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.

solutions-diagram solutions-diagram
CB4
No hardware
installation at stores
CB4
Uses simple
point-of-sale data
CB4
Proof of concept
in an hour
CB4
Implementation
in a single day

Operational impact? Less than a minute a day.

CB4
CB4 recommendations highlight SKUs whose sales are affected by
operational issues such as on-shelf visibility, out-of-stocks,
mislabeling, etc.
CB4
Managers can review the responses and lost sales recovered on
mobile or web based dashboards
CB4
Average time spent per manager to implement CB4
recommendations is just 20 minutes

We can deliver a POC in less than an hour
and a full implementation takes less than a day

SCHEDULE A DEMO TODAY

Revolutionizing retail is as easy as 1-2-3

laptop

1

Run a free analysis on sales data and
generate actionable recommendations

20 MINS

phone

2

Deploy recommendations to select
store managers and merchandising
teams

VIA MOBILE APP, WEB APP OR EMAIL

laptop2

3

Let CB4’s solution measure revenue
gain and ROI

SCHEDULE A DEMO TODAY