How AI Is the New Growth Engine for CPG Chief Growth Officers
Executives at the world’s leading CPGs have huge amounts of data and insights available to them, and with R&D centers around the globe and huge insight budgets at their fingertips, some might argue that the role of the Chief Growth Officer would be an easy one. But the reality is that, despite their extensive corporate resources, agile startups are nipping at the heels of large product manufacturers and the sheer size and complexity of CPGs doesn’t always work in their favor. The huge amount of data and insights coming in from the market is creating analysis paralysis – a situation where multiple feeds of static data are sitting in silos in different parts of the organization and delaying crucial strategic decisions.
It’s never been a more challenging time to be a Chief Revenue or Growth Officer. To survive and thrive in the increasingly challenging economic environment, brands and retailers need to collaborate closer than ever before to put the customer at the center of their business decisions. Yet in practice, retailers are highly protective of their consumer data and rarely share their real strategic agenda with their suppliers. With dramatically and quickly shifting consumer tastes, preferences, and shopping behaviors, as well as own-label powerhouses like ALDI and Trader Joe’s, many factors are gradually luring people away from branded products.
Multiple pressures are on the Chief Growth Officer: colleague and shareholder pressures, shorter product innovation cycles, retailer and consumer demand for fresh items more frequently, and a vicious grab for market share in new and interesting categories.
There is also a paradox within modern CPGs: they want to act agile in order to compete with the new string of direct-to-consumer innovators, but these startups are often funded by bullish venture capitalists willing to take a risk across multiple bets. Yet CPGs must be prudent and are typically risk-averse. So how can you act agile if you’re not willing to take a test-and-learn approach?
AI and machine learning hold the key to making agile innovation less risky and can make predictions with unbelievable accuracy. Solutions like Insite AI sit within your own cloud, securely bringing together millions of data points from inside and outside the business. Historical sales data, category data, consumer insight data, 3rd-party data from the leading research houses such as Nielsen and Kantar…add to that unstructured data such as the latest trends reports and insights from countless social media posts where consumers share their emerging behaviors, ideas, opinions, shopping trends, and future intent.
As a leader responsible for growth and revenue, you need to combine millions of disparate data points and connect the dots between things that would be impossible for any team of people to achieve. This is where AI and ML come in. Imagine a world where millions of scenarios are created in hours (not days or months), with a result of clear intelligence on categories and product concepts with highly accurate and granular forecasts of how they will perform in each channel (retail, e-commerce, direct to consumer) and at different price points. Just imagine the step change in your organization when you can sift through your innovation pipeline and have the confidence to commercialize projects, removing the nagging doubt of failure.
If you’re ready to see how Insite AI’s proven engine will drive incremental growth, customer-market fit, and added value for your retail and distribution partners, then you’re ready to turn the uncertainty of rapid change into your CPG’s opportunity.
