APEX - 2 weeks assessment

LTIMindtree Limited

APEX: Accelerates CPG Sales Using Azure Big Data, Analytics and Mobility with Store specific Assortment and Sales Recommendations

Crisp description of the service (What & why) - APEX: Accelerates CPG Sales Using Azure Big Data, Analytics and Mobility with Store specific Assortment and Sales Recommendations • Pools, validates and maps data from sales, stores and products, third-party data (Nielsen, etc.) and demographic data. • Uses proprietary algorithms and performs predictive modeling for each store • Recommends must-sell using Logistic Regression algorithm and cross-sell products using ANN and Collaborative filtering which are proven Industry standard algorithms. • Provides “out of stock” predictions. • Enables recommendation adjustments. • Delivers recommendations to mobile sales force.

Customer Challenges/Pain points/Asks – • Dependency on Sales representatives intuition to identify products to be sold
• Difficult to generate and maintain store specific recommendations • Out of stock products lead to loss of sales • Identifying right cross-sell for every store • 10-15% attrition in field salesforce Benefits/Differentiators Although APEX is platform since 2015 and is based on real work that Mindtree has done behind the scenes with a Top 3 global CPG over the last three years. Across eight different countries, we have used bigdata, analytics and mobility to boost FMCG sales.

The results have been smashing: • $40 million in incremental revenue thanks to smart cross-sell recommendations at the store level. • 17% increase in repeat purchase rate 4.5 million out of stock recommendations. • 26% increase in unique lines sold per store. For new partners, we predict a minimum 10% increase in lines per call and a 15% increase in average store order value after a one-year period with APEX. And because it’s a cloud-based solution, you can expect the onboarding process to take five to six weeks rather than five to six months.

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https://store-images.s-microsoft.com/image/apps.58475.ac772150-c6cf-4440-b785-4c0289e3c3f2.609f7c95-a344-403a-8c2f-686b2e91e622.1c4226b1-f08d-4056-8400-f64301d756f2