Predictive Analytics For CPGs: 6 Ways to Drive Greater Marketing Performance

Whether you’re launching a new CPG (consumer packaged goods) product or managing a billion-dollar brand, the key to growth lies in proactively targeting first-time buyers. By focusing on these “opportunity audiences” marketers can translate massive amounts of data into insights that reliably forecast a shopper’s next move.

1. Predictive analytics improves conversion rates for CPG

Broadly speaking, companies use predictive CPG analytics to convert massive amounts of data from first- and third-party sources into statistical algorithms that inform business decisions. They can combine it with look-alike modeling to expand reach. These finely tuned audiences typically deliver better conversion rates than demographically driven ones and result in higher return on ad spend (ROAS).

Yet, many marketers have been reluctant to invest in more sophisticated CPG analytics tools—likely because the privacy landscape continues to evolve. As recently as January 2023, the Harvard Business Review• reported that interest in investing in data, analytics, and AI had waned – with only 40% of Fortune 1000 executives reporting that they managed data as a business asset, down from 47% the year before.

Then, a sea change: The emergence of ChatGPT and Google Analytics 4 (GA4) has fueled a supercharged interest in the ROI of predictive analytics. A Wavestone survey♦ reports nearly 90% of respondents now consider data and analytics a top organizational priority worthy of increased investment. Businesses are now actively exploring how to use artificial intelligence to analyze everything from supply chain management to demand forecasting and risk assessment.

2. Identify, measure, optimize

For the CPG industry, predictive analytics offers X-ray vision into the privacy-protected shopper universe. These AI-powered consumer insights give marketers a way to anticipate and react to an individual shopper’s needs that is compliant with their privacy preferences.

At its core, predictive CPG analytics use data, statistical algorithms, and machine learning techniques to predict customer behavior, personalize campaigns, and optimize marketing budgets. By harnessing its power, brands can gain a deeper understanding of their customers, measure what’s working and what’s not, optimize your marketing efforts, and drive sustainable growth.

3. Targeting and measurement at scale

One of the most powerful applications of predictive CPG analytics lies in targeting and defining audiences at scale. By analyzing vast datasets, marketers can segment customers based on a variety of factors, including demographics, lifestyles, purchase history, and online behavior.

4. Uncover purchase-based insights

Accenture‡ estimates that CPG companies scaling data, analytics, and AI strategically outperform their peers with a 32% increase in their price-to-earnings ratio. Predictive CPG analytics uncover purchase-based insights and actions that deliver:

• Improved customer satisfaction by understanding customer needs and preferences, marketers can deliver more relevant, personalized experiences, leading to increased satisfaction and loyalty.

• Increased sales and revenue by identifying high-value customers and potential new audiences, optimizing marketing spend, and growing sales and revenue.

• Reduced costs by understanding which creative messaging and promotions work best in real time to convert sales, brands can optimize campaigns to reduce media waste and stretch budgets.

• Competitive advantage by allowing you to take an agile approach to targeting category users and brand switchers to deliver incremental sales beyond current buyers.

5. Anticipate your brand’s likely triers

Catalina’s BuyerScience engine synthesizes purchase data and shopper insights from nearly all U.S. households with 3.5 million predictive data variables. Our data analysts leverage the most advanced predictive models, enhanced with AI and machine learning, to craft highly efficient audiences, including those most likely to try – and like/love – your product.

Our expertise and practical use of AI and ML techniques allows the CPG industry to pull, process, and activate data efficiently across digital and in-store channels to drive greater differentiation and performance in an increasingly crowded digital media landscape.

We uncover the interactive effect between demographics, shopper personalities, contextual factors and market dynamics, enabling brands to target specific purchase patterns such as likely triers, defectors, and category shifters. Particularly valuable is the ability to anticipate how a shopper’s lifestyle is transitioning, whether they’re starting a new diet, having a baby, or trading up to more premium brands.

6. Capitalize on transitioning purchase patterns

For a leading baby care brand Catalina’s BuyerScience platform identified high-potential households for babies transitioning to toddlers. Using predictive modeling to target these parents, the campaign delivered an in-store offer that generated a 30% repeat rate and a 59% return rate from a second coupon following purchase. Sales doubled, resulting in a $19.20 return on ad spend (ROAS), while always respecting privacy.

Learn more here about how your brand can benefit from Catalina’s precise and flexible targeting, best-in-class programmatic media, and in-flight optimization techniques.

• Harvard Business Review: Has Progress on Data, Analytics, and AI Stalled at Your Company, 2023
♦ Wavestone: Annual Survey of Fortune 1000 and Global Data and AI Leadership, 2024
‡ Accenture: The Insight Track: Five No Regret Capabilities to be a Data and Analytics-Driven CPG Business, 2021