Revenue Maximization using Analytics
- In India, beer prices and profitability are governed by two primary factors – increasing input costs and government excise duties.
Business Challenge
- Increasing input costs & high taxation is compelling companies to increase prices , leading to share loss
- Our client with a large variety of brands across different segments wanted a pricing strategy that can help increase prices with minimalistic share loss across two key markets
Our Approach & Solution
- We adopted a two-pronged approach to meet the objectives:
- Econometric Modeling: using time series modeling, focus was on volume forecast and to uncover interactions across the brands
- Conjoint Research to measure brand price elasticities & estimate change in revenue & volumes with increased future prices
- Volumes from the modeling were integrated with Conjoint to measure future price increase impact
Value delivered & Client Action
- Insights
- Client brands enjoyed different levels of brand equity across the two markets. The pricing recommendation was thus brand, market & format specific.
- Category was operating at different levels of price sensitivity across the 2 markets. One of the markets was inelastic and thus allowed for higher price increases than the other market
- Impact
- Volume Prediction : Integrating conjoint findings with modeling output, predictive market shares & volumes could be computed
- Increased revenue: recommended pricing options can lead to gains in both volume & revenue
- Market level deployment of Pricing: Presentations made to Business Leadership. Client went ahead with recommended pricing strategy and maximized revenue with minimal volume share loss
