BUC-2.2 — Insight Intelligence Hub.

One place to ask for consumer, shopper and competitor insight – from past research and AI surveys to virtual shelf tests. Today: • New consumer questions often trigger a new research project even when similar questions have been asked before. Many hours of interviews and FGDs sit idle in videos and agency decks, locked to one research objective and rarely reused. Rewatching footage takes more time than teams typically have. • Today, research forces a choice between scale and depth. Quantitative studies tell WHAT people choose, but fixed questions often cannot reveal WHY. Qualitative research uncovers the deeper “why”, but only from a small sample. To bridge the gap, teams run separate quantitative and qualitative phases. This adds time, cost and handovers while insights lose momentum. • PPA, pricing, planogram and display decisions rely on historical data, shopper research and physical store tests. The real choice spans pack, price, channel, shopping mission, display and competitor context. Testing every combination physically is not practical. Teams narrow options early, and some growth opportunities remain untested. • Traditional product concept testing approaches are limited in speed and depth, which reduces the ability to accurately predict market success and the likelihood of launching winning innovations. • Consumer insights are often fragmented across multiple research studies, consumer feedback sources and market data, making it difficult to identify recurring themes, unmet needs and actionable business opportunities. • Consumer feedback and social listening come from many sources and need to be classified as positive/negative and by topic (product, advertising, plans and more); not just one team but many teams need this. • Shopper needs and category shifts are spread across research, sell-out data, social comments and channel reports. Competitor price, promotion, pack, assortment and shelf moves are captured differently across TT, MT and eCommerce. These signals are not connected into one forward-looking view for innovation and commercial decisions. → As a result, pieces of the market are visible, while the next growth space and how to win it could be clearer. • Competitor monitoring remains largely ad hoc and periodic, limiting the ability to capture timely market moves and respond effectively to competitive threats.

• Help the business understand consumers, shoppers, category and competitors faster, and decide quickly from universal data. • Faster, evidence-based decisions – shorten insight gathering and synthesis through one consumer, shopper and competitor data hub. • Unlock idle consumer content; fewer repeat studies; instant debriefs; consumer truth available to marketers on demand. • Deeper insight at quantitative scale; faster decisions through immediate synthesis; fewer handovers between quantitative and qualitative phases. • Higher innovation hit-rate; more combinations explored before physical testing; faster, evidence-based alignment between Marketing and Sales. • Enhance the winning ability of new innovation; faster insight generation and stronger consumer-centric decision making. • One cross-channel intelligence view detects shifts in shopper needs, category demand and competitor activity across TT, MT and eCommerce; competitor price/promo capture accuracy improves and material moves are translated into a decision-ready response. • AI does not replace qualitative research or real shopper research – it adds a layer / narrows the field. Human researchers remain in control of objectives, guardrails and interpretation.

• Every marketer, insight and brand team that needs consumer truth on demand • Marketing, insight and innovation teams across the learning journey • Marketing, Sales and shopper teams deciding pack, price, display and planogram • Branch Sales, CDT, BU (Business Unit), CMI

• Focus group video, audio and transcripts; past and future qualitative studies • Survey responses and open-ended conversations at quantitative scale • Shopper, sales and behavioral data for AI shopper personas • External data / benchmark • U&A, Concept Tests, Product Tests, Social Listening, Business Reviews, Nielsen, Consumer Complaints • CDT, Channel, Sales, PG inline • Sell-out data, social comments and channel reports; competitor price, promotion, pack, assortment and shelf moves across TT, MT and eCommerce Note: Synthetic data could be potentially provided during the process, upon approval of the Nestlé team and in compliance with data law.

• To be determined Note: Nestlé's current environment includes Microsoft Azure and Microsoft Copilot. Solutions that can work with this environment are welcome, and final integration and deployment requirements will be confirmed with the Nestlé team.