Consumer demand patterns in brick and mortar stores often result from local factors that are not always available as structured dataset features. For example, the presence of a school near a store can affect one subset of products (e.g., products that children prefer), while a local product's promotion by a nearby competitor can affect another subset of products (e.g., complementary or competing products).
Accordingly, a store can share demand patterns related to specific subsets of products with specific stores that are affected by similar hidden factors, even if these stores are different in size, format or region and do not necessarily belong to the same stores' segment.
Trying to explain such complex demand behavior is extremely time consuming and practically impossible when many of these external and internal factors are hidden and dynamic. At the same time, with the right technology, the actual demand patterns that are reflected in point-of-sales data can be extracted and used to trigger granular and actionable recommendations.
CB4's technology is based on proprietary data-compression algorithms that can automatically capture the local demand patterns at each store, regardless of their affecting factors. Detected patterns are not bound by assumptions on how stores are segmented or clustered, or how various internal and external factors affect the demand behavior locally.
CB4's algorithms automatically detect similar demand patterns among products and stores, regardless of whether those products were purchased together by the same customer ('basket analysis') in specific stores. The algorithms automatically identify the related 'fuzzy-clustered' patterns and define an exact sales benchmark for target products at each store. It then generates recommendations based on anomalies and unmet opportunities that are detected for specific products in specific stores.