Retail - Price & Forecast Optimizer

Problem

Retailers find ways to optimize the price and forecast predictions to have a better stand in the market. They do have historical data of their customers but there is no sophisticated solution to optimize price and forecast for all sizes of retailers, the ability to strategically optimize price and forecast, by leveraging the customer data.

Challenges

  • Currently no solution available to optimize product price and forecast
  • Highly volatile market prices
  • No demand forecasting tool available. Even a slight price drop during off-season would trigger demand of the product

Tools

  • Google BigQuery

Result

  • Sophisticated and flexible rules engine that sets and relaxes rules as needed.
  • Delivers a consistent pricing architecture across line priced families, and different pack sizes.
  • Price products using proven science that moves beyond cost-plus pricing to consider elasticities, Customer reaction, product level relationships and cannibalisation, seasonality, and promotional effects when selecting an optimal price.
  • Understand forecasted commercial outcomes to a variety of strategic approaches and understand the most effective strategies