Transaction Matching connects anonymous in-store purchases to real customers and households. Learn how it works, what it unlocks for retailers, and what to look for in a solution.
Every transaction tells a customer's story. What they buy, how frequently they shop, which products they prefer, and how their purchasing behavior changes over time can all reveal valuable opportunities for retailers.
The challenge is that much of this information remains disconnected from the people behind the purchases.
A customer might visit the same grocery store every week, use different payment cards, or make purchases without participating in a loyalty program. Each transaction generates valuable data, but without a reliable identity connection, retailers may see a series of unrelated purchases rather than a returning customer with an evolving relationship with their brand.
This creates gaps in customer understanding that extend well beyond the point of sale. It becomes harder to recognize loyal shoppers, personalize marketing, measure campaign performance, and understand how digital engagement influences in-store purchases.
Transaction Matching bridges that gap by connecting previously unidentified purchases to customer identities, turning fragmented transaction data into actionable customer intelligence.
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PhoneCRMTransactionsLoyaltyHouseholdLocationDeviceCredit cardEmailBut how does it work, what opportunities can it unlock, and what should retailers consider when choosing a solution?
What Is Transaction Matching?
Transaction Matching is the process of connecting purchase transactions, including previously anonymous in-store purchases, to known customer or household identities using available transaction signals, identity data, and matching methodologies.
For retailers, this means gaining visibility into purchasing activity that might otherwise remain disconnected from their customer relationship management (CRM), loyalty, and marketing systems.
Consider a shopper who regularly visits a grocery store but has never enrolled in its loyalty program. The retailer can see what was purchased, when the transaction occurred, and how much was spent, but may have no reliable way to associate that purchase with a known customer.
Transaction Matching uses available information and identity resolution techniques to help establish that connection where sufficient evidence exists.
Once retailers associate purchases with reliable customer identities, they can connect transactional behavior to broader customer profiles, helping them understand shopping frequency, recognize returning customers, develop more relevant audiences, and improve marketing measurement.
Importantly, Transaction Matching is not limited to retailers with established loyalty programs. Depending on the available data and matching methodology, it can help identify shoppers through other qualifying signals, extending customer intelligence beyond the portion of the population already represented in a CRM.
Why Are So Many Retail Transactions Still Anonymous?
Retailers have invested significantly in customer data platforms, CRM systems, loyalty programs, and marketing technology. Yet collecting more data doesn’t necessarily mean developing a more complete understanding of customers.
The issue is often fragmentation.
Customers interact with retailers across different stores, channels, and payment methods, while the systems capturing those interactions don’t always share a consistent identity.
A shopper might use a loyalty account during one visit but not another, alternate between credit cards, or pay through a mobile wallet. Without sufficient identity signals, these transactions can become disconnected from existing customer records.
For businesses without loyalty programs, the challenge can be even greater, as most purchases may enter their systems without any attached customer identity.
These blind spots affect far more than customer profiles. They can limit the ability to distinguish new shoppers from returning customers, create relevant marketing segments, keep existing buyers out of acquisition campaigns, and connect advertising investments to in-store revenue.
The opportunity, therefore, isn’t simply to collect additional information. It’s to unlock more value from the transaction data retailers already have.
How Does Transaction Matching Work?
Transaction Matching combines available purchase information with identity resolution technology to connect transactions to individuals or households.
While methodologies differ across providers, more advanced approaches can evaluate multiple types of information rather than depending on a single identifier.
At Deep Sync, Transaction Matching can draw on three complementary categories of information: personal, transactional, and geographic.
- Stage 1Personal informationLoyalty accounts, existing customer records, and other qualifying identity signals.Known identifiers
- Stage 2Transactional informationEligible card-level or payment-related signals for purchases made without a loyalty account.Payment signals
- Stage 3Geographic informationStore locations, trade areas, and household identity data for remaining transactions.Probabilistic
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Personal Information: Connecting Transactions to Known Customers
When personal information or an existing customer identifier is available, it can provide a strong starting point for identity resolution.
This might include information associated with a loyalty account, an existing customer record, or another qualifying identity signal.
By connecting these records to a reliable identity graph, retailers can associate purchases with known individuals and households, providing a foundation for more consistent customer recognition.
The effectiveness of this process depends heavily on data quality. Names, addresses, and other identity attributes may change over time, contain inconsistencies, or appear differently across systems.
This is why data hygiene and identity maintenance matter. Standardizing and validating customer information can strengthen matching opportunities and improve consistency across records.
Transactional Information: Finding Connections Beyond Loyalty Data
When existing customer information can't identify a transaction, payment-related signals may offer another opportunity.
Depending on the available data and processing relationships, Transaction Matching can use eligible card-level or payment-related signals to connect purchases to known identities.
This is particularly valuable for retailers whose customers frequently shop without identifying themselves through a loyalty account.
Rather than treating every unidentified purchase as an entirely new or unknown customer interaction, retailers can use qualifying transactional information to recover additional customer connections.
An important consideration is that not every solution supports the same processing infrastructure. Some approaches depend on a particular payment processor, while others can operate across different processing relationships or use provided transaction records securely.
For businesses managing multiple payment providers, the flexibility of the matching approach can be as important as the matching technology itself.
Geographic Information: Extending Identity Connections Beyond Direct Signals
Even after evaluating personal and transactional information, some purchases may remain unidentified. Geographic information can provide an additional layer of insight by considering store locations, surrounding trade areas, and household identity data.
By combining these geographic signals with additional matching criteria, retailers can uncover potential customer connections that might otherwise remain unresolved.
Depending on the methodology, these connections may involve probabilistic techniques or approximate matching criteria, such as variations in first or last names. Because not every match carries the same level of confidence, retailers should be able to understand how each connection was established and decide which matching methods are appropriate for their needs.
Deep Sync allows customers to configure which matching rules to include or exclude, helping them balance match coverage with their desired level of confidence.
Why a Multi-Stage Matching Approach Matters
The real value of combining these methods comes from how they work together: Rather than relying on a single signal, a matching waterfall evaluates transactions through multiple approaches, allowing each stage to address records that previous methods could not identify.
Deep Sync’s Transaction Matching methodology builds on decades of identity matching expertise, combining data hygiene, a comprehensive identity graph, and flexible matching techniques to help retailers improve coverage while maintaining visibility into the criteria behind each match.
Because different matching techniques produce different levels of confidence, retailers should be able to understand how records were resolved and determine which results are appropriate for their intended use.
This distinction is important: a higher match rate can expand customer visibility, but the quality, explainability, and suitability of those matches ultimately determine their business value.
What Can Retailers Do With Transaction Matching?
Recognizing previously unknown shoppers is an important first step, but the real opportunity begins when that information becomes useful across customer engagement, analytics, and marketing.
Build a More Complete Understanding of Customer Behavior
Loyalty programs and CRM databases provide valuable insights, but they often represent only a portion of the people shopping at a retailer’s stores.
By connecting more transactions to customer identities, businesses can develop a broader view of purchasing frequency, shopping patterns, product preferences, and customer relationships.
This helps retailers understand not only their most engaged loyalty members, but also customers whose purchasing activity was previously difficult to recognize.
Identify Returning Customers and Improve Retention Strategies
When transactions remain disconnected, returning shoppers can appear as unrelated buyers, making customer acquisition and retention difficult to measure accurately.
Transaction Matching can reveal recurring purchase activity, helping retailers distinguish returning customers from potentially new shoppers and develop more relevant engagement strategies.
With a stronger understanding of purchasing behavior, marketing teams can identify retention opportunities, refine customer segments, and better understand changes in shopping frequency.
Personalize Campaigns Using Actual Purchase Behavior
Customers’ purchasing histories can offer a more meaningful foundation for marketing than broad audience assumptions alone.
Once transactions are linked to usable customer identities, retailers can build segments based on actual shopping behavior, including purchase categories, frequency, and product affinities.
These insights can support more relevant offers, cross-selling opportunities, and personalized campaigns, helping brands connect customer engagement to demonstrated interests and purchasing patterns.
Reduce Wasted Advertising Spend
Without reliable customer recognition, retailers may continue targeting existing buyers with campaigns designed to acquire new customers.
Transaction Matching supports audience suppression by helping identify more existing shoppers within addressable customer data.
This lets marketing teams refine acquisition audiences, reduce unnecessary impressions, and direct more investment toward customers they are genuinely trying to reach.
Connect Digital Advertising to In-Store Sales
Connecting digital marketing investments to physical-store purchases remains a significant challenge for many retailers.
A customer might see an advertisement online and purchase a product in-store days later, but without a recognizable identity connection, evaluating the relationship between campaign exposure and purchase behavior becomes difficult.
Transaction Matching can help create the identity foundation needed for closed-loop attribution and retail media measurement.
When combined with compatible advertising and measurement systems, retailers can better understand how digital campaigns contribute to in-store activity and use those insights to improve future media decisions.
Extend Customer Intelligence Across Marketing Technologies
The value of resolved transaction data shouldn’t end with a single campaign or platform.
When identity connections are portable and compatible with a retailer’s technology ecosystem, the resulting intelligence can support CRM enrichment, customer analytics, audience development, and activation across different channels.
Depending on the solution and applicable permissions, retailers may be able to use resolved data within their own systems, customer data platforms (CDPs), clean rooms, and other authorized environments.
This makes Transaction Matching part of a broader customer identity strategy, not an isolated marketing capability.
Transaction Matching in ActionHow a Grocery Retailer Resolved Millions of Anonymous Purchases
The impact of Transaction Matching becomes clearest when you look at a real-world retailer operating without a traditional loyalty program.
A fast-growing national grocery chain processed approximately 43.8 million in-store transactions every month, yet nearly 95% of those transactions had no customer identity attached.
Without a traditional loyalty program or customer app, the company had limited visibility into its shoppers, leaving valuable purchasing behavior disconnected from its customer intelligence and marketing strategies.
Deep Sync applied a three-stage Transaction Matching methodology, beginning with available loyalty signals, expanding through card and payment information, and using store trade area modeling to evaluate remaining unmatched transactions.
Each stage contributed additional coverage, helping the retailer uncover customer connections that would otherwise have remained unidentified.
In a single monthly production run, Deep Sync resolved approximately 32 million transactions out of 43.8 million processed, achieving a reported resolution rate of 73% of total transaction volume.
That expanded visibility created opportunities for the retailer to build addressable audiences, personalize marketing based on purchase activity, suppress existing shoppers from acquisition campaigns, and connect digital advertising investments to in-store behavior.
The lesson extends beyond grocery retail. Businesses don’t necessarily need to launch a new loyalty program or redesign their customer experience to begin improving customer recognition. In many cases, valuable customer intelligence already exists in the transactions they process every day.
How to Choose the Right Transaction Matching Solution
As more retailers look for ways to identify unknown shoppers, it’s important to recognize that not all Transaction Matching solutions offer the same capabilities.
Matching coverage matters, but evaluating a provider solely on its reported match rate can overlook differences in data quality, methodology, integration flexibility, and the usefulness of the resulting information.
A stronger evaluation considers not just how many transactions can be matched, but how those matches are established and what the retailer can accomplish with them.
- Data qualityHow is identity data built, updated, and maintained?
- Infrastructure fitDoes it work with your payment processors and data environment?
- TransparencyCan you see how each match was made and how confident it is?
- PortabilityCan resolved identities move across your systems and partners?
- What you receiveOnly an activation ID, or full profiles and enrichment?
Look Beyond Match Rates to Data Quality and Matching Expertise
A reliable Transaction Matching solution depends on the strength of its identity data and the methodology used to establish connections.
Retailers should understand how often identity information is updated, how customer and household records are maintained, and what processes address incomplete or inconsistent information.
Deep Sync brings 30 years of experience in consumer identity matching, including expertise developed through direct mail and offline data applications. Its identity capabilities are supported by a deterministic foundation of individual- and household-level information, persistent identifiers, and regular data updates.
Combined with comprehensive data hygiene and a multi-stage matching methodology, this foundation supports customer recognition across different data environments.
Retailers should also distinguish between the deterministic identity data underlying a solution and any probabilistic techniques used to extend transaction-matching coverage.
Choose a Solution That Adapts to Your Data Infrastructure
Retailers don’t all use the same payment processors, data environments, or technology platforms. Their Transaction Matching solution should be flexible enough to accommodate those differences.
Some businesses may prefer to securely provide transaction data to an identity provider for matching, while others may require transaction data to remain within their payment processor’s environment.
Deep Sync supports both approaches. Retailers can use Deep Sync’s proprietary matching methodology to process provided transaction data, or work through supported payment processing environments where matching takes place without moving the underlying transaction data to Deep Sync.
This flexibility can be especially valuable for organizations operating across multiple payment providers or looking to maintain control over their data infrastructure.
Demand Transparency Into How Matches Are Made
A match rate alone doesn’t explain the quality or methodology behind a result.
Before selecting a provider, retailers should understand whether they can distinguish between matching techniques, evaluate confidence levels, and establish criteria for which matches are appropriate for different applications.
Customers can also configure their matching waterfall by selecting which match types and criteria to include or exclude, including available approximate matching rules. This flexibility allows businesses to tailor their matching strategy to their specific use cases and confidence requirements, rather than relying on a fixed, one-size-fits-all methodology.
This transparency helps teams make more informed decisions about how to use matched information across analytics, audience development, and measurement.
Consider Identity Portability and Integration
Retailers should also evaluate whether resolved identities can support their broader customer data and marketing ecosystems.
A solution that returns identifiers usable only within a limited environment may restrict opportunities for enrichment, collaboration, and cross-channel activation.
Deep Sync’s persistent, pseudonymous DS ID can support authorized data connections between compatible partners and environments without requiring the exchange of directly identifying personal information.
This provides an identity foundation that can extend across systems and help businesses maintain flexibility as their technology needs evolve.
Understand What You Receive After the Match
Connecting a purchase to a customer identity creates value, but the information returned after matching determines how broadly that value can be applied.
Retailers should consider whether their provider returns only an activation identifier or also supports usable customer profiles and enrichment attributes.
When Deep Sync performs matching, retailers can receive fully resolved customer profiles, along with requested enrichment data, supporting applications beyond advertising activation.
This gives businesses opportunities to strengthen their CRM, improve customer analytics, develop more informed audience strategies, and use transactional intelligence across multiple business functions.
From Anonymous Transactions to a Deeper Understanding of Customers
Retailers already have access to valuable information about how people shop, but too much of that intelligence remains disconnected from the customers generating it.
Transaction Matching bridges that gap by connecting more purchase activity to customer identities and helping businesses make better use of their existing data.
As organizations evaluate solutions, the most important considerations extend beyond matching volume. Data quality, methodology, transparency, flexibility, and the usability of resolved customer information all influence the business value of the results.
With 30 years of identity expertise, flexible transaction matching approaches, and transparent matching methodologies, Deep Sync helps retailers better understand the real people behind their transactions and turn previously disconnected purchases into actionable customer intelligence.
Frequently Asked Questions About Transaction Matching
Does Transaction Matching Require a Loyalty Program?
No. Although loyalty information can provide valuable signals for identity resolution, Transaction Matching can also use eligible transactional and geographic information to help connect purchases to customer identities.
The available methods and achievable results depend on the data, matching technology, and applicable privacy requirements.
Can Transaction Matching Work Across Multiple Payment Processors?
Yes, depending on the solution provider and its supported integrations.
Some implementations depend on a specific payment processing relationship, while more flexible approaches can use different processing environments or securely provided transaction data.
Retailers should evaluate compatibility with their current infrastructure and their ability to support additional payment providers as business needs evolve.
Is Transaction Matching the Same as Identity Resolution?
Transaction Matching is a specific application of identity resolution that connects purchase activity to customer identities.
Identity resolution is the broader process of connecting fragmented identifiers and records to develop a consistent understanding of individuals or households across datasets and channels.
Transaction Matching uses those capabilities to make purchasing activity more useful for customer intelligence, marketing, and measurement.
How Accurate Is Transaction Matching?
Accuracy depends on available data, identity graph quality, matching methodology, and confidence thresholds.
Some transactions can be associated with identities through direct deterministic signals, while others may be evaluated using probabilistic methods.
Because matching techniques differ, retailers should consider match confidence and validation alongside coverage, rather than assuming every resolved transaction carries the same level of certainty.
How Can Retailers Use Matched Transaction Data?
Depending on the information returned and the retailer’s permitted use cases, matched transaction data can support customer segmentation, CRM enrichment, personalization, audience suppression, retargeting, customer analytics, and marketing attribution.
Activating those insights depends on the retailer’s connected systems, applicable data permissions, and the capabilities of its identity and marketing technology partners.
How much of your in-store customer activity could you be missing?
Explore Deep Sync’s Transaction Matching case study or connect with our team to discover how much of your anonymous transaction volume could become addressable.
Julia is Director of Product Marketing at Deep Sync, specializing in identity resolution, customer data, audience activation, and go-to-market strategy. She helps brands turn complex data into actionable insights that power better marketing, personalization, and customer engagement.
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