They settled on Tredence, a provider of artificial intelligence and data science solutions, and executed a 10-week project across the United States, Canada, Brazil, South Korea, Argentina, South Africa, and Paraguay.
A range of internal and external data sources were used to develop the automated models to compute price elasticity, including AB InBev’s revenue, volume, and sell-in price. It also used such sources as market research data and information from Euromonitor, income data from World Bank, COVID info from the University of Oxford Government Response Tracker, and even sports-related data from national sports websites.
The tool and visualizations are designed to be easy to use, digest, and shared with teams, and about 140 models were used in the seven markets, increasing accuracy of around 80-90%, according to Tredence.
Kurup says the biggest benefit thus far has been for AB InBev’s revenue growth management team, which is part of its commercial team. But in a switch from previous initiatives, the company integrated many other teams, including its supply chain and trade teams, and, as a result, the benefits are extending beyond this core RGM team.
This scaling was key to the integration. “The idea was not that the analytics team will do anything, and then just give a number out to the business team,” he says, noting that the risk of adoption challenges. “The idea was that you build something which you can give to the business, and they can play around with it. They can input their own business understanding. They can play with scenarios, etc., and then take the [pricing] decision in all the right confidence while we enable all of it.”
It’s not only been successful on that front, but AB InBev now has a framework that’s more scientific and data-driven, growing the confidence of their pricing decisions, he says. Looking ahead, the company has developed a strong foundation to scale up to different countries, brands, and setups. “The fundamentals are pretty solid.”
As a result, the project tallied another win in pushing forward the company’s mission of integrating data and analytics throughout the business — not just at the beginning or the end of an initiative. And while some of the more tangible benefits are still TBD until the end of the year, the company is executing nearly all of the recommendations in the market.
“We are executing at almost 100%, so you can consider that as a big win for analytics,” says Kurup.
Some common challenges have cropped up along the way, including gathering data together and getting Tredence acclimated to AB InBev’s tech stack and systems. Timing has also posed an issue as government regulations often constrain when and how often AB InBev can take price increases.
But all of these efforts are ultimately geared towards driving more volume, and given today’s inflationary environment, particularly with the commodities headwinds AB InBev is facing, the investment has come at an especially valuable time given how important — and challenging — pricing decisions have become.
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“Consumers are also super price sensitive now,” he notes. “We operate across the spectrum, so we have premium products, our core value products, affordable products — we have all of them. It's very important for us to price all our products correctly, and also to make sure that we don't get into different segments, and we don't price a premium product so low that it becomes like a core product. All of that becomes very important for us, and this does help with that.”
As AB InBev’s initial goal was focused on internal pricing, the solution is not fully integrated into its supply chain, but even the partial integration has served as a benefit.
“You can do the pricing right … but if the product is not on the shelf, it's all quite meaningless."