How To Optimize Pricing Algorithms With Smarter Data Strategies

Consumer goods leaders are increasingly looking to AI to optimize their sales strategies so they can better respond to real-time changes in consumer behavior. Having an agile and smarter pricing approach, however, requires a firm foundation in clean data practices, precise measurement practices, and models that support prediction capabilities. In this Expert Exchange video, CGT spoke with Joel Cartwright, industry principal of consumer products for Vistex, about how organizations can use AI-based pricing models to adapt to consumer behavior and improve sales and profitability while navigating challenges like consumer trust and data accuracy.At Vistex, Cartwright helps identify and understand the evolving trends impacting the consumer products industry. Over the last 13 years, he’s focused on the software side of tech innovation. Prior to that, he began his career with manufacturers such as Sarah Lee Foods, Sunday Delight and Kimberly-Clark.AI should be wrapped around core analytics capabilities, he said. When used for pricing purposes, AI and data science can help build algorithms that predict the impact of pricing changes across the organization. This approach allows companies to pull in different data sources around the shopper experiences, taking into account such factors as external media and seasonality to accurately predict consumer behavior. According to one particular manufacturer Vistex works with, consumers have become immune to price increases, requiring CPGs to expand their understanding of consumers to decide which levers to pull regarding pricing and promotions. “Does that pricing from a promotional standpoint take the form of different deal offerings? Are we going to have to go over different promotional pricing thresholds?” Where they found success is on the frequency and depth, such as having a slightly higher promotional price point that is used more frequently but during a shorter time span. Using AI, companies can now use predictive analytics to run different scenarios for retailers to show what types of impact they may have, he said. Once implemented, however, how might a CPG company measure the success of smart pricing? There are two particular numbers they should be going by, per Cartwright: the accuracy of the predictions and then that accuracy factored in with volume to determine sheer net revenue increase over the month, quarter or year. Without consistent data sources, companies will never be able to provide an accurate measure of success. Many times, he said, he’s seen manufacturers quickly grab data without visiting data sources as frequently as they should. CPGs should be working on managing and formatting that data and implementing data checkpoints to ensure data integrity. Validity checks, he said, should compare data to prior year, month and quarter inputs to “shroud off the black box when the information is being predicted and forecasted.”Companies are using a multi-pronged approach to sales and marketing, combining tactics such as driving promotional influence or using co-product merchandising to improve the consumer experience and drive sales, overcoming ongoing sensitivities to pricing.

How To Optimize Pricing Algorithms With Smarter Data Strategies

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