Business Situation

The client wanted to have a model to optimize cost price of stock, rationalize assortment of items and have better demand forecasting.

GrowYT Approach

• We integrated salesforce, promotion campaign, customer feedback, google analytics and other store data on a cloud-based data warehouse.
• We developed pricing optimization tool for Stock Keeping Units.
• Found key central items using state of the art algorithms and designed basketing or promotions around those items.
• Used RFM model and Bayesian Models to find customer lifetime value (CLTV).
• Designed Algorithms to forecast Visitor count at store to optimize resources.

Engagement

• We generated valuable insights based on above models to drive item assortment.
• We used above models to achieve customer lifetime value which is unique to each store.

Benefits & Outcomes

• Enabled marketing team planning promotions.
• Provided advance forecasts for optimal allocation of labour at all the retail stores.
• Enabled stores to retain old customers and increase new customer base.
• Optimized item stock based on demand and visitor forecasting.

Key Takeaways

• We enabled all the retail stores to measure their performance as well as take insight-based action to improve sales, revenue and profit.
• Hypothesis based data sanity is indispensable to cater dynamic human behavior.