The big box retailer had recently launched a chatbot to help customers find answers to product related questions. Very quickly, they were faced with a massive increase in bot usage that resulted in more than 100K conversations every month. Unfortunately, they quickly learned that, while intents were being triggered, their customers were leaving the bot and escalating to an agent, or bypassing the bot altogether. Live agent wait times were up to five hours long. The chatbot team knew their customers were unsatisfied with the automated answers and frustrated with the overall experience, however, existing reporting tools prevented the bot team from knowing where the bot was struggling, and it was next to impossible to prioritize what needed to be fixed first.
The team started by analyzing conversation performance. Taking advantage of the AI-based topic modeler, the system grouped each and every conversation into topics, and provided detail on volume, containment, agent escalation, bot automation and bot experience for each of those topics. This helped the team identify problem areas, and helped them prioritize what to tackle first. Next, through the use of transcript search and entity extraction, the system revealed additional detail, and exposed that customers were most frequently inquiring about product inventory status, including order delays, damaged, and missing items.
- $1.6M in annual savings
- 75% positive sentiment for top contact reasons
- 19% deflection in inventory use case
With a deeper understanding of customer inquiries gave the insight needed for the team to upgrade the bot. They identified the need for a new product inventory use case, that would include API integration to their ERP system, as well as new content to better respond to specific customer questions. These upgrades led to a 9% increase in deflection rate, resulting in $1.6M cost savings, from a reduction in agent handle time, and an impressive boost to customer sentiment of more than 30% in just a few months.
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