online clothing case study

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Fashion eCommerce Case Study

OUR ROLE FOR TABITA OUTLET

• UX/UI Design
• CRM Development
• Consulting Services
• Email Marketing
• Social Media Marketing

The Project

Life is too short to wear the same boring clothes every day. So why not make the most of fashion to express your true self? Especially when it comes at ridiculously low prices!

That’s the story behind Tabita, a fashion outlet with a history of 23 years in the retail industry of Cluj-Napoca. They’ve built their reputation through several brick-and-mortar stores, but the rise of an online store was both necessary and inevitable. That’s how they ended up selling famous brand wear at affordable prices nationwide.

We had to draw attention to fashion enthusiasts and cover their needs, which can be a lengthy process. However, the main focus was on their behaviour since they basically spend 24 hours a week online.

That meant creating a completely new experience for the fashionistas by blending the best e-commerce design and development practices with a fail-proof marketing strategy.

Our main goal was to perk up the website in a way that properly reflects the potential of a renowned brand from Cluj-Napoca. A whole new level of online presence was charted in order to take things to the next level and enable the business to serve an entire country.

Finding Clarity in Chaos

The strategy and tactics were clearly established from the beginning so that no disconnect or handoff could emerge between what we planned and what we executed.

This meant regular meetings for us to truly understand the business and to transparently discuss the steps that were to be implemented. The next phase was to involve our executional experts in the collaborative strategic processes.

Kudos to the participants on both sides! They managed to ensure consistency and effectiveness across each and every initiative we implemented: 2 back-end developers, a front-end developer, a designer, a marketing specialist, a QA specialist, a SEO specialist, a product manager, a few members from the client’s side

Discovery & Research

First things first 01

Customizing the website according to the right audience is an essential move, so we carried out a target market analysis and a competitive assessment. The result was a detailed buyer persona who represents a large percentage of the customer audience.

The Journey 02

Now that we knew exactly to whom the website was speaking to, we could focus on the sales funnel. We have created a series of steps – from tailored newsletters to inspiring social media posts – that guide visitors toward a buying decision. However, a major cornerstone was considering the perspective of the buyer too.

Immersing into the context of the customer allowed us to create a user journey that could quickly enable the user to achieve their goal. All this by educating the public about how to get dressed, but also by means of pivotal tweaks on the platform.

For instance, the mobile website leads the customer directly to the promotional campaigns and focuses on a clean and simple view of the clothing categories. On the other hand, the desktop version takes you through a journey centred around visual categories and famous brands.

Information Arhitecture 03

The next phase was structuring the website and customer relationship management in order to get each and every segment right.

Prototyping & Testing

4 Interviews

+49 Wireframes

4 A/B Tests

2 Prototypes (Desktop / Mobile)

6 Iterations

+60 Detailed UI Screens

We didn’t just shape Tabita’s online platform for a visually clean, but smooth shopping experience. We created an environment that balances the users’ desires with the business’s needs.

This also meant integrating a payment gateway that enabled a positive checkout experience. Then we customized the admin panel so that product addition would not require any hassle.

• On top of that, we have created a separate mobile website from scratch in order to offer a seamless user experience. Mobile responsiveness is the foundation upon which the entire digital experience occurs.
• Therefore it is critical for customers to stick around. As soon as we have published a smooth mobile version, the customers were enabled to order on the go and the online exposure substantially increased.

Error Prevention

We left no stone unturned when we implemented our solutions. But what if we’ve missed something? That’s when we came up with various scenarios for each step of the funnel in order to anticipate user behaviour and prevent unwanted circumstances.

As an example, we have noticed that the images on the mobile websites were putting a strain on the buying process, so we switched to a minimalist version to avoid conversion loss.

Another instance was enabling the customers to reserve the chosen items in their shopping cart without rushing them to quickly check out. It’s just like when you’re in a physical store and you keep an item in your hand while you continue browsing. We have basically copied the natural behaviour of offline customers and implemented it online.

Website Design

When we started working on the design process, we already had a clear idea of what was needed: a minimalist design, very clear, making the content stand-out and easy to follow by any user.

Therefore, each page has the most important information and the most appealing visuals at the top, so the user can easily find what he’s looking for.

Women’s Clothing

Product Listings

Product Details

Shopping Cart

Frequent Questions

Blog Categories

Product categories

Product listings

Product filters

Product details

Know your audience

Constantly study their behaviour! You may need to adapt, since they could change, just like fashion trends do.

Means long-term marketing success. There are no overnight miracles involved, but when SEO hits, your brand stays.

Keep an open mind !

Sometimes you may have different priorities than the client does, which is fine, as long as you agree to disagree and focus on the client’s needs.

Online Marketing

Digital marketing is simply the best way to improve brand presence and sales for an online store. Truth is that it’s easily trackable, you get immediate feedback and you can determine whether a campaign is working or not.

Therefore, it was easy to report the 80% increase of traffic after only 2 months of engaging with this project.

• We’ve started with a high focus on SEO to drive traffic to the website by optimizing it for search engines. That implied a top-notch infrastructure of the website, quality content, a spotless SEO-centered mobile version of the website, but especially the use of relevant keywords. Thus, we carried out a thorough analysis of search trends and demographics to get the highest return with the least amount of competition.
• At the same time, for the offsite SEO part, we placed social media into the spotlight. Pictures speak a thousand words so we launched Facebook and Instagram campaigns with inspiring fashion images, but also attractive content. Then we relied on striking outfit collages with articles from the shop to “wow” the Facebook community built around Tabita.
• However, we managed to work miracles with the weekly newsletters too since we arrived at hundreds of clicks and conversions every day. All this, by luring the customers with stylish images and content around the most inspiring products.
• 58% – 1st month
• 79% – 2nd month
• Within one month of us handling their SEO, website traffic went up with 58% and another month later, with 79%.

Average Position

• 6.2 – from 03.04.2016 to 17.08. 2016
• Our optimization processes helped Tabita gain an average position in SERP of 6.2 in less than four months.

Succesful Sales

• 328 – 1st month
• Following our project delivery, the online shop sold 328 products and still counting.

Weekly Campaigns

Social Media Posts

Facebook Ads

Final Thoughts

Since we started this collaboration, tabita.ro made it to the first page on google for some of their top keywords. The number of daily sales has increased substantially and it has quickly become a staple of online shopping across the entire country.

Feeling inspired? Let’s make a stylish project together!

Fashioning a sustainable future for an online clothing retailer

EY teams have helped fashion retailer ASOS to identify opportunities to unlock value, making it more resilient and better prepared for the future.

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EY UK&I Vice Chair

Silvia Rindone

EY UK&I Strategy and Transactions Managing Partner

The better the question

What should a growth-oriented company do to fuel more growth?

ASOS engaged the EY Reshaping Results team to help it identify new opportunities to unlock value in an increasingly competitive marketplace.

I n early 2020, ASOS was at a crossroads. After close to two decades of stellar performance, the UK online fashion retailer found itself faced with increasing competition, profit and growth headwinds, and challenging operational issues resulting from the simultaneous implementation of two state-of-the-art international distribution centers.

To address these issues, CEO Nick Beighton instituted a comprehensive plan to refocus the business and set it on course for the next stage of its growth. As a pioneer in online fashion, the company had enjoyed first-mover advantage, but market competition had put pressure on margins, creating a need for greater efficiencies and flexibility.

However, as a relatively young, fast-growing company, it had neither the experience nor sufficient internal capacity to deliver value transformation programs.

Beighton had not previously employed outside consultants. “I was always very proud that ASOS had the capability internally and was able to think differently,” he says. “We were always able to turn left when others turned right.”

But, having been introduced to EY teams, he reconsidered that stance. “I realized that it was a great moment for us to re-examine what we do. The EY process and challenge has built on and complemented that internal capability to assist us with the next level of growth.”

The better the answer

A focus on cost and cash

EY teams drove value by identifying, quantifying and prioritizing cost and cash opportunities.

“We could see that the opportunity for the organization was massive,” says Beighton, recalling his brief to the EY team. “What we were less clear about was how we extract the right fuel to fund our growth aspirations.”

Mona Bitar, UK&I Consumer Leader, Ernst & Young LLP says that the approach of EY teams was to help the ASOS leadership team look at their business as an external investor would: “We took an “outward-in” approach and challenged the team to think about their business in a different way.” She adds that it was important to bring the executive team on a journey, ensuring they were engaged so they felt comfortable and accountable for owning targets and plans.

The EY teams focused on a rapid diagnostic that covered extensive areas – including looking at the product range, customer segmentation, supplier performance, logistics, inventory and support functions – and was able to identify significant opportunities to help super-charge ASOS’s ambitions. This was achieved by bringing together people from a number of service offerings, including Strategy; Restructuring and Turnaround Strategy; Valuation, Modeling & Economics; and Consulting, as well as retail professionals – all working as one team.

The COVID-19 pandemic posed an additional challenge and meant that the EY team had to present their findings to ASOS on a video call. Beighton praises the way EY adapted to the unexpected new working conditions: “I was impressed with their ability to pivot with the new circumstances and still bring us solutions, in a moment when I’m sure EY staff’s lives were turned upside down in the same ways our lives were turned upside down,” he says. Indeed, many of the key team members on both sides have still never met face to face.

How EY-Parthenon can help

Reshaping results.

We help you respond to the challenges of COVID -19, providing trusted leadership in these urgent, critical and complex situations to help you recover and preserve value for a better future.

The better the world works

Different ways of working and increased agility

The work EY teams have done with ASOS has helped the retailer to change the way it thinks and given it the confidence to pursue its ambitions.

Had we not reshaped ourselves, we would not have had the ability to buy that brand that we so dearly wanted.

ASOS incorporated the EY team’s findings into its three-year strategic plan, enabling it to set clear targets to achieve its ambitions. To do so, the company has established an enterprise-wide value transformation program, which EY is now helping it to shape.

Beighton believes one of the reasons the relationship between ASOS and EY teams have worked so well is that both are purpose-led organizations. “That helped build and maintain trust,” he says. “At the core of what we’re doing, we’re both aligned on achieving outcomes in a way that suits both of us. Also, when you have bumps in the road during the process, it allows you to recommit to trust. You can say, I disagree with this, you disagree with that, but actually, we both want something that’s better. So it brings you back together.”

The work of the EY teams has already started to bear fruit, with strong savings in cash and costs made in the first year.

As Beighton explains, it also helped enable the company to achieve a long-held ambition. “During January, the comfort we’d built in the business and the cushions we’d built in terms of greater earnings and cash flow – programs EY was working with us on – enabled us to buy Topshop [and other Arcadia brands],” he says. “Had we not reshaped ourselves, we would not have had the ability to buy that brand that we so dearly wanted.”

As ASOS prepares for business in a post-pandemic world, it has identified clear efficiencies and now has a smart strategic plan, making it more resilient, better prepared for the future and, ultimately, able to continue inspiring and delighting its fashion-loving 20-something customers in a way that is difficult for others to copy.

Strategy consulting

EY-Parthenon professionals recognize that CEOs and business leaders are tasked with achieving maximum value for their organizations’ stakeholders in this transformative age. We challenge assumptions to design and deliver strategies that help improve profitability and long-term value.

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Gravity-Defying Fashion: a Business Case on Digital Fashion

“Gravity-defying fashion” business case has been published recently on The Case Centre . Luxury expert, professor of Marketing Anne Michaut, investigated books, articles, and online databases on the fascinating yet still unknown topic of digital fashion, and from this study, shared insights through a fictitious business case.

Virtual dress that a consumer can buy online

Digital fashion, or ‘contactless cyber fashion,’ in other words, virtual digital fashion is 3-D clothing designed for humans and digital avatars. The clothing articles are created with 3-D computers without any fabrics or materials and do not exist in a three-dimensional format. Buying digital clothing online, the ease of purchase is as simple as any other online purchases of products and services. 

One of the main platforms for digital fashion is online gaming. Nearly 3 billion people around the world play video games, and that figure just keeps growing. The average gamer is 33 years old and upper-middle class, which nicely aligns with many fashion and luxury brands’ target demographic. Players spend an average of seven hours a week on games and collectively spend an estimated €100 billion ($107 billion) on goods in the virtual arena. 

Daphnée Prometheus, CEO of a luxury apparel company called Grass (a fictitious company and character), made it a habit to follow fashion trends on the internet. Grass, although founded in 1946 in Paris with a turnover of over €850 million ($855 million), had built its reputation on timeless and elegant pieces of clothing for those who could afford them and opened self-owned boutiques in 20 major cities. Daphnée was concerned about the staying power of the brand to keep it relevant and fresh for the younger generations of potential customers.

Moreover, she felt that the design process would be more creative and boundless, just the rejuvenation she had been looking for her company. In this context, Daphnée was considering whether digital/virtual/contactless fashion would be an opportunity. She was trying to analyze why customers would spend from 30€ ($32) to 1,000€ ($1,100) or much more on clothing that does not exist in the real world, only online.

Why this case

The case addresses questions such as: •    Why are customers paying real money for virtual clothes? What is the value proposition for these customers?  •    Is it a good moment for luxury brands to enter the virtual fashion business?  •    What are other positive spillover effects for fashion companies like Grass to enter the virtual fashion market?   

Anne Michaut's current research interests are in the field of luxury, focusing on the perception of luxury, sustainable development, entrepreneurship...

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The 24 Best eCommerce Retail Case Studies Worth Reading

In the fast-paced world of retail and eCommerce, staying ahead of the game is not just a goal; it’s the lifeline of our industry. For seasoned retail executives, inspiration often comes from the experiences and successes of industry giants who paved the way with their innovative thinking and managed to thrive through thick and thin. That’s why we’re excited to bring you an exclusive collection of the 30 best eCommerce case studies meticulously curated to provide you with a wealth of insights and ideas to fuel your strategies. These case studies are more than just success stories; they are beacons of guidance for retail professionals navigating the ever-changing landscape of our industry.

In this article, we delve deep into the journeys of retail giants who have not only weathered the storms of disruption but have emerged as trailblazers in eCommerce. From adapting to shifting consumer behaviors to mastering the art of online engagement, this compilation offers a treasure trove of wisdom for the modern retail executive. 

Table of Contents

• > Case studies for grocery/wholesale eCommerce retailers
• > Case studies for fashion eCommerce retailers
• > Case Studies for home & furniture eCommerce retailers
• > Case Studies for health & beauty eCommerce retailers
• > Case studies for electronics and tools eCommerce retailers
• > Case Studies for toys and leisure eCommerce retailers

Case studies for grocery/wholesale eCommerce retailers

Retail case study #1: tesco .

Industry : Grocery stores

Why worth reading: 

• Historical evolution: Understanding Tesco’s rise from a group of market stalls to a retail giant provides valuable lessons on growth and adaptation to market changes​.
• Customer service focus: Tesco’s long-term emphasis on customer service, which is consistent across their physical and online platforms, showcases the importance of customer-centric strategies.
• Innovation in eCommerce: The case study covers Tesco’s pioneering of the world’s first virtual grocery store in South Korea, a testament to its innovative approach to digital retailing.
• Crisis management: Insights into how Tesco handled the Horse Meat Scandal, including efforts to tighten its supply chain, contributing to its logistical success​.
• Financial integrity: The study discusses the Accounting Scandal, offering a sobering look at financial transparency and the repercussions of financial misreporting.

Read the full Tesco case study here .

Retail case study #2: Walmart 

Industry : Discount department and grocery stores

• Data-driven success: The case study provides a wealth of data, showcasing Walmart’s remarkable achievements. With an annual revenue of almost $570 billion, a global presence in 24 countries, and a customer base exceeding 230 million weekly, it’s a testament to the effectiveness of their strategies.
• Marketing strategies: The case study delves deep into Walmart’s marketing strategies. It highlights their focus on catering to low to middle-class demographics, the introduction of the Walmart Rewards loyalty program, and their commitment to environmental sustainability, all of which have contributed to their success.
• eCommerce transformation: As eCommerce continues to reshape the retail landscape, this case study details how Walmart shifted significantly towards omnichannel retail. Readers can learn about their innovative technologies and approaches, such as personalized shopping experiences and augmented reality, that have helped them adapt to changing consumer behavior.
• Supply chain innovation: Walmart’s proficiency in supply chain management is a crucial takeaway for retail executives. Their decentralized distribution center model , in-house deliveries, and data-driven optimization exemplify the importance of efficient logistics in maintaining a competitive edge.

Read the full Walmart case study here .

Retail case study #3: Sainsbury’s 

Industry : Grocery stores

• Omnichannel success amidst pandemic challenges: With the fastest growth in online shopping among major retailers, the study illustrates how Sainsbury’s adapted and thrived during unprecedented times.
• Dynamic brand positioning: The analysis delves into Sainsbury’s strategic shift in brand positioning, demonstrating a keen responsiveness to changing consumer preferences. This shift showcases the brand’s agility in aligning with contemporary health-conscious consumer trends, supported by relevant data and market insights.
• Supply chain and quality assurance: The study highlights Sainsbury’s commitment to a stellar supply chain, emphasizing the correlation between high product quality, ethical sourcing, and customer loyalty. With data-backed insights into the extensive distribution network and sourcing standards, retail executives can glean valuable lessons in maintaining a competitive edge through a robust supply chain.
• Innovative technological integration: Sainsbury’s implementation of cutting-edge technologies, such as Amazon’s “Just Walk Out” and Pay@Browse, demonstrates a commitment to providing customers with a seamless and convenient shopping experience. 
• Diversification beyond grocery: The case study unveils Sainsbury’s strategic partnerships with companies like Amazon, Carluccio’s, Itsu, Leon, and Wasabi, showcasing the brand’s versatility beyond traditional grocery retail. 

Read the full Sainsbury’s case study here .

Retail case study #4: Ocado 

• From startup to industry leader: The Ocado case study presents a remarkable journey from a three-employee startup in 2000 to becoming the UK’s largest online grocery platform.  
• Omnichannel excellence: The study emphasizes Ocado’s success in implementing an omnichannel approach, particularly its early adoption of smartphone technology for customer engagement. 
• Operational efficiency: From automated warehouses with machine learning-driven robots to digital twins for simulating order selection and delivery processes, the data-rich content sheds light on how technology can be leveraged for operational efficiency. 
• Navigating challenges through innovation: Ocado’s strategic response to challenges, particularly its shift from primarily a grocery delivery service to a technology-driven company, showcases the power of innovative thinking. The case study details how Ocado tackled complexities associated with grocery deliveries and embraced technology partnerships to stay ahead.  
• Strategic partnerships: The study sheds light on Ocado’s strategic partnerships with grocery chains and companies like CitrusAd for advertising opportunities on its platform. 

Read the full Ocado case study here .

Retail case study #5: Lidl

Industry : Discount supermarkets

• Longevity and evolution: The article provides a detailed overview of Lidl’s origins and evolution, offering insights into how the brand transformed from a local fruit wholesaler to a global retail powerhouse. Understanding this journey can inspire retail executives to explore innovative strategies in their own companies.
• Global success: Retail executives can draw lessons from Lidl’s international expansion strategy, identifying key factors that contributed to its success and applying similar principles to their global ventures.
• Awards and recognitions: The numerous awards and accomplishments earned by Lidl underscore the effectiveness of its marketing strategy. Marketers and eCommerce professionals can learn from Lidl’s approach to quality, innovation, and customer satisfaction. 
• Comprehensive marketing components: The article breaks down Lidl’s marketing strategy into key components, such as pricing strategy, product diversification, and target audience focus. Readers can analyze these components and consider incorporating similar holistic approaches in their businesses to achieve well-rounded success.
• Omnichannel transformation: The discussion on Lidl’s transformation to an omnichannel strategy is particularly relevant in the current digital age. This information can guide executives in adopting and optimizing similar omnichannel strategies to enhance customer experiences and drive sales.

Read the full Lidl case study here .

Retail case study #6: ALDI

Industry : FMCG

• Omnichannel approach: Aldi’s growth is attributed to a robust omnichannel strategy that seamlessly integrates online and offline channels. The case study delves into how Aldi effectively implemented services that can overcome the intricacies of a successful omnichannel approach in today’s dynamic retail landscape.
• Target market positioning: Aldi’s strategic positioning as the most cost-effective retail store for the middle-income group is explored in detail. The case study elucidates how Aldi’s pricing strategy, emphasizing the lowest possible prices and no-frills discounts, resonates with a wide audience. 
• Transparency: Aldi’s commitment to transparency in its supply chain is a distinctive feature discussed in the case study. For retail executives, understanding the importance of transparent supply chain practices and their impact on brand perception is crucial in building consumer trust.
• Differentiation: Aldi’s successful “Good Different” brand positioning, which communicates that low prices result from conscientious business practices, is a key focus of the case study. Effective differentiation through brand messaging contributes to customer trust and loyalty, especially when combined with ethical business practices.
• CSR Initiatives: The case study highlights Aldi’s emphasis on social responsibility to meet the expectations of millennial and Gen-Z shoppers. By consistently communicating its CSR efforts, such as sustainable sourcing of products, Aldi creates a positive brand image that resonates with socially conscious consumers and builds brand reputation.

Read the full Aldi case study here .

Retail case study #7: ASDA

Industry : Supermarket chain

• Omnichannel implementation: The case study details how ASDA seamlessly integrates physical and virtual channels, offering customers a diverse shopping experience through in-store, digital checkouts, Click & Collect services, and a dedicated mobile app. 
• Market segmentation strategies: The incorporation of partnerships with young British designers and influencer collaborations, coupled with socially progressive messaging, reflects a strategic shift that can inspire marketers looking to revitalize product lines.
• Crisis management and ethical branding: The study highlights ASDA’s strong response to the COVID-19 crisis, with ASDA’s actions showcasing a combination of crisis management and ethical business practices. This section provides valuable insights for executives seeking to align their brand with social responsibility during challenging times.
• Product and format diversification: ASDA’s product categories extend beyond groceries, including clothing, home goods, mobile products, and even insurance. The case study explores how ASDA continues to explore opportunities for cross-promotion and integration.
• Website analysis and improvement recommendations: The detailed analysis of ASDA’s eCommerce website provides actionable insights for professionals in the online retail space. This section is particularly beneficial for eCommerce professionals aiming to enhance user experience and design.

Read the full ASDA case study here .

Case studies for fashion eCommerce retailers

Retail case study #8: Farfetch

Industry : Fashion retail

• Effective SEO strategies: The Farfetch case study offers a detailed analysis of the company’s search engine optimization (SEO) strategies, revealing how it attracted over 4 million monthly visitors. The data presented underscores the importance of patient and dedicated SEO efforts, emphasizing the significance of detailed page structuring, optimized content, and strategic backlinking.
• Paid search advertising wisdom and cost considerations: The study delves into Farfetch’s paid search advertising approach, shedding light on its intelligent optimization tools and the nuances of running localized advertisements. Moreover, it discusses the higher cost of visitor acquisition through paid search compared to organic methods, providing valuable insights for marketers navigating the paid advertising landscape.
• Innovative LinkedIn advertising for talent acquisition: Farfetch’s unique use of LinkedIn advertising to attract talent is a standout feature of the case study and highlights the significance of proactive recruitment efforts and employer branding through social media channels. 
• Strategic use of social media platforms: Exploring the brand’s highly consistent organic marketing across various social media channels, with a focus on visual content, highlights Farfetch’s innovative use of Instagram’s IGTV to promote luxury brands. The emphasis on social media engagement numbers serves as a testament to the effectiveness of visual content in the eCommerce and fashion sectors.
• Website design and conversion optimization insights:   A significant portion of the case study is dedicated to analyzing Farfetch’s eCommerce website, providing valuable insights for professionals aiming to enhance their online platforms. By identifying strengths and areas for improvement in the website’s design, marketers, and eCommerce professionals can draw actionable insights for their platforms.

Read the full Farfetch case study here .

Retail case study #9: ASOS

Industry : Fashion eCommerce retail

• Mobile shopping success: eCommerce executives can draw inspiration from ASOS’s commitment to enhancing the mobile shopping experience, including features such as notifications for sale items and easy payment methods using smartphone cameras.
• Customer-centric mentality: ASOS emphasizes the importance of engaging customers on a personal level, gathering feedback through surveys, and using data for continuous improvement. This approach has contributed to the brand’s strong base of loyal customers.
• Inclusive marketing: ASOS’s adoption of an ‘all-inclusive approach’ by embracing genderless fashion and featuring ‘real’ people as models reflects an understanding of evolving consumer preferences. Marketers can learn from ASOS’s bold approach to inclusivity, adapting their strategies to align with the latest trends and values embraced by their target audience.
• Investment in technology and innovation: The case study provides data on ASOS’s substantial investment in technology, including visual search, voice search, and artificial intelligence (AI). eCommerce professionals can gain insights into staying at the forefront of innovation by partnering with technology startups.
• Efficient global presence: ASOS’s success in offering a wide range of brands with same and next-day shipping globally is attributed to its strategic investment in technology for warehouse automation. This highlights the importance of operational efficiency through technology, ensuring a seamless customer experience and reduced warehouse costs.

Read the full ASOS case study here .

Retail case study #10: Tommy Hilfiger 

Industry : High-end fashion retail

• Worldwide brand awareness: The data presented highlights Tommy Hilfiger’s remarkable journey from a men’s clothing line in 1985 to a global lifestyle brand with 2,000 stores in 100 countries, generating $4.7 billion in revenue in 2021. This strategic evolution, exemplified by awards and recognitions, showcases the brand’s adaptability and enduring relevance in the ever-changing fashion landscape.
• Adaptation and flexibility to changing market trends: The discussion on how the brand navigates changing trends and overcame market saturation, particularly in the US, provides practical insights for professionals seeking to navigate the challenges of evolving consumer preferences.
• Successful omnichannel marketing: Tommy Hilfiger’s success is attributed to a brand-focused, digitally-led approach. The analysis of the brand’s omnichannel marketing strategy serves as a map for effective promotion and engagement across various channels. 
• Decision-making and customer engagement: The case study emphasizes the brand’s commitment to data-driven decision-making with insights into customer behavior, leveraging data for effective customer engagement.

Read the full Tommy Hilfiger case study here .

Retail case study #11: Gap

• Overcoming challenges: The case study provides a comprehensive look at Gap Inc.’s financial performance, and growth despite the challenges. These insights can offer valuable takeaways into effective financial management and strategies for sustained success.
• Strong branding: Gap’s journey from a single store to a global fashion retailer reveals the importance of strategic brand positioning. Understanding how Gap targeted different market segments with unique brand identities, can inspire retail executives looking to diversify and expand their brand portfolios.
• Omnichannel adaptation: The case study delves into Gap’s omnichannel strategy, illustrating how the company seamlessly integrates online and offline experiences.
• Unique use of technology: By exploring the technologies Gap employs, such as Optimizely and New Relic, retail executives can learn about cutting-edge tools for A/B testing, personalization, and real-time user experience monitoring. This insight is crucial for staying competitive in the digital retail landscape.
• Inspiring solutions: The case study highlights challenges faced by Gap, including logistical, technological, financial, and human resource challenges. 

Read the full Gap case study here .

Retail case study #12: Superdry

• Success story: The case study emphasizes SUPERDRY’s successful transition to an omnichannel retail strategy, with in-depth insights into their adaptation to online platforms and the integration of technologies like the Fynd app. 
• Mobile-first and social-first strategies: As mobile internet usage continues to rise, understanding how SUPERDRY leverages videos and social media to engage customers can offer valuable takeaways for optimizing digital strategies.
• Sustainable fashion focus: Executives looking to appeal to environmentally conscious consumers can gain insights into how SUPERDRY navigated the shift towards sustainable practices and became a leader in eco-friendly fashion. 
• Data-driven marketing strategies: The case study delves into SUPERDRY’s social media marketing strategies, showcasing how the company uses targeted campaigns, influencers, and seasonal keywords. 
• Global market understanding: By exploring SUPERDRY’s experience in the Chinese market and its decision to exit when faced with challenges, the case study offers valuable insights into global market dynamics. 

Read the full SUPERDRY case study here .

Retail case study #13: New Look 

Industry : Fast-fashion retail

• Strategic pivots for profitability: A decade of revenue contraction led New Look to adopt transformative measures, from restructuring credits to withdrawing from non-profitable markets.
• Omnichannel strategy: Marketers and eCommerce professionals can study New Look’s journey, understanding how the integration of physical stores and online platforms enhances customer experience, reduces costs, and improves profitability.
• Social media mastery: The case study underscores the pivotal role of social media in engaging audiences, showcasing how New Look leverages user-generated content to build brand loyalty and maintain a positive brand perception. 
• Effective partnerships for growth: New Look strategically partners with major eCommerce platforms like eBay & Next to expand its brand presence, and tap into new audiences and markets.

Read the full New Look case study here .

Retail case study #14: Zara

• Rapid international expansion through innovative strategies: Zara’s unique approach to continuous innovation and quick adaptation to fashion trends fueled its global success. Marketers can learn how to build brand narratives that resonate across diverse markets, and eCommerce professionals can glean strategies for seamless international expansion.
• Revolutionary eCommerce tactics: The case study provides a deep dive into Zara’s eCommerce strategy, emphasizing the importance of agility and responsiveness. The brand can be a bright example of implementing supply chain strategies for a swift market adapting to rapid fashion cycles. 
• Visionary leadership: Amancio Ortega’s low-profile persona and visionary leadership style are explored in the case study, aiding retail executives to learn about leadership strategies that prioritize customer-centric business models. 
• Omnichannel marketing and integrated stock management: Zara’s successful integration of automated marketing and stock management systems is a focal point in the case study. With insights into implementing integrated stock management systems to meet the demands of both online and offline channels, Zara can inspire professionals to improve their operations.
• Co-creation with the masses: Zara’s innovative use of customer feedback as a driving force for fashion trends is a key takeaway. Marketers can learn about the power of customer co-creation in shaping brand identity, and eCommerce professionals can implement similar models for product launches and updates.

Read the full Zara case study here .

Case Studies for home & furniture eCommerce retailers

Retail case study #15: john lewis.

Industry : Homeware and clothing retail

• Omnichannel perspective: The data-driven approach, especially in tracking orders and customer behavior, serves as a blueprint for any retail business aiming to enhance its omnichannel experience.
• Strategic growth factors: This case study offers concrete data on the strategies that contributed to the company’s sustained success, inspiring similar endeavors. 
• Innovative customer engagement: John Lewis’s take on customer engagement showcases the brand’s agility and responsiveness to evolving consumer needs, supported by data on the effectiveness of these initiatives.
• eCommerce best practices and pitfalls: The analysis of John Lewis’s eCommerce website provides a data-backed evaluation of what works and what could be improved. The critique is grounded in data, making it a valuable resource for those looking to optimize their online platforms.

Read the full John Lewis case study here .

Retail case study #16: Argos 

Industry : Homeware catalog retail

• Adaptation to the changing retail landscape: Argos’s journey from a catalog retailer to a retail giant demonstrates its ability to successfully adapt to the evolving retail landscape. 
• Omnichannel success story: The case study provides a detailed analysis of Argos’s omnichannel strategy, showcasing how the company effectively integrated online and offline channels to achieve a seamless shopping experience across multiple touchpoints.
• Market share and financial performance: The inclusion of data on Argos’s market share and financial performance offers retail executives concrete metrics to evaluate the success of the marketing strategy. Understanding how Argos maintained a robust market share despite challenges provides actionable insights.
• Technological advancements: The case study delves into the technologies employed by Argos, such as Adobe Marketing Cloud, New Relic, and ForeSee. 
• Overcoming obstacles: By examining the challenges faced by Argos, including logistical, technological, financial, and human resources challenges, retail executives can gain a realistic understanding of potential obstacles in implementing omnichannel strategies. 

Read the full Argos case study here .

Retail case study #17: IKEA

Industry : Home & furniture retail

• Data-driven evolution: This detailed case study offers a data-rich narrative, illuminating the brand’s evolution into a leader in omnichannel retail.
• Pandemic response: This exploration delves into the integration of eCommerce strategies, online expansions, and the balance between physical and digital customer experiences.
• Advanced mobile apps and AR integration: A deep dive into IKEA’s innovative applications, notably the AR app “IKEA Place,” showcases how the brand leverages technology for a seamless customer experience.
• Democratic design approach: The study meticulously breaks down IKEA’s success factors, emphasizing the brand’s holistic approach through the lens of “Democratic Design.” 
• DIY mentality and demographic targeting: A detailed analysis of how IKEA’s affordability is intertwined with a Do-It-Yourself (DIY) mentality. The case study explores how IKEA strategically tapped into a shift in consumer behavior, particularly among younger demographics, influencing not only purchasing patterns but also reshaping industry norms.

Read the full IKEA case study here .

Retail case study #18: Marks & Spencer

Industry : Clothing and home products retail

• Valuable lessons in eCommerce: The Marks & Spencer eCommerce case study offers a profound exploration of the brand’s journey from a latecomer to the online scene to a digital-first retailer.
• Real-world application of effective solutions: By diving into the history of Marks & Spencer, the case study provides tangible examples of how a retail giant faced setbacks and strategically pivoted to revitalize its eCommerce platform. 
• Data-driven analysis of eCommerce failures: The case study meticulously analyzes the pitfalls Marks & Spencer encountered during its eCommerce journey, offering a data-driven examination of the repercussions of a poorly executed website relaunch. 
• Multichannel customer experience: Marks & Spencer’s shift towards a multichannel customer experience is dissected in the case study, emphasizing the significance of a seamless user journey for increased customer satisfaction and loyalty.
• Embracing technology: Exploring Marks & Spencer’s technological innovations, such as the introduction of an intelligent virtual assistant can enhance the customer shopping journey, foster engagement, and contribute to revenue growth.

Read the full Marks & Spencer case study here .

Retail case study #19: Macy’s 

Industry : Clothing and homeware retail

• Resilience and adaptability: The case study showcases Macy’s ability to navigate and triumph over obstacles, especially evident during the COVID-19 pandemic. Despite hardships, Macy’s not only survived but thrived, achieving $24.4 billion in net sales for 2022.
• Omnichannel innovation: Macy’s successful transition to omnichannel retailing is a standout feature. The case study delves into Macy’s implementation of a seamless omnichannel strategy, emphasizing the integration of physical and digital retail channels. 
• Private label strategy: The introduction of new private brands and the emphasis on increasing the contribution of private brands to sales by 2025 provides a strategic lesson. Retailers can learn from Macy’s approach to enhancing control over production and distribution by investing in private brands, ultimately aiming for a more significant share of profits.
• Groundbreaking retail media strategy: Macy’s innovative approach to retail media and digital marketing is another compelling aspect. For marketers, this presents a case study on how to leverage proprietary shopper data for effective advertising, including entry into connected TV (CTV).
• Community engagement and social responsibility: The case study explores Macy’s “Mission Every One” initiative, highlighting its commitment to corporate citizenship and societal impact, integrating values into business strategies.

Read the full Macy’s case study here .

Case Studies for health & beauty eCommerce retailers

Retail case study #20: the body shop .

Industry : Beauty, health, and cosmetics

• Activism and ethical values: The Body Shop has pioneered promoting eco-friendly, sustainable, and cruelty-free products. The brand’s mission is to empower women and girls worldwide to be their best, natural selves. This strong ethical foundation has been integral to its identity.
• Recycling, community fair trade, and sustainability: The Body Shop initiated a recycling program early on, which turned into a pioneering strategy. It collaborates with organizations to create sustainable solutions for recycling, such as the Community Trade recycled plastic initiative in partnership with Plastics for Change.
• Product diversity: The Body Shop’s target demographic primarily focuses on women, but it has expanded some product lines to include men. Its products include skincare, hair and body treatments, makeup, and fragrances for both men and women.
• Omnichannel strategy, technology, and eCommerce best practices: The Body Shop has embraced an omnichannel approach that incorporates personalization, customer data and analytics, and loyalty programs. The Body Shop utilizes technology, including ContactPigeon, for omnichannel customer engagement, personalization, and data-driven decision-making.

Read the full The Body Shop case study here .

Retail case study #21: Boots

Industry : Pharmacy retail

• Long-term success: Boots’ rich history serves as a testament to the effectiveness of the brand’s strategies over time, offering valuable insights into building a brand that withstands the test of time.
• Strategic omnichannel approach: The Boots case study provides a deep dive into the marketing strategy that propelled the brand to success, with valuable insights into crafting effective omnichannel growth. 
• Impactful loyalty program: Marketers can glean insights into designing loyalty programs that resonate with customers, fostering brand allegiance. 
• Corporate Social Responsibility (CSR) as a pillar: The case study sheds light on how Boots addresses critical issues like youth unemployment and climate change, showcasing how a socially responsible approach can positively impact brand perception.
• Adaptive strategies during crises: Boots’ proactive role during the COVID-19 pandemic, offering vaccination services and supporting the National Health Service (NHS), demonstrates the brand’s agility during crises. 

Read the full Boots case study here .

Retail case study #22: Sephora

Industry : Cosmetics

• Authentic customer experience-focused mentality: Backed by an impressive array of data, the case study meticulously outlines how Sephora transforms its in-store spaces into digital playgrounds, leveraging mobile technologies, screens, and augmented reality to enhance the customer shopping experience. 
• Exceptional omnichannel business plan: The early adoption of an omnichannel strategy has been pivotal to Sephora’s ascendancy. The case study delves into the mobile app’s central role, acting as a comprehensive beauty hub with data-driven insights that drive the success of groundbreaking technologies. 
• Omnichannel company culture: The case study illuminates this by detailing how this amalgamation allows a holistic view of the customer journey, blurring the lines between online and in-store interactions. This unique approach positions Sephora as a global leader in turning omnichannel thinking into a robust business strategy.
• Turning data into growth: Sephora’s adept utilization of mobile technologies to harness customer insights is a beacon for retailers in an era where data reigns supreme. The case study dissects how a surge in digital ad-driven sales, showcases the power of data-driven decision-making.

Read the full Sephora case study here .

Case studies for electronics and tools eCommerce retailers

Retail case study #23: screwfix.

Industry : Tools and hardware retail

• Innovative omnichannel approach: The case study highlights how the company strategically implemented online ordering with in-store pickup, creating a seamless shopping experience that contributed to a significant sales growth of 27.9% in just one year.
• Customer-centric strategies: Marketers can gain insights from Screwfix’s emphasis on customer experience. By studying customer feedback and incorporating personalized shopping experiences, Screwfix achieved success in the competitive home improvement sector. 
• Supply chain management for rapid growth: The company strategically opened distribution centers to keep up with demand, ensuring efficient inventory management for both online and in-store orders.
• Mobile-first approach for trade professionals: With a customer base primarily consisting of trade professionals, the company’s mobile app allows for easy inventory search, order placement, and quick pickups, catering to the needs of time-sensitive projects.
• Commitment to employee well-being and community: Retail executives and marketers can draw inspiration from Screwfix’s commitment to building a positive workplace culture.

Read the full Screwfix case study here .

Case Studies for toys and leisure eCommerce retailers

Retail case study #24: lego.

Industry : Toys and leisure retail

• Global reach strategies: LEGO’s case study meticulously outlines LEGO’s focused approach, investing in flagship stores and understanding the local market nuances.
• Diversification and licensing brilliance: LEGO’s commitment to diversification through licensing and merchandising emerges as a beacon for marketers. The collaboration with well-established brands, the creation of movie franchises, and themed playsets not only elevate brand visibility but also contribute significantly to sales. 
• Social media takeover: The case study unveils LEGO’s unparalleled success on social media platforms, boasting over 13 million Facebook followers and 10.04 billion views on YouTube. LEGO’s adept utilization of Facebook, Instagram, and YouTube showcases the power of social media in engaging customers. 
• User-generated content (UGC) as a cornerstone: LEGO’s innovative use of digital platforms to foster a community around user-generated content is a masterclass in customer engagement. This abundance of UGC not only strengthens brand loyalty but also serves as an authentic testament to LEGO’s positive impact on users’ lives.
• Education as a marketing pillar: LEGO’s unwavering commitment to education, exemplified by its partnerships and $24 million commitment to educational aid, positions the brand as more than just a toy. Aligning brand values with social causes and leveraging educational initiatives, builds trust and credibility.
• Cutting-edge mobile strategy: Sephora’s foresight into the mobile revolution is dissected in the case study, presenting a playbook for retailers aiming to capitalize on the mobile landscape.

Read the full LEGO case study here .

Tons of eCommerce retail inspiration, in one place

In the realm of business, success stories are not just tales of triumph but blueprints for aspiring executives to carve their paths to growth. The case studies explored here underscore a common theme: a mindset poised for evolution, a commitment to experimentation, and an embrace of emerging trends and technologies are the catalysts for unparalleled growth.

For any executive eager to script their growth story, these narratives serve as beacons illuminating the way forward. The dynamic world of retail beckons those ready to challenge the status quo, adopting the strategies and technologies that promise scalability. The key lies in constant optimization, mirroring the agility demonstrated by industry leaders.

As you embark on your growth journey, consider the invaluable lessons embedded in these success stories. Now is the time to experiment boldly, adopting new trends and technologies that align with your brand’s ethos. If you seek personalized guidance on navigating the intricate landscape of growth, our omnichannel retail experts at ContactPigeon are here to assist. Book a free consultation call to explore how our customer engagement platform can be the linchpin of your growth strategy. Remember, the path to scaling growth begins with a willingness to innovate, and your unwritten success story awaits its chapter of transformation.

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Neue: An Online Clothing Store for Blind People [Case Study]

Project overview.

The product: Neue is an online clothing store specifically focused on making purchases convenient for blind people. The app is designed around providing meaningful accessibility features that benefit users with a disability, helping them browse the store faster and make confident purchases with easy refund policies.

Project duration: November 2021 to January 2022

The problem: It is very inconvenient for people with visual impairment to visit the physical store to buy clothes and many of the online alternatives don’t have helpful accessibility features.

The goal:  Design an app for the Neue Clothing Store that allows users with a disability to easily browse through products and make purchases online w/o having to visit the physical store.

My Role: UX designer designing an app for Neue Clothing Store from conception to delivery.

Responsibilities:  Conducting interviews, paper, and digital wireframing, low and high-fidelity prototyping, conducting usability studies, accounting for accessibility, and iterating on designs.

User Research: Understanding the user

I conducted interviews and created empathy maps to understand the users I’m designing for and their needs.

Every individual who participated in the research showed a different approach for making purchases online and I confirmed that people with disabilities rather face the hassle of having to use workarounds to overcome online-platform shortcomings rather than having to visit the physical store.

Pain Points

• No support for assitive technologies: Assitive technologies like Screen readers are rarely supported in most apps.
• Non-descriptive copies: Online shopping platforms generally have undescriptive Call-to-actions and product descriptions.

User Persona: Rebecca

Problem statement: Rebecca is a blogger with visual impairment who needs an accessible online store to purchase outfits for her articles because going to the physical store is very inconvenient.

User journey map

Mapping Rebecca’s user journey revealed how helpful it would be for users to have access to a dedicated Online Clothing app for the blind.

Starting the Design

Paper wireframes.

Taking the time to draft iterations of each screen of the app on paper ensured that the elements that made it to digital wireframes would be well-suited to address user pain points. For the home screen, I prioritized the search field to help users with disability easily browse products.

Digital wireframes

As the initial design phase continued, I made sure to base screen designs on feedback and findings from the user research.

The primary goal is for users to find items they are looking for fast and easily.

Low-fidelity Prototype

Using the completed set of digital wireframes, I created a low-fidelity prototype. The primary user flow I connected was for the “Making a purchase” manual flow.  View the Neue app low-fidelity prototype .

Usability study: Findings

I conducted two rounds of usability studies. For synthesizing my results I create an affinity diagram.

Findings from the first study helped guide the designs from wireframes to mockups. The second study used a high-fidelity prototype and revealed what aspects of the mockups needed refining.

• Users want a filter options to find specific results.
• User want to have easier search typing feature.
• Users want to have well written descriptions & CTAs for screen readers.

Refining the Design

My early designs didn’t have contextual headings and placeholder texts for screen readers. So, I added a bit more context to every actionable task such as descriptive headings and search input placeholders.

After the second round of usability studies, I noticed users wanted specific results as search output instead of a never-ending list of results. To improve on this, I added a prominent filter button to help users narrow down search results.

Here is how my final design looks like:

High-fidelity prototype

View the Figma Link for hi-fi Prototype

Responsive designs

The designs for screen size variation included mobile, tablet, and desktop. I optimized the designs to fit the specific user needs of each device and screen size.

Accessibility considerations

• Used appropriate contrast and tested colors so that color-blind users can easily differentiate the UI elements.
• Used detailed instruction text and typography hierarchy to help all users better understand the design.
• Used gesture navigation wherever possible to make navigating through the app a lot easier.

Going forward: Takeaways & Next steps

The app helps users with disabilities buy clothes online w/o having them visit the store physically.

“All I wanted was a good search bar. Didn’t know this was the one I wanted.”

What I Learned:

While designing the Neue Clothing app, I learned that there are some biases I needed to overcome to make design accessible. Usability studies and peer feedback influenced each iteration of the app’s designs.

Next Steps:

• Add more accessibility features which will result in a better experience for all.
• Add overlay tips for users who are not tech savvy since many of our user base falls in that category.
• Research more on the workflows of how are users actually use the app.

New insights in online fashion retail returns from a customers’ perspective and their dynamics

• Original Paper
• Open access
• Published: 19 March 2021
• Volume 91 , pages 1149–1187, ( 2021 )

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You have full access to this open access article

• Björn Stöcker   ORCID: orcid.org/0000-0001-7119-7371 1 ,
• Daniel Baier   ORCID: orcid.org/0000-0001-6525-8094 2 &
• Benedikt M. Brand   ORCID: orcid.org/0000-0002-4250-4704 3  

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Returns are an inconvenient problem in the mail-order business, not only for the merchant but also for the customer. With an estimated return rate of 50% in the fashion sector, the seller has to deal with the expense of restocking and possibly reprocessing, the buyer, who must reship the return, and the environment. We do not consider returns to be generally bad, but rather an explicit, integral part of the online business model. Therefore, we investigate potentially suitable measures to avert or avoid returns in the pre-purchase, purchase, and post-purchase phases. We look at current and technological developments in return management and the most critical drivers for fashion assortment returns. The measures we investigate deliver a holistic view of the issue and target all three purchase phases. The resulting measures were assessed via an online questionnaire with 8393 participants (customers of a German fashion online retailer) to impact customer satisfaction using Kano’s method. There are clear measures that promise high customer satisfaction (such as 360° view) and a clear hierarchy regarding monetary and non-monetary measures. By applying a new method, the segmented Kano perspective, we found different customer segments, which are different in their expectations towards returns. That allowed us to conclude dynamics regarding return management. This assessment is followed by discussing the results, conclusions, and indications for further research fields.

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1 Introduction

While serving consumers online provides multiple benefits for online retailers (e.g., reaching consumers worldwide), it is also tied to some disadvantages inherent to distance trading. Especially product (fit) uncertainty (Hong and Pavlou 2014 ) and the missing touch and feel of products (Shulman et al. 2011 ) result in large amounts of product returns. These product returns are not only causing enormous costs for online shop operators (Samorani et al. 2019 ; Yan and Pei 2019 ) but additionally negatively affect the environment (Dutta et al. 2020 ; Pålsson et al. 2017 ). The number of returns shows to be very high in the online fashion business, in particular, due to its less standardized products (Difrancesco et al. 2018 ; Saarijärvi et al. 2017 ), the need for clothing to fit correctly (Gallino and Moreno 2018 ; Gelbrich et al. 2017 ) and the importance of apparel’s texture (Ofek et al. 2011 ). Since handling the return policy more or less lenient in this business will trigger higher purchase frequencies or prevent consumers from buying products (Hjort and Lantz 2016 ; Janakiraman et al. 2016 ), it is crucial to ascertain the golden mean for managerial implications.

While the vast majority of previous studies focused on finding optimal countermeasures for keeping return rates low without scaring off potential customers before or after purchasing separately, we contribute to the literature by examining the problem of returns holistically. Therefore, we extend the two-step decision perspective from Wood ( 2001 ), according to which online purchase decisions are divided into the (first) decision for or against a purchase, and the (second) decision for or against keeping the product, by analyzing measures to prevent product returns in three stages. These measures comprise supporting consumers searching for fashion products (pre-purchase stage), assistance in the ordering process (purchase stage), as well as strategies inducing consumers to keep the product (post-purchase stage). While the vast majority of literature focuses on preventing returns either before or after the purchase, we enable a direct comparison of measures for reducing returns by investigating all three stages with the same methodological approach. We use Kano's “Theory of Attractive Quality” (Kano et al. 1984 ) as a basis, from which we have respondents categorize several measures. Besides, to the best of the authors’ knowledge, we are the first to apply (segmented) Kano’s method in product returns and thus revealing those return measures that increase customers’ satisfaction the most.

Furthermore, we address potential solutions for product returns by implementing the most recent technological advances, such as virtual fitting of articles or 360° views of the products. Hence, we want to shed light on how consumers evaluate measures for preventing product returns in the context of online fashion shops at each of the three stages and to what extent they affect consumers’ satisfaction. By answering this question, we cover recently postulated research gaps (Janakiraman et al. 2016 ; Samorani et al. 2019 ) and indicate how managers could efficiently allocate financial budgets regarding their return policy.

Therefore, this study is structured as follows: first, we illustrate return management, its most recent developments, and technological improvements, as well as drivers of returns. We then describe our methodical approach leading to the results yielded. After discussing these, we end with a conclusion and directions for future research.

2 Theoretical background

With ever-increasing numbers of online shopping orders, the issue of product returns also becomes more critical. Even if the current return ratio remains constant, the consequence will negatively affect the environment heavily (Dutta et al. 2020 ; Pålsson et al. 2017 ). Furthermore, product returns constitute a cumbersome, unpleasant task for companies and consumers, likewise. As the e-commerce industry still struggles to provide sufficient and appropriate product information for customers to prevent (or at least reduce) returns (Gelbrich et al. 2017 ), and thus might not be able to offer suitable solutions soon, it is essential to explore product returns in comprehensive depth and based on recent technological advancements. Following the theoretical framework of the Confirmation–Disconfirmation paradigm in the context of products bought online (Hong and Pavlou 2014 ), the satisfaction with the delivered product (post-purchase) might be (1) lower than expected, resulting in a negative confirmation, (2) as expected resulting in zero (dis)confirmation, or (3) higher than expected resulting in positive confirmation.

2.1 Return management and recent developments

The emergence of a return is to be understood due to a comparison of expectations (while shopping online) and reality (when receiving the product), as illustrated in Fig.  1 . In the context of fashion, the expectations regarding the nature of the article (correct article) and the fit (correct fit) should be understood as a logical consequence, whereby the comparison of the expectations to the actual product can be moderated by curating the offer, e.g., through personal or personalized outfit recommendations. Resolving the information gap then leads to satisfaction or dissatisfaction with the ordered article. Nevertheless, it can be assumed that satisfaction alone does not directly affect the return behavior. A customer can be satisfied with a delivered article but still return it (selection order of several sizes or budget reached). It is also conceivable that an unsatisfied customer does not make a return but avoids buying a product from the supplier/manufacturer as a result. The influence of perceived service quality and its influence on the return behavior (e.g., delivery time) was not considered in this study.

Pre- and post-purchase stages with corresponding return prevention starting points

To categorize product returns properly, we refer to product returns before the purchase decision as “return avoidance”, whereas those return measures after the purchase decision will be named “return averting”. In the second case, the aim is no longer to influence expectations towards an article but to negotiate with the customer about the intended return. This negotiation can be done with, for example, money or an appeal. We assume that it is easier to negotiate with a customer satisfied with the article than with dissatisfied customers. In the latter case, the company must also consider whether suppressing the return is beneficial for the customer relationship or conceptualize an offer, which avoids lasting customer annoyance. In general, these measures should be applied with caution because once customers have understood this mechanism, they could actively use it to their advantage and change their ordering and purchasing behavior in this direction (Gelbrich et al. 2017 ).

The return literature dealing with these issues could be segmented into different groups based on their approach (Table 1 ). While some studies model different scenarios based on researchers’ assumptions (Difrancesco et al. 2018 ; Dutta et al. 2020 ; Letizia et al. 2018 ; Li et al. 2019 ; Ülkü and Gürler 2018 ) or founded on observable online shopping data (Gallino and Moreno 2018 ; Hjort and Lantz 2016 ; Lohse et al. 2017 ; Minnema et al. 2016 ; Petersen and Kumar 2015 ; Rao et al. 2018 ; Sahoo et al. 2018 ; Samorani et al. 2019 ; Walsh et al. 2016 ), we analyze measures of return avoidance and averting, by focusing on the customer’s voice; as finally, customers’ evaluation contributes to a more or less successful implementation of these measures. Thus, we conducted a literature review about recent articles (published between 2015 and 2020) that either include “product return”, “return prevention”, “reverse logistics”, or “return policy” in common scientific databases. After screening them by abstracts, we highlight those incorporating customers’ viewpoints derived from survey-based investigations.

It becomes evident that most studies investigating return measures from a customer’s viewpoint explore either the purchase or the post-purchase (returning) stage, and thereby not allowing a direct comparison of the effectiveness of the measures analyzed. In the same vein, the meta-analytic review by Janakiraman et al. ( 2016 , p. 234) concludes that “[p]rior research has largely examined these effects separately”. In contrast, studies interviewing the same respondents on product return prevention measures for both purchase and post-purchase are scant. To the best of our knowledge, we are the first to analyze return prevention measures for all three stages by applying the Kano method.

2.2 Drivers of returns and potential solutions

Whether to buy online instead of in a store also depends on the disadvantages of the mail-order business (Hong and Pavlou 2014 ; Shulman et al. 2011 ), which are common knowledge. If someone orders online, they have already familiarized/acquainted with it in advance (Ülkü and Gürler 2018 ) and might even take advantage of vendors’ lenient return policy (Pei and Paswan 2018 ).

Reasons for product returns are multi-faceted and very individualistic in the field of fashion in particular, but not all cases of product returns can be prevented. Based on a recent investigation with n = 1024 respondents (ibi research 2017 ), the drivers of product returns reveal to be product did not fit (62%), consumers did not like the product (39%), the product was defective or delivered in damaged conditions (30%), the product was not as described (30%). Followed by multiple variants were ordered (20%), wrong delivery (7%), delivery took too long (5%), the product was found cheaper in another shop (2%) or other reasons (2%), which is comparable to prior investigations (Gelbrich et al. 2017 ; Lee 2015 ). These drivers identified (Table 2 ) could be condensed into an information gap related to return reasons and those caused by online shopping operators' service. However, in some cases, customers return articles due to consumer behavior related causes, such as impulsive purchases (Ülkü and Gürler 2018 ), so-called “showrooming” behavior (Bell et al. 2018 ), or not fulfilled returns, which might result in dissatisfaction. Besides this categorization, ordered products were intended to be worn as a set and cannot be delivered or combined fall in-between consumer behavior and fulfillment/service reasons.

Based on these reasons, we collected potential measures for the three stages (Fig.  1 ) in Table 3 . These are substantiated based on literature and illustrated by practical examples, representing the measures used in our investigation. (As we intend to explore customers’ viewpoint for technological-advanced and state-of-the-art measures, some of the items applied have not yet been investigated in established journals.) Apart from that, we focused on rewarding rather than sanctioning measures. Most online retailers try to avoid the adverse effects of a less lenient return policy, such as ordering elsewhere (Gelbrich et al. 2017 ). This avoidance is in line with the operant conditioning theory (Skinner 1965 ), where the intended customer behavior (from a retailer’s perspective) is assumed to occur more frequently when this behavior is linked to a pleasant consequence (“positive reinforcement”). This theory has been applied in many areas of consumer behavior research (Wells 2014 ), such as online product selections (Perotti et al. 2003 ), corporate behavior (Vella and Foxall 2013 ), the effectiveness of TV commercials (Nathan and Wallace 1971 ), and even in the context of product returns (Gelbrich et al. 2017 ).

Hence, we also incorporate recent measures yet only discussed in blogs and contained in market research reports. Additionally, we assume an influence on the categorization by the market standard (MS) and the degree of user integration (DoIU).

Unfortunately, there are no relevant publications on the MS or the diffusion of the measures. We have decided to rate the MS in three dimensions: 1 = very common, 2 = partly common, 3 = very rare/not (yet) existing. For this purpose, we went among others through the top 20 German fashion online stores in 2018 to be consistent with the customers surveyed, who also live in Germany. In our assessment, only three measures can be considered very common (MS = 1): “360° view”, whereby we have also included an all-around photo series. Personalized newsletters were also offered by all providers, although not every newsletter contained a personalized element. We categorized measures as partially common (MS = 2) if they were not shown consistently or only for selected articles in the top 10 providers, which was the case with “catwalk videos” or “information model size.“For measures that were hardly shown (MS = 3), we had to search outside the top 20. In general, it can be said that measures from the post-purchase phase are hardly widespread (and challenging to investigate from an outside position), probably also because a mention of returns after purchasing could encourage a considered return. Bonus points for retained goods are an exception.

For the DoUI, we have also decided on three categories: ○ = no user interaction needed, ◑ = user interaction needed, but can still be used without, ● = can only be accomplished by integrating the user. Many of the measures do not rely on active user participation. We have assigned “No user interaction needed” if, on the one hand, no direct interaction is required, and the result does not change with even partial user interaction (e.g.,”Size advice—figure types”). This category is followed by measures that deliver results even without user input, but user interaction leads to improved results (e.g.,”Favorite article for comparison”). The highest requirements are measures that can only be achieved together with the user. These include virtual try-on or self-measure.

2.3 The relationship between expectation fulfillment and satisfaction

The effect of the individual (service) attributes on customer satisfaction is not always linear (Kano et al. 1984 ; Shahin et al. 2017 ; Shahrestani et al. 2020 ) and changes over time (Kano 2001 ). We would like to provide an informative insight into the different measures’ expected effects with our work from a customers’ perspective. For this purpose, various approaches are available (Mikulić and Prebežac 2011 ). Kano’s model (Kano et al. 1984 ) is a proper way to capture effects in the design stage of a product or service and later to derive managerial strategies. Therefore, we will use Kano’s method for our investigations.

In the literature, Kano’s model is not precisely distinguished. The following shall apply to this work: Kano’s model (Matzler 2003 ) is the term used to describe the work of Kano ( 1968 , 1987 , 1995 , 2001 ) and Kano et al. ( 1984 ), which is often referred to as “Theory of Attractive Quality”. Kano describes that the relationship between expectation fulfillment and customer satisfaction is not always linear. It should serve us as a theoretical concept for the multi-factor structure in customer satisfaction. Kano’s model is in contrast to the Kano method. It describes a procedure that can be used for categorization.

According to Kano et al. ( 1984 ) and Kano ( 2001 ), there are four primary patterns for cause-effect relationships: must-be, one-dimensional, attractive, and indifferent (Fig.  2 ) supplemented by two relatively rare and theoretical cases (Matzler et al. 1996 ; Mikulić and Prebežac 2011 ; Nilsson‐Witell and Fundin 2005 ) from which strategies for companies are derived.

Must-be (M) items are items for which poor performance has the strongest effect on customer satisfaction in its entirety; meeting or even exceeding expectations cannot increase overall customer satisfaction. Strategy: Securing primary performance via, e.g., service level agreements, following no further investment.

One-dimensional (O) items are items with a direct influence on overall satisfaction for good and bad fulfillment. Strategy: Ensure primary performance and increase it further.

Attractive (A) items are usually not expected by the customer and, if present, lead to an improvement in satisfaction. Absence or poor performance does not affect overall satisfaction. Strategy: If the necessary services (M and O) are acceptable, they can differentiate in the market.

Indifferent (I) items have no neither a positive nor negative influence on customer satisfaction. Strategy: Avoid Investments.

Reverse (R) items lead to a decline in satisfaction when present, but their absence leads to an improvement. Strategy: Not only should any investment be avoided, but consideration should also consider whether a consciously externally communicated demarcation can be perceived as A.

Questionable (Q) items are forfeited if none of the five correlations listed could be determined; subsequently, no general strategy applies.

Kano’s model (Kano et al. 1984 ) with the illustration of its life cycle (Kano 2001 )

Kano ( 2001 ) also addresses a dynamic change over time. In his view, a successful quality element of a product or service passes through this sequence or life cycle: I → A → O → M. Nevertheless, also other sequences can be found. Nilsson‐Witell and Fundin ( 2005 ) have shown that when an adoption level is taken into account, the answers can be categorized differently. For example, one service studied was during introduction I and later A. Respondents referred to as early adopters already categorized this service as O or even M instead of A. Further studies have also shown in time series comparisons that the attributes change dynamically over time. Hölzing ( 2008 ) examined services for people with diabetes at an interval of 6 months (2005, 2006), Raharjo et al. ( 2010 ) for characteristics of notebooks with ten data points at a 2-month rhythm, Löfgren et al. ( 2011 ) quality attributes of commodity packaging (2003, 2009) and Stöcker and Nasseri ( 2020 ) touchpoint satisfaction of customers of an e-commerce retailer (2011, 2013).

2.4 Hypothetical framework

We will now derive our hypotheses about the measures presented in Table 3 . These can be divided into two main groups: characteristics that concern the measure itself (time effects, type of incentive, and user interaction) and variations in customer attributes (age and order frequency).

2.4.1 Measure-related hypotheses

As illustrated in Fig.  2 , new service features will first be evaluated as A and perceived as O with a linear increase regarding satisfaction and, finally, the M dimension (Kano 2001 ). However, online shopping operators need to consider the features’ adoption rates in terms of time and incorporate the potential competitive advantage by being the first to offer specific measures. According to the law of differentiation dynamics, the prospective competitive advantage will diminish if competitors are already providing such features (Rudolph and Becker 2003 ). While some measures (those with high levels of MS, see Table 3 ) are already widely implemented in online shops, others are still in an evolving stage with only a few practical examples existing. Therefore, we assume:

Measures with a low level of MS are more frequently categorized as I and A instead of O and M than those representing a high MS level.

Within the post-purchase stage measures, those related to compensation or rewards might be perceived as positive, as they will trigger reinforcement according to the operant conditioning theory (Skinner 1965 ). Hence, they result in higher consumer satisfaction than other sanctioning measures (such as displaying return behavior or return impact information). So, measures that reward consumers seem to pay off more than sanctioning them (Gelbrich et al. 2017 ; IFH Köln and AZ Direct 2016 ). Although we have excluded re-purchase behavior from this study, it should be evident that, especially in a buyer's market with many suppliers, respectively, a negative sanction leads to customers' churn. Therefore, the implementation of these measures must follow with great sensitivity. Accordingly, we hypothesize that monetary measures (“Discount on next order”, “Discount on current order”, “Bonus points for purchase”, “Bonus points for non-return orders”, and “Waiver of shipping costs”) will result in a higher increase in customer satisfaction, especially in contrast to measures sanctioning customers (“Display of the return behavior” and “Return impact information”).

Monetary measures have a stronger positive influence on customer satisfaction (CS + ) than non-monetary measures.

In the measures described for the avoidance of returns, some can only succeed with the user’s active collaboration (see Table 3 , column DoUI). Here, such measures’ success depends on customers’ willingness to engage in these measures (Lai et al. 2014 ). Since the fashion market is a buyer’s market, we assume that these are less appealing.

Measures that require the direct engagement of users are less frequently categorized as A, O, and M compared to other measures.

2.4.2 Customer-related hypotheses

Technical innovations undergo a life cycle, according to Kano ( 2001 ). We assume that newer measures, which cannot be considered the MS, are preferred more by younger than older customers. In this study, the measures “Virtual fitting of articles”, “Self-measurement via webcam”, “Curated shopping”, “Assisted shopping”, and “Online shop as a social platform”. This effect is actual true for the millennial generation, who possess excellent technological skills (Ladhari et al. 2019 ).

Innovative measures positively influence customer satisfaction (CS + ) by younger customers.

In line with extant literature (Gelbrich et al. 2017 ), we assume purchase frequencey to moderate the categorization of return averting measures. Meanwhile, customers with high shopping frequency are used to handle product returns as part of the shopping online (Ülkü and Gürler 2018 ). Hence, they easily hazard the related consequences and sometimes even take advantage of a merchant’s lenient return policy (Pei and Paswan 2018 ). Therefore, we expect:

Customers with a high purchase frequency tend to categorize the queried measures in the three purchase stages as A and O.

With the hypotheses that have been formulated, we try to determine structures within the individual measures, which can later be generalized. Using the segmented Kano perspective, we also investigate whether the answers already show signs of a life cycle for the measures. For this purpose, we use a structured questionnaire, which also includes questions on buying and return behavior. Thus, we hope to isolate additional descriptive characteristics that can profile our findings even more precisely.

3 Research design

To shed a light on the customers’ voice, we decided to use an online questionnaire sent to all customers. In this questionnaire, we asked one functional and one dysfunctional question for each measure; these questions were combined in the evaluation.

3.1 Survey and descriptive statistics

While many studies in return management literature applying self-report surveys suffer from acquiring an adequate sample and use student samples instead (Gelbrich et al. 2017 ; Oghazi et al. 2018 ; Pei and Paswan 2018 ), we want to overcome this issue by enquiring actual customers from a leading online shop in Germany. This approach provides multiple advantages. First, in contrast to students, actual customers exhibit higher income levels and, therefore, higher purchase power (Iyer and Eastman 2006 ), leading to more realistic responses regarding price issues. Second, even though elderly consumers represent a fast-growing segment in e-commerce, literature on consumers’ online shopping behavior older than 50 years is still very scant (Lian and Yen 2014 ) and should be examined. Third, while students’ answers for hypothetical scenarios might not reveal their actual shopping and return behavior, we expose our questions within the determined online shop's framework addressing this specific online shop's customers, which results in more realistic findings. For our research, we had the opportunity to contact customers of BAUR Versand (baur.de), a top 10 online retailer for fashion in Germany (EHI Retail Institute 2019 ). BAUR's product range focuses on fashion, shoes, and home, including furniture, and concentrates primarily on female customers between 40 and 55. BAUR relies primarily on well-known brands, and around 90% of the business volume is handled via the online shop.

The invitation to participate in the survey was sent by e-mail on December 14, 2018, to all BAUR customers providing the opportunity to answer the questionnaire until January 18, 2019. A raffle of 15 shopping vouchers worth EUR 20 for the BAUR online shop was announced among all participants in the invitation. To not overstrain respondents with the very time-consuming questionnaire, three surveys with different clusters of measures were used, randomly assigned to the e-mail addresses. All questionnaires had the same structure and differed only in the return measures exposed using the Kano methodology (survey 1: 10 measures, survey 2: 11, and survey 3: 9, see Table 5 ). In the beginning, the aim and purpose of the study were explained. It was pointed out that this was a joint research project of BAUR and students of a near-by University. The initial questions on the current ordering and returns behavior were subsequently asked (no further validation via the customer database). The self-assessment of the respondents serves, on the one hand, as an icebreaker question; on the other hand, the respondent should reflect his or her return behavior at this point and thus form the basis for further answers. They were following these questions by the evaluation of one of the three clusters of measures. The Kano questioning technique, unusual for many respondents, was first introduced using an example. Finally, presenting the questions on socio-demographics and space for comments and the opportunity to participate in the raffle. Pretests helped to test the comprehensibility of the questions and the structure during the questionnaire development.

For describing the respondents in more detail in the following analysis, other characteristics were queried: (a) On the one hand, the current ordering behavior, whereby the ordering frequency, the average expenditure on fashion, for who is mainly purchased, where individual product ranges are purchased preferentially (online or offline), whether these purchases are mainly spontaneous or planned and how fashion buying online is generally perceived. Afterward, (b) the current return behavior: how often a return took place, the reasons for it, how complex a return is perceived, and whether the return behavior differs between orders from different shops. Finally, in addition to age and gender, (c) the residence place’s size was also surveyed to detect any differences in an assumed imbalance of supply.

A total of 8393 complete questionnaires were evaluated (survey 1: n = 2789 completion rate 68%, survey 2 n = 2855 completion rate 70%; survey 3 n = 2749 completion rate 64%). The three samples are structured as follows about their purchasing behavior and socio-demographic characteristics (for full detail, see Appendix ).

The majority of customers order fashion online between once a month (30.6%) and once a quarter (32.6%). At the same time, 85.8% of those surveyed stated that they spend up to 150 EUR. Regarding their shopping behavior, 22.8% describe themselves as planning, 36.0% as partly/partially planning, and 41.1% as browsing and discovering. Only very few of the respondents (8.0%) answer that they avoid online shopping when possible. Besides, the vast majority (62.0%) answered that they love buying fashion online. Concerning the number of returns, customers state that they have also returned in 32.6% of (all) orders transacted.

Regarding the reasons for a fashion return, 87.1% of the respondents answered with “Item does not fit” 45.9% with “I do not like this item” 41.6% ordered several sizes to choose from, 21.0% “not as described” and 4.2% bought more to choose from at home due to a promotional measure. In the upper third of the scale, 55.4% rate a fashion return’s effort as “not elaborate”. Here too, bias is to be assumed from the survey of active online shoppers. When asked whether the return behavior differs among different providers, 52.4% explicitly answered “no” while 77.7% of the answers tended to be “no” in the first half of the 6-point Likert scale.

Among the respondents, 79.8% are female, 29.1% are between 29 and 44 years old, 32.9% are between 45 and 54 years old, and 38.1% are older than 55, slightly above average in small and medium-sized cities (5 to 100 thousand inhabitants) and firmly below average in cities with millions of inhabitants.

3.2 Categorization of the measures

In order to determine the cause-effect relationships for each item in Table 3 , two questions were asked: the functional (“imagine that … has [item] …”) (Kano et al. 1984 ; Matzler et al. 1996 ; Mikulić and Prebežac 2011 ) and dysfunctional (“imagine that … has not [item] …”) questions (Berger et al. 1993 ; Matzler et al. 1996 ; Nilsson‐Witell and Fundin 2005 ). The answer is given on an ordinal scale with a middle option [“(1) I like it that way”, “(2) It must be that way”, “(3) I am neutral”, “(4) I can live with it that way”, “(5) I dislike it that way”]. The classification can be determined via the Kano table by combining the two answers to the two questions (Table 4 ).

The characteristic is now derived from the Kano table. If all survey results of one question are plotted as value pairs in a coordinate system, the characteristic Kano curves are obtained (see Fig.  2 ).

In the literature, however, another approach is also common. In this case, no curves are shown; the character is reflected here in the position of the individual measures in the respective quadrants. This approach presents the positive and negative impact on customer satisfaction as two coefficients (Berger et al. 1993 ; Shahin et al. 2013 ; Shahin and Zairi 2009 ). Assuming a positive factor on the customer satisfaction (CS + ) for answers falling into classes A and O, a negative factor (CS − ) for O and M. Answer combinations from classes Q and R are not considered. The results are then displayed graphically in a coordinate system representing the two axes CS + and CS − orthogonally. The two coefficients tell us how often an item has been categorized into the mentioned groups. For CS + , the mentions are counted positively influencing satisfaction when the expectation is fulfilled positively (A and O). For CS − those where a negative fulfillment negatively influences satisfaction (O and M). A high value consequently shows a high correlation with customer satisfaction. Since Kano’s categorization can only be interpreted by translating the terms into one of the corresponding curves, the coefficients dispense with this step. The positive as well as the negative effect, can be read off directly.

#A, #I, #M and #O represent the response frequencies of the categories or the number of responses categorized as A, I, M, or O. The indices are between 0 and 1 and − 1, respectively, and reflect the impact on satisfaction. From the location of the points, their categorization is again apparent. The coordinate system is divided into four quadrants:

If points are close to the origin, no influence can be proven at all. If a point lies precisely in the middle, a positive and, at the same time, the negative influence is detectable in 50% of the respondents. The coordinate system position can now be determined (see values in Table 5 ) using the formulas described in Chapter 3 or read directly from Table 5 . For example, the item “Discount on next order” has a CS + of 0.8 and a CS − of -0.23. It can therefore be found in quadrant “A” in the upper left corner.

Also, the total strength (TS) represents the number of mentions categorized as A, M, or O compared to all mentions. Items with a high TS also have a strong influence (positive or negative) on total customer satisfaction. The TS serves to prioritize the individual items concerning their effect on customer satisfaction. Improvements to items with a high TS should have a high impact on the change in customer satisfaction:

Recently, other papers apply an additional variant to the method described above. The Segmented Kano perspective descends one level deeper by searching for clusters within the answers. The new approach makes it possible to identify different customer segments with different expectations, otherwise not visible in the aggregated form. For this purpose, the answers enter the functional and dysfunctional question as a metric feature into the cluster analysis (Baier, Rese and Röglinger  2018 ) or using one-mode non-metric cluster analysis concerning the derived categories (Rese et al. 2019 ). The number of clusters is then determined iteratively under the observation of the Bayesian Information Criteria (BIC) concerning the likeness functions.

Table 5 displays the overall assessment of the measures based on Kano’s model, indicating category frequencies, the total strength (TS), the customer satisfaction index CS + , and the customer dissatisfaction index CS − .

The surveyed measures’ results are evaluated solely as A (not expected, but if there is a positive influence on overall satisfaction) or I (no evident influence on overall satisfaction). Regarding category A, the measures “360° view”, “Discount on current order”, “Discount on next order”, “Bonus points for purchases”, and “Waiver of shipping costs” stand out. More than 50% of the respondents rated these measures as A, suggesting that these measures could substantially contribute to customer satisfaction. In contrast, the measures “Curated shopping”, “Assisted shopping”, “Commenting on reviews”, “Online shop as a social platform”, “Photos from social networks”, “Outfit recommendations from influencers” and “Return impact information” are also categorized I to more than 50% of the mentions. Here, no influence on customer satisfaction is expected when implementing the measures. None of the measures can be described as M. The only measure that could be considered O is “Waiver of shipping costs”. Here, the closest mentions are for A 1373 and O 900. Measures are categorized as R if their interrelationship towards satisfaction is precisely the opposite. An exemplary implementation has a negative effect and a bad one, a positive effect on satisfaction. Here, the measure webcam size is particularly striking. Categorized as I, with 1125 mentions, but with 996 mentions, it is also very close to R.

Considering the categorization of the measures and the MS’s degree that we have assumed (H 1 ), no consistent picture emerges (Table 6 ). The measures investigated are distributed equally between I and A, depending on the MS’s level. Interestingly, even measures that have been in the market for a long time and established are only categorized as A.

Nor can a uniform picture be formed for the DoUI (H 3 , Table 7 ). Suppose we additionally exclude return averting measures, which do not prevent returns in the narrower sense but negotiate the conditions under which the customer would refrain from returning, a similar distribution between I and A can be observed here as well. Consequently, this hypothesis must also be rejected.

Figure  3 shows all measures based on their impact coefficients CS + and CS − . Here, too, the same picture emerges. All measures presented are located in the two quadrants I and A. Furthermore, an exciting pattern becomes visible: most measures with a monetary reward show the most considerable positive impact (“Waiver of shipping costs”, “Discount on next order”, “Discount on current order”, “Bonus points for purchases”). As we stated in H 2 , monetary measures have a stronger positive influence on customer satisfaction than the other examined measures and measures sanctioning the customer. These are followed by measures that primarily result in an improvement of the presentation by the vendor (“360° view”, “Find out individual size”, “Size advice—figure types”, “Size recommendation—previous purchases”, “Presentation via video”, “One model wears all sizes”, “Size recommendation—previous purchases”). A third block can be seen in the I quadrant. This cluster contains measures that either include external content in the shop (“Online shop as a social platform”, “Outfit recommendations from influencers”, “Commenting on reviews”, “Photos from social networks”), require the customer to be involved (“Assisted shopping”, “Self-measurement via webcam”, “Photos from social networks”) or reflect their return behavior. Since our study examines all stages of the purchasing process, we can clearly show this hierarchy of measures at this point.

Depiction of the overall assessment of possible measures (n = 8396)

We then have examined all the proposed measures regarding dependencies (linear or segmental) in the answering behavior to their age (H 4 ) and shopping frequencies (H 5 ) with no significant differences found.

We apply the before-mentioned segmented Kano perspective to reveal more meaningful insights based on the overall results and derive more clear implications. We have used the well-known two-step clustering approach, according to Chiu et al. ( 2001 ). In each record, each measure is categorized according to Kano’s evaluation table. For the resulting nominal data matrix, independent multinomial distribution of the categories over the clusters’ attributes is assumed. The optimal number of clusters is now determined iteratively, taking into account the BIC. In this case, three clusters have proven to be ideal.

From the results in Table 8 and Figs. 4 , 5 and 6 , initial findings can already be deduced. A closer look reveals that the three surveys' segments follow similar patterns: each segment can be assigned to a quadrant. We named segments primarily in I “Indifferents”, those in A “Enthusiastics” and O “Demanders”. A segment in M we would call “Taken-for-granteds”. Nilsson‐Witell and Fundin ( 2005 ) have found a similar starting position in their study for “e-service”. When introduced, the service was perceived as I, it became later A. They investigated the mentions classified as A with a technology adoption level and found segments in O and M, which they refer to as “early adopters”, a term also used to the diffusion of innovations theory (Rogers 1962 ).

Depiction of the assessment of possible measures survey 1 (n = 2792)

Depiction of the assessment of possible measures survey 2 (n = 2855)

Depiction of the assessment of possible measures survey 3 (n = 2749)

These segments can again be depicted graphically, where each graph represents one of the three surveys. In the graphs, there are three data points (segments) for each measure. Different symbols indicate the affiliation to the respective segment. For the sake of clarity, we have refrained from displaying all 84 data points in one graph. Therefore, we have staggered the graphs according to the surveys. We see more information value in directly comparing the clusters’ positions to each other for each item. Also, the clusters were calculated independently for each survey.

Analogous to Kano’s life cycle theory, there are already segments with statistically significant differences. In the segmented Kano perspective, not the measures differ among themselves, but the persons confronted with the measures. It is possible to see in Figs. 4 , 5 and 6 that a general MS apparently has no influence or otherwise cannot be determined in the first place. Instead, it seems that customers have individual expectations regarding the measures, referring again to H 1 ,

Naturally, measures that show the highest and lowest influences aggregated (Table 5 ) also show the strongest or lowest influences in relative terms for the individual segments. To have a strong influence overall, many respondents have to answer in the same way, which is also the case after dividing into the three segments. Therefore, measures with a very high or meager impact on customer satisfaction are found in similar (relative) positions after splitting into segments.

To provide a more detailed characterization of the segments, we also investigated them regarding their buying and return behavior and socio-demographic. No significant differences were found here either.

5 Discussion

5.1 theoretical contribution.

While the majority of previous studies analyzed return behavior either before/during, or after the purchase decision (Janakiraman et al. 2016 ), we contribute to the literature by expanding the view on return management to a 3-stage approach, which is investigated in the pre-purchase, post-purchase, and purchase stage based on a large data pool of actual customers (n = 8396). This holistic approach reveals that return measures in the post-purchase and actual purchase stage are more applicable to increase consumers’ satisfaction than those related to the pre-purchase stage. For this purpose, we have extended the already existing approaches in Fig.  1 . Besides, to the best of our knowledge, this study represents the first to analyze consumer return behavior by applying the Kano method. Hence, we enable an overview of product return avoidance and averting measures to satisfy consumers the most. This juxtaposition shows for the first time how strongly monetary approaches differ from the remaining measures. Without a combination of the three stages, this finding would not have been possible.

5.2 Managerial implications

Returns in the mail-order business, especially fashion, are a great nuisance for the customer, the company, and the environment. However, not all of the proposed measures can be effectively implemented by a company. It is, therefore, essential to focus on a few but effective measures. Our paper offers new insights in this respect. As one might expect, measures that positively sanction customers are prevalent. Since these, in turn, actively influence pricing policy, such measures must be weighed up carefully. The next exciting group includes measures that aim at improving the presentation of merchandise without requiring further effort from the customer. The “360° view” stands out in particular. This measure has even prevailed over more elaborate presentations such as “Model type photos”, “Presentation via catwalk videos”, “Virtual fitting of articles”, "Presentation via video”, or “Information model size”.

The second important finding is that the measures were categorized as exclusively I or A on the overall level. A lack of or poor performance in these measures still has little effect on satisfaction. Two conclusions can be drawn from this: either returns are hardly an issue for the respondents. More than half of the respondents answered that they do not consider returns to be costly (the bias could be that only active mail-order customers participated in the survey). The other conclusion may be the real MS in terms of avoiding and averting returns is still deficient, and so are the expectations.

However, a more precise segmentation into clusters already reveals the first one-directional measures. Thus, there are already customers whose expectations are significantly higher and whose absence or poor performance leads to dissatisfaction. In the sense of early strategic detection, this customer group should be observed more closely. If this group grows significantly over time, investment in return management is no longer just nice to have but essential for customer satisfaction. Ultimately, it can be assumed that measures will migrate to the M quadrant in some time, which means that investments in this area will not even increase satisfaction but will only prevent dissatisfaction. Unfortunately, we were not able to describe these clusters more precisely with the customer characteristics queried. Further research in this area would, therefore, be highly desirable.

In brief, this means that customer expectations in return management are generally still in a very early life cycle stage. However, this does by no means indicate that this is unimportant. On the contrary, vendors can set themselves ahead of the competition and gain a competitive advantage by, for example, improving the presentation of their products. Our categorization of the MS for Germany clearly shows how few measures can already be considered established. The online market, which is still growing dynamically, will also be joined by different groups of consumers who can no longer be described as early movers. Here the demands will change even more significantly, also concerning return management. The current social discussion in Western countries is also bringing the environmental impact of human activity more into focus. Here, too, vendors can already differentiate themselves from the competition today and use the first-mover advantage for themselves.

Although monetary incentives such as vouchers or discounts promise a high impact, these mechanisms are usually easy to comprehend. On the other hand, we are firmly convinced that a focus on monetary incentives alone does not represent a differentiating feature and can also be easily copied by the competition.

Finally, it should be noted that some measures, albeit unintentionally, can have a negative impact on repeat purchase behavior. A test and learning approach is, therefore, advisable here.

5.3 Limitations and future research

This paper is limited in some respects. First of all, the respondents are active, mail-order customers acquired via newsletters. Potential customers who, for example, do not buy by mail order at all due to the problem of returns are not present. Neither the age nor gender structure is representative of Germany. It is also conceivable that BAUR customers differ from other mail order customers in their attitude to, among other things, new technologies, precisely because in the survey, mainly women with an aging focus over 45 years answered (see  Appendix ). They differ significantly from the millennial generation regarding their technological skills (Ladhari et al. 2019 ).

Secondly, the survey was conducted in the German market. Thus, no assertions about possible cultural influences are possible, nor can the industry structure be transferred to other markets without adjustments. Competition may be more intense or extensive, which also affects expectations. For instance, based on the cultural dimensions (Hofstede 1980 ), Germans are assumed to be more likely to avoid uncertain outcomes (rating: 65), compared to, e.g., Americans (rating: 46) or Chinese (rating: 30; Hofstede Insights 2020 ). Hence, return avoidance and return averting measures can be expected to be of higher interest among Germans to avoid such potentially wrong decisions.

The formed segments strongly indicate a dynamic over time, but unfortunately, could not be described in more detail using the other characteristics that were queried. Therefore, no further contribution could be made here about the presumed adoption behavior, leaving space for further investigations.

There is also strong evidence in the literature that a very restrictive or inconvenient return policy can also affect purchase and re-purchase behavior, especially in a competitive environment like fashion retail. In our view, this field also still receives little attention in research.

Finally, Kano’s method has its limits. Especially in innovation research, many measures are categorized as I or A. The method can only indicate the current status without providing direct trends for individual attributes’ future progression. A life cycle is only determined retrospectively. Especially in very dynamic markets such as (fashion) e-commerce, new features are often simply trialed without the need to go through a classic life cycle. Therefore, the context of the featured solutions must always be considered. Also, the special questioning is quite time-consuming and requires a high level of concentration in answering it, diminishing long surveys.

6 Conclusion

We wanted to investigate the most effective strategies to counteract returns from a customers’ standpoint. Based on a newly developed three stages process purchase model, a view of several measures have been investigated towards their potential impact on customer satisfaction. Using the Kano method and its subsequent segmented Kano perspective, exciting results were obtained. Among other things, we were able to show that an improvement in the presentation of the products on offer is generally an excellent choice for counteracting returns and that different expectations regarding return management can already be observed today. We thus confirm prior findings, revealing that enhanced product presentation features, such as zooming (De et al. 2013 ), or in our case a 360° perspective, paves the way for fewer returns or higher customer satisfaction, respectively.

Similarly, photos from social networks or, more generally, alternative product photos are perceived indifferently or might even result in more returns (De et al. 2013 ). Moreover, we validated that offering virtual fit information enables declined returns (Gallino and Moreno 2018 ), as virtual reality tools lead to increased customer satisfaction. Generally, our insights emphasize monetary gratifications to represent the measures increasing customer satisfaction the most, which contradicts elder findings derived from online shop return rates below the usual average in the fashion industry (Walsh and Möhring 2015 ). Besides the nature of gratification and contrast to previous literature, our holistic perspective demonstrated that measures from the post-purchase stage are most likely to increase customer satisfaction, as five measures are among the eight most practical measures (highest CS + ). With this work, we hope to have provided valuable insights into the avoidance and prevention of returns, leading to a reduction of returns in practice.

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Stöcker, B., Baier, D. & Brand, B.M. New insights in online fashion retail returns from a customers’ perspective and their dynamics. J Bus Econ 91 , 1149–1187 (2021). https://doi.org/10.1007/s11573-021-01032-1

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• Drivers for returns
• Avoiding and averting returns
• Kano (method)
• Segmented kano perspective
• Customer perspective
• Kano dynamics

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Consumer values, online purchase behaviour and the fashion industry: an emerging market context

PSU Research Review

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Article publication date: 21 September 2021

• Supplementary Material

This study examines consumer online purchase behaviour in the Nigerian fashion industry.

Design/methodology/approach

A cross-sectional study was conducted with a total useable sample size of 241 respondents contacted through on-site visitation. Descriptive and inferential statistics were used to test the influence of customer value on online purchase behaviour in the fashion industry.

Consumer values are categorised into terminal (happiness, love and satisfaction) and instrumental (time-saving, price-saving discount, service convenience and merchandise assortment) values. The findings show that both values have significant influence on online consumer purchase behaviour, while fashion consciousness moderates the relationship between consumer values and online purchase behaviour.

Practical implications

Online fashion retailers should focus on increasing the terminal and instrumental values of their products and making available goods that meet the needs of different generational cohorts in society.

Originality/value

Studies have examined various factors, for example, consumer values that are determinants of consumer online purchase in the fashion industry; however, there has been limited focus on the nature of fashion and online purchasing in emerging markets, particularly those in Sub-Saharan Africa.

• Customer values
• Online purchase behaviour
• Digital retailing
• Technology innovation

Adeola, O. , Moradeyo, A.A. , Muogboh, O. and Adisa, I. (2021), "Consumer values, online purchase behaviour and the fashion industry: an emerging market context", PSU Research Review , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/PRR-04-2021-0019

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Introduction

The fashion industry dates back to over a hundred thousand years, right from the availability and use of textiles by mankind ( Botti, 2019 ). The industry, over time, has added economic and material value to humanity, evolving with society, making it a very relevant aspect of human life and also a common area of research, particularly in this technology-driven world ( Bruce and Daly, 2006 ; Botti, 2019 ; Kilduff, 2005 ; Xue et al. , 2019 ). Globally, the fashion industry contributes about US$3000 bn, an estimated 2% of the world's gross domestic product (GDP) ( Botti, 2019 ). Today, technological revolution and the Internet have enabled the establishment of online fashion retail systems to displace aspects of the traditional store patronage ( Johnstone et al. , 2013 ; Kautish and Sharma, 2018 ; Pantano and Viassone, 2015 ).

The term “fashion” is a concept that is widely accepted by committees, class or groups of people and is directly affected by marketing factors, such as low predictability, high impulse purchase, short-life cycle and the high volatility of market demand ( Fernie and Sparks, 1998 ; Bhardwaj and Fairhurst, 2010 ).

Digital retailing in the fashion industry has gained prominence, providing ample opportunities for marketers to reach out to different generational cohorts (i.e. generations X, Y and Z) ( Pentecost and Andrews, 2010 ). Generational cohort is a theoretical approach to understanding the diverse group of individuals in a society. The term is used to describe individuals who share similar political, social, cultural and economic events during their childhood ( Fernández-Durán, 2016 ). The most widely used categorisation is Gen X, Y and Z ( Sima, 2016 ). Individuals who fall into these classifications are considered to share similar behaviour, perceptions of reality, values and consumption patterns, which must be understood from a marketing standpoint ( Fernández-Durán, 2016 ; Liang and Xu, 2018 ; Mahmoud et al. , 2021 ; Sima, 2016 ; Tan et al. , 2019 ). For example, individuals in Gen X (1965–1981) are regarded as digital immigrants while Gen Y (1982–1999) and Gen Z (2000–2012) are regarded as digital natives ( Mahmoud et al. , 2021 ). To contextualise the distribution of consumers in the fashion market, this classification must be well understood.

Retail digitisation has changed the process of shopping for consumers and the process of selling for organisations in the fashion industry by providing convenient and affordable services ( Hagberg et al. , 2016 ; Kautish and Sharma, 2018 ; Renko and Druzijanic, 2014 ). Consumers' desire to shop for clothing online has, however, been hindered by challenges of “fit” and “size” of cloths ( Miell et al. , 2018 ). There have been several studies (e.g. Loker et al. , 2004 , 2008 ; Song and Ashdown, 2012 ; Kim and LaBat, 2013 ; Beck and Crié, 2018 ) that focused on providing solutions to the challenges that can impede the benefits of online fashion retailing for businesses, shoppers, and generally hinder the growth of the industry.

These challenges have negatively influenced consumers' perception of online purchases in the fashion industry, especially with clothing purchase. Digital “fit” and “sizing” technologies have been introduced to address this challenge and give customers the needed satisfaction in their online fashion purchases in developed nations ( Miell et al. , 2018 ). Online purchase is gaining prominence in Nigeria ( Aminu, 2013 ; Usman and Kumar, 2020 ), but the rate and pace of online fashion (apparel) purchase have been low despite having a large population of Internet users ( Falode et al. , 2016 ). Falode et al. , investigated online and offline shopping motivation of apparel consumers in Ibadan, Nigeria and found that consumers prefer offline purchase of apparel to online. This is quite worrying as Nigeria has an active online population which offers fashion organisations enormous opportunities ( Falode et al. , 2016 ). Hence, understanding the factors that will engender the consumer's online purchase in the fashion industry is sacrosanct to the sustainability of the online fashion space in Nigeria.

Extant studies have attempted to provide predictive direction regarding what influences consumers' online purchases in the fashion industry. For example, Schmidt et al. (2015) posit that what consumers see and hear online, influences their buying behaviour. Pentecost and Andrews (2010) established that gender influences the rate of purchase and that females purchase more items in the fashion industry than their male counterparts. Pentecost and Andrews also found that Gen Y consumers have higher purchase frequency and impulse buying than other generational cohorts. Kautish and Sharma (2018) examined consumer values, fashion consciousness and behavioural intentions in the online fashion retail sector and found a significant relationship between consumer values, fashion consciousness and behavioural intention of the consumers in India. Their study was conducted to highlight the basic factors that influence consumer purchase and patronage of online retailing in the country's fashion industry. The authors identified three variables that determine the consumer's online behaviour: consumer values, fashion consciousness and behavioural intentions.

Generally, countries in Africa are known for their distinct socio-cultural values, which influence their fashion behaviour ( Aminu, 2013 ; Falode et al. , 2016 ). The role of socio-cultural values on consumer purchase behaviour has also been explored (see Agnihotri and Bhattacharya, 2019 ; Ansari, 2018 ; Craig and Douglas, 2006 ; Kacen and Lee, 2002 ; Koon et al. , 2020 ; Nwankwo et al. , 2014 ; Pepper et al. , 2009 ; Tendai and Crispen, 2009 ); however, there is a dearth of studies on consumer online purchase behaviour, in the fashion industry, with reference to sub-Saharan Africa. A key country in this region is Nigeria, known for its multi-ethnicity and large population. The country's median age is 18.4 years, which indicates the propensity of a technology-driven youthful population ( Varrella, 2020 ). With the challenge of “fit” and “size” and patronage of online fashion space in Nigeria ( Falode et al. , 2016 ; Ogbuji and Udom, 2018 ), this study assesses consumer purchase behaviour in online fashion retailing of an emerging market, particularly in a technology-driven society. Following Kautish and Sharma's (2018) study, we adopt the variables – values, fashion consciousness and behavioural intention to purchase – as predictors of online consumer purchase behaviour in the Nigerian fashion industry.

Theoretical framework

Theory of planned behaviour.

This paper adopts the theory of planned behaviour (TPB) by Azjen (1985 , 1991 ) to explain the purchase and patronage of online fashion retailing. Azjen (1991) asserts that an individual's behaviour is not spontaneous but rather is influenced and determined by various factors, such as intention, social norm and perceived control over certain phenomena. TPB is an extension of the theory of reasoned action (TRA) ( Azjen and Fishbein, 1980 ; George, 2004 ). The TRA proposed that intention is crucial in exhibiting certain behaviours, and it is measured by attitude and subjective norms ( Hagger, 2019 ). The theory focused on explaining behaviours within the individual's control, and the scope did not capture explanations on why individuals are not in total control of some of their behaviours, and this led to TPB. Azjen extended TRA with the propositions of the TPB and included the construct of perceived behavioural control to explain behaviours beyond the control of the individual ( Hagger, 2019 ).

According to George (2004) , the attitude towards a target behaviour and the subjective norms surrounding it determine intention. Several studies have applied the assumptions of TPB to purchase behaviour ( Arora and Sahney, 2018 ; Conner, 2020 ; George, 2004 ; Verma and Chandra, 2018 ) and also in studies on Internet purchasing behaviour (i.e. Battacherjee, 2000 ; George, 2002 , 2004 ; Jarvenpaa and Todd, 1997a , b ; Khalifa and Limayem, 2003 ; Limayem et al. , 2000 ; Pavlou, 2002 ; Song and Zahedi, 2001 ; Singh and Srivastava, 2019 ; Suh and Han, 2003 ; Tan and Teo, 2000 ; Verma and Chandra, 2018 ). The three antecedents of online-purchasing behaviour are measured and defined on the premise of TPB ( Ham et al. , 2015 ). These include attitude and intention (Do I want to do that?), subjective norms (Do others want me to do that?) and perceived control (Do I have the necessary ability to do that?).

Azjen (1991) proposes that intention is determined by an individual's attitude, subjective norms and perceived behavioural control. Attitude can either be positive or negative, and it is influenced by an individual's beliefs, which, in turn, inform the norms. Azjen (1991) adds that an individual's possession of resources and opportunities needed to engage in the behaviour would influence whether the individual will exhibit such behaviour. In other words, it is not sufficient to have intentions to purchase; individuals must also have the ability to purchase the product. For example, two individuals might have the same level of intention and belief in purchasing a particular product, but the one with the resources to purchase the product is more likely to make the purchase decision.

In the context of this study, behaviour is determined by intentions and beliefs (social norms) that align with the individual's values. Individuals will act in calculative ways, such that decisions are made based on the most favourable outcome. This paper hypothesises that consumers' values (terminal and instrumental values) and consumers' fashion consciousness are factors that determine their online purchases in the fashion industry. This implies that in an emerging market, despite the challenges of fit and size of apparels bought online ( Kaushik et al. , 2020 ), consumers' instrumental values, terminal values and fashion consciousness will stimulate purchase using the same medium. TPB is, therefore, adopted to explain and predict consumer online purchase behaviour in the fashion industry and in an emerging market; this is premised on the tenets of the theory that consumers' values (instrumental, terminal) and fashion consciousness will determine consumers' purchase in the online fashion industry.

Technology and the fashion industry

The retail business is experiencing continuous changes due to the dynamics in taste, innovation and consumer behaviour in the market ( Kennedy et al. , 2019 ; Suzuki and Park, 2018 ; Tendai and Crispen, 2009 ). The fashion industry, which is one of the oldest industries in the history of mankind, has been very dynamic, evolving according to the tastes, trends and needs of society. Xue et al. (2019) emphasise that retailers must understand how to use technology to facilitate consumer purchase behaviour in local and global markets of this era. Xue et al. (2019) project that proper investment in electronic retailing would enhance the business performance of retailers, sustain their competitive advantage and attract a larger population to the electronic market, if the purchase behaviours of consumers within the markets are understood. The fashion industry has evolved and imbibed the online retailing system to attract the attention of the majority in the market. As society is becoming more technology-driven, the fashion industry must position itself in line with this trend; however, some studies show that challenges emanating from online fashion commodities, like apparels, have negatively affected rather than boost retail sales ( Bonetti et al. , 2018 ; Hope-Allwood, 2016 ; Xue et al. , 2019 ).

Therefore, having a technology-driven retail strategy without understanding or paying attention to factors that influence consumer purchase behaviour will result in negative sales outcome, for consumers are driven by social and psychological factors in their purchase intention. Niemeier et al. (2013) as well as Xue et al. (2019) found hedonic factors, convenience (friendly-user interface and easy process) and entertainment as determinants of consumers' purchase of virtual products. Contributing to the array of knowledge on consumer purchase of virtual products, consumer values, fashion consciousness and behavioural intention are tested in this study.

Consumer values, fashion consciousness and behavioural intentions in the online fashion retail sector

Instrumental value influences consumer online purchase behaviour in emerging markets.

Terminal values influence consumer online purchase behaviours in an emerging market

Fashion consciousness influences consumer online purchase behaviour in the fashion industry

The relationship between consumer values (terminal and instrumental) and consumer online purchase behaviour is moderated by consumers' fashion consciousness

Research sample

We employed a cross-sectional design and surveyed 282 individuals through convenience sampling. The data collection method yielded a useable total of 241 survey reports through onsite visitation, representing a response rate of 88.5%, which is considered adequate. The remaining 41 survey reports were rejected due to incomplete information. The survey questionnaire contained close-ended questions and was administered to the respondents in August 2019. The study was conducted in an environment comprising both students and the working class, where a major public university in Lagos, Nigeria, is situated. The demographic characteristics of respondents are as follows: 52.3% of the respondents are students; 13.3% are unemployed; 2.90% are self-employed and the remaining 31.5% constitute other professions ( Table 1 ). Most of the respondents in the study fall within Generation Y (21–30 years, 45.6%; 31–40 years, 21%) and Z (Below 20, 28%) category. The descriptive statistics and correlation of the constructs are provided in Table 2 .

To ensure high content validity, all the measurement scales used for the consumer values, fashion consciousness and online consumer purchase behaviour were adopted from extant literature ( Kautish and Sharma, 2018 ). The survey asked respondents to indicate on a 7-point Likert scale, ranging from 1 = “strongly disagree” through to 7 = “strongly agree”, the extent to which each statement applied to them.

Control variables

We controlled for four variables in the analyses to account for other factors that were not captured in the research but could affect customer online purchase behaviour in Nigeria. These control variables include age of respondent, educational qualification, monthly income and online purchase frequency.

Scale validity and reliability

The Cronbach alpha reliability test ( α ), which shows internal consistency for each item that makes up a construct is as follows: consumer value has α value of 0.70; fashion consciousness has α value of 0.72 and consumer online purchase behaviour has α value of 0.80. These Cronbach alpha values are all above 0.7, which is the recommended minimum acceptable level ( Hair et al. , 1998 ). Confirmatory factor analyses (CFAs) of the adopted measures which confirm the discriminant validity are as follows: normed chi-square value ( χ 2  = 537.48; df = 129), the fit indices Non-Normed Fit Index (NNFI) = 0.70, Normed Fit Index (NFI) = 0.70, Goodness of Fit (GFI) = 0.80, Comparative Fit Index (CFI) = 0.74, p -value = 0.00000 and Root Mean Square Error (RMSEA) = 0.115. The CFA results confirmed the discriminant validity of the constructs. Table 2 shows the means, standard deviations and correlations of the variables. The ( χ 2 /df) value for the model is 4.2, which is within the acceptable range of 2–5 ( MacCallum et al. , 1999 ; Marsh et al. , 1988 , 1998 ; Kautish and Sharma, 2018 ).

Analysis and results

The following regression model was used to estimate the consumer online purchase behaviour influence of the two independent constructs: consumer value and fashion consciousness: Y i = β 0 + β 1 C V + β 2 F C + β 3 C V F C + e i

The subscript i denotes each respondent ( i  = 1,…, 241). Y is the dependent variable (Consumer online purchase behaviour). CV represents the vector for the variants, terminal and instrumental values, FC represents the vector for fashion consciousness, CVFC represents the vector for the moderating effects and e i is the error term. β 1 – β 3 represent the parameters of the coefficients. Figure 1 shows the research model.

Multiple regression analysis was carried out using the hierarchical method ( Cohen and Cohen, 1983 ). In this case, the independent variables were sequentially introduced, one after the other. The hierarchical regression analysis was carried out using six separate multiple regression analyses, as shown in Table 3 . In the first regression model, all the control variables were included. In the second regression model, consumer terminal value was regressed on the consumer online purchase behaviour and the control variables. In the third regression model, the instrumental value was regressed on the consumer online purchase behaviour and the control variables. In the fourth regression model, consumer values (terminal and instrumental values) were regressed on the consumer online purchase behaviour and the control variables. Finally, the interaction terms and consumer values (terminal and instrumental values) were regressed on the consumer online purchase behaviour and the control variables.

Overall, the four hypotheses are supported, as indicated in Table 3 . From model 1, none of the control variables is significant. From model 2, the results show that terminal value is significantly positively related to consumer online purchase behaviour ( β  = 0.633 at p  < 0.01), thus, supporting H1 ; all the control variables are not significant. From model 3, the results show that instrumental value is significantly positively related to consumer online purchase behaviour ( β  = 0.451 at p  < 0.01), thus supporting H2 ; all the control variables are not significant. From model 4, the results show that fashion consciousness is significantly positively related to consumer online purchase behaviour ( β  = 0.413 at p  < 0.01), thus supporting H3 ; almost all the control variables are not significant, except the age of respondents, which is significant ( β  = −0.169 at p  < 0.05). From model 5, the results show that terminal value is significantly positively related to consumer online purchase behaviour ( β  = 0.048 at p  < 0.01), thus also supporting H1 . Instrumental value is significantly positively related to consumer online purchase behaviour ( β  = 0.219 at p  < 0.01), thus also supporting H2 .

Fashion consciousness is significantly and positively related to consumer online purchase behaviour ( β  = 0.142 at p  > 0.05), thus also supporting H3 . All the control variables are found to be insignificant. From model 6, the results show that the interaction term (terminal value × instrumental value × fashion consciousness) is significantly positively related to consumer online behaviour, thus supporting H4 . Instrumental value is not significant, whereas terminal value is significantly related to consumer online purchase behaviour. Fashion consciousness is not significantly related to consumer online purchase behaviour.

All the control variables are found to be insignificant. From model 6, the interaction between consumer value and fashion consciousness accounted for significantly more variance than just consumer value and fashion consciousness alone; R 2 change = 0.008, p  < 0.01, indicating that there is potentially significant moderation between consumer value and fashion consciousness on consumer online purchase behaviour. The Durbin–Watson ranges from 1.6–1.9, which are approximately 2, and shows no evidence of autocorrelation ( Gujarati, 2003 ). The overall statistical measures, such as ( R 2 , R , F and p -value) indicate the adequacy of the model (see Table 3 ).

Discussions and implication

The role of consumer values in influencing online purchase has been documented in the literature ( Limayem et al. , 2000 ; Nwankwom et al. , 2014 ; Kautish and Sharma, 2018 ). However, very few studies have examined the role of technological innovation in influencing customer value towards online purchase, especially as related to the fashion industry. Kautish and Sharma (2018) examined consumer values, fashion consciousness and behavioural intentions in India's online fashion retail sector and suggested that similar studies should be conducted in emerging economies with diverse cultures. This study, thus, fills this gap by examining consumer values and purchase in the fashion industry through technological platforms in emerging markets, like Nigeria.

Consumer values were grouped into instrumental and terminal values to illustrate the practical implications of the study. The first hypothesis examined the influence of instrumental value on consumer online purchase behaviour in an emerging market, and the result shows that there is a positive significant relationship between instrumental values and online purchase of fashion apparels. This implies that purchasing apparel online saves consumers' time, cost of purchase, convenience, discount in services received and it offers varieties of goods to choose and buy. In other words, key factors that attract and influence the purchase of fashion items online using technological innovation are the convenience, low cost, discount and variety of commodities offered by online stores. This result supports the theoretical proposition by Azjen (1991) that behaviours of individuals are influenced by calculative permutations on the cost and benefits of their actions. Consequently, intentions become actions when it is perceived that the action has more benefit than cost. This finding also supports the observations of Kautish and Sharma (2018) that instrumental values to be derived by consumers in the purchase of a commodity online will influence their purchase decision.

The second hypothesis on the influence of terminal values and consumer online purchase behaviour in an emerging market reveals a significant and positive relationship between terminal value and consumer online purchase behaviour. This implies that happiness, love and satisfaction are consumers experience when they purchase fashion apparels online. In addition, customers perceive a sense of freedom and comfort when they successfully make online purchases. This also supports the submission of Allen et al. (2002) as well as Kautish and Sharma (2018) that terminal value reward from online purchase of a product influences consumer purchase. Online stores, hence, must ensure that their products provide ease of purchase and are low cost and also that the apparels reflect the desires of the customers, such that they provide comfort, satisfaction and happiness when worn.

The third hypothesis examines the influence of fashion consciousness on consumer online purchase behaviour in the fashion industry, and the result shows a significant and positive relationship between fashion consciousness and purchase behaviours. This implies that students, professionals, the employed and unemployed in emerging markets like Nigeria, support and are mindful of fashion trends; the result also showed that students are more interested in fashion trends than the unemployed and self-employed; this result might be associated with the fact that Gen Y and Z consumers are the most represented in this study. This result supports the observations of Babin and James (2010) , Fernandes (2013) , Kautish and Sharma (2018) that fashion consciousness influences the decision to purchase apparels and other related fashion items online. Kautish and Sharma's (2018) submitted that Gen Y consumers have a higher purchase frequency and impulse buying than other generational cohorts. However, this study extends knowledge from the work of Kautish and Sharma (2018) , which was focused on students to show that it is not only this category of individuals who are fashion-conscious but also professionals, the self-employed and even the unemployed in emerging markets, like Nigeria.

The fourth hypothesis tested the moderation of customer values (terminal and instrumental) and online purchase behaviour by fashion consciousness, and the result shows that fashion consciousness moderates the extent to which consumers' values influence their purchase behaviour. A society with a high rate of fashion-conscious individuals will purchase fashion apparels online more than a society with less fashion-conscious people. In addition, it shows that an individual's consciousness for fashion plays a primary role in the online purchase of fashion apparels and other fashionable items.

Additional findings in the study reveal that terminal value has a greater influence on online consumer purchase of fashion apparel. This is indicated by its higher coefficient score (0.633) compared to the scores for the instrumental value (0.451) and fashion consciousness (0.413) (see Table 3 ). This shows that happiness, love, satisfaction, a sense of freedom and comfort derived from online purchase of fashion apparels influence customers' behaviour more than instrumental values (ease of purchase, cost, convenience, discount and product varieties). Interestingly, these findings do not support the observations of Kautish and Sharma (2018) in India, which indicated that instrumental value has a greater influence on consumer purchase. The reverse is the case in this study, as the terminal value reflects the highest coefficient among the two constructs. Nigerians in the study were more interested in the terminal value obtained from the purchase of fashion apparels online, as opposed to customers in India, which might be due to their social and cultural differences.

Implication for practice

The findings from this study have both business and technology-use implications. First, organisations and businesses in the fashion industry must continue to implement innovative and technological ideas on how to provide customers with the values that appeal to them from the online purchase of apparels as this has proven to be a key factor influencing customers' purchase. Consumers in this study are influenced by the convenience and time efficiency of purchase, cost-effectiveness, discount and availability of varieties; hence, managers, business owners and app developers for the fashion market must ensure that their services take into consideration all of these factors for online purchase to be continually stimulated.

Additionally, managers and app developers must understand the kind of apparels that conform to consumers' satisfaction and design, such apparels to meet this need, as this is also paramount to stimulate purchases. Consumers in the Nigerian fashion market are conscious of apparels that give them comfort, a sense of love, happiness and are trendy; therefore, online fashion retailers must have in stock apparels that possess these characteristics. In addition, the targeted audience should not be students or the younger generation alone, as this study has shown that the larger Nigerian populace is fashion conscious. Business owners should have apparels that cut across generations X, Y and Z; they should ensure that there are various offerings to capture different population classifications in the market, thereby meeting all needs. Businesses can focus more on generation Y and Z as they are the most populous in emerging markets and are more used to digital innovations. In spite of this, generation X must still be captured in their product offerings and designs.

The focus should be on increasing terminal values (happiness, love and satisfaction, a feeling of freedom and comfort) of fashion apparels purchased. Instrumental values (ease of purchase, cost, convenience, discount and product varieties) values are important to the Nigerian market; however, there is a preference for clothes that satisfy more terminal values.

Limitations and direction for future research

The study covered consumer values, fashion consciousness and online purchase in the fashion industry in an emerging market – Nigeria. This study is limited to the online fashion (apparel) market and did not take into consideration other viable sectors. Hence, future studies can fill this gap. Other markets, for instance, electronics and automobiles, can be examined in future studies to extend the knowledge of online purchasing and the impact of technological innovations.

Through the lens of a cross-sectional methodology and quantitative techniques, convenience sampling was used to select respondents from a mixed population of students, working-class professionals, the self-employed and unemployed within a multi-cultural and industrial environment, Lagos.

Future studies can consider using random sampling techniques, triangulate their methods and expand the geographical coverage of the sample as non-attention to these factors can be considered a limitation of the study.

The model above represents the direct effects models ( Hypotheses 1 , 2 , 3 ) and the moderation model ( Hypothesis 4 )

Demographic characteristic of respondents

Descriptive statistics and correlations

Note(s): n  = 421; standardised regression coefficients are reported

* p  < 0.10; ** p  < 0.05; *** p  < 0.01

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Tendai , M. and Crispen , C. ( 2009 ), “ In-store shopping environment and impulsive buying ”, African Journal of Marketing Management , Vol. 1 No. 4 , pp. 102 - 108 .

Usman , M.U. and Kumar , P. ( 2020 ), “ Factors influencing consumer intention to shop online in Nigeria: a conceptual study ”, Vision , pp. 1 - 8 , doi: 10.1177/0972262920926797 .

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Further reading

Harrison , D.A. , Mykytyn , P.P. and Riemenschneider , C.K. ( 1997 ), “ Executive decisions about adoption of information technology in small business: theory and empirical tests ”, Information Systems Research , Vol. 8 No. 2 , pp. 171 - 195 .

Pantano , E. ( 2016 ), “ Engaging consumer through storefront: evidences from integrating interactive technologies ”, Journal of Retailing and Consumer Services , Vol. 28 , pp. 149 - 154 .

Taylor , S. and Todd , P.A. ( 1995 ), “ Understanding information technology usage: a test of competing models ”, Information Systems Research , Vol. 6 No. 2 , pp. 144 - 176 .

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Is Online Shopping Bad for the Planet?

In theory, getting deliveries can be more efficient than driving to the store. But you may still want to think before you add to cart.

Credit... Naomi Anderson-Subryan

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By Dionne Searcey

Dionne Searcey is part of a rotating cast of Climate reporters and special guest writers who will answer your burning climate questions.

• Published April 22, 2024 Updated April 29, 2024

Q: How much do I need to worry about the impact of my online shopping?

The convenience of online shopping is hard to beat. But it uses a lot of energy and resources and can lead to more waste.

Transportation needed for online shopping spews greenhouse emissions. Three billion trees are cut down every year to produce packaging for all kinds of things, e-commerce included, according to some estimates . The data centers needed to store and retrieve orders consume about 10 times the amount of energy of a typical home and gulp precious groundwater for cooling.

Sounds bad, right? Read on.

Online shopping isn’t always the worst choice. Efficiency is a big factor.

Think of it like this: A single truck delivering orders to several homes could be less of a drain on the environment than several shoppers hopping in cars to drive to stores. That’s especially true if people group their purchases into less-frequent deliveries.

One study from M.I.T . even found that online shopping could be more sustainable than traditional shopping in more than 75 percent of scenarios that researchers came up with. Those scenarios imagined things like an online shopping experience with all-electric shipping and reduced packaging.

Online retailers and delivery companies have been trying to make online shopping more climate friendly. Some have embraced electric vehicles.

Amazon.com, for instance, has pledged to have 100,000 electric delivery vehicles on the road by 2030, a move that it says will prevent millions of metric tons of planet-warming carbon from being released into the atmosphere. UPS has plans for updating its fleet with electric vehicles, but those plans hit a snag when the company it had contracted to provide the new trucks ran into financial problems . FedEx plans to make half its purchases for its pickup and delivery fleet E.V.s by next year, and to have the fleet fully electrified by 2040 .

Some companies are also experimenting with robot and drone deliveries . But there are other things to consider.

Packaging and waste are also important.

Companies like Amazon have also started to cut back on packaging, which in the early days of online shopping produced laughable mountains of boxes, Bubble Wrap and other padding for tiny items. It still happens from time to time now , even with the effort to reduce. Some companies have begun using more reusable, recyclable and even biodegradable packaging. But millions of pounds of plastic from packaging still end up in rivers, oceans and landfills.

Maybe the biggest thing: How much stuff we buy.

So, it’s complicated. But there’s one foolproof thing you can do for the planet and for your bank account: Buy less stuff.

The production and use of household goods and services are responsible for 60 percent of greenhouse gas emissions worldwide, one 2015 study found . In the United States, more than 20 percent of emissions are directly attributed to household consumption, according to researchers at the University of Michigan .

Many of those lamps, toasters, sweaters and other items are imported, arriving in the United States on carbon-emitting cargo ships or airplanes. The shipping industry alone accounts for 3 percent of global greenhouse gas emissions.

Things to try: Buying in bulk, slow shopping and bundling orders.

Climate organizations encourage buying secondhand items or fixing the broken things you already own. An increasing number of companies offer repair services, sometimes for free. YouTube videos offer step-by-step guides for fixing a surprising number of items. Local meet-ups for mending clothing or repairing appliances are becoming a thing.

If you are going to buy stuff online, there are many ways you can make your online shopping more sustainable.

Take a minute to look at size charts and read reviews to cut back on returns. Many studies say online shoppers are five times more likely to return an item, which means a lot more transportation emissions.

If you’re ordering several items, try to group your orders into one shipment. Many companies will ask if you want to do so; don’t forget to seek out that option. The Better Business Bureau suggests buying in bulk to cut down on packaging for individual items and taking advantage of delivery to pickup locations.

Practice slow shopping . Pause and think about whether you need an item. It’s easy to get a rush from buying something new, but environmentalists suggest getting your dopamine fix from something entirely different: Try taking a walk instead.

Have a question for reporters covering climate and the environment?

We might answer your question in a future column. We won’t publish your submission without contacting you, and may use your contact information to follow up with you.

An earlier version of this article described incorrectly FedEx’s plans to convert its pickup and delivery fleet. The company plans to make half its purchases for the fleet E.V.s by next year, it does not plan to convert half its fleet to E.V.s by next year.

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Dionne Searcey is a Times reporter who writes about how the choices made by people and corporations affect the future of the planet. More about Dionne Searcey

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Difference Between Ikat and Double Ikat

In this article, we'll explore Ikat fabrics, focusing on the distinctions between single Ikat and double Ikat. Let's start by understanding what Ikat is.

What is Ikat?

Ikat is a technique that follows a form of tie-dye to create patterns on yarns before they are woven into fabric. These patterns emerge during the dyeing process, where the yarns are strategically bound and dyed to form specific motifs once woven. The resulting fabric made from these patterned yarns is known as Ikat fabric. For clarity, we'll refer to these as Ikat-dyed yarns.

The difference between Ikat and double Ikat

Let's understand the difference between a single Ikat and a double Ikat.

Single Ikat: 

This type involves Ikat-dyed yarns used either in the warp (vertical yarns) or the weft (horizontal yarns) direction, but not both. If the Ikat-dyed yarns are used in the warp, it is called a warp Ikat, and if used in the weft, it is known as a weft Ikat. The other direction typically uses solid-colored yarns.

Double Ikat: 

In double Ikat, the Ikat-dyed yarns are used in both warp and weft directions. This technique is intricate, requiring precise dyeing and weaving to ensure the patterns align perfectly.

Comparative analysis:

In the following, a comparison is shown based on the 4 key factors design patterns, visual appeal, craftsmanship, and cost of production. 

• Patterns: Single Ikat often features basic and predominantly geometric motifs, such as triangles and diamonds. Double Ikat, however, allows for more complex and sharply defined patterns due to the dual use of Ikat-dyed yarns in both weaving directions.
• Visual Appeal: Double Ikat designs generally appear more vibrant and have clearer edges compared to Single Ikat.
• Craftsmanship: Double Ikat requires highly skilled weavers due to the complexity of aligning patterns from both warp and weft yarns.
• Manufacturing costs and End product price: To make double Ikat highly skilled labour is required. This makes double Ikat fabrics more expensive to produce compared to the single Ikat.

Related posts:

What is Ikat: Ikat Fabric, Dyeing techniques, Origins, and Weaving Designs

Batik Printing: An Overview, Dyeing Method, Its History and Application

Pochampalli Sarees – Brief History and Its Production Process

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