Scalable Machine Learning Applications in Retail

Arrikto introduces some new use cases where machine learning is being used to solve retail problems.
Explore use cases pertaining to retail where machine learning is being used to solve complex business problems:

The retail market has changed globally, requiring retailers to rapidly adapt throughout the value-chain from supply chain, inventory management, consumer buying behaviors, and launching new channels.
To gain a competitive advantage, retailers need to unlock data gathered in all channels and build scalable machine learning models to meet new customer demands and maximize value generated.

Personalize value in every part of the customer’s journey. Whether it is assortment selection, promotions or communications – deploy highly targeted campaigns to understand shopper intent and to provide individualized solutions that increase ROI.
Shopping Patterns
Model tune individual item performance by store, improving forecast replenishment orders to warehouses and suppliers to meet daily demands. Reduce shrink and provide customers with fresh products – just in time.
Pricing and Promotion Optimization
Accurately predict the best time to promote and the optimized discount price while maximizing the value generated. Demonstrate success of specific promotions such as increase in basket sizes or store level customer foot count metrics.
Customer Loyalty
Use predictive analytics to review customer buying patterns to customize and incentivize customers to complete the purchases as well as customize the assortment offer that is promoted to increase basket size.
Supply Chain
Accurately forecast models and place purchase orders to fill customer demand with adequate lead times, helping retailers improve service and save on inventory holding costs.
Labor Management
Predict store operational needs and better plan colleague schedules taking into account shopping patterns and timing. Optimize labor allocation in the store to better serve the customer and thereby increase NPS scores.
Real Estate
Use geospatial data analytics to identify critical new prospective locations and where to upgrade existing assets based on customer location, competitor presence, market and assortment preferences to optimize business outcomes.
Fraud Detection
Efficiently analyze transactional data at scale to accurately predict buyer behavior and historical purchases to identify fraudulent transactions and block them as they occur.
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