Product
Customer Feedback Instore Testing
Understand the drivers for customer satisfaction, when analyzing test and control groups of own stores and local competitors
KEy FACTS
Customer: Global Retail Company
Challenge: Testing new store concepts for a group of stores
Objective: Monitor customer feedback of test stores vs. control groups vs. local competition
Solution: Dashboarding and NLP Analysis of Google Reviews for certain topics. Comparison between test group of stores vs. control groups, as well as selected competitors in neighborhood
What were the key challenges?
One of the country organization was going through a major rehaul, as it was competing against very mature competing chains.
The integrated customer project team, was testing a new instore concept for 10 pilot stores, ranging from changes in staffing, selection as service at checkout.
One key challenge was, how to measure quickly the difference in customer perception between test stores vs. the remaining stores in the country. In addition, the project group was keen on understanding, how customer would rate the pilot stores vs. selected competitors in the neighborhood.
How does our solution differ?
Rather than incentivizing customers to fill in structured survey data, we used the NLP platform of our MIGO suite to analyze Google reviews of pilot vs. control vs. competing stores.
The advantage of this approach is, that customer feedback is unbiased, as google nudges Android users to fill in a short review , when leaving the store.
Sourcing all historical reviews as well as daily sourcing of new reviews, the project team was able to analyze customer feedback from to 500k reviews. Hereby, the project team was able to understand the topics of interest as well as the sentiment (positive, negative, neutral)
Predict42 did set up an automatic sourcing routines for our own as well as relevant competitor data within days. By applying a customized topic model, we are now analyzing this data on a regular basis. We are also monitoring changes in customer feedback between certain pilot stores (for which new concepts are tested) and all other stores in our portfolio
Predict42 did set up an automatic sourcing routines for our own as well as relevant competitor data within days. By applying a customized topic model, we are now analyzing this data on a regular basis. We are also monitoring changes in customer feedback between certain pilot stores (for which new concepts are tested) and all other stores in our portfolio
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