Most brands see only what their customers buy from them. Cross-retailer purchase data reveals the rest — the 70% or more of category spending that happens at other retailers, invisible to loyalty programs, CRM systems, and first-party analytics. For any team making decisions about competitive positioning, pricing, or customer retention, that invisible majority is where the answers are.
The Blind Spot
Brands know their own transaction data deeply. They know what customers buy from them, when they buy it, at what price, and how often they come back. A decade of investment in loyalty programs, CRM platforms, and first-party data infrastructure has made this internal view genuinely sophisticated.
What they cannot see is what those same customers buy everywhere else. The competitive purchases. The category spending at Amazon, Walmart, Target, Costco, DoorDash. The brands they are quietly trying. The products they are returning.
This is not a minor gap. For most brands, their own transaction data captures somewhere between 20% and 30% of their customer’s total category spending. The other 70% to 80% happens at other retailers — and it is completely invisible to every internal system the brand operates.
Loyalty data tells you what a customer bought from you last Tuesday. It tells you nothing about what they bought from three other retailers the same week.
What Exists Today (And What’s Missing)
Several approaches attempt to close this visibility gap. Each solves part of the problem while leaving a significant piece unresolved.
Receipt panels. Some providers recruit consumers to scan or upload their receipts in exchange for rewards. This produces a cross-retailer view of purchasing, but from a general panel population — not from a brand’s own customers. The data describes what strangers buy, not what your specific users are doing.
Credit card and payment data. Transaction-level data from payment processors shows where money was spent and how much. But it does not show what was purchased. A record that reads “$60 at Amazon” contains no information about which products were in that order, what brands were chosen, or whether anything was returned.
Syndicated market data. Traditional syndicated providers project category-level market share estimates from relatively small panels. The largest U.S. household panels cover roughly 0.15% of all households.1 Useful for macro trends, too coarse for customer-level decisions.
The gap that remains. No existing approach lets you take a specific group of your own customers and see exactly what they bought, item by item, at other retailers. Receipt panels give you cross-retailer detail, but from strangers. Payment data gives you your customers, but without product detail. Syndicated data gives you the market, but at category level. The intersection of all three — your customers, at SKU-level, at other retailers — has not been available.
What Changes with SKU-Level Cross-Retailer Data
When you can see what your own customers actually buy elsewhere, at the item level, several things become visible that no survey, panel, or internal dataset can surface.
Returns as unmet demand
A customer bought and returned three different brands of wireless earbuds over four months. They have a clear, active need — but they have not found the right product yet. That signal never appears in a survey. But the return sequence makes the unmet demand obvious, and it identifies the exact customer experiencing it.
Competitive timing
You can see that a customer bought your competitor’s product two weeks after your price increase. Not “they might have switched because of price” — you see the exact sequence. The price went up on March 1st. They bought the alternative on March 14th.
Basket context
A person buying premium dog food, organic groceries, and high-end supplements is a fundamentally different customer than someone buying the same dog food alongside value-brand everything else. In your own loyalty data, these two customers look identical — same product, same purchase frequency, same spend with you. Cross-retailer data reveals that they are entirely different people.
Category entry
You can see the moment someone starts buying in a new category — their first baby product, their first pet purchase, their first home-office setup — before they have ever told anyone about a life change. These category entries are among the most valuable targeting signals in consumer marketing, and they are structurally invisible to any single retailer’s data.
Brand preferences people don’t report
Private-label products, store brands, hard-to-pronounce brands, and algorithmic bestsellers take significant market share but rarely appear in survey data. Consumers don’t think of them as “brands” and don’t recall purchasing them. Purchase data has no such recall bias.
How It Works
Consent-based account connection. Consumers connect their retailer accounts — such as Amazon — in exchange for clear value. This is first-party data with explicit, informed consent.
SKU-level detail. Every item, every price, every date. Not category-level approximations, not dollar-amount-only summaries. Individual products, specific brands, exact quantities, and whether items were kept or returned.
Longitudinal history. Up to five years of purchase records become available the moment someone connects their account. There is no ramp-up period. A consumer connects today and their historical purchasing is immediately accessible.
Zero fraud. The data comes directly from the retailer’s transaction record. There are no receipts to photograph, no surveys to fill out, no recall to rely on. The data is what was actually purchased, verified by the retailer’s own system.
Meaningful visibility, not omniscience. Even seeing a single additional retailer — like Amazon — is a significant expansion of the picture. The value is the delta: going from zero visibility outside your own four walls to real, item-level visibility into a major channel.
Frequently Asked Questions
What is cross-retailer purchase data?
Transaction-level records of what consumers buy across multiple retailers — not just what they buy from one brand or store. It includes specific items (SKUs), prices paid, purchase dates, and purchase frequency. Unlike aggregate market data, cross-retailer purchase data ties back to individual consumers, showing their complete purchasing behavior across retail channels.
How is cross-retailer data different from panel data?
Panel data comes from a recruited sample of the general population — strangers who may or may not resemble a brand’s actual customers. Cross-retailer purchase data from consent-based account connection comes from a brand’s own customers, providing verified transaction records rather than projected estimates from a separate population.
What level of detail does cross-retailer data provide?
SKU-level — individual items, not categories. You can see specific products, brands, sizes, price points, and whether items were returned. This is a meaningful difference from credit card data, which only shows dollar amounts per retailer, and from syndicated data, which only shows category-level estimates.
How do consumers share their purchase data?
Through consent-based account connection. Consumers link their retailer accounts (such as Amazon) in exchange for clear value. The data is pulled directly from the retailer’s transaction records with explicit consent. Consumers understand what data is being collected, can see it themselves, and can revoke access at any time.
What can brands learn from cross-retailer data that they cannot learn today?
Which competitors their customers actually buy from — not just which ones they claim to consider in a survey. What triggers brand switching and when it happens relative to pricing or promotional changes. What other categories their customers are spending in. Whether price increases are causing defection to specific alternatives. And early life-stage signals, like category entries, that occur in purchase data before any survey could detect them.
Is cross-retailer purchase data privacy compliant?
Yes. The data is first-party, consent-based, and consumer-initiated. The consumer chooses to share, understands what is being collected, and can revoke access at any time. There is no scraping, no inference from payment networks, and no reliance on third-party cookies or device tracking.