For retail brands, returns are anything but a small issue. On the one hand, offering flexible returns is a key part of meeting expectations and supporting an effective overall customer experience. On the other, so-called “serial returners” and too-flexible policies can drive incredibly harmful costs.
“Serial returners” is the term reserved for the most overt repeat offenders. According to one study, this group makes up about 11% of online returners but generates 24% of all online non-food returns. However, even outside of this cohort, returns can combine to create a cycle that negatively impacts both profitability and customer satisfaction. For brands, the ripple effects include increased return processing costs, disrupted inventory management, and potential damage to customer relationships.
Right now, the whole retail industry is contending with the issue, with one analyst describing returns as a potential “trillion-dollar problem.” However, there is an under-used resource that can help mitigate this problem: customer interaction data.
Customer support teams engage with thousands of customers each month, gathering invaluable insights directly from the end users. Manually reviewing each conversation to extract actionable data is daunting and time-consuming, making it impractical for most companies.
Fortunately, advancements in AI have improved the impact of conversation intelligence (CI), providing a scalable solution for deriving meaningful insights from customer interactions. In this blog, we’ll explore exactly how support teams can leverage CI insights to tackle return challenges and combat serial returners.
Analyzing Return Patterns with Intent Data
Understanding the reasons behind product returns is crucial for tackling repeat return customers. To get to this stage, staying close to the Voice of the Customer is the first place to start.
By tapping into customer conversations, brands can uncover valuable insights into customer intent and sentiment. New developments in technologies like Generative Conversation Intelligence (GenCI) enable Large Language Models (LLMs) to sift through vast amounts of customer data from various channels, such as call recordings, emails, chats, and social media.
By doing so, businesses can pinpoint specific issues that lead to returns, whether they relate to product quality, misleading descriptions, or unmet customer expectations.
Consider a beauty brand that specializes in skincare products. Let’s say the skincare brand experienced a surge in returns for its new anti-aging serum. By leveraging conversation intelligence tools, the brand can analyze thousands of customer interactions and discover, for example, that many returns were due to customers experiencing irritation after use. This type of learning can signal that customers with sensitive skin were unaware of certain ingredients that could cause reactions.
Armed with this insight, the brand can update its product descriptions to highlight the reactive ingredients and add a recommendation for a patch test before full application. They can also include more detailed usage instructions and launch a customer education campaign focusing on product safety. In this case, the return rate for the product can be addressed with facts, improving customer satisfaction and loyalty.
Enhancing Product Descriptions and Images
Accurate and detailed product descriptions are essential in reducing return rates. Customers need clear and comprehensive information to make informed purchasing decisions. This includes not only the product features but also usage instructions, ingredient lists, and potential benefits.
To improve these product descriptions, support teams can leverage customer feedback from returns via the ‘reason for return’ comment box, structuring questions in such a way that customers provide more detailed information. For example, once customers select the drop-down category, require a character count to encourage longer responses.
Here, however, is also an instance where GenAI-powered conversation intelligence plays a role – using this technology, brands can analyze unstructured customer data and gain insights beyond “wrong size” or “no longer needed.”
For example, an outdoor apparel company facing high return rates for a new line of hiking boots can use CI to discover that most returns are due to, say, customers finding the boots less waterproof than advertised. Using AI-powered conversation intelligence, the company can drill into which specific complaints about waterproofing were most common (e.g., water seeping in during rain, moisture accumulating during long hikes).
The company is now empowered to respond by creating more detailed product descriptions, including specifying the waterproof rating and explaining the limitations of the waterproofing technology. They can also produce videos demonstrating the boots’ waterproof capabilities in different conditions. These types of enhancements help customers make better-informed decisions. By investing in better product content, the company can set realistic expectations and reduce the likelihood of returns due to mismatched expectations.
Improving Customer Education and Support
Educating customers about product usage is another effective strategy for minimizing returns. Often, returns occur because customers are unsure how to use a product correctly or do not see the expected results. Providing detailed tutorials, usage tips, and frequently asked questions can empower customers to use products effectively.
But, the question remains – how does a brand make sure that its FAQs and related tips reflect what customers truly need?
Generative insights from customer data is one way to get this information. The power of these insights is their ability to autonomously pull detailed information that customers mention on support tickets and in conversations with contact center teams. GenCI can aggregate top product questions, and knowledge base managers can use them to inform knowledge base updates, product pages, and more in-app education articles to address top questions promptly.
Furthermore, these insights can be used to empower contact center agents with the knowledge they need to proactively address customer concerns and questions. By integrating generative insights into agent dashboards and knowledge bases, brands can ensure that agents have access to the latest information on product usage, common issues, and effective solutions. This enables agents to provide more accurate and helpful support, reducing the likelihood of returns and improving overall customer satisfaction.
For example, using GenCI, a dietary supplement brand identified that many customers returned the products because they didn’t notice immediate benefits. To address this, the brand can launch an educational campaign explaining that dietary supplements often require consistent use over a period of time to show results, using customer comments to define key messages and flip the script on the concern behind a lack of immediate results.
Taking the Steps Towards Reducing Product Returns
Tackling the challenge of serial returns in particular, and costly returns in the retail industry in general, is no easy feat but gaining insight into customer conversations at scale is crucial to get started.
By analyzing customer interactions, support teams can uncover the underlying reasons for returns and take targeted steps towards improving product descriptions and customer education to help customers get the information they need before they buy.
Conversation intelligence technology can play a pivotal role in this process. With the right solution powering root cause analysis efforts, it’s possible to forge a path forward to mitigate the impact of returns and foster stronger customer relationships.
By harnessing the power of customer insights, brands can not only solve current challenges but also pave the way for a brighter, more customer-centric future.
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