CX
Jun 25, 2024
In the beauty and wellness industry, 'repeat return' customers pose a significant challenge. Studies show that nearly 30% of online beauty purchases are returned, creating 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.
However, there is a pretty under-used resource that can help mitigate this problem: customer interactions. 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 generative AI have up-leveled the impact of conversation intelligence (CI), providing a scalable solution for deriving meaningful insights from customer interactions. This technology enables brands to understand the root causes of returns and address them effectively.
In this blog, we’ll explore how support teams can leverage Generative Conversation Intelligence (GenCI) to tackle return challenges. By identifying return patterns, enhancing product descriptions, and improving customer education through the deep analysis of customer feedback, brands can reduce return rates and boost customer satisfaction.
Table of Contents
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 with technologies like Generative Conversation Intelligence 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 GenCI, the skincare brand can analyze thousands of customer interactions and discover that many returns were due to customers experiencing irritation after use, for example. 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 beauty 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 anti-aging serum 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’s where GenCI plays a role – using this technology, brands can analyze unstructured customer data and gain insights beyond “wrong color” or “no longer needed.”
For example, a cosmetics brand facing high return rates for a new foundation line can use GenCI to discover that most returns are due to, say, customers receiving shades that didn't match their skin tone. Using GenCI, the cosmetics brand can drill into which shades were being returned or exchanged the most.
The company is now empowered to respond by creating more detailed product descriptions, including comparisons to popular shades from other brands. They can also produce tutorial videos showing how to choose the right shade, starting with the highest-returned ones. These types of enhancements help customers make better-informed decisions. By investing in better product content, the cosmetics brand 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 usage 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, conversations, even third-party reviews. 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.
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 Fewer Repeat Returns
Tackling the challenge of repeat returns in the beauty and wellness industry 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.
Generative Conversation Intelligence (GenCI) can play a pivotal role in this process. With the right technology 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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