This article is brought to you by InStore.ai.
Whether it’s listening to a customer’s feedback, making upsells on promotions and encouraging loyalty program sign ups, or simply being friendly and helpful, interactions between a cashier and consumer at checkout can be pivotal in influencing nearly every aspect of a convenience retailer’s business.
“Traditionally, operators must trust that employees are saying the right things, responding to feedback adequately and communicating it to supervisors, and putting in the effort to enhance customer experience,” said Jay Blazensky, CEO and co-founder of voice analytics tech firm InStore.ai. “But with the introduction of voice analytics using artificial intelligence tools such as InStore.ai, retailers can monitor what’s being said in the store and to make interventions and improvements.”
InStore.ai uses microphones to record conversations between cashiers and customers at different touchpoints around the store, including the register, foodservice areas or near self-checkout. The conversations are then interpreted and analyzed using AI analytics, gathered in a database and presented as an interactive dashboard that can help retailers make decisions to improve operations.
Developed by Blazensky and his partner Marc Della Torre, Silicon Valley entrepreneurs with backgrounds in bringing voice analytics to customer call centers, InStore.ai is introducing voice analytics to the convenience store industry. The goal is to enhance both retailer operations and customer experience.
With a program like InStore.ai, there are several ways to glean insights through the dashboard’s semantic search function, which allows users to search all recordings across all stores using natural language.
“We put the pulse of what's happening at all stores, remotely, at the fingertips of the operation executives,” said Blazensky.
For example, a simple search on ‘no receipt at the pump’ can instantly reveal which stores across the entire chain have the biggest problems with not providing receipts to customers at the pump. Being able to quickly validate the findings by listening to a few of the interactions enables operators to quickly message the stores needing attention, and better delight their guests.
“The ability to query all the conversations across every store based on topic and see how customers feel about their experience, such as facilities not working properly or rewards cards not being offered with the correct language, operators can quickly identify and correct issues in the stores,” said Blazensky.
“Loyalty card sign-up attempts” instantly give a high-value peek into all the times employees are making effort in some way to increase a store’s loyalty program. By seeing which store has the most successful sign ups and which conversations resulted in a customer signing up, operators can determine which language was most effective and use that to train employees on how to increase future loyalty sign ups.
Or an operator could search “compliments on food items,” for example, to see what customers are enjoying and which food items they like the most, or the phrase “promotions mentioning cigarettes,” to find out how often employees are mentioning a deal that’s running and if they are able to successfully upsell those items.
“Once you find something that's important to your store, track it. Save that search result on the dashboard and get a weekly report on how that's trending,” added Blazensky.
This is the second article in a series on InStore.ai. Read part one here.