CB4 Analytics

CB4 Analytics

Description

From the trillions of patterns that exist within your POS sales data sets, CB4’s patented solution uncovers customer purchasing patterns that detect and capture consumer demand.

The patterns themselves are the result of myriad external and internal conditions that impact consumer preferences at each store location. These conditions could be related to demographics, climate, a promotion (either your own or a competitor’s) and many others, few of which a retailer has access to.

When a store is part of a purchasing pattern that indicates a high local demand for a specific product, but the product’s actual sales at that store does not meet the expected potential -- an opportunity is uncovered.

CB4’s solution identifies whether the opportunity could be captured by fixing an operational execution issue within the store, or by making a better-informed merchandising decision.

The solution is not focused on understanding which conditions created these purchasing patterns, but rather on extracting the patterns themselves and applying them to detect unmet demand. In other words, the software analyzes the sales data to uncover lost or hidden sales opportunities.


Benefits

  • Uncover hidden consumer purchasing patterns by using just basic point of sale data
  • Detect and correct operational inefficiencies as they occure at a store and SKU level
  • Fine tune assortments to better fulfill local consumer preferences and respond to local competition
  • Understand local demand preferences to improve category and brand strategies
  • No installation or integration of hardware required
  • Fully automated from data to actionable recommendations that are delivered to the right stakeholders at the right time

About the Company

CB4 Analytics provides a patented solution for brick and mortar retail companies that increases same store growth by up to 3% using simple sales data. The software uncovers hidden purchasing patterns that capture unfulfilled consumer demand at the SKU and store level.  These patterns are translated into actionable recommendations to fine tune product assortment in order to better meet local consumer preferences, and to improve store execution by detecting and correcting operational inefficiencies.


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