How Retailers are Using AI to Listen to Customers
In the world of retail, the customer is indeed king. Retailers strive to minimize returns by selecting the right (quality) products for each and every aisle, all while managing social media fury and customer preferences across every channel imaginable.
So, how are the best of the best keeping up with the onslaught of feedback coming from their customers?
Well, many retailers are certain they can make sense of it all by simply looking into a small sample of their overall feedback. But this rarely paints a complete (or accurate) picture. And with the manual work required to scoop even a small ladle into the cauldron of unstructured text feedback, the results are both underwhelming and potentially misrepresenting your customers.
For innovative, forward-thinking retailers that seek to understand and quantify all of their customer feedback, they look to Luminoso. Find out how we capture each and every customer’s opinions and preferences to provide retailers with an actionable view of the most important topics their customers are talking about.
Retailer Use Case #1 (U.S. Based)
This particular retailer sells everything from cereal to lawn furniture, so when they came to Luminoso to learn how they could minimize returns and support inquiries, we had them covered.
With text from over 170,000 monthly support chat sessions, they wanted to discover ways to reduce how often their customers needed to call in or live chat to complete an action. Another top concern was being able to understand what customers were saying about the top five most returned products in their stores.
In the first training session, six business analysts focused on the Customer Experience were each able to load the 170,000 live chat sessions from the previous month. They were then able to focus on an area of interest and develop actionable insights based on the data with no extensive setup or training. All they needed was a little education on how to maneuver through Luminoso’s proprietary data exploration workspace, and away they went.
During the analysis, they discovered product issues like missing bottles in cases of water. They found that many bottles were often crushed, empty, or simply too malformed to stand up in the refrigerator, and were able to make changes to the way they delivered and stocked this item to remedy that issue. They also dove into customer experience issues like “recycling ink cartridges,” or “price matching,” to understand the confusing elements of those programs for customers.
They’re currently integrating even more customer touch points like website reviews, satisfaction surveys, social media data, and product reviews to explore similar findings and further improve the business.
Retailer Use Case #2 (U.S. Based)
This retailer needed a way to sift through millions of tweets they had collected around Halloween, but time was running short to make changes in their stores. Luminoso’s response was to clean the data of spam and duplicates from some 3 million messages, pairing it down to 300,000 using proprietary cleaning algorithms. Luminoso then automatically clustered the conversations into topical groups so they could explore topics like “Costumes”, “Candy”, or “Movies”.
However, it was the investigation of subtopics that revealed the emotions connected to these broad halloween-related themes. They discovered that customers were feeling nostalgia for older movies they'd watched as kids, and wanted to share that experience with others. They also found that there were strong emotions of excitement for Pumpkin Spice M&Ms to come back into stores.
The social media team was then able to make a plan to ramp up both excitement and nostalgia. Rather than just selling products, they were now engaging with their customers on an emotional level. Not only did they tweet and post about the highly sought-after M&Ms, they also rearranged their store layouts in the days after the Luminoso report. They even placed Pumpkin Spice M&Ms right up front as the first item encountered when entering all of their stores with great success.
The retailer has since been experimenting with social media data year-round, as the topics their customers talk about change as quickly as the seasons.