Pricing Optimization and the adoption of Machine Learning

A young man holding a product while using a smart phone in a grocery store

The past decade ushered in a massive wave of new technology that reshaped the retail industry, in particular how brands collect, analyze and use customer data. Thanks to the prominence of online shopping and rise of digital-driven loyalty programs, retailers have access to more detailed and in-depth customer behavioral data than ever before. The challenge now is understanding how to mine that information to create better customer relationships and grow business. The answer may lie in even more technology, this time in the form of machine learning.

Machine learning, and AI in general, has come to prominence in the past few years, most notably thanks to virtual assistants like Apple’s Siri and Amazon’s Alexa. The practice unleashes the potential of computers and the cloud to analyze vast amounts of data faster and more thoroughly than is possible for humans. This processing power makes it ideal for retailers desperate for more efficient methods of sifting and understanding their customer data. It’s especially useful in the complex area of pricing optimization, where it can help maximize ROI in both the retail and B2B settings.

If you’re considering implementing a machine learning program to improve your approach to pricing, here is a look at how introducing one can make a difference for your retail and B2B strategy.

Retail pricing optimization

Retail pricing optimization is a complex undertaking, requiring data analysis at a granular level for each customer, product and transaction. Endless factors need to be considered, from how sales are impacted by changing price points over time to if customers will continue to buy a product in smaller quantities at higher price points. All of those factors must then be analyzed, in context, factoring in timing and causal conditions, as well as controlling for factors like seasonality, weather, promotions and even product relationships like how lunch meat prices might impact bread prices. 

A well-crafted machine learning program can factor in all of these variations, combining them with additional details like purchase histories, product preferences and more. In a real-world example, Precima applied a machine learning system for a leading European grocer to help them offer customers the lowest possible prices. The system analyzes approximately one billion transactions across 40,000 products in 800 stores, something that just isn’t possible without this technology. The program produces monthly optimized product-level price-point recommendations that have helped to generate stable revenue alongside an incremental lift in profit.

B2B pricing optimization

Leveraging machine learning for B2B pricing optimization is similar to retail, but far more complex. While two grocery store customers are always offered the same price for a whole chicken, CPG manufacturers and goods distributors often offer different, customized prices on the same product to different business customers based on anything from volume to delivery time. For a B2B pricing optimization program to offer actionable recommendations specific to each customer, it needs to analyze literally millions of data points and scenarios. What makes machine learning so ideal for this process is that more data actually leads to more power behind decisions and more evidence from which to draw more accurate conclusions.

In another real-world example, Precima has developed a machine learning program for one of the largest food distributors in the U.S. to help guide their B2B pricing strategy for customers and products while factoring in localized situations. This system analyzes approximately one billion observations a month, across 230,000 customers, 150,000 products and 60 different markets, providing more than 29 million personalized customer and product-level price recommendations per month, that have led to consistent incremental lift in sales and profit. This number of recommendations, and the sophistication and impact they have, wouldn’t be possible without machine learning.

The B2B and retail industries are still in the early stages of implementing machine learning but as with all technology, the speed of adoption will continue to increase, allowing retailers, B2B organizations and CPG manufacturers to solve problems more accurately than ever before. The organizations already taking advantage of machine learning to increase the ROI of their pricing strategies are ahead of the game. Those that don’t begin leveraging it soon could be in jeopardy of being left behind.