A national quick-service restaurant chain wanted to evaluate purchasing behavior and item popularity to answer the following question:
- Are there differences in item preference based on low, medium, and high visit frequency?
The client used Guest Analytics to analyze their transaction data and separate guests in the relevant groups for comparison. Guest Analytics applied the transaction data for each customer to the analysis and provided the client with purchasing behavior for each group.
Light users demonstrated broader product exploration in their purchasing behavior and were unlikely to include a staple item in their decisions. Medium users were likely to stick to just two categories while Heavy and Low users ordered from a broader variety.
The client used the knowledge of differences between guest segments to introduce segment-specific communication strategies with specific goals.
- Light users – sample a larger variety of products
- Medium and Heavy users – drive incremental visits by featuring favorite products