PepsiCo Simulates Market Conditions
Consumer packaged goods companies are increasingly placing their trust in data to understand consumers better and create more robust engagement models. PepsiCo (www.pepsico.com) brand managers envisioned greater use for their data. They believe that analyzing the data helps them in understanding consumer choices, at depths sufficient enough to get insight into the products they should likely retain, discard or even revalue. The idea took shape when a PepsiCo brand manager wanted to understand the ideal combination of product features, price point, pack size, etc., before launching new product variants.
To make this vision a reality, PepsiCo developed the Pricing Pack analytics solution in partnership with TCS (www.tcs.com) that would simulate various market conditions based on different combinations and assess the corresponding effect on market share, volume and revenue variance at brand and SKU level.
Additional insights into price and cross elasticity at the SKU level provided greater understanding of the simultaneous impact of multiple product-level changes in a single portfolio. This helped in determining the ideal combination of pack sizes and price points to achieve successful product pricing strategies. The use of dynamic data generation and plotting techniques enabled the solution to take into consideration all possible attributes to provide real-time yet accurate market simulation.
What’s more, now the tool is additionally analyzing the competitor product pricing strategy to judge its impact on the PepsiCo product portfolio.
FAST FACTS
A Market Need
Brand managers at PepsiCo wanted to use data differently to maximize market share through dynamic adjustments to price, pack size availability or both.
The Analytical Solution
A Pricing Pack analytics solution now generates simulated conditions of market share and revenue to changes in product price, pack size and availability.
Big Benefits
PepsiCo brand managers can now assess the impact of the introduction of new product variants on market share and on the extent of cannibalization, as well as determine the optimal “price points” to enable market share/revenue maximization.