Identity resolution: What it is and how to use it for customer personalization

Competitive companies want to deliver personalized experiences to their customers, but with customer data spread across dozens of devices, accounts, products, and marketing campaigns, this is easier said than done. The solution? Identity resolution.

Data in the real world is messy and irregular, and customer identity data is no exception. Identity resolution creates a clean and cohesive view of this data. Once separate interactions from different platforms and sites have been combined into one single identity, AI/ML teams can build accurate behavioral models that can be used as input to personalization engines that improve customer experience.

What is identity resolution?

Identity resolution, sometimes referred to as identity stitching, is the process of assembling a distributed mosaic of behaviors from a given customer (spread across social media, browsing history, brand engagement, etc.) into a concrete data point useful for sales leads, market research, or resale.

The mix of identity data is initially composed of a company's personal relationship with that customer, stored in customer relationship software. This starting point can be supplemented by third-party services that specialize in the detective work involved in connecting device IDs, geolocations, or purchasing behavior. Ultimately, the identity resolution process should give your company an accurate view into all individual customer relationships, enabling not only customer decision-making but also high-level, strategic insights from fine-tuned demographic knowledge.

Ultimately, identity resolution leverages the interconnected web of modern life to supercharge your business and deliver an instantaneous personal relationship with every one of your customers.

How does identity resolution work?

Customer identity resolution starts with an identity graph. In certain fields of mathematics, a graph refers to a network of nodes connected to one another by lines (like a subway map, or a spider web). Identity graphs sew individual scraps of a single customer’s information into a quilt that represents their whole identity. For example, if one device is frequently linked to another via Bluetooth, and both devices are logged into the same account, an identity graph would include the connections between the two devices as well as their link to the account’s email address. This lets you develop a complete picture of an individual customer.

Simple databases are not an efficient way of storing complex graphs, and as online footprints become larger and more complex, it has become necessary to shift away from simple database solutions to more complex representations of data. Data warehouses are a good tool for storing sensitive and high-demand data used by identity resolution algorithms.

How deterministic or probabilistic matching affect identity resolution

The difference between first- and third-party data is always relevant in issues of customer data. In the case of consumer identity resolution, your data’s provenance can have a deep impact on the type of identity resolution that can be performed. Loosely speaking, first-party data (directly from customers to your databases) allows for more comprehensive identity resolution, whereas third-party data (large-scale anonymized data from a vendor) only enables a more nebulous resolution of identity.

Deterministic identity matching

Many of the classic cases of identity resolution fall into the category of deterministic identity resolution. Deduplication and device stitching are necessary to get a clear view of everything from customer journeys to ad campaign efficacy. Clickstream data resolution, which retroactively matches the actions a user took to an identity provided later, is a crucial tool for observing onboarding and understanding the impression your brand makes.

All of that utility is generated by first-party data — data collected from a customer and stored only for your own company’s purposes. By holding a microscope to user behaviors, you gather “known identifiers,” useful identity data that allows for a highly personalized relationship with your customers.

An example of this in action would be a user visiting your website and receiving a certain anonymous ID. If that user signs up for an account or makes a purchase, this anonymous ID is now connected to an email address, a phone number, or an internal user ID. When that same user visits your website from a different device, like a smartphone, they would receive a new anonymous ID and initially appear as two unique users. However, if the user then logged into their existing account, their new anonymous ID would also be connected to the same email address or user ID. In deterministic matching, one can now reliably connect the first anonymous ID to the second one, starting the process of ID resolution.

Probabilistic identity matching

Unlike deterministic matching, probabilistic matching draws conclusions about likely customer identity using suggestions from non-deterministic data sources. There could be device co-location suggesting shared usage, IP address or digital fingerprints to help narrow device identity, or fuzzy matching algorithms that collate additional ambiguous variables into a sensible guess about user identity.

In many cases, the raw data needed to effectively use probabilistic matching comes from a third-party vendor. Third-party data typically arrives in large volumes, which makes guesses based on data distribution easier and more reliable. Vendors like Liveramp Identity or Softcrylic are brands you may be familiar with in this field.

Neither approach is necessarily better for identity resolution, but deterministic matching covers most use cases and is generally cheaper (because you already own the raw data). Deterministic matching is also a more conclusive approach to identity resolution when data security or precision is an important requirement.

For example, if your organization uses identity resolution for compliance purposes with privacy legislation, “accidentally” merging customer profiles based on probabilistic matching could cause irreparable harm. That being said, when handling large-scale identity needs, especially with anonymous data (which deterministic matching handles poorly), probabilistic solutions might be the better or even only feasible approach. It all depends on what type of error is worse for your business: missing out on potential opportunities by focusing on deterministic matching, or potentially making the wrong conclusions and taking wrong actions based on imperfect probabilistic matching

All customer information matters

The most basic function of identity resolution is filtering out duplicate data points from various devices and accounts — replacing anonymous device IDs with a human customer. This means the most fundamental data in the identity graph is the ID tag associated with the device, account, network, session, transaction, or other anonymous identifier that can engage with your company. Once you’ve collected these and associated them with a single customer identity where possible, your essential customer data becomes more reliable and you can move on to higher goals.

At this point, cookies, demographic information, geolocation, and other personal data become relevant. These are the details that will personalize your product outreach and facilitate the customer's journey. Most of these tidbits are warehoused and delivered to you by large identity resolution services, but it’s likely that you will also generate customer details in the course of your business relationship. All such details are valuable — with automation from a data warehouse or proprietary resolution software, ads and services can be updated in real time, supported by instant machine inference. Properly implemented, the customer relationship evolves in real time, resulting in more pleasing customer interactions and a more profitable revenue stream for the business.

Not only do the fine-grained details yield bounty for the individual customer, but the identity resolution process also serves to standardize and sanitize incoming customer data. This makes large-scale modeling and analysis much easier, giving you an unprecedented view of your entire audience. Especially important here is the efficient filtering of duplicate IDs. If buckets of redundant data can be reliably reduced to unique humans or households, any census of customers gains explanatory power.

This kind of de-duplication is not limited to activity in your warehouse. Properly implemented identity resolution can enable your organization to clean data in source systems automatically. This has the benefit of preventing issues where one customer receives the same (or sometimes even worse, a set of different) marketing or sales emails because of a duplicate entry in an email tool or CRM system.

Understanding the benefits of identity resolution for business

With identity resolution, businesses can create a comprehensive view of their customers from every data point. Understanding customers better is good for any business, but there are a number of other practical benefits to identity resolution.

Increased data quality

You’ve undoubtedly heard many business experts say that “data is the new gold” - and they’re not wrong. Much like gold, data is valuable — but only when you can guarantee its truthfulness and that it’s high quality.

This is precisely what identity resolution delivers. Refining and merging customer behavior data eliminates duplicates and other data quality issues. With more reliable customer information at your fingertips, you can make more confident decisions across product, marketing, and customer support.

Stronger compliance

Duplicated, disparate data will hinder your data compliance efforts — especially when it comes to performing audit trails and honoring subject access requests.

Consolidating your data enables you to access and retrieve your data in a more efficient manner, as well as easily identify any anomalies, providing a strong foundation for important data governance policies and compliance.

Better customer insights

Identity resolution consolidates disparate data points (including anonymous user IDs) and assigns them to a real-world individual. This means you obtain reliable, fleshed-out profiles of all your customers and prospects. The profile automatically grows as these individuals continue to interact with your business across different channels and touchpoints.

This goes beyond simple data points, such as names and email addresses. Identity resolution empowers you to match disparate behaviors, preferences, and historical interactions to each profile to build a comprehensive Customer 360. This enables you to understand who you’re selling to and how they behave. With this valuable information, you can better target your ideal customer profile through personalized messaging and marketing tactics.

Reduced customer churn

Retaining the customers you already have is just as important as gaining new ones.

By analyzing your user data and identifying unexpected behaviors, you can flag “at risk” customers and proactively stop them from churning by delivering personalized campaigns, increasing customer lifetime value.

Enhanced omnichannel experiences

Your customers and prospects value convenience. They expect to receive the same experience at every touchpoint, whether it’s on your website, in your mobile application, or on the phone with your customer service representatives.

Identity resolution merges disparate interactions, behaviors, and preferences across your touchpoints. This gives your users a seamless, personalized omnichannel experience, and ensures they don’t have to re-fill or repeat information.

Exploring identity resolution use cases

Fine-tune your marketing and sales efforts

When you think you know your customers, you may end up pushing marketing and sales campaigns that don’t resonate. With identity resolution, there’s no guesswork involved because the data doesn’t lie. You know your customers, prospects, and users inside-out, which means you know what they’re interested in and what they’re not interested in.

With infallible data in their toolkit, your sales and marketing teams can:

• Create targeted, personalized campaigns: Your teams can reach your ideal customers in the right place, in the right way, and at the right time. This may take the form of targeted advertisements on social channels, personalized product recommendations in emails, or exclusive offers in an SMS workflow.
• Communicate better with prospective customers: Identity resolution gives your sales teams a solid foundation on which to base their outreach and nurturing campaigns. They can better understand who they’re talking to, what they’re interested in, and what services or products may alleviate their pain points.
• Attribute lead conversion and other marketing metrics: Identity resolution associates behaviors, such as form-filling and click-through rates, with your unique users. As a result, it’s easier to attribute marketing KPIs, like conversions, to your campaigns. From here, your marketing teams can optimize their efforts and identify customer-winning tactics.

Strengthen fraud detection and security

For businesses in the finance, insurance, and government sectors (to name a few), proactive fraud detection is critical. Organizations must be able to flag and escalate questionable behaviors in an accurate, timely manner. If they can’t, they may face financial losses and reputational damage.

Of course, identifying these often subtle indicators is easier said than done. It requires accessible, trustworthy data—data you can confidently attribute to specific identities. If your data is disparate and unattributed, how can you be certain an action is actually fraudulent?

This is where identity resolution comes into play. With a comprehensive data profile of your users, you’ll know which behaviors are expected and which aren’t. This is crucial for identifying and deescalating fraud accurately and at speed.

Identity resolution vs entity resolution: What’s the difference?

Entity resolution is a data management principle wherein you link data from single or multiple sources to a real-world entity. In this context, an entity is an umbrella term that refers to any unit or identifier that your organization can quantify.

This can change from business to business, but common entities include:

• Households
• Products
• Subscriptions
• Accounts

The most common entities are individual people or identities, meaning that identity resolution is a type of entity resolution.

Choosing the right identity resolution tool

When choosing an identity resolution tool, you need to focus on their specific needs of your company.

Firstly, you must consider how much customer data your business has exclusive access to. You may not want to share your data with a third-party system, preferring instead to solve identity resolution in-house with an integrated data warehouse or similar. In general, the rule of thumb is to begin with deterministic matching to start, and then explore other options if your system has sufficient volume.

On the other hand, if you don't have sufficient data to generate demographic insights or filter redundancies, you should look at third-party support. Identity resolution increases in power with more data, so using services that allow you to supplement your data if it is scarce can help you achieve better customer insights.

You will also need to consider the laws around data privacy, for example GDPR. Other personal data regulations exist in most areas, and more laws are expected as internet usage grows. When handling identity resolution, prioritize privacy and use adaptable tools to comply with changing regulations.

Create better personalized customer experiences by solving identity resolution with RudderStack Profiles

Identity resolution is essential for businesses aiming to consolidate disparate data points into comprehensive customer profiles. The benefits are significant: increased data quality, stronger compliance, and better customer insights.

RudderStack Profiles provides an out-of-the-box, warehouse-native identity resolution solution directly in your warehouse, enabling data teams to create complete Customer 360 views without the need for complex modeling. Simplify your customer identity resolution, save time, and accelerate data projects to create the best experiences for your customers every time.

If you want to dive deeper into the customer data landscape, our learning center helps you unpack concepts like:

• Identity Graphs
• What is Customer Data?