Smarter Decisions — 4 Ways CPGs Can Use New Data and LLMs to Outpace Competition

The Consumer Goods industry suffers from a serious problem — many important decisions are based on very little data. According to industry reports, most promotions are not evaluated, and even those that are analyzed are often too general, giving little useful information.

To overcome this lack of knowledge, the industry invests a lot of money in after-the-fact measurement studies that cannot change what has already been done. The main issue? Bad decision-making due to insufficient detailed and practical data and the ability to generate insights quickly.

Deep dive into the decision problem

• Promo efficiency: This reliance on outdated research creates a blind spot – resulting in up to a third of promotion dollars being wasted due to improper targeting and evaluation.

• Ad effectiveness: CPGs (Consumer Packaged Goods) are still grappling with personalization, leading to up to 30% wasted ad spend (around US$ 40 billion globally), according to media investment group GroupM.

• Out-of-stocks: Globally, the lack of accurate on-shelf availability (OSA) alerts translates to a staggering US$1 trillion missed opportunity, with the U.S. alone suffering losses exceeding US$100 billion per industry estimates.

• Supply chain strains: The consumer Industry suffers huge fines (up to $100M annually) due to a lack of real-time inventory visibility and shared forecasts with retailers. This translates to billions in avoidable expenses industry-wide.

Decision transformation coupled with LLMs — The disrupting force

The CPG data transformation is well underway. Here are four ways forward-thinking CPGs are leveraging these shifts to make more informed and profitable decisions:

1. Decision blindness to precision with third-party data sources: Partnerships with third-party data providers like Nielsen grant CPGs access to granular, zip code-level data on pricing, promotion, assortment, and consumer activation. In a secure clean room environment, CPGs merge this with their data and leverage elasticity models to extract game-changing insights. These fuels fine-tuned, ROI-positive decisions based on hyper-precise market understanding and OSA strategies.

2. Real-time promo impact with retail giants: Data platforms like Amazon Marketing Cloud and Walmart Connect provide CPGs access to consumer purchase paths. This replaces slow, outdated panel studies with immediate granular measurement, and with LLM models enabling in-flight optimization of campaigns identifying shifts in interest at region levels and optimizing ad spend in that area, avoiding wasted promo spend while achieving greater promotion ROI.

3. Unlocking personalization with next-gen data sources: Receipt data providers like Fetch, etc., expand the CPG data arsenal – instead of relying on small panel studies, they grant access to longitudinal purchase behavior across census-normalized households.

Clean rooms, and activation solutions, emerge as game-changers, empowering CPG brands to deliver personalized marketing campaigns at scale by creating targeted messages, and personalized visuals like dynamic product packaging designs or targeted video ads, for maximum impact on individual consumers who are most likely to engage.

For CPG firms aiming to double product consumption, this shift provides the precise tools needed to achieve goals that were previously unachievable.

4. AI-powered supply-chain agility: Leading retailers are increasingly sharing complex supply chain data. Traditional ERP implementations are not designed to consume and act on the influx of new data.

These data models coupled with LLM capabilities, generate multiple, adaptable supply chain scenarios providing actionable insights like stock transfer smoothing, lane-level freight forecasting, and dynamic order management, boosting OTIF, decreasing waste, and saving millions for CPGs. The value generated in a much shorter time horizon by the white box AI models helps break the decision silos.

The future of decision-making is connected and collaborative

Consumer goods and retailers are changing how they work together as retail media networks emerge. Retailers see the benefits of using data to form partnerships, following Amazon's example of retail media success.

Clean rooms allow data-sharing that respects ethical and consumer-privacy standards. This collaborative shift empowers CPGs with greater agility and insights while unlocking new opportunities for retailers.

Call to action

Consumer Goods Industry leaders must overcome the data crisis if they hope to win in this new era and avoid flying blind in a data-driven world. Evaluating your existing data availability is step one and enabling LLM adoption is step two to drive comprehensive decision transformation.

GenAI emerges as a revolutionary force, transforming CPG operations into dynamic ecosystems capable of real-time adaptation, ultimately driving growth and profitability.

About the Authors

Deepak Jose is Global Head of One Demand Data and Analytics Solutions at Mars Wrigley and a Mars Global Customer Leadership member. He was part of Mars Digital Technologies, leading data and analytics transformation programs for Pet Care, Mars Wrigley, and Food & Multi Sales segments.

Before Mars, he was part of global brands like Coca-Cola, ABB, Asurion, and Mu Sigma in strategic roles driving business growth. He holds an MBA from George Washington University and a mechanical engineering degree from NIT Calicut, India. He completed an executive education program from Oxford University Said School of Business in Economics of Mutuality.

Jose is an in-demand speaker and analytics thought leader in the CPG and Retail Industry. He is also the author of his book “Leading through Pandemic - Unconventional Wisdom from Heartfelt Leaders,” and whitepapers on machine learning, trade promotion optimization, and revenue growth management.

Sagar Balan is Chief Business Officer for CPG at Tredence. He brings a wealth of experience collaborating with top CPG Chief Data Officers (CDOs) to bridge the critical gap between data strategy and business goals. His expertise lies in unlocking the potential of data across the value chain, consistently delivering impactful results in areas like commercial transformation and supply chain optimization.