Data was de-siloed, and a centralized data mart was created to integrate data from multiple sources using APIs, and scraping tools.
Company wide KPIs, sales reports, and product planning reports were generated on an automated basis until stakeholder trust was built over a period of time.
The descriptive analysis reports and predictive model insights were fed into power BI to create compelling business-focused visualizations. The easy to navigate and personalized dashboards immensely helped the business planning, sales & marketing teams to carry out daily tasks with more precision and take data-driven decisions.
Proprietary ‘Sales prediction’, and ‘Inventory optimization’ machine learning models were developed using advance boosted regression algorithms and deep recurrent neural networks. These models captured key business inputs like internal promotion data, social media data, consumer sentiments, external factors (weather, pandemic) and historical sales data to predict future sales, which in turn were used to preplan and optimize the inventory. The models were not only able to accurately predict the sales of continuing products but also provided a good estimate of sales of newly launched products.