Expert Thoughts on Future AI Trends

Kristine Joji, former VP of merchandising at Walmart, and now executive vice president of strategy consulting at Insite AI, an artificial intelligence and strategy partner, talked to the Path to Purchase Institute about how marketers can leverage AI to as a forecasting tool. 

P2PI: How can marketers leverage AI to forecast future product, channel and buying behavior? 

Joji: Leveraging AI to forecast the future comes down to how technology can interact with data. Predictive AI can ingest a wide array of data — whether that is a brand’s internal data or external, macro-economic factors — and instantly produce explainable results while also forecasting demand at granular levels. 

Even more than that, AI can layer those insights on top of a market forecast. When AI is crunching data, it can reveal the fragments or segments driving the predictions. As such, it can reveal the groups of shoppers that are changing and shifting and uncover short-burst trend cycles that have the potential to become long-term patterns. 

For example, AI can reveal to a snack brand that consumers in one region are particularly apt to purchasing its low sodium chip product, which allows the brand to not only ensure the stock on that product will meet demand, but also understand the potential that the region is experiencing a preference shift toward healthier snack alternatives. Now, that brand is keen on a future trend that could not just impact inventory, but pricing, assortment and other factors, as understanding demand is a key step when making those decisions. 

AI-powered demand forecasting ultimately allows brands to understand their different consumer segments, geography, movement, trends and buying patterns as it anticipates shopper needs and underlying demand drivers.

P2PI: Can you share some pros and cons? Or benefits and gaps?

Joji: A key benefit of AI forecasting is “explainability.” It can be difficult for humans in the CPG industry to grasp the mounds of data at their fingertips, but AI can step in and lay it out neatly. It can uncover in-depth meaning behind existing insights and therefore make it easy to convey important messages across a brand’s stakeholders and internal teams. 

Another benefit I’ve seen is that AI can allow brands to run scenario simulations, which help inform next steps on optimizing various factors such as assortment or pricing. Scenario simulations involve the creation of virtual models that mimic real-world situations, allowing decision makers to explore various scenarios and their potential outcomes. These simulations are based on algorithms, data analytics and predictive modeling techniques, enabling organizations to simulate different scenarios and their potential impact on key performance indicators (KPIs). Instead of relying on intuition or guesswork, decision makers can utilize simulations to identify the most favorable course of action based on data-driven insights.

In the era of AI and machine learning, the role of human expertise is irreplaceable. While AI algorithms can analyze vast amounts of data and generate insights, they lack the nuanced understanding and strategic vision that human decision makers possess. Integrating human expertise into the decision-making process is essential for AI to generate the most meaningful results. This symbiotic relationship is often referred to as "human in the loop," and it ensures that AI-driven insights are aligned with business rules, goals and strategic plans, which      optimizes decision-making outcomes.

P2PI: How has forecasting and predicting how products/promotions will perform evolved with the rise of AI and what are some recent capabilities?

Joji: With its ability to reveal market trends and forecast demand, the rise of AI has allowed brands to take those predictions a step further and also determine optimal next steps. I think a good example of this is what we’re seeing with Walmart Luminate. 

For context, Walmart Luminate is a tiered data platform that allows the retailer’s consumer goods partners an in-depth look into data around consumer shopping behaviors, pickup, delivery and more. 

However, managing that data and deciding what’s next is where AI can step in and change the game. Predictive analytics and AI models can take insights from Walmart Luminate data and create accurate forecasts on a product’s demand. It can also explain why sales may spike or decline in the coming months. From there, AI can run scenarios to help brands decide how to optimize key factors such as assortment, pricing and shelf space.

P2PI: Is there a strong use case you’ve seen or been involved in that leveraged AI to plan and/or execute product and pricing decisions? Can you share a little about that?

Joji: An example that we have seen recently at Insite AI of successfully leveraging AI to make product and pricing decisions was with a craft beer brand. The brand had the mission to understand its demand drivers and forecast demand over a two-year duration. 

Using AI-powered predictive capabilities, we helped the brand track data across multiple retailer accounts, and we provided visibility into growth and atrophy predictions around innovation trends. This enabled the brand to quickly develop data-driven forecasts using macroeconomic factors, shopper behaviors and more. We saw the brand achieve 10% to 30% above its fair share of market capture, an annual revenue increase of $20 million and a market share gain of 10% to 20%. 

P2PI: Based on the successes (and possibly mishaps) you’ve observed from brands using AI as a tool to target/engage with retailers, what types of trade promotions are particularly resonating?

Joji: In a market characterized by volatility and uncertainty, optimizing trade investment is paramount for CPG brands that want to drive volume and maintain profitability. Before embarking on any trade investment strategy, brands need clear objectives. Whether the goal is to increase sales volume, enhance brand visibility or maintain profitability amidst inflationary pressures, having a defined objective provides the guiding framework for decision-making.

In our work with brands, we have seen the transformative approach that AI offers in optimizing trade spend, particularly in the realms of pricing and promotion. By leveraging AI, CPG brands can develop pricing plans that drive volume while aligning with profitability targets. AI enables CPG brands to implement dynamic pricing strategies that adjust in real time based on market dynamics, demand elasticity and competitor actions. This dynamic approach ensures that pricing remains competitive and responsive to changing market conditions.

Similarly, AI empowers CPG brands to develop personalized promotion plans tailored to specific stores, brands and product features. Rather than adopting a one-size-fits-all approach, AI analyzes customer segmentation data, historical sales performance, and promotional effectiveness to identify the optimal promotion mix for each store and product category. This targeted approach maximizes the impact of promotions, driving sales volume while minimizing unnecessary spend.