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Machine Learning in Retail and eCommerce

Expand engagement and captivate customers across channels with machine learning ‚Äč


Leverage your extensive consumer data to maximize buyer engagement. Make customer experience hyper personal with cross-channel advertising, recommendations, and optimize special offers for increased purchases. Attain maximum operational efficiency with intelligent automated ML systems.



Recommendation engines

Increase sales and upsell with hyper personalized recommendation engines. Utilize lifetime customer data to retain customers, enhance the customers experience, and increase their intent to buy.


Lifetime Value prediction

The data used to determine Customer Lifetime Value (CLV) can be used by ML algorithms to predict total CLV, which in turn translates to differential benefits and levels of investment in various customers according to their predicted CLV, increasing revenue and customer satisfaction.

Pricing & Promotion Optimization

Identify peak purchasing moments for your customers. Utilize machine learning to determine optimal timing, placement, channel and device to promote your product. Give optimized promotional offers for better conversion with real-time models.


Drive sales by providing better service in a cost-effective way. With machine learning you can turn this simple tool into a valuable asset for your customers and your business. Engage with customers, and answer their questions with limited resources necessary.


Inventory management

Optimize inventory with intelligent and automated inventory management systems. Utilize account sale data, past inventory trends, seasonality and predict demand for a more efficient inventory management.

Fraud detection

Fraud and theft are major pains for both retail and eCommerce. Train models to identify suspicious behaviors, abnormalities and extraordinary events to be flagged for suspected illegal activity, at a much higher success rate and lower cost.