Using Data to Improve In-Store Customer Engagement

How is your team localizing in-store audience strategies? 🏪 Not all store visits are the same, and localized trip missions vary by region, store format, and shopper demographics. A convenience store in Manhattan serves a different mission than a suburban Sam’s Club. Understanding these distinctions is critical. 🎯 To build an effective in-store audience strategy, we need to align messaging, media, and promotions with two key dimensions: 1️⃣ Why is the shopper here? Each store visit serves a unique purpose based on geography, shopping habits, and store format: 🛒 Stock-Up Trip (Bulk Buy) – Larger baskets, typically planned for weekly or monthly needs. Common in warehouse clubs and large-format stores. 🛍️ Fill-In Trip – Smaller, more frequent visits for fresh or missing essentials. Typical in urban grocery and neighborhood markets. ⚡ Urgent Need (Immediate Consumption) – A grab-and-go mission for an essential (e.g., medicine, baby care, dinner ingredients). Key for convenience stores and pharmacies. ☀️ Daily Shopping (Habitual Trip) – Regular visits, often in dense urban areas, where fresh food and quick-stop items are a priority. 2️⃣ How do shoppers make decisions? Beyond trip type, decision-making mode varies based on location, occasion, and shopper intent: 📅 Pre-Planned Purchases – Shoppers know what they need before they walk in. Personalized app-based reminders, aisle signage, and digital coupons for planned replenishment items. 🛍️ Impulse Purchases – Shoppers are open to discovering something new. Localized product recommendations, in-store sampling, and digital shelf-edge media. 🎯 Focused vs. Browsing Behavior – Some shoppers are on a mission, while others explore. 💡 Time-sensitive shoppers need efficient checkout options and wayfinding tools, while browsers respond to interactive displays, storytelling, and product bundling. 🏪 Retailers who integrate purchase history, mobile app engagement, and real-time in-store behavior can create hyper-localized retail media experiences that feel intuitive and tailored to the moment. The result? More relevant messaging, increased basket sizes, and higher shopper engagement.

Advanced personalization work involves 'growth engineering' as a new 'role' to connect the dots and architect the data, from tools/data like: • CDPs • Data warehouses • Testing tools that enable adaptive approaches, e.g., Contextual Multi-Arm Bandits (or similar) • And advanced 'intent' or 'propensity' data and models. The last point is where Mr. David Mannheim comes in. He just pushed out a cool Ecom report on intent. (check it here, https://lnkd.in/gFcB-s7f) Whats in there are concepts, vocabulary, taxonomy that influences the last point of propensity data. Things like (from the top of the report): - 63% feel manipulated by ecommerce tactics (only 11% don’t) - 46% feel overwhelmed on ecommerce sites - 83% use discount codes when they would have bought at full price - 1 in 5 will stop shopping if they get an early pop-up The TRICK is to get this data accessible to the testing and engagement platform setup. Feature attribute data: > CDP defined User-level attributes: account tier, number of past upgrades purchased, engagement metrics (time on site, feature usage). > Session-level attributes: current time of day, day of the week, user’s device type, current navigation path or product page. > External attributes (optional): Geo-location, known seasonal promotions, pre-determined propensity model data All this sounds cool, but WHY/WHERE to apply this stuff? Here's my thinking: > Adaptive Learning: A dynamic personalization approach continuously updates the probability distribution of reward for (offer/product/promo) as new data is collected. Unlike a static A/B test, it doesn’t wait for a full experiment cycle to end before updating which offer to show next. (we don't care what wins, just push to what is working best now) > Context Utilization: This setup leverages user and environmental context (e.g., user account age, user’s current usage tier, time of week, location). This allows for personalized experiences rather than one-size-fits-all solutions. Add in explicit propensity and 'intent' data (h/t to David here) and you really get cooking. > Handling Concept Drift: If certain upgrades become more or less attractive over time, the testing/personalization algorithm automatically adjusts. This adaptability ensures that the system remains optimal in the face of changing market or user conditions. Yes this is where AI experimentation tools come into play, but the foundation of tooling and explicit data ontology (use case and model connections) needs to be there first. A personalization (also AB testing) recipe is only as good as it's ingredients. Bottom line? The right data, connected smartly, powers personalization that actually works—and keeps evolving. Want to dig deeper into David’s intent report, architect your own growth engineering setup, or just swap ideas on making this real for your team? I’m all ears—DM me or drop a comment below. Let’s cook up something impactful together!

🛒 In‑Store Ads: The Spark That Ignites the Shopper Journey . As CPG professionals, we're always chasing that perfect moment when a shopper’s curiosity turns into a purchase. According to new data from Placer.ai and EMARKETER (March 2025), 40.6% of US adults say they've researched a product after seeing an in‑store ad. But here’s where it gets interesting: 75.5% of marketers say ads featuring discounts or special offers grab attention — and 53.9% of consumers say those offers actually get them to buy something unplanned. That means in-store ads aren’t just awareness builders – they can activate purchase behavior. 🚀 So what does that mean for CPG brands? 1. Use in-store ads as the spark, not the full funnel. Static shelf talkers alone won’t cut it. Instead, incorporate mobile-first elements – QR codes, digital demos, microsites – to keep the shopper journey alive after they’ve left the store. 2. Incentivize meaningfully. Discounts and coupons aren’t just nice-to-haves; they drive behavior. But conversion means pairing them with digital follow-through – think incentive-linked loyalty points or exclusive promo codes. 3. Build seamless mobile experiences. Make sure every physical ad links to a frictionless mobile journey—store locators, recipe ideas, reviews, loyalty rewards. Think omnichannel. 4. Measure what matters—and attribute it. Track mobile searches, clicks, scans, app downloads, and even foot traffic spikes. The data isn’t just “nice to have,” it’s essential for proving ROI on in-store media programs. 🔍 What this means for us in CPG: We need to treat store shelves as digital touchpoints — not endpoints. When in-store ads are smartly integrated into a broader mobile-first, value-driven experience, they’re not just sparks; they’re converters. If you’re navigating in-store media for a CPG brand: * Explore QR-to-mobile campaigns with clear CTAs. * Pair discounts with loyalty or retargeting follow-ups. * Invest in analytics (app, web, POS) to close the loop between spark and sale. #digitalmareting #omnichannel #instore #cpgbrands

Had lunch with an old friend over the weekend. A former Software Developer, he is now the GM of a successful Jaguar Land Rover India dealership. It was interesting to hear how he uses data to improve sales outcomes. When he switched careers, being a "data guy", he quickly realized that the automotive business has a TON of data that they are not using at all. Further, he felt strongly that they were relying on the WRONG data. His thesis was that using ACTUAL customer data, collected live from real humans, was way more valuable than what the industry pundits and profiteers in the back office were telling him to rely on. He started by having his Sales Team record the first 3 questions that anyone who came to the dealership asked, jot them on a clipboard and aggregate The top 3 questions in the first 2 months: "How cold does the A/C blow"? "What trims does this come in"? "What's the best price you can do?" He built answers to all 3 questions directly into his sales playbook. Here is how he did it: 1. Before anyone went for a test drive he would go start the vehicle, turn the A/C on full blast so that when people got into the car, it was already cold. So they would say "Wow, that A/C really blows great. This will be great in the summer. (Dealership is in a hot climate) 2. The Reps would mention as they walked out to the car, "this vehicle comes in three trims, I'm gonna show the base trim and go up from there. You will get to see all three available trims today. Does that work for you?" 3. The Reps would close with, "If you like this model or any of the trims you see you today, then we can go back to my desk and work together to get you the best possible price for that vehicle" Easy, right? The results were remarkable. These small changes lead to customers asking less questions, asking different questions, and a 12% increase in new vehicle sales by month 3. He has since continued to iterate on this model and they are now one of the top producing Land Rover dealerships in the United States as a result. Listen to your customers. Incorporate those learnings into your business processes. #customerexperience #data #problemsolving #automotive #sales