- Client: A global cosmetic company
- Industry: Cosmetics and Chemicals business
- Area of operations: Cosmetics, Skin Care and Hair Care, Human Health Care, and Fabric and Home Care, in addition to the chemicals business
- No. of Employees: 33,409
- Net income for 2020: 128.1 billion yen
A leading chemical and cosmetics brand wanted to improve its on-shelf availability and reduce out-of-stock scenarios. They hoped to develop new AI/ML algorithms to capture better customer insights and enhance the customer experience, as well as create an optimized assortment and inventory, increasing accuracy of stock forecasts. The client hoped to gain a more holistic view of sales and shipment data for their sales team, and create better, more actionable insights and consolidate customer sentiment data that was held in different sources, both structured and unstructured.
Wipro developed a robust framework solution that consolidated data from all retailers, establishing a single data repository that serves as a single source of truth. In addition, Wipro developed advanced natural language processing (NLP) algorithms to classify consumer feedback insights by sentiment and provided the ability to analyze these insights according to demography, product categories, brands and products. This framework also provides real-time alerts to sales analysts and brand managers and extends reporting and analytics capabilities to the sales and marketing team. Finally, the framework allows forecasting at the item level, helping merchandise planning by identifying key sales attributes using advanced forecasting models. A stable and repeatable forecasting model was developed for 1500 SKUs.
The client found swift, robust gains, reducing data silos for sales and marketing information and providing quicker access to data needed to enhance the customer experience. Customer sentiments created a better understanding of customer pain points via reviews and ratings, and the reduction in manual efforts improved efficiency, boosting POS reporting by 25%. Forecast accuracy increased to 85% and stock out scenarios decreased, increasing customer satisfaction and providing advanced analytics and creating actionable insights for the sales team in real time.