Your trade spend has a housing problem
The CPG industry wastes billions on "impersonal" trade spend by ignoring the structural reality of the home.
- Foot traffic lacks nuance; property intelligence reveals if a shopper has the equity to spend or the space for bulk.
- Optimize shelf space by aligning products with local housing—single-serves for renters vs. multipacks for homeowners.
- Shift from ROAS to asset-anchored CLV, treating the home as the key predictor of brand stability.
In the CPG boardrooms of Chicago, Cincinnati, and London, the mandate is clear: "Personalization at scale." But for an industry that spends over $500 billion annually on trade promotions—roughly 11% to 27% of revenue—the execution is surprisingly impersonal.1
For decades, we have relied on a two-dimensional view of the consumer. We know what they bought (Point of Sale data from NielsenIQ or Circana) and where they went (foot traffic data from point solutions like Placer.ai). But we are missing the third, and perhaps most critical, dimension: the financial and structural reality of the home.
The RMN paradox: spending more to know less?
As retailers aggressively push brands into Retail Media Networks (RMNs), demanding hundreds of millions in digital ad spend, the old data isn't enough. It is no longer sufficient to know that a shopper visited a store. You need to know if they have the home equity to absorb a price increase, the storage space to buy in bulk, or the mortgage lifecycle that signals a propensity to switch brands.
Retailers like Walmart (Walmart Connect), Kroger (KPM), and Target (Roundel) are pitching their media networks as the ultimate closed-loop solution. They have the loyalty data. They know who the shopper is. But even loyalty data has a ceiling.
A loyalty card tells you that "Shopper A" buys premium dog food. It doesn't tell you why. At Cotality, we believe property isn’t just about investment; it’s about the deeper stories and social currents that define our times. Without property-level intelligence, you cannot distinguish between a high-net-worth homeowner and a renter in a high-churn complex facing an imminent rent hike.
Mapping markets: the "home buyer" context
This is the new frontier of category management. For giants like Procter & Gamble (P&G), unlocking granular property data is the difference between "renting" a sale and owning a customer. We view the property market as a mirror of society's broader shifts—aspirations, anxieties, and financial realities.
Imagine if a category manager at Kraft Heinz could overlay traditional demand planning with homeowner financial health:
- Scenario A: A neighborhood of 1970s split-levels with low home equity. These shoppers are cash-constrained; they need value but cannot afford the high cash outlay of a bulk pack.
- Scenario B: A neighborhood of new builds where owners have a high propensity to buy and significant equity. These shoppers value convenience over cost.
By connecting loyalty IDs to property deeds and mortgage data, CPGs can stop guessing. P&G could identify neighborhoods with high "new mover" velocity—the one moment in a consumer’s life where they are structurally guaranteed to buy cleaning supplies in bulk. This is about moving from reactive data to a proactive stance that redefines market stability.
Breaking the "national planogram"
The era of the "average store" is over. The future belongs to the CPGs that can operationalize home buyer intelligence. This isn't just about marketing; it’s about supply chain and assortment.
- Coca-Cola doesn't need to fight for the same shelf space in every region. In areas with high "renter" density (limited storage), the strategy should be single-serve cold consumption. In high-equity "garage" suburbs, the focus shifts to ambient multipacks.
- Nestlé can predict pet care demand by looking at property lot sizes and fence permits—leading indicators of pet ownership that POS data lags by months.
The measure of success
How do we measure this? It’s not just ROAS (Return on Ad Spend). It’s customer lifetime value (CLV) anchored in asset reality. Ownership is about more than possession; it is a promise of stability. If you acquire a "high equity homeowner" who just refinanced, that customer represents a stable, five-year annuity for the brand.
We aren't suggesting you rip out your existing data infrastructure. Nielsen, Circana, and Placer.ai are vital for the what and the where. Cotality provides the who and the how.
We enable category managers to see the financial constraints of the neighborhood before they ship the product. We help brand managers target ads based on the homeowner's ability to spend, not just their desire to buy. In an age of uncertainty, the companies that win won’t be the ones with the most data. It will be the ones that understand the homes of their shoppers.
