When Customer Returns Evolve: The Hidden Cost of Customer Loyalty

An Interview with Dr. Siham El Kihal by Isin Acun, WU Vienna 

In the online fashion industry, product returns not only clog warehouses but also quietly erode margins and distort customer lifetime value (CLV). In Customer return rate evolution, Dr. Siham El Kihal (WU Vienna) and co-authors Tülin Erdem, Christian Schulze, and Weiqing Zhang show that return rates rise, not fall, as customer-retailer relationships mature.

The numbers are striking: the average customer's return rate climbs from 25% on the first purchase to 37% by the tenth, an increase of 48% that drives a 24% drop in contribution margin (the money left over from each sale after covering direct costs like shipping and return processing) across those purchases. If managers fail to factor in this drift, they risk overestimating (CLV, the total profit a customer generates over their relationship with the company) by a whopping 40% over ten purchases.

“When we plotted the return rate, it was increasing… we were like, OK, what is going on?”

— Siham El Kihal

Source: GPT5 Generated

The Ten-Year Paper: Collaboration and Persistence

The research that became "Customer Return Rate Evolution" began during Dr. El Kihal's PhD, a journey that started with an unexpected pivot into marketing. Initially trained in electrical engineering, through a happy misunderstanding during her search for PhD programs, she stumbled onto e-commerce research. While searching for "electrical engineering," she discovered "electronic commerce" and was hooked by the field's blend of technical problem-solving and consumer behavior. Her unique background, combining rigorous quantitative training with marketing perspectives, would prove essential for tackling the complex modeling challenges ahead.

Working with Christian Schulze, she accessed rich longitudinal data capturing individual customers' purchase and return histories. Their starting hypothesis was straightforward and intuitive: as customers learn about a retailer's assortment, sizing, and quality, their return rates should decline.

A turning point came when Tülin Erdem visited Frankfurt for a seminar. Recognizing her expertise in consumer learning, Dr. El Kihal proposed collaborating. Later, Weiqing Zhang joined the team while pursuing his PhD at NYU's Stern Business School.

“I really appreciate that throughout the years, Tülin was never pushy or impatient,” Dr. El Kihal says. “Every couple of months, it was just, ‘Shall we meet? What’s the status? What should we do?’ It was always supportive.”

— Siham El Kihal

The road to publication was long. The team's first two submissions were rejected. Other demands relegated the paper to the back burner. But the team never abandoned it. "Not giving up on ideas is one of the most important factors in a research career," she reflects. When the paper finally reached IJRM, the review process significantly strengthened the paper.

 "Our model and method part got more rigorous thanks to the great reviewer comments, and the managerial implications section got richer and clearer."

— Siham El Kihal

The Surprising Discovery: Return Habit Beats Learning

While theory predicted a learning effect, with more purchases leading to fewer returns, the data told a different story. Two forces were at work. Brand experience through prior purchases reduces the likelihood of returns, while return habituation increases the likelihood of returns for customers who have returned products in the past. Surprisingly, the second force was the winner.

“There is a strong state dependence in returns; people develop these habits, and they return more and more over time. Even with learning, it’s often not enough to offset that habit.”

— Siham El Kihal

The team confirmed the robustness of the effect across different customer groups and measurement.

What Managers Should (Not) Do

By recognizing the return rate effect and building it into their planning, managers can make smarter decisions that protect profitability. Recognizing return rate evolution allows retailers to accurately forecast customer value, allocate acquisition and retention budgets more effectively, and focus resources on customers who will remain profitable over time—especially important for retailers with tight profit margins. Dr. El Kihal suggests differentiated return policies where legal, such as charging for returns from chronic returners.

Some customers who start with high return rates will remain unprofitable despite any learning. However, she cautions against firing those customers as this can trigger negative publicity.

“Instead, don’t activate those customers,” she advises. “Don’t send them newsletters, don’t target them with campaigns.”

— Siham El Kihal

Beyond the Paper: Predicting Returns from Pre-Purchase Signals

The next frontier, Dr. El Kihal says, is predicting returns before they happen. Her current work investigates how pre-purchase browsing and search behavior can indicate future return risk.

“If I’m on a site searching for jeans, filtering by color, narrowing by size, sorting by price, these are all signals about how certain or uncertain I am.”

— Siham El Kihal

Retailers already have this data sitting in their systems. If Dr. El Kihal's research confirms that these browsing patterns can reliably predict returns, retailers could use these signals to intervene earlier, before the purchase happens. The challenge isn't privacy or data collection; it's figuring out which patterns matter. This is where academic research can make a real difference: by collaborating with researchers and sharing anonymized data, retailers can unlock insights that benefit the entire industry.

Read the paper 

Interested in diving deeper into how return rates evolve and what this means for customer lifetime value calculations? Read the full paper here.

Want to cite the paper? 

El Kihal, S., Erdem, T., Schulze, C., & Zhang, W. (2025). Customer return rate evolution. International Journal of Research in Marketing. https://doi.org/10.1016/j.ijresmar.2025.03.003

---

Meet Siham

Professor of AI and Marketing Analytics, WU Vienna, Austria

What drives you to do research at the intersection of marketing analytics and customer behavior?

"My goal is to solve intriguing, complex problems and come up with relevant solutions. I enjoy working on challenging and interesting projects, but I also want to see the purpose behind what I've done. Often, we work on projects that stay a bit theoretical, and then we write what a manager could do with those insights, but I'd really like to see these things applied in practice."

If you weren't an academic, what would you be?

"Maybe a life coach; not because I have my life together, but because I feel I'm good at communicating and giving advice. I'd also love to be a sports coach; it was on my bucket list to become a fitness instructor. It never happened, but it's something I think I would really enjoy."

What's the best piece of advice you've received?

"There's a quote I love from the movie Rocky. It's not about how hard you hit — it's about how hard you can get hit and keep moving forward. That mindset helps me a lot: always thinking about moving forward, no matter what comes, because that's life."

If you could have lunch with any researcher (dead or alive), who would it be?

"For me, it would clearly be Albert Einstein. The way he changed how we think about the universe and physics, especially in the context in which he worked, is incredibly impressive and inspiring. I'd love to have a meal or coffee with him and ask a few questions."

What's the number one question you hope to answer during your career?

"I'd like to make a significant change that has a great impact, and it doesn't have to be within academia. That could mean coming up with a new theorem that changes our understanding of how consumers think or supporting and empowering young women around the world."

---

This article was written by

Isın Acun

Ph.D. candidate at the WU, Vienna