Shopper Behavior Analysis

Your Price Elasticity is wrong the moment you use it. If you work in #Pricing or #RGM, you see it constantly: "the elasticity is -2". It's in spreadsheets, dashboards, presentations. It's the foundation for price recommendations, portfolio decisions, promotion evaluations. It feels solid. It isn't. Not because the measurement was bad. That's a real problem, but it's not the interesting one. The interesting problem is structural: Even if the number is perfectly measured, it still is wrong the moment you use it. Here's why. 𝗣𝗿𝗶𝗰𝗲 𝗲𝗹𝗮𝘀𝘁𝗶𝗰𝗶𝘁𝘆 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝘄𝗶𝘁𝗵 𝗽𝗿𝗶𝗰𝗲. Say your elasticity is -2 at the current price of €1.00. You're considering a 10% price increase. The elasticity tells you to expect roughly a 20% volume drop. So far, so good. But after you raise the price to €1.10, your elasticity is no longer -2. It might be -2.5. Or -3. The sensitivity of demand has changed. Because at a higher price, a different set of customers is now marginal. The ones who were barely buying at €1.00 are gone. The ones still buying at €1.10 have different price sensitivities. This isn't a measurement error. It's a mathematical certainty. 𝗪𝗵𝗮𝘁'𝘀 𝘂𝗻𝗱𝗲𝗿𝗻𝗲𝗮𝘁𝗵: 𝘁𝗵𝗲 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝘆𝗼𝘂'𝗿𝗲 𝗻𝗼𝘁 𝘀𝗲𝗲𝗶𝗻𝗴. What you actually need — and what the elasticity number throws away — is this full demand curve. That curve encodes the distribution of customer preferences, and it tells you the revenue and profit implications at every price point. Elasticity is a single point on that curve. It captures almost none of the information. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝘆𝗼𝘂 𝗺𝗮𝗸𝗲. When you use an elasticity of -2 to evaluate a pricing decision, you are implicitly assuming three things: 1. The elasticity you measured is still accurate at the price you're moving to. 2. The competitive context that produced that elasticity hasn't changed. 3. The customer base whose behavior generated the number is the same customer base you'll face after the change. None of these are usually true. And the further you move from the price at which elasticity was measured, the less reliable it becomes, precisely when you most need it to be right. This doesn't mean elasticity is useless. It's a reasonable summary statistic for small, local price movements in stable conditions. But it is a terrible foundation for the decisions that actually matter: significant price changes, portfolio restructuring, or anything involving a new competitive dynamic. 𝗔 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝘁𝗼 𝗮𝘀𝗸. When working with elasticities, try asking: "At what price was this measured? And how far are we moving from that price?". If the answer is more than a few percent, the number has already drifted. See how AI in RGM can help: http://bit.ly/4bhEvpn #pricing #RGM #priceelasticity #commercialstrategy #CPG

The Evolving Face of the US Homebuyer The National Association of Realtors' (NAR) 2024 report provides a fascinating snapshot of the US housing market’s buyer profile that looks significantly different than it did just a few years ago. The data reveals a changing homebuyer. The average buyer age has climbed to a record 56, underscoring the impact of high housing costs and rising interest rates that have sidelined younger would-be buyers. For first-time buyers, the average age is now 38, nearly a decade older than it was in the early 1980s. These changes signal a more mature buyer who brings accumulated wealth and likely more significant financial security to the table. Additionally, a fifth of all home purchases were made by single women, a notable demographic shift reflecting both a societal change in homeownership goals and an economic shift in who can afford to buy. By contrast, single men comprised only 8% of recent buyers. This snapshot highlights what many are calling a “bifurcated housing market,” where those able to buy homes are increasingly established, wealthier individuals, often using home equity from previous properties to secure cash purchases or make substantial down payments. This market has been largely inaccessible to younger buyers, who continue to face affordability challenges, limited savings, and reduced opportunities for financial support in the form of lower mortgage rates. With affordability gauges near record lows, first-time homebuyers hold a mere 24% share of the market, down dramatically from the 40% share held in pre-Great Recession years. Rising prices and interest rates have compounded these barriers, leading to a market where nearly three-quarters of all buyers have no children under 18 at home, reflecting an older and more established buyer profile than in decades past. While this report offers a look back, the trends it captures underscore a potential turning point. Recent mortgage application data suggests that prospective buyers who had previously been priced out or sidelined may begin to re-enter the market as interest rates stabilize. If these sidelined buyers do return, particularly younger and more diverse demographics, the profile of the typical buyer could again start to shift, gradually increasing diversity in age, household composition, and race among homebuyers. At Havas Edge, we’re continually analyzing these demographic shifts to support brands in delivering timely, targeted strategies that meet the realities of today’s buyers and the anticipated resurgence of those who’ve been waiting on the sidelines. #RealEstate #Homebuyers #MarketTrends #HousingEconomics #ConsumerInsights

Sensitivities have always been the real computational bottleneck. Especially for complex derivatives, XVA, and under regulatory frameworks like FRTB. The cost of computing Greeks often dominates the cost of pricing itself. The industry standard remains “bump & reprice”. Shift an input slightly, rerun the full Monte Carlo simulation, and observe the price change. It’s simple, robust, and versatile, but computationally expensive. The core issue is scaling: with N risk factors, the cost grows as O(N). 📈A well-known example is interest rate products. Here, “spot” isn’t a single number, it’s the entire yield curve. Each tenor point is a risk factor, so simulations quickly balloon. The same applies to volatility: not just ATM, but the full implied surface across strikes and maturities, with each node a risk factor. On top of that, finite differences introduce a fundamental trade-off: too small a bump leads to numerical noise, too large a bump leads to biased sensitivities. 💡 Adjoint Algorithmic Differentiation (AAD) takes a fundamentally different approach. Instead of recomputing the system for every perturbation, it computes all first-order sensitivities in a single pass: one forward simulation and one backward sweep. The result is effectively O(1) with respect to the number of risk factors. ⏮️The intuition is straightforward. A Monte Carlo simulation is a chain of transformations. AAD applies the chain rule in reverse, propagating sensitivities from the payoff back to the inputs. If you come from machine learning 🤖, this is immediately familiar-AAD is simply backpropagation. The Jacobian is the local gradient at each step (layer-wise derivative), the Adjoint is the backpropagated gradient flowing backward through the system. 💡 The impact is material. In XVA, AAD can reduce runtimes from days to minutes. Under FRTB, it removes what is otherwise a major computational bottleneck. There are challenges, of course: 📌 Difficult to retrofit into legacy systems 📌 Requires careful implementation for higher-order adjoints 📌 Memory-intensive (storing the forward pass / “tape”) 📌 Discontinuous payoffs require smoothing techniques 🔧 That said, implementation has evolved. A common approach today is hybrid: combine AAD with bump & reprice. This balances performance, flexibility, and implementation complexity. 💡 Crucially, this is where AAD extends beyond first-order risk. One backward sweep gives all first-order Greeks; an additional sweep recovers second-order Greeks and cross sensitivities. No pairwise bumping, no quadratic scaling, just the full sensitivity surface in a few passes. 🌍 As risk analytics evolve, particularly in areas like climate risk, portfolio optimization, and multi-scenario analysis, the dimensionality of the problem continues to grow. Moving beyond coarse scenario outputs toward actionable insight requires understanding how risk responds to underlying drivers. That’s where scalable sensitivity methods become essential.

I walked into Miniso just to browse, but a tiny design detail caught my attention I reached for a perfume tester, expecting to spray it on my wrist. But there was no push-button. Just an open nozzle, forcing me to bring it close and take a sniff. Observations: 🛍️ Smart Product Placement: Perfumes were neatly arranged in visually appealing color blocks, making selection feel intuitive. 👃 Tester Trick: The tester bottles had no push-button sprays! Instead, customers had to directly sniff the nozzle—reducing impulse spraying by passersby and ensuring serious buyers engage more deeply. 👉 Behavioral Science in Action: 📌 Commitment Bias: If you take the effort to pick up and sniff, you're more likely to consider buying. 📌Scarcity Effect: No free-flowing spray means the product feels more 'exclusive.' 📌Decision Fatigue Reduction: Minimal distractions, clear choices, and a structured layout make buying easier. Retailers are getting smarter—it's not just about WHAT they sell but HOW they sell it. Have you noticed any clever behavioral tactics in stores lately? #BehavioralScience #RetailPsychology #ConsumerBehavior #MarketingStrategy #BrandExperience 

the "boring" marketing channels that outperform your flashy one-off campaigns (data from actual b2b companies). while everyone's chasing the latest tiktok trend or ai-powered whatevs, the unsexy channels are quietly delivering the best roi. here's what the data actually shows: 𝗿𝗲𝗳𝗲𝗿𝗿𝗮𝗹 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝘀: the silent revenue machine 84% of b2b decision makers say their buying process starts with a referral. yet most companies treat referrals like an afterthought. referrals have 3-5x higher conversion rates than any other marketing channel and 71% of b2b companies report higher conversion rates from referrals than other customers. but here's the kicker: only 11% of salespeople actually ask for referrals, even though 91% of customers say they'd give them. (stats from 👉 Referral Rock + Influitive + Propello) 𝗲𝗺𝗮𝗶𝗹 𝗻𝘂𝗿𝘁𝘂𝗿𝗲 𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝘀: email is defs not dead. if marketing sends more than 8 emails between deal creation and closure, the close rate increases by 47%. yet 94% of emails are sent before any pipeline qualification - meaning most companies abandon prospects right when nurturing matters most. the average conversion rate from email marketing campaigns in b2b is 2.5%, but companies with solid nurture sequences see much higher returns because they're playing the longgg game. (stats from 👉 Powered by Search + HockeyStack) 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴: 𝘁𝗵𝗲 𝗲𝘅𝗽𝗮𝗻𝘀𝗶𝗼𝗻 𝗴𝗼𝗹𝗱𝗺𝗶𝗻𝗲 this is the most overlooked channel. 75% of marketers use abm for customer marketing as it helps increase client retention rates. existing customers are 50% more likely to try new products and spend 31% more than new customers - yet most marketing budgets focus almost entirely on acquisition. (stats from 👉 Terminus (by DemandScience) UserGems 💎) 𝘄𝗵𝘆 𝗯𝗼𝗿𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝘀 - longer-term thinking = compound returns - relationship-focused vs transaction-focused - less competition for attention - sustainable without constant optimisation the flashy stuff gets the conference talks.  the boring stuff gets the revenue.

During my four years at Amazon Ads, one thing brands could never get enough of was benchmark data. March 2026 just delivered a massive efficiency breakthrough: Google ROI surged +291% (from 4.23 to 16.55) while Walmart Connect Onsite Display ROI exploded by +166%. I can’t wait to see what the Q1 numbers look like. Retail media continues to drive significant results, but performance is concentrated in a few top channels. The gap between these high-efficiency channels and where most teams are still allocating budget is widening. Here's what the data is actually telling you: Retail media has become the primary growth driver. In CPG and Food & Beverage, Amazon Search and Instacart conversion growth is running +30% to +100%+ YoY. Walmart Search is up +59%. This reflects a true structural shift, not outliers. Reallocating budget is answer. Several channels in this benchmark show the same pattern: spend up, clicks up, conversions flat or down. This indicates low ROI despite higher engagement.. The brands winning right now are moving budget toward proven efficiency breakout channels, not simply adding investment across the board. Last year’s channel mix is already wrong. If you're still running the same allocation you built in 2025, the data says you're behind. Google (16.55 ROI), Walmart Onsite Display (19.34 ROI), and MSN (10.50 ROI) are pulling away. Low-ROI, high-click-volume channels are pulling in the opposite direction. Three things worth acting on now: Scale what's working. Double down on Google and ADSP. Google’s 291% ROI surge shows massive intent momentum, while ADSP CPCs improved by 55%, proving offsite efficiency is scaling. Cut the false growth. Social media is currently the False Growth trap, as CPCs dropped 20%, but ROI remained flat at 0.43. It's efficiency without effectiveness. Capitalize on the Local explosion. Local channel ROI grew from 1.73 to 90.07 this month. If your brand has a physical footprint, the window to move efficiently is now. Join Josh Dreller (Skai) and Kelly Gerrard (Marshall Associates) on April 23 for an in-depth look at Q1 digital advertising performance, featuring our exclusive data on retail media, paid search, social, and GenAI-powered marketing.

Most conventional cost-effectiveness models in pharmacy assume drug prices never change – they are static. That’s a problem. In the real world, prices fall dramatically after patent loss. --- New #research from Center for the Evaluation of Value and Risk in Health (CEVR) and National Pharmaceutical Council shows just how far off conventional cost-effectiveness analyses (CEAs) can be when they ignore real-world #DrugPricing dynamics. The study compared static CEAs to dynamic models that incorporated drug price changes over time, which is particularly crucial to account for price changes after loss of exclusivity. --- The result: static CEAs consistently overstated #DrugCosts, skewing the cost-effectiveness ratio by 27% to 82%, depending on the treatment type. Key takeaways: -Chronically administered treatments are most affected. Their price drops after losing exclusivity were the single biggest factor in shifting cost-effectiveness. -One-time treatments are less sensitive to price changes, but they are highly influenced by baseline patient age and discount rates. -Dynamic models offer a more realistic view of opportunity costs and better reflect the #pharmacoeconomics of patented drugs over time. --- Why does this matter? Conventional CEAs may currently be penalizing certain #pharmacy therapies, especially those treating chronic conditions, by ignoring the competitive market forces that eventually drive prices down. This may distort resource allocation, payer negotiations, and long-term pricing strategy. Models should reflect how prices behave in the real world, and not assume a fixed price indefinitely. --- How are you accounting for future price shifts in your trends and projections? Most drug prices act pretty similarly after patent loss. It may be worth creating some patent loss assumptions to incorporate into your models if you aren't already.

I’m delighted to launch our latest thought leadership research with Transportation, Shipping, & Logistics at Amazon, looking at how delivery can drive loyalty. 🔍 Our pan-European analysis across UK, Spain, France and Italy uncovered some super interesting insights. For one (see graph), the affluence-age relationship isn't just a demographic split – it's aligned to a lifetime value predictor that’s heavily influenced by delivery. Knowing which consumer cohort to target and how, is a critical component of profitability. The data highlights a growing divide in consumer behaviour, emphasising the need for a tailored approach: agile, customer-centric delivery for the younger, affluent segments, and value-driven strategies to attract and convert older, more cautious shoppers. Another way of identifying target cohorts is to look at repeat purchases. Our research reveals a clear trend: affluent GenZ and Millennial shoppers not only buy more frequently, but also exhibit higher loyalty. From these cohorts, fast and convenient delivery options are crucial to capture their repeat business. Conversely, older and less affluent consumers are more price-sensitive and cautious, indicating a different value proposition is needed to engage and retain them. 🎯 The Strategic Imperative: This isn't just about who's buying more – it's about the fundamental reshaping of retail economics: 💥 The Loyalty Multiplier Effect: When high-affluence millennials increase their purchase frequency, they don't just buy more – they create a compound growth effect. Each additional delivery satisfaction point translates to a higher likelihood of repeat purchase. 💥 The Hidden Cost Dynamic: Less affluent customers show more price sensitivity, suggesting a different value proposition is needed to engage and retain them. When retailers align delivery pricing with segment-specific price thresholds, they can potentially reduce the cost to serve by consolidating consignments or extending delivery windows. Smart delivery segmentation can be a profit opportunity when mapped correctly to purchasing power. 💥 The Generation Bridge: The 35-44 affluent segment isn't just buying more – they offer foresight into the behavioural patterns that are likely to cascade down to other segments. Their behaviours today provide a glimpse into tomorrow's consumers in terms of life-stage, omnichannel behaviour and loyalty drivers. Ultimately, delivery options require a tailored strategy depending on the customer. There is no one-size fits all. Our report with Amazon Shipping is packed full of more insights so download for free and take a look! Download our FREE report now 🔗 https://lnkd.in/eJnCu3wW

We spent the last 12 months trying to find a better B2B channel than LinkedIn. And we failed. Over the past year, we’ve quietly tested different ways to expand our service. We’ve trialled new platforms. We’ve experimented with distribution techniques. We’ve looked at outbound channels. And we’ve run head-to-head comparisons to see what would actually drive demand faster. Every single time, LinkedIn won. (Often it wasn’t even close.) We’d build a campaign on another channel, using a sharp message, clear ICP and watch it underdeliver. Engagement would stall. Leads would drip, not flow. Conversations took longer to start, and even longer to convert. Then we’d go check back on LinkedIn, and… - We’d have consistent inbound leads. - 100s of conversations started monthly. - Content driving millions of impressions. It’s more visibility, more relevance, but most of all, it’s just easier to start conversations with B2B buyers here. Mainly because of the sheer number of them on this platform. Within 11 seconds, I can open Sales Navigator and see over 9,500+ people in our ICP who’ve visited my profile in the last 6 months. Not a cold list, 9500 people who’ve shown some interest in what we do. No ad platform does that. No other social platform does that. No newsletter sponsorship does that. Yes, other channels can work. But nothing works like this. For me, it’s easily the most effective B2B marketing channel right now. That’s why we’re now all-in on it. My advice: If you’re a B2B company, you probably should be too. ---- Follow me for more B2B marketing Niall Ratcliffe

Lasting impacts of the pandemic on consumption. It is now more than 4 years since the pandemic started. Probably enough time to observe the permanent changes in US consumer spending. The chart below shows the change since the pre-pandemic period in consumer purchases for selected categories. Since Q4 2019, U.S. consumption behavior has experienced some significant shifts, largely influenced by the pandemic's long-lasting effects on lifestyle and spending patterns. One of the most pronounced changes is the dramatic increase in purchasing of technology and communication equipment. For instance, purchases of telephone-related equipment have surged by 140%, and spending on video, audio, photographic, and information processing equipment has risen by 93%. Another notable trend is the shift toward purchasing of recreational goods rather than services. Purchases of sporting equipment, supplies, guns, and ammunition increased by 51%, while purchases of recreational books rose by 39%. These figures suggest that consumers have turned to home-based or individual recreational activities, perhaps as a lasting change from pre-pandemic behavior. This shift is further emphasized by the relatively modest 4% increase in spending on recreation services, indicating that group-based recreational activities and services have not fully recovered and may face ongoing challenges in returning to pre-pandemic trajectories. Health and wellness have also emerged as a key area of consumer focus. Purchases of paramedical services has increased by 26%, and pharmaceutical and other medical products have seen a 25% rise in expenditures. In contrast, purchases of tobacco have experienced a significant decline. Overall, the data indicates some profound shifts in U.S. consumer behavior since the pandemic, with an increased emphasis on technology, home-based recreation, and health, while some traditional services and goods have seen reduced demand. #economy #consumption #pandemic #labormarkets