Case 1
Analyzing offline store visits and customer perception through social listening
Background
Brand A wanted to understand actual customer perception of 10 specialty stores across the country.
At first, it tried offline surveys for visitors at each store, but response rates were extremely low and the team failed to secure meaningful data.
Later, it attempted to raise participation by offering event prizes, but most responses then became formulaic positive answers driven by the incentive itself.
As a result, data reliability dropped sharply, while surveying all 10 stores through an outside vendor still required significant time and expense.
Solution and outcomes
Instead of relying on unreliable surveys, Brand A registered a range of keywords including store names such as specific branches and store titles, then collected voluntary customer visit reviews comprehensively from blogs, communities, and social platforms.
The system automatically extracted positive and negative factors related to the store experience from the large body of collected review text.
It also grouped the extracted keywords into meaningful categories such as brand image, product or menu evaluation, visit experience, and store atmosphere so they could be compared at a glance.
This allowed Brand A to secure unbiased voice-of-customer data while dramatically reducing time and cost versus offline surveys and completing a multi-angle deep analysis across all 10 stores.
