A multi brand global leader in the CPG sector faced a challenge to optimize product offering for point of sale in a highly competitive market. With hundreds of products, the client faced a risk of over-contacting their distributors and POS customers with sub-optimal product offers.
Use of Qualitest’s Machine Learning for retail solution to predict the best product variety for any point of sale. By predicting demand more accurately the company increased the diversity of products per point of sale, decreased inventories and returns and lifted revenues.
The company partnered with Qualitest to use its Machine Learning solution. Final model includes three main components:
For each component a separate prediction model was created, each model was fed with hundreds of commercial and location expletory variables. Our auto Machine Learning tool distilled the crucial features that contributed most and optimized the inner algorithm settings to secure the highest level of models’ performances.
Qualitest used complex data streams from multiple brands and product categories. The main model of demand prediction was built based on Machine Learning models ensemble. The next phase included the use of Market Basket analysis combined with prediction modeling for each product. An optimized quantity for each product was applied. The model components are now part of the company’s production environment.
Qualitest’s AI-powered Machine Learning Predictive Analytics solution proved to be fast, easy to use and accurate when applied to complex data. The solution will enable the company to expand sales for each POS and increase operations efficiency while ensuring POS satisfaction.