In-Store Consumer Behavior Analysis
From Patterns to Recommendations
CB4’s patented machine learning engine and advanced data compression
algorithms detect hidden consumer purchasing patterns in basic sales and inventory data sets.
These patterns are used as dynamic benchmarks that accurately predict expected demand at a SKU-in-store level.
Capitalize on operational opportunities
When a SKU fails to meet its expected local demand at a store the software automatically sends a recommendation to store managers and supervisors that helps them identify and fix a variety of operational issues which are hindering sales.
Granularly localize your assortment
When high local demand is detected at a store for an item which is not yet in the assortment, recommendations to add the item are sent to the merchandising team along with an accurate prediction of expected revenue impact.
- No hardware
installation at stores
- Uses simple
- Proof of concept
in an hour
in a single day
Operational impact? Less than a minute a day.
- CB4 recommendations highlight SKUs whose sales are affected by
operational issues such as on-shelf visibility, out-of-stocks,
- Managers can review the responses and lost sales recovered on
mobile or web based dashboards
- Average time spent per manager to implement CB4
recommendations is just 20 minutes