This is our story of co-creating a solution with a leading Consumer Packaged Goods (CPG) company

As we know…

In the retail and CPG industries, pricing strategies often change within a span of few weeks. Hence, pricing is both a sensitive and complex aspect for companies. It is a sensitive aspect because a slight change in price can significantly impact a company’s own product and that of the competition as well.

The complex aspect of pricing is due to the nature of relationship and negotiations that happen across channels of distribution, eventually impacting the margins of key stakeholders like retailers. New channels of distribution such as online stores have made things even more challenging.

While traditionally, customers compared the price of a product across brands, today they compare the price of the same brand across different channels to find the best deals. Analytics therefore has become an indispensable tool for retail and CPG companies that want to stay ahead of the competition in building a dynamic pricing strategy which is right for their business.

The challenge for the CPG company was…

In understanding the impact of different price points of a brand on its volume of sales, and study the impact of changes in price of competing brands. This analysis had to be done for each channel separately as the pricing strategy varies across channels. The company partnered with WNS to leverage its robust analytics capability and build the right pricing model that could enable a dynamic pricing strategy across channels.

Here’s what we co-created as a solution…

WNS developed a pricing model by deploying statistical tools on the pricing data of the brand across every channel. The key steps in the process included:

• Using descriptive statistics to identify and validate the outliers and deviations in the data set

• Considering key variables such as volume share, value share, promotions, price of the different stock keeping units of the brand and the price of all competing brands

• Conducting multiple iterations with the client to build a logarithmic regression model with a high R-squared value

• Determining price elasticity and cross-price elasticity of the brand (extent of change in volume share due to a change in the company’s own price and competitors’ price respectively)

Our learnings and outcomes from the process of co-creation are…

That the pricing model helped the client make dynamic pricing decisions. The CPG company was able to build a pricing strategy suited to the uniqueness of each channel. The model strengthened the client’s capability to take quick and well-informed pricing decisions in response to changes in market conditions and competitors’ strategies.