Your Q3 brand health tracker came back clean. Purchase consideration for your brand within Apparel and Footwear is stable at around 40%. Favorability is up 2 points. Brand attributes are holding steady across the board. Your agency congratulated you on another quarter of strong brand-building.
Then you saw the sales deck. Revenue down 9%, with softer retail traffic and weaker e-commerce performance. Your premium line, a central part of your growth strategy, stalls. Your CFO wants answers. Your brand team suspects a lack of brand differentiation or a highly competitive retail environment.
But here's what your brand tracking system never measured: Consumer confidence in your key markets dropped an average of 11 points this year (see trending chart below for the US market). Tariffs have started to hit the category, with apparel prices surging 0.7% month-over-month since September. Your core customer segment, households earning $50k-$120k, saw discretionary spending contract by over 5%.
Your brand wasn't getting weaker. Your main customers were getting poorer. And your measurement system was designed to detect brand problems, not economic headwinds.
This isn't a hypothetical. It's the pattern we've observed across QSR chains, retail brands, automotive categories, and consumer services over the past 18 months. Brands maintain stable tracking metrics — sometimes even improving them — while losing meaningful share because the economic terrain shifted beneath what their instruments could detect (see example in the charts below for the Grocery retailers industry sector).
Recent comments by Chipotle CEO Scott Boatwright illustrate what's really at stake for brands. Like many of its peers, the chain experienced declining visit frequency across all income segments earlier in 2025, but households earning under $100k, representing roughly 40% of sales, started to pull back even further in recent months, likely the result of heightened economic anxiety and inflation pressure. Worse, the 25-35 age range proves most challenging, given the brand's younger demographic skew. As Boatwright observed, "The chain is losing these customers to grocery stores and food at home."
Brand tracking wouldn't reveal this substitution pattern, but tracking savings depletion, credit utilization, and income-stratified inflation pressure would have signaled the contraction months earlier.
Classic economics still explains the lion's share of consumer demand: Employment and wages determine ability to buy, prices and inflation shape willingness, interest rates and credit access govern financing-dependent purchases, savings buffers and debt capacity determine resilience when income falters, and consumer confidence influences timing. These aren't abstract macroeconomic indicators for your CFO to worry about; they're systemic forces that collide and amplify each other, actively shaping the value calculations happening in your customers' brains at scale as they decide what to buy (or not to buy) next.
Yet standard brand measurement approaches, designed to assess the state of the brand and offer guidance for planning the year ahead, typically ignore these economic realities. It's like flying with one eye closed while the terrain changes faster than your instruments can detect.
No amount of awareness-building or attribute ownership will solve a context problem. The growth edge now belongs to leaders who start with ambient macro context, compete for the main category entry points that trigger demand for the industry they're in, and grow mental availability so their brands show up when minds are made up as well as forge better emotional connections.
When brand performance shifts, awareness holds steady but sales decline, consideration remains strong but conversion drops — that’s when managers reflexively diagnose brand problems. But economic forces and their impact on real-life choices often explain what most traditional brand tracking systems cannot.
These aren't micro-contextual factors like occasion or company that influence which option consumers choose. They're structural conditions that determine whether consumers are in-market for your category or brand at all. Employment uncertainty delays big purchases. Inflation shifts price thresholds and channel choices. Interest rates, credit access, and BNPL (Buy Now, Pay Later) availability govern financing-dependent categories. Depleted savings and maxed credit cards force category exit regardless of brand preference. These forces operate beneath category and brand-level metrics, invisible to standard tracking systems, yet they drive the macro shifts in performance that managers are tasked with explaining.
Yes, who you're planning to eat with or time of day influences which restaurant you end up choosing on a given Tuesday. And it's important for your brand to be present across as many of the ways people enter your category. But employment trends, interest rates, and inflation determine whether you're eating out at all this month or trading down from casual to QSR from QSR to grocery. These macro shifts shape and reshape category demand before micro-situational factors ever come into play. And when they do, your brand may not be top of mind.
Consider QSR. A few years ago, fast food's value positioning looked unassailable. But recent events have effectively eroded the perceived gap with casual dining. Food-away-from-home prices have outpaced groceries, narrowing the value equation in real time. For frequent QSR customers doing mental math at the point of decision, the premium to upgrade is shrinking. As consumers conclude that paying "a little more" delivers a meaningfully better experience when the price gap has compressed, casual dining chains have started to win share.
The lesson: Economic context determines what consumers are choosing between — which category type they'll shop before brand preference enters the equation. Micro-context then drives the search for options within the category (e.g., "I need fast food under $15 near my office"). Macro-context determines which category is on the table at all, and this can hurt the brand.
If economic forces shape category and brand demand beneath what brand tracking can see, what system should managers build instead? The answer requires seeing both what you can control and what you must navigate.
Two complementary pillars: First, custom research that maps how consumers actually enter categories and navigate choice — the situations, constraints, and goals that precede brand selection. This reveals your strategic playing field: which entry points drive volume, where your brand has presence, and where mental availability gaps create opportunity.
Second, syndicated economic data layered by the dimensions that matter for your business: region, income level, store footprint, sector-specific sentiment. This reveals which segments have the capacity to respond to your strategy right now — separating aspirational opportunities from realistic growth paths given current economic conditions.
Together, these create visibility into both the behavioral mechanisms and the structural conditions driving performance, offering a direct line of sight into where to focus your next actions.
The Ehrenberg-Bass Institute (EBI) advanced the conversation beyond generic brand awareness metrics. Their framework — including mental availability as the likelihood your brand comes to mind in buying situations — represents progress, particularly through Category Entry Points (CEPs) that capture real-world purchase triggers. These situations, scenarios, and motivations precede brand choice and function independently from brands themselves.
Understanding how your brand performs across priority CEPs provides a crucial strategic perspective: It reveals whether your brand substantially participates in the category at all — measured by presence in the high-volume, high-value situations that drive the majority of category purchases. A brand scoring well on low-frequency peripheral entry points while absent from the dominant purchase drivers isn't building mental availability where it matters.
But mental availability, as conceptualized by EBI, only measures whether CEP-brand links exist somewhere in memory, retrievable through deliberate recall during abstract contemplation. Neuroscience research, however, demonstrates that the brain constructs value dynamically in the moment, integrating stored knowledge with current context (e.g. personal financial situation), immediate goals, emotional state, and social benchmarks — not by retrieving fixed preferences from memory.
Asking "Which brands do you associate with [situation]?" measures whether knowledge is stored in memory but not whether it surfaces automatically when real constraints bind — time pressure, budget anxiety, social context, or economic uncertainty. Cognitive psychologists distinguish between mental availability (what's stored) and mental accessibility (what activates when it matters).
Self-relevance is a major input to the subjective worth our brains assign each option we choose from. Before evaluating specific attributes or brand characteristics, our brains assess whether options feel like "me" or "not me".
Someone answering a survey about electric vehicles might easily associate Tesla with environmental responsibility or fuel savings motives — Tesla is "mentally available" in memory, and survey research will confirm this. But this knowledge might never activate in real purchase contexts if Tesla feels psychologically distant due to reasons as varied as political affiliations, or lifestyle misalignment.
Our June 2025 study of 2,200 consumers illustrates this gap precisely. When we measured psychological distance—asking respondents how "close" or "distant" they felt to what Tesla represented—the pattern was striking: a majority felt strongly distant from the brand (scores of 1 out of 7), while very few felt strong emotional connection (scores of 7). Despite high brand awareness, Tesla failed to connect emotionally with most consumers because it didn't feel like "me," though differences emerged across political affiliations. Self-brand connection determines whether stored associations translate to consideration, regardless of how strong those associations appear in mental availability traditional measures.
From this perspective, the Ehrenberg Bass framework is necessary but not sufficient to explain choice. Accessibility (stored knowledge being activated) matters at least as much, if not more. Whether stored knowledge rises to consciousness depends on past experience with the brand (direct or indirect), social dimensions, and whether current context makes that knowledge feel relevant and applicable in the moment.
A brand associated with "premium quality" as measured via survey research might have high constructed value when the consumer just received a bonus and feels financially secure, but dramatically lower constructed value when layoff rumors are circulating and budget anxiety spikes.
So, it’s the same stored association, but under a different economic context. It will require a completely different activation and value construction in the moment that matters.
Standard Mental Availability measurement captures brand-situation links in stable, abstract conditions. But economic pressure doesn't just change which brands people prefer — it fundamentally alters which mental processes (which aspect of the choice) dominate at the point of decision: Financial constraint and uncertainty shift thinking from abstract benefits ("premium quality") to concrete costs ("$4 more per visit × 3x/week = $48/month"), budget anxiety makes "not like me" filters more aggressive, eliminating brands that felt accessible in better times and when every dollar counts, the mental effort to consider multiple options shrinks — consumers satisfice rather than optimize, often defaulting to the cheapest acceptable option
This is why brands can maintain high equity scores (e.g. favorability) while losing ground. The associations that create differentiation in memory can become liabilities when economic context shifts. A "premium quality" association might strengthen brand equity in surveys yet suppress activation when budget constraints heighten psychological distance and make premium feel inaccessible.
If Pillar One reveals how consumers navigate choice under soft constraints, Pillar Two reveals which constraints are actually operating — and for whom.
Standard brand tracking treats all consumers as facing roughly equivalent decision contexts. But economic pressure isn't uniform. It varies by region, income bracket, employment sector, credit access, savings buffers, debt service burden, and household composition. A brand losing share in the Southeast while holding steady in the Northeast isn't necessarily facing a brand problem — it may be facing divergent economic realities that traditional surveys can't detect.
Properly layered syndicated economic data makes these structural forces visible.
Consumer sentiment, employment trends, and inflation experiences vary dramatically by geography. Morning Consult's state-level Index of Consumer Sentiment reveals which markets are experiencing confidence shocks versus stability. For multi-regional brands, this explains why identical marketing performs differently across territories. A QSR chain seeing traffic declines in Michigan but growth in Texas isn't dealing with creative fatigue — it's navigating different economic headwinds.
Aggregate inflation numbers obscure how price increases hit different income cohorts. Lower-income households spend proportionally more on food, fuel, and housing — the categories experiencing above-average inflation. Syndicated data tracking spending patterns by income quintile reveals which customer segments are trading down, delaying purchases, or exiting categories entirely. This isn't "losing customers to competitors." It's losing customers to economic constraints.
Not all industries face the same employment uncertainty. Tech layoffs don't impact healthcare workers. Interest rate hikes matter dramatically for auto and housing, but barely register for grocery. Sector-specific confidence indices show which professional cohorts are experiencing income anxiety versus security. For B2B brands or categories with distinct professional customer bases, this explains demand shifts that brand metrics miss entirely.
Interest rates govern credit-dependent categories, but credit access and financial resilience vary dramatically by segment. Syndicated data on credit utilization rates, approval rates, debt service burdens, and savings rates reveals who can still finance purchases versus who's been priced out—and who can weather income shocks versus who must contract spending immediately.
When auto sales decline, is it brand preference shifting or is it that your target customers can no longer afford 7% APR on a $40k purchase? When appliance purchases drop, is it awareness or is it that BNPL (Buy Now, Pay Later) approval rates have tightened among mid-income consumers who historically financed these purchases?
For categories where 30-50% of purchases involve financing (automotive, furniture, appliances, travel, even premium grocery through BPNL, tracking credit conditions by income tier and region isn't optional context; it's the primary demand driver. A brand can have perfect mental availability yet see sales collapse when credit tightens or savings deplete, especially among segments that rely on financing to access the category at all.
Economic data becomes actionable when mapped to your actual store locations or service territories. A national brand discovering that 60% of its footprint sits in counties with below-average consumer confidence and above-average inflation can stop diagnosing "brand health" issues and start addressing structural accessibility problems: pricing architecture, value messaging, credit options, or even portfolio strategy.
Custom research and economic data aren't just complementary—they're multiplicative. Each answers a different strategic question, and you need both to make high-confidence decisions (see case study italicized below).
Custom research identifies growth territories while syndicated economic data identifies the right levers to pull for growth
A specialty grocer known for organic produce and artisanal products discovered through custom research that they had strong brand equity for "healthy quality produce and discovery" but weak mental availability for "weekly shopping" and "saving money" — the dominant category entry points (see illustration below). The strategic implication seemed clear: to drive in-store traffic, build associations with routine shopping occasions that drive 80% of grocery shopping volume. And this is where most insights teams would have stopped.
While routine occasions could indeed eventually drive growth across all income segments, our large-sample syndicated data revealed their customer base skewed toward higher-income adults, driving higher and more resilient consumer sentiment than the general population. Affluent households in their MSAs had both the means and the appetite for premium grocery experiences: they offered the fastest, most profitable path forward given current economic realities.
The recommendation shifted from vague ("become more relevant for everyday shopping") to precise: "Make this brand the default weekly shop for affluent households in markets where you have store density, competing on 'premium that reduces waste' rather than discounting, and building routine around meal planning and pickup convenience."
Given the rich metadata we collect — including detailed demographic, psychographic, and political profiles of their target, plus the media channels they overindexed on — we could then provide actionable direction and examples of how this recommendation could be brought to life. This is the difference between "insights" and "decision intelligence."
We built our system to capture this integration systematically—not as two separate studies that clients must connect themselves, but as a unified diagnostic framework.
The Custom piece maps the strategic landscape: which entry points drive category volume, how your brand performs across them, where psychological distance creates barriers, and which territories offer growth potential.
Our syndicated economic tracking reveals which consumer segments have capacity right now: state-level confidence trends, income-stratified pressure points, sector-specific employment stability, financial resilience (savings buffers, debt capacity, credit access), and regional inflation realities.
The fusion layer is where strategy becomes actionable.