Identity Resolution is defined as the process of connecting unique ‘identifiers’ to create a single, unified, real-time, persistent customer identity.

In this article, we talk about what identity resolution is, its components and process, who needs it and why – with examples and use cases for marketers.

Table of Contents

• What Is Identity Resolution (Definition)
• Who Needs Identity Resolution and Why? Use Cases and Examples
  • Core Components & Functionalities of an Identity Resolution System
  • Personalized Customer Experiences: Typical Challenges for Enterprise Marketers
  • How Identity Resolution Helps with Personalized Marketing and Customer Experiences
• The Identity Resolution Process
• 2 Types of Identity Resolution – Probabilistic and Deterministic Identity Matching
• Advantages of Identity Resolution for Enterprises

What Is Identity Resolution?

Identity Resolution is the process of connecting unique ‘identifiers’ to create a single, unified, real-time, persistent customer identity. Identifiers include device identity, browser behaviour, transactions and other contextual data that help connect the same individual across devices, platforms, and channels. The outcome is an accurate, complete and 360-degree view of each customer that can then be communicated to, in the most engaging and relevant way possible.

The goal of Identity Resolution is to get a holistic view of the customer’s interactions with the brand in an omnichannel environment, which is key to turning data complexity into an opportunity for exceptional customer experience.

Marketers are only too aware that their prospects and customers live in a device-dominated, multi-channel world. Marketers need to deliver a consistent brand experience irrespective of what device, platform or channel the customer chooses to engage – whether online or offline. That is where identity resolution helps.

In this feature, we will try and set out a detailed primer for enterprise marketers interested in understanding the concept of identity resolution, why they may need it, how to go about it and the types of identity resolution possible, even as they go about finding ways to incorporate the approach into their customer data management strategy.

Who needs Identity Resolution, Why, and When?

Challenges, Use Cases, and Examples

Progressive marketers have always been focused on understanding the customers journey and addressing the most relevant messages to them, but it has been a long evolution from the time when the technology simply was not sophisticated enough for the kind of segmentation we can do today.

In fact, modern identity resolution and customer data management platform technology can help deliver the holy grail of ‘segment of one’ where each individual customer gets a personalized and unique brand experience based on their own characteristics, behavioral triggers and journey map.

Between the rising inadequacy of cookies as a way to track customers and the increasing complexity of the device, platform and channel landscape, customer-centric marketers, who are focused on marketing efficiency, effectiveness and delivering a seamless, friction-free and optimal customer experience (CX) to their prospects and customers will prioritize identity resolution to better engage and retain customers.

While the early adopters of identity resolution technology to enhance customer data management outcomes are in industries such as retail, travel & hospitality, ecommerce etc., it is a useful technology for any consumer-focused and customer-centric industry that wants to prepare for a world where customers increasingly expect to be recognized and treated to personalized and contextual journeys in an ever more complex environment. The primary use-cases for identity resolution are in adtech and martech applications for customer acquisition, engagement, retention and loyalty.

Core Components & Functionalities of an Identity Resolution System

1. Data onboarding: the process of bringing all the available online and online customer data into a single system. Speed, accuracy and security are key to successful data on-boarding.
2. Real-time and persistent matching and resolution at scale: once all the data is in one place, typically on the vendors identity resolution system or on the customer data platform (CDP), the process of deduplication, (probabilistic and deterministic) matching; hashing or anonymizing; and suppression begins*. The final outcome is the creation of one unique individual profile of each customer, which is persistent (changes with any change in customer’s use of device, channel, platform or address) and real-time.
3. Identity Graph: proprietary identity graph models differ across vendors, but the basic idea of an identity graph is to further enrich the PII (personally identifiable information) collected in the customer profile with additional external channel, device or behavioral data that can act as digital identifiers. This could include third-party data from marketing partners or data vendors; municipal data in the public domain such as house or car ownership, or voter data; online surveys, event attendance, cookie and IP data, device data, mobile advertising IDs etc. The outcome is a complete customer identity – digital and online – created from both – owned and external data sources – that can inform the design of campaigns and customer experience.
4. Integration & Activation: making these complete and addressable customer profiles available to other systems – such as martech or adtech platforms – that can then further activate the data by running campaigns and delivering experiences.
5. Compliance: adhering to prevailing regulations that define what ‘personal information’ means in a particular geography, industry or time frame. Usually, this means anything that can be associated or linked with an individual or household is subject to privacy and data security compliance. Identity resolution solutions must cater to all that are applicable in the context of your brand.

Personalized Customer Experiences: Typical Challenges for Enterprise Marketers

When trying to create a personalized journey experience for an individual customer, the typical challenges marketers face include:

1. No specific information about a customer: the customer is just a number or ID or a transaction, and there is no concrete information about her preferences, devices used, etc.

2. Disjointed online and online identities: connecting an individual’s online and online identities is a complex task due to technology silos, non-persistent data, and data loss as multiple vendors are involved

3. Fragmented identities: customers engage across various channels and touchpoints on numerous devices. Identifying each digital source and associating a customer identity with them is a challenge

4. Campaigns are designed based on educated guesses: for instance – cookies – which may tell us about the basic device but everything else is just an assumption

5. One-size-fits-all campaigns: due to an inability to identify individual customer preferences, we end up creating generic campaigns, which fail to engage the prospect or customer

6. Loss of engagement as a customer switches device: when customers change devices or evolve their platform and touchpoint preferences, we are faced with losing all the engagement built on the previous device

As customer data platform (CDP)Opens a new window , observes in this Opens a new window article on MTA, “The challenge for many brand advertisers is the sheer volume of data. There is a seemingly infinite volume of offline and online attributes, such as name, address, email address, cookies, date of birth, transaction history, mobile device identifiers and the list goes on. Each one of these attributes alone gives you a glimpse into the customer, but the ability to connect these disparate data sets in a privacy compliant manner, can create a 360-degree view of the customer and help marketers make the right decisions. And it all starts with the right resolution and matching process.”

How Identity Resolution Helps with Personalized Marketing and Customer Experiences

Are you treating one customer as one single individual (irrespective of where you meet them) or as multiple different individuals? Is their experience seamless and consistent no matter what platform or device they interact with your brand; or are they still getting retargeted with ads for that little black dress – long after they have already purchased said little black dress?

When I visit a shop or restaurant regularly today, I ask ‘why should I have to be a card-carrying loyalty program member for your on-ground outlets to recognize my preferences’? Shouldn’t the phone number from where I booked my table be enough to set off a chain of fortunate incidents that lead to a delightful restaurant experience?

Enter identity resolution. A unified view of the customer is the core of CDPs, but a critical subset of creating a unified view is matching the identity of customers across diverse devices, platforms, channels and locations to help resolve multiple interactions as coming from the same individual.

Identity resolution has, off late, grown into an independent solution category within customer data management. Depending on what technology and methodology is being used for the identity resolution, marketers can arrive at a probabilistic or deterministic match of their customers across devices, platforms and online/offline channels.

For example Starbucks uses Amperity for customer 360 identity stitching to create more holistic customer experiences. Earlier they had a lot of disparate data around both known (loyalty) customers and a lot of ‘loyal’ customers they couldn’t reconcile. Identity resolution helps them reconcile a lot of their customer data, identify unique customers and personalize the experiences with much more coherent marketing campaigns, including the food and beverages the barista can offer to them based on their identity. They are able to send ‘proximity alerts’ to its mobile app users to promote special offers when they are in the vicinity, based not just on their location but also their customer preferences.

Similarly, retailer FinishLine used LiveRamp identity resolution solutions across its paid search and shopping campaigns, to identify segments containing customers who transacted online and in store, and send more powerful offers to them based on that behavior

The Identity Resolution Process

The outcome of identity management is as complete view of an individual customer as possible. Identity resolution helps marketers join the dots across all online and offline touchpoints where prospects and customers engage with the brand. This includes all digital, telephonic, virtual, mobile, physical channels of contact – and any other new touchpoint that may emerge in the future. These touchpoints are typically a combination of an individual’s terrestrial, device and digital identity**.

[**Terrestrial Identity: includes data such as home address, work address, home phone number

Device Identity: includes IP/other identification data of various devices associated with individual.

Digital identity: includes various email IDs, social profiles, blogs, website registrations etc.]

For example, if you have three records, you may think you have 3 unique customer records.

• The first record has a link between the first name, last name and email address
• The second record has a link between first name initial, last name and phone number
• The third record has a link between first and last initials and email id and phone number.

Without identity resolution, you would send out 3 sets of all communication, annoying the customer and wasting marketing dollars. Indeed, any two of these records may fail a comparison, but taken together, with an identity resolution approach, it can give a very high-confidence match that this is almost certainly the same customer, and you can then do a much more targeted and focused campaign to this individual.

A comprehensive identity resolution process involves several components:

1. Identify – the various channels, platforms and devices that dominate the customer’s journey
2. Connect – the dots between those devices, platforms and channels* along the customer’s path to purchase or larger journey across the lifetime
3. Match – individuals or households to each device/ set of devices and platforms – based on a defined set of attributes
4. Validate – with confidence– that it is definitely / probably the same individual across all those devices/ platforms
5. Activate – the data to orchestrate relevant, personalized adtech or martech campaigns based on this understanding[* Channels include in-store, ecommerce or mobile app. etc.

Platforms include online, offline, social media, websites etc.

Devices include smartphones, laptops, tablets, smart speakers, IoT, PoS kiosks, SmartHome IoT etc.]

The 2 Types of Identity Resolution: Probabilistic and Deterministic Identity Resolution

With identity resolution solutions, it is possible to do complex matching across millions of data points and records in near real-time or real-time. Once the identity resolution system gathers all the terrestrial, digital and device data, depending on the technology and data sets, it can deliver one of two types of matches – a probabilistic or deterministic match.

In essence, the type of match is not just about making the connections between data points, but the confidence level with which the match is made.

Probabilistic ID Matching

With probabilistic matching, profiles are matched through an estimate of the statistical likelihood that two identities are the same customer. The ‘identifiers’ are in fact millions of anonymized or anonymous data points from diverse digital sources, including IP address, device type, browser or OS, locational data, type of wi-fi network, timings and patterns of browsing and other behavioral data. The logic or confidence in defining something as a probable match comes from the combination of attributes selected for matching in each use case. There are several data vendors who have created a ‘foundational databank’ of billions of data points that can be leveraged for probabilistic matching. If you have multiple similar records, all using different devices but can find some linking identifiers, you can conclude that this is probably the same person. Probabilistic matching is very useful for modeling ‘look-alike’ audiences for digital advertising segmenting and targeting purposes.

Deterministic ID Matching

With deterministic matching, customer records are matched by searching for equality across identifiers such as hashed email, phone number, or logged-in username. This high-confidence approach works best when first-party data is readily available. This ‘first-party’ data usually includes personally identifiable information (PII) such as email address, home or work address, telephone or credit card numbers, sign-ons and log-ins etc. There is usually no doubt about who the individual is. For example, if someone logs into your website on their desktop, and then into the mobile app on their smartphone a few days later, you can conclude with high confidence that this is definitely the same individual on different devices.

Learn More: Top 10 Identity Resolution Software Companies for 2020Opens a new window

Key Advantages and Outcomes of Identity Resolution for Enterprise Marketers

Intelligent identity resolution at scale can transform marketing outcomes at the enterprise level:

1. Increased marketing efficiency

1.1 Reduced customer acquisition and retention costs: streamlining communications across touchpoints and devices reduces messaging wastage due to overlaps, duplication or mistargeting; and improves returns on cross and upselling efforts. It is not just who you target- it is also about who you don’t target anymore. Think of all the dollars wasted retargeting ads to people who have either already purchased or have stopped looking for a product.

1.2. Improved campaign effectiveness: based on unified data, campaigns activated across the martech stack – on marketing automation platforms, DSPs or DMPs – all work to deliver the right message at the right time on the right channel; thus improving conversion rates and campaign ROI.

1.3. Improved attribution, tracking and decision-making: knowing what worked, when it worked, on what device it worked (and didn’t work) lead to data-driven decision making and help improve subsequent campaign outcomes.

1. 4. Improve integration and collaboration between functions (finance, sales, customer service, marketing) to serve a single customer seamlessly – even beyond the buying journey.

2. Increased marketing effectiveness

2.1. Drive growth:

At the macro (segment) level, identity resolution enables improved segmenting and targeting by helping marketers better define ‘look-alike’ audiences, and even discover new audience segments to deliver more targeted campaigns at scale.

At the micro (individual) level, marketers can identify new cross-sell, upsell and reactivation opportunities and better monetize these opportunities across the buyer’s journey than they could with disjointed data.

2.2. Drive conversion, retention and loyalty: highly personalized offers on the right device or channel, at the right time leads to improved business outcomes.

2. 3. Performance and attribution tracking across every touchpoint and device enables complete transparency in measurement – both at the individual level and at an anonymized omni-channel view of segment behavior.

2. 4. Ability to evolve and be flexible to changing market dynamics: individual customers evolve and change their preferences. With strong identity resolution capabilities, marketers can easily evolve with these changes and continue to remain relevant.

2. 5. Compliance: A robust view of customer’s ongoing preferences will ensure the brand remains compliant to all regulatory and ethical privacy requirements.

2. 6. Showcasing marketing outcomes: by far, Customer Data Management – including Identity Resolution – is the strongest and most compelling way to demonstrate the role of marketing in converting and retaining customers at the lowest cost and highest efficiency possible.

Probabilistic versus Deterministic Identity Resolution: Who Needs Which?

In general, deterministic matching is thought to be more accurate than probabilistic matching. But that does not mean one is better than the other. It comes down to the marketer use-case. When approached this way, either kind of matching could be the more appropriate one for the use case.

When marketers tend to work with first-party data, and are more interested in accuracy (high confidence of a match) than scale, deterministic matching methodologies would be more apt. Typically highly personalized martech applications – upselling an insurance policy to known customers, showing a returning user a certain type of content on the website or personalizing offers to a loyalty member – would rather use deterministic matching.

When the goal is reach and scale, then probabilistic matching would be a better bet. In this case, accuracy may not be as crucial as reaching the best possible target segments – people who are most likely to respond or convert. Typically, programmatic adtech platforms would use this sort of matching to find the best fit prospect segments to drive maximum ROI on advertising dollars.

Of course, enterprise marketers can seldom survive communicating with only one group or the other group. The best-case scenario is to ensure a combination of both capabilities. A solution that can deliver personalization for known customers based on deterministic matching; while still making the most relevant offer possible to unknown customers based on a probabilistic match to a set of defined attributes. In fact, insight into buyer behavior based on deterministic matching can be applied to find more ‘look-alike’ groups through probabilistic matching in larger data sets, thus expanding campaigns to the best set of known and look-alike prospects.

To learn more about customer data platforms and identity resolution, don’t miss these industry insights from leading practitioners and vendors in the space.