Buying Patterns Analysis

We analyzed consumer spending patterns across three major marketplaces heading into Q4. The data reveals a fundamental shift in buyer behavior: FINDING #1: High-income shoppers are trading down across categories Consumer sentiment dropped to near-record lows despite 4% GDP growth. Even households earning $100K+ are cutting holiday spending by double digits. This isn't temporary belt-tightening. FINDING #2: Gen Z adoption of AI shopping tools jumped to 43% Nearly half of younger consumers now use AI to validate purchases before checkout. Traditional product detail pages alone no longer close the sale. The decision happens before they reach your listing. FINDING #3: Buy-now-pay-later usage crossed 75% penetration Over three-quarters of shoppers plan to use payment flexibility options this season. Brands without BNPL integration are leaving revenue on the table before Black Friday even starts. FINDING #4: Early shopping behavior accelerated by two full weeks 58% of consumers started holiday purchasing before November. The old playbook of launching promotions Thanksgiving week is now arriving after peak traffic already converted elsewhere. FINDING #5: Basket sizes contracted while transaction volume increased Shoppers are making more frequent, smaller purchases. Average order values dropped across apparel, electronics, and grocery categories. Your unit economics need recalibration. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗶𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Brands optimizing for last year's consumer behavior will underperform competitors who adapted to these five shifts. The marketplace doesn't reward nostalgia. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗰𝗵𝗲𝗰𝗸𝗹𝗶𝘀𝘁: → Test promotional calendars starting two weeks earlier than 2024 → Add BNPL options to high-ticket SKUs before Cyber Week → Build content strategy around AI discovery patterns, not just human search 𝗬𝗼𝘂𝗿 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲: Pick one finding above and stress-test your Q4 strategy against it this week. 𝗥𝗲𝗺𝗶𝗻𝗱𝗲𝗿: These trends accelerate heading into 2026. What worked during the last holiday cycle is already outdated.

Email frequency matters more than most marketers assume. An analysis of 53,000 emails and 5,300 purchases across 200 customers revealed a clear pattern: the best results come when brands tailor frequency to buying behavior.  The optimal monthly cadence: ↳ 5-7 emails for frequent buyers ↳ 6-10 for medium buyers ↳ 12-14 for occasional buyers When customers aren’t segmented, 7 emails a month deliver the strongest performance. The highest open rates and most purchases over time. Sending only 4 emails reduces lifetime profit by 32%, while sending 10 cuts it by 16%. The reason is simple. Frequent buyers already know the brand, so too many emails create fatigue. Occasional buyers, on the other hand, read more when they’re still exploring and learning. This makes segmentation strategy the real growth lever. Instead of treating every subscriber the same, match communication frequency to purchase behavior. The balance is all about timing and relevance. The right message to the right segment builds stronger engagement, higher retention, and more revenue over time. How often do you adjust your email frequency based on buyer type?

Half our marketing budget targeted women 25-34. Our highest converting audience? Men 45-65 buying gifts. Discovered this by accident when analyzing order patterns from last Diwali season. These gift-buying men were completely invisible in our targeting strategy. Weird pattern we noticed: ⤵︎ They never used discount codes ⤵︎ Always chose express shipping ⤵︎ Bought our highest-priced items ⤵︎ Had near-zero return rates Our acquisition cost for this segment was 4X lower while average order value was 3.2X higher. Instead of ignoring this insight, we rebuilt our entire holiday strategy around it: ↗︎ Created "gift concierge" landing pages with curated selections ↗︎ Added gift wrapping and personalized message options ↗︎ Developed email sequences specifically for gift occasions ↗︎ Built lookalike audiences based on this high-value segment These changes increased our holiday revenue by 142% year-over-year while reducing marketing spend by 17%. The most profitable audience segments rarely match your brand's imagined customer avatar. Data reveals who's actually buying, not who you think should be buying. What hidden audience segments are you overlooking?

I have spent years analyzing hundreds of eCommerce launches, and one pattern always emerges: Most purchase decisions are irrational, but predictable. Customers don't compare specs like a spreadsheet. They’re influenced by cognitive biases, i.e., mental shortcuts that steer attention, value perception, and urgency. Here are 9 biases I see shaping buying behavior every day: 1) Category Heuristics: → Customers focus on a few key specs to compare quickly. → Highlight top attributes to guide decisions instantly. 2) Power of Now: → Immediate offers drive faster action. → Delays reduce perceived value and urgency. 3) Social Proof: → Reviews and ratings boost trust. → Recommendations from others validate purchase decisions. 4) Scarcity Bias: → Limited availability creates urgency. → “Only a few left” nudges faster buying. 5) Authority Bias: → Expert endorsements reduce hesitation. → Recognizable brands or figures build instant credibility. 6) Power of Free: → Small freebies increase perceived value. → Free add-ons motivate purchase without extra cost. 7) Anchoring Bias: → First price sets the mental reference point. → Subsequent options feel more valuable or affordable. 8) Loss Aversion: → Fear of missing out drives immediate action. → People avoid losses faster than they seek gains. 9) Decoy Effect: → Middle option nudges buyers toward higher-margin choice. → Position options to shift perception without force. Here’s the truth: Cognitive biases allow you to design buying experiences that feel intuitive, effortless, and even inevitable. When applied to pricing, offers, bundles, and landing pages, these biases: → Increase conversion rates → Strengthen perceived value → Accelerate buying decisions → Reduce hesitation and cart abandonment Next time your conversion lags or launches underperform, Ask: Are you designing the experience, or leaving it to chance? Save & share this to help others in your network. Follow Asim Khaliq for more applied growth strategies.

A customer adds a product to their cart. Then they leave. Then they come back an hour later and buy a different product. What just happened? A. They changed their mind B. They compared you to a competitor and you lost. C. They found something you don't sell and bought the closest thing. Most brands assume A. Some assume B. Almost no one considers C. But here's the truth: people rarely change their minds randomly. They change because they discovered a missing feature, a better price, or a need they didn't know they had. If they bought a different product from you, they didn't leave. They self corrected. And that self correction is data. Go look at your abandoned carts. Compare them to what people actually bought instead. You'll see patterns. "People who abandoned the leather bag bought the canvas tote". That tells you price sensitivity "People who abandoned the small size bought the large size". That tells you usage anxiety "People who abandoned the blue bought the black". That tells you color preferences Most brands only look at what sold You need to look at what sold instead of what was abandoned. That's where your product gaps live.

🛒 How Basket Analysis Can Drive eCommerce Growth: A Bangladeshi Scenario As eCommerce continues to grow rapidly in Bangladesh, businesses are dealing with more and more customer data. One of the most valuable and often overlooked ways to make sense of that data is through basket analysis. Whether you’re working at a platform like Daraz, Chaldal, Pickaboo, or even running your own online shop, basket analysis can help uncover what products people are buying together. These insights can help you make smarter decisions when it comes to marketing, product placement, bundling, and personalized offers. 🔍 What is Basket Analysis? Basket analysis (also known as market basket analysis) is a method used to find associations between products based on customer purchase history. For example: - What do people usually buy with rice? - Are customers who buy smartphones also buying covers or screen protectors? - Are snack items more popular during weekends? By identifying patterns like these, eCommerce platforms can: - Increase average order value - Run more effective cross-sell campaigns - Deliver personalized recommendations - Make better inventory decisions 🧺 Real-Life Example: A Case Based on Chaldal While analyzing data from Chaldal, one of Bangladesh’s largest online grocery platforms, we noticed something interesting. Many customers in areas like Dhanmondi and Mirpur were buying instant noodles and tomato ketchup together, especially during the evening. This pattern suggested a common need: quick dinner solutions, likely for students or working professionals. Based on this insight, we tested a few simple strategies: - Introduced a combo offer with noodles and ketchup - Showed both products in the “Frequently Bought Together” section - Ran targeted push notifications in the evening with a message like “Need a quick dinner? Grab our Noodles + Ketchup combo now!” The early results were promising: - Better product visibility - More engagement during evening hours - A small bump in basket size for repeat users We’re still monitoring the data, but it’s a great example of how even small insights can be turned into smart decisions. 💡 Final Thoughts You don’t need AI or complex tools to start using basket analysis. A simple SQL query or spreadsheet analysis can help you uncover product relationships that lead to real business value. #eCommerce #BasketAnalysis #DataAnalytics #DigitalBangladesh #CustomerInsights #BusinessGrowth #SQLforBusiness #OnlineGrocery #MarketingStrategy #StartupBangladesh

The biggest gap in B2B marketing isn't understanding your ICP. It's understanding how your buyers actually buy. Some of my biggest takeaways from The Hidden Buyer Journey by Scott Gillum, discussed in today's CMO Coffee Talk: Personas are only the starting point. Buying decisions are shaped by motivations, behaviors, and communication preferences. Buyers generally fall into four behavioral styles: Dominant, Influencer, Steady, and Conscientious. Each responds to a different message, pace, and proof point. The same pitch that wins one buyer can lose another. Tailor your messaging to how they process information, not just what they do. Job titles don't tell the full story. Two CIOs at different companies may have completely different decision-making styles. Every stakeholder plays a different role in the buying committee. Some influence, some drive urgency, some challenge assumptions, and some block deals. Buying behavior becomes harder to predict as you move from an individual to a buying committee, company, and industry. Context matters as much as the individual. Personalization should go beyond first name and company. Adapt your messaging, assets, proof points, and calls to action based on buyer preferences. Different buyers consume information differently. Some want data-rich white papers, others prefer visuals, peer stories, webinars, or conversations. Buying signals come from many sources, not just CRM data. Engagement history, sales conversations, online behavior, intent data, and relationship context all paint a more complete picture. AI makes this far more practical. Instead of creating one campaign for everyone, marketers can personalize messaging, content, and outreach based on behavioral patterns at scale. One insight that really stuck with me: buyers don't evaluate your company the way marketers organize campaigns. They evaluate you through their own motivations, biases, communication preferences, and role in the decision. The more we understand the hidden buyer journey, the more relevant our marketing becomes, and relevance is what earns attention and drives pipeline.

Take a guess: Which two products cross-sell best with Coke at the major US pharmacy chains? Keep reading and learn how I did the analysis. I worked on trade spend optimization at Coca-Cola with a simple goal: generate more revenue from the same shelf space. Here's exactly how I did it. The most impactful analysis I worked on at Coca-Cola had nothing to do with dashboards. The solution? Market basket analysis. We wanted to understand what people were buying with Coca-Cola. In real stores, with real purchasing behavior. One result still sticks with me. In two major pharmacy chains in the US, two of the top products purchased alongside Coca-Cola were Jim Beam and Jack Daniels. Was that your guess? Not likely. Most people say chips, chocolate, any of your other favorite junk foods. I found this really ironic, given these are pharmacy chains, not liquor stores. That insight mattered. It told us something simple but powerful. If people are buying these products together, placement matters. So we tested a small change. We placed Coca-Cola closer to those products in-store. That one decision unlocked tens of millions of dollars in revenue. Not because we sold more products overall, but because we made it easier for customers to buy what they already wanted together. That’s the real power of market basket analysis. The technique itself is relatively straightforward. The impact comes from knowing what the results mean, how to interpret them, and how to act on them. This week in an advanced Next-Level Tableau class, we recreated that kind of analysis using grocery store purchase data. The GIF shows how quickly these patterns jump out when you visualize them properly. Once you see relationships instead of rows, you stop asking "What sold?" and start asking "What should be next to what?" That’s where analysis starts to change outcomes. I’d love to hear, what kinds of use cases could you apply market basket analysis to?

Every time I look at transaction data, I see stories people don't realize they're telling. Gas purchases are one striking example. Some folks drop $50 and fill the tank. Others drop $11 here, $15 there. Just enough to get through the day. That pattern tells you something about financial confidence. When someone consistently buys gas in small increments, they're managing cash flow day-by-day. They're preserving liquidity because they may need that $35 difference for something else this week. The full-tank buyers have breathing room. They know next week's expenses are covered. This subtle behavioral signal is highly correlated with credit risk. Traditional credit scores focus on obvious, long-term relationships. Debt-to-income. Length of trade lines. But these nuanced behavioral patterns reveal a more detailed up-to-date story. How people navigate daily financial decisions tells much about where they're headed.

When we analysed 180 dropped deals across 12 accounts, 41% of “lost” deals had the same patterns! Here’s what the patterns revealed:   1️⃣ Budget - the objection that isn’t random When we reviewed dropped deals tagged as “no budget,” something obvious (but invisible at first) emerged: Budget correlated with revenue bands. Below a certain revenue threshold, companies never “had budget” for this spend. Above it, the objection rarely appeared. Once we tightened the revenue threshold inside the ICP: → Prospecting became sharper. → We stopped chasing accounts that would never close. 2️⃣ Timing - not an objection, a context signal “Not right now” is almost never about your product. It’s about what’s happening internally: ➡️Mergers ➡️Leadership changes ➡️Budget season ➡️New procurement processes We learned this the hard way after losing a deal mid-merger because we were speaking to only one side of the new structure. That one insight reshaped how we qualify, map stakeholders, understand timing and run deals. 3️⃣ Priority - the hardest stall, and the most important “This is important, but not urgent.” You can’t force urgency. But you can surface the cost of delay. Our best-performing reactivation message is a simple one: “Has something more urgent taken priority? If yes, can we help you keep this moving without adding pressure?” 30% of these messages restart stalled deals. Not because the objection disappears, but because we  come in with better awareness, and context instead of pressure. Understanding the consumer’s priorities helps us reframe our offer in the context of their priorities. 🔥Why this analysis matters: When you actually study dropped deals, you learn: → Who your real ICP is → Which revenue bands can actually buy → What signals matter most during prospecting → Where discovery needs to go deeper → How early you need to multi-thread → When to apply urgency vs patience → Where pricing or messaging needs refinement. Dropped deals are insights. And if you follow up and study them properly, they’ll tell you exactly how to sell better, long before your next pipeline review.