Tracking Changes In Consumer Buying Behavior Over Time

𝗧𝗵𝗲 𝘁𝗿𝗲𝗻𝗱 𝘄𝗮𝘀 𝗵𝗶𝗱𝗶𝗻𝗴 𝗶𝗻 𝗽𝗹𝗮𝗶𝗻 𝘀𝗶𝗴𝗵𝘁. 𝗡𝗼 𝗼𝗻𝗲 𝗲𝗹𝘀𝗲 𝘀𝗮𝘄 𝗶𝘁. But one sock brand did. A sock brand I spoke with spotted a tiny shift in consumer behavior—𝘣𝘦𝘧𝘰𝘳𝘦 it blew up—and turned it into their lowest CPA campaign ever. The wildest part: they found the trend using a 100% free tool. 𝗪𝗵𝘆 𝗱𝗶𝗱 𝗶𝘁 𝘄𝗼𝗿𝗸? 𝗧𝗶𝗺𝗶𝗻𝗴. The biggest difference between a winning DTC brand and a struggling one isn’t budget—it’s timing. 👉 Move too late, and you’re in a price war. 👉 Move early, and you print money. Here’s what happened: On a call last week, I casually mentioned Pinterest Predicts—because Pinterest has an 80% accuracy rate in forecasting viral trends months before they peak. 𝗔𝗞𝗔: 𝟴𝟬% 𝗼𝗳 𝘁𝗵𝗲 𝘁𝗶𝗺𝗲, 𝗣𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁 𝗰𝗮𝗻 𝘀𝗲𝗲 𝘄𝗵𝗮𝘁 𝘁𝗿𝗲𝗻𝗱𝘀 𝘄𝗶𝗹𝗹 𝘁𝗮𝗸𝗲 𝗼𝗳𝗳 𝗯𝗲𝗳𝗼𝗿𝗲 𝗮𝗻𝘆𝗼𝗻𝗲 𝗲𝗹𝘀𝗲. This sock brand took action. We noticed that "cherry" patterns were quietly trending up. Within 24 hours, they launched a cherry sock collection, tied their creative to the aesthetic, and started testing new ads. 🚀 Today: → The product/ad combo is their 3rd highest spender → Lowest CPA across all campaigns → Crushing their new customer acquisition costs 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝘆 𝗰𝗼𝗻𝘀𝘂𝗺𝗲𝗿 𝘁𝗿𝗲𝗻𝗱 𝗺𝗶𝗻𝗶𝗻𝗴 𝗶𝘀 𝗮 𝗰𝗵𝗲𝗮𝘁 𝗰𝗼𝗱𝗲. 🚀 Brands that move fast have: ✅ Ads that convert immediately ✅ Higher margins (first-mover advantage) ✅ Less reliance on discounts & promos 🐢 Brands that wait too long: ❌ Spend more to compete ❌ Launch when the market is saturated ❌ End up in a price war just to survive 𝗪𝗮𝗻𝘁 𝘁𝗼 𝘀𝗽𝗼𝘁 𝘁𝗿𝗲𝗻𝗱𝘀 before they blow up? Here are 4 free tools I use daily to track consumer trends before they hit mainstream: 📌 Pinterest Trends/Predicts → Forecasts viral trends months in advance 🛍️ Etsy & Amazon Search Data → Shows what niche buyers are actively searching for 🎥 TikTok Comments → Raw, unfiltered consumer obsession in real time 💬 Reddit Threads → Where micro-trends are born Most brands have access to this data—but never use it (or aren’t using it enough). 𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝗴𝗿𝗼𝘄𝘁𝗵 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝘀𝗽𝗲𝗻𝗱𝗶𝗻𝗴 𝗺𝗼𝗿𝗲. 𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗹𝗮𝘂𝗻𝗰𝗵𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗮𝗱 𝗮𝘁 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘁𝗶𝗺𝗲.

A lot of us still rely on simple trend lines or linear regression when analyzing how user behavior changes over time. But in recent years, the tools available to us have evolved significantly. For behavioral and UX data - especially when it's noisy, nonlinear, or limited - there are now better methods to uncover meaningful patterns. Machine learning models like LSTMs can be incredibly useful when you’re trying to understand patterns that unfold across time. They’re good at picking up both short-term shifts and long-term dependencies, like how early frustration might affect engagement later in a session. If you want to go further, newer models that combine graph structures with time series - like graph-based recurrent networks - help make sense of how different behaviors influence each other. Transformers, originally built for language processing, are also being used to model behavior over time. They’re especially effective when user interactions don’t follow a neat, regular rhythm. What’s interesting about transformers is their ability to highlight which time windows matter most, which makes them easier to interpret in UX research. Not every trend is smooth or gradual. Sometimes we’re more interested in when something changes - like a sudden drop in satisfaction after a feature rollout. This is where change point detection comes in. Methods like Bayesian Online Change Point Detection or PELT can find those key turning points, even in noisy data or with few observations. When trends don’t follow a straight line, generalized additive models (GAMs) can help. Instead of fitting one global line, they let you capture smooth curves and more realistic patterns. For example, users might improve quickly at first but plateau later - GAMs are built to capture that shape. If you’re tracking behavior across time and across users or teams, mixed-effects models come into play. These models account for repeated measures or nested structures in your data, like individual users within groups or cohorts. The Bayesian versions are especially helpful when your dataset is small or uneven, which happens often in UX research. Some researchers go a step further by treating behavior over time as continuous functions. This lets you compare entire curves rather than just time points. Others use matrix factorization methods that simplify high-dimensional behavioral data - like attention logs or biometric signals - into just a few evolving patterns. Understanding not just what changed, but why, is becoming more feasible too. Techniques like Gaussian graphical models and dynamic Bayesian networks are now used to map how one behavior might influence another over time, offering deeper insights than simple correlations. And for those working with small samples, new Bayesian approaches are built exactly for that. Some use filtering to maintain accuracy with limited data, and ensemble models are proving useful for increasing robustness when datasets are sparse or messy.

If your sales process has a longer timeline—big-ticket products, high-consideration purchases, or seasonal demand—traditional reporting can mislead you. ❌ Month-over-month tracking ignores when leads actually converted. ❌ It doesn’t account for different buying cycles. ❌ It treats all customers as if they behave the same way. This is where Cohort Analysis changes the game. Framing Data The Right Way: Cohort Analysis Instead of tracking when sales happen, you track when customers first enter the funnel and how they behave over time. ✅ Cohort 1: Leads from January → How many converted in Feb, March, April? ✅ Cohort 2: Leads from February → How many converted in March, April, May? ✅ Cohort 3: Leads from March → How many converted in April, May, June? Now, instead of reacting to short-term fluctuations, you’re seeing how different groups actually convert over time. Why This Matters Let’s say you’re selling custom furniture with a 6-month sales cycle. If you only look at raw monthly sales numbers, February looks weak. But if you run a cohort analysis, you see that January’s leads are closing at a higher rate than ever. 💡 Your marketing isn’t failing—it’s working better, just on a longer timeline. Without cohort analysis, you might cut budgets on a winning campaign simply because it doesn’t fit into a traditional reporting structure. The Takeaway: Frame Data Correctly, or Risk Making Bad Decisions Cohort analysis is just one example, but the principle applies everywhere. The way you structure your data impacts the way you think about your business. 🚀 Are you tracking the right trends, or just chasing short-term numbers? Let’s talk about it below. 👇 #AmericanMade #CohortAnalysis #MarketingAnalytics #CustomerAcquisition #ScalingYourBrand

Two major updates to Amazon Marketing Cloud (AMC) today: First, the long-awaited 5-year historical purchase data view is now available for everyone. This shows us customer behavior patterns we've never seen before. Here's what I mean: I recently looked at data from a CPG manufacturer: * 1-year window: 37% repeat purchasers, $24 average GMV * 5-year window: 85% repeat purchasers, $185 average GMV The difference is striking. With five years of data, brands can now: * Spot product lifecycles * Map seasonal patterns across multiple years * Track how customers move through product portfolios * Understand actual customer value over time Second announcement - Amazon is removing cost barriers for AMC features. For example, Amazon Insights, which was previously a paid feature, is now available at no cost. These signals allow you to Analyzes custom audience segments to show behavior patterns, media exposure, shopping activity, and purchase trends. This Helps to refine your media strategy by showing what’s resonating with your most valuable audiences and enables advanced segmentation for future targeting or suppression strategies. For anyone wanting to try the 5-year data view, or learn about building AMC audiences, reach out to your AMC tool provider or contact your Amazon Ads PDM.

The early signals are here. And they look familiar. We’re seeing the return of two major patterns on Amazon: 📉 Consumers are trading down 📦 Some categories are showing stockpiling behavior This moment reminds me of early COVID—lagging economic impact, real-time shifts in behavior, and executive teams urgently revisiting their assumptions. At Momentum Commerce, we analyzed the top products on Amazon and found that the 𝐚𝐯𝐞𝐫𝐚𝐠𝐞 𝐬𝐞𝐥𝐥𝐢𝐧𝐠 𝐩𝐫𝐢𝐜𝐞 (𝐀𝐒𝐏) 𝐢𝐬 𝐝𝐨𝐰𝐧 𝟎.𝟖% 𝐲𝐞𝐚𝐫-𝐨𝐯𝐞𝐫-𝐲𝐞𝐚𝐫. But it’s not because brands are lowering prices. It’s because 𝐜𝐨𝐧𝐬𝐮𝐦𝐞𝐫𝐬 𝐚𝐫𝐞 𝐜𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐰𝐡𝐚𝐭 𝐭𝐡𝐞𝐲 𝐛𝐮𝐲. 🔹 In 𝐃𝐢𝐚𝐩𝐞𝐫𝐬, historic top sellers (which have raised prices +6.0% YoY) are losing share to cheaper alternatives—today’s top sellers are down -3.9% in ASP. 🔹 In 𝐕𝐚𝐜𝐮𝐮𝐦𝐬 & 𝐅𝐥𝐨𝐨𝐫 𝐂𝐚𝐫𝐞, ASPs for historic best sellers are up +14.4% YoY—consumers are shifting to more affordable models. 🔹 In 𝐒𝐤𝐢𝐧 𝐂𝐚𝐫𝐞 𝐚𝐧𝐝 𝐏𝐞𝐭 𝐒𝐮𝐩𝐩𝐥𝐢𝐞𝐬, we’re still seeing pricing resilience. These are the “affordable luxuries” consumers are holding onto—for now. 🔹 And in 𝐁𝐚𝐛𝐲 𝐅𝐨𝐫𝐦𝐮𝐥𝐚, we just saw a 26x week-over-week unit sales spike. That’s a clear sign of stock-up behavior taking root. Brands are responding—fast. The smartest ones are running the playbook we saw work in 2020: 1️⃣ Renegotiating with suppliers 2️⃣ Raising prices selectively on inelastic SKUs 3️⃣ Accelerating shipments before tariffs hit harder 4️⃣ Tracking consumer behavior weekly, not quarterly And yes, this all reminds me of Hitchhiker’s Guide to the Galaxy. The cover famously reads: “Don’t Panic.” For brands right now, it’s a useful mantra. But it only works if it’s followed by a plan. We’re helping our clients write that plan: ✅ Real-time category pricing trackers ✅ Trade-down indicators ✅ Margin risk diagnostics ✅ One-on-one strategy sessions and playbooks Our data tells the story. Our job is to help you write the next chapter.