Recommendation Engine for Retailers

Increase store level sales through better brand portfolio decision making.

knime_icons_rz View workflow on KNIME Hub

The Challenge

A trial and error approach to optimizing merchandise levels for a single retail store is inefficient and ineffective. To reduce the amount of unsold stock, Merchandising Managers and Brand Portfolio Managers must have a good idea of what will sell and what will not. Capitalizing on regional data to predict brands or products with higher sales potential puts any retail company on a better path.

knime_icons_rz Our Solution

A data science team builds a workflow in KNIME Analytics Platform which is based on an approach similar to collaborative filtering. It makes automatic predictions of individual customer interests by collecting preferences from many customers. The workflow starts with ETL and other data preparation steps, before creating a recommendation engine, and lastly through to determining interaction points for the Analytical Application. The workflow is then deployed on KNIME Server as a Guided Analytics Application.

Why KNIME Software

Tasks like ETL and data prep require a certain degree of technical knowledge - as does creating a recommendation engine. In this case, data scientists can focus on creating and deploying an Analytical Application from which Merchandising and Brand Portfolio Managers can draw insights and make decisions.

This Innovation Note was written by our trusted partner EPAM.

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