ML in Retail Blog

Online shopping has uplifted the competition in the industry. With thousands of products and prices available for customers to compare 24/7, retailers’ pricing strategies face new challenges. Is yours ready?

Markdowns are essential to clear stock and manage margin. However, they're not used to full potential. In this article we discuss the benefits of markdown optimization with DSLab's machine learning.

How to launch machine learning initiatives that bring measurable business results and a step-by-step approach to implementing machine learning projects.

Standard demand forecasting approaches are no good for fresh food. The characteristics of perishables and associated demand patterns to take into account when forecasting demand for fresh assortments.

Challenges of online demand forecasting and common issues grocers need to bear in mind to succeed.

Changes that grocery retailers need to undertake in order to succeed in the near future.

Grocery retail is greatly effected by the coronavirus outbreak: from stockpiling and growth of online grocery segment to cannibalization of future sales and possible supply chain disruptions. In this article we share our recommendations on managing effects of the outbreak.

Three high-impact areas and easy wins of implementing ML that allow retailers to gain measurable effects and return on their investments

Overstocks and out-of-stocks are results of poor forecasting. How to measure forecast quality? Is the most accurate forecast always the best? The article on the pitfalls of measuring forecast accuracy

Fresh products with shelf life of 1 to 7 days pose a forecasting challenge to every planning department. AI, however, can give a fresh perspective on demand forecasting for perishables.

Learn how Lenta automates demand forecasting with DSLab AI technology. Download case study here