6 Things Every Marketer Needs to Know About Identity Graphs

Is your marketing performance suffering from an identity crisis?

If you don’t have a customer intelligence solution that allows you to truly understand the person behind the ever-changing screen of choice, odds are, it is.

Everyday, consumers already switch among four connected devices like smartphones, laptops and tablets. With new smart technologies continually hitting the market– kitchen appliances, cars, even umbrellas – customers’ cross-channel habits will become even more complicated. Which means so, too, will the marketer’s ability to recognize and relate to them with relevancy, consistency and timeliness across all touchpoints.

Fortunately, there is a solution: an identity graph. In this age when data runs rampant, the identity graph offers marketers a means to tie online and offline data sets across multiple platforms to a single customer view in order to gain a unified customer view.

Here we break down everything you need to know about identity graphs.


What is an identity graph?

An identity graph, or ID graph, is a database that houses all the known identifiers that correlate with individual customers.

Across a consumer’s journey, one or many personal identifiers may be associated with an individual — email addresses, a physical address, mobile phone numbers, device IDs, account usernames, customer IDs, loyalty numbers and an ever-changing array of cookies picked up in browsers. The ID graph collects these identifiers and connects them to the customer’s profile and any related data points, including behavioral data like browsing activity or purchase history.

Different ID graphs offer different types of customer profiles, matching methodologies, speed and control. It’s important to understand your options before making a choice.


How do customer profiles work within the ID graph?

Basically, there are two types of customer profiles: authenticated and not-authenticated.

Authenticated profiles, or persistent profiles, are rooted in authenticated IDs. These IDs include email addresses or customer IDs that require a log-in, making them more durable than cookie-based data that expires and is restricted to the Web. When a customer “authenticates,” i.e., logs on to a site or makes a purchase with a credit card, the ID graph links this first-party data to the various bits of data used to uniquely identify this person across channels or devices. With each customer interaction, the persistent profile accumulates data, increasing clarity and value over time.

Non-authenticated profiles, on the other hand, are built from identifiers like cookies or device IDs, which are shorter-lived or don’t translate across devices. They work fine within a single channel or for a single campaign, but because they are unable to continually collect and connect customer data, they result in just a partial view of a customer; essentially, a snapshot in time.


What is the difference between deterministic and probabilistic matching?

ID graphs use two different data matching methodologies: deterministic and probabilistic. ID graphs may use just one method or a combination of both.

Deterministic matching uses known customer information, such as anonymized log-in data or hashed email addresses, to match and recognize individuals on whatever device they may be using. Because the data is authenticated, the match is made with 100% certainty.

Probabilistic matching uses anonymized data signals, like IP address, device type, browser type, location and operating system, to create likely statistical connections between devices. While probabilistic matching achieves greater scale, the match is not perfectly accurate.


Who controls the ID graph?

Brands may own their ID graphs or they may tap into ID graphs assembled by third-parties, such as social networks, advertising partners or onboarding providers.

“Renting” third-party ID graphs increases marketing reach. Yet it does little to increase marketing intelligence. Marketers are limited to the tools and insights these vendors uncover. And if their partners don’t share back user-level data or insights, it’s impossible for marketers to connect what happens inside their ecosystem with the rest of a brand’s marketing efforts.

The advantage to owning your ID graph is that you can take full advantage of this holistic data asset and control how it is used. You can share the data with any partners in your vendor ecosystem. Continuously recognizing customers across screens, in the context of their preferences, history and needs, allows marketers to understand customers as real people and deliver genuine customer experiences.


How fast should an ID graph process data?

Some ID graphs collect data by retrieving static batch files from a marketer’s various channel platforms. This process typically takes five to seven days to upload, match and push out customer IDs. Therefore, marketers are unable to leverage the most recent data.

Other ID graphs enable real-time data collection through their API, browser pixel and mobile SDK. Integrating live streaming data from all digital touchpoints and simultaneously synchronizing these insights with offline data, such as CRM storages, call centers, POS systems or other engagement points, provides marketers with recognition capabilities that are always-on.

What can marketers do with an ID graph?

    Power people-based targeting.

A consumer’s journey spans an array of devices, channels and platforms. Unfortunately, all associated identifiers don’t. An ID graph collects and connects all different types of personal identifiers to one person, so a customer can be recognized and targeted with the right content and in the right context across screens.

    Enhance customer engagement.

The more insightful data you have about customers, the more able you are to personalize brand experiences. Uploading and matching offline data with digital identifiers and behaviors within the ID graph delivers a 360-degree view of a customer. Marketers can use these insights to anticipate what consumers may need and strategize future interactions, upsell or cross-sell opportunities, even ways to reengage lapsed customers.

    Attribute and optimize performance.

Having all customer device, channel and behavior data in one place allows advertisers to accurately measure reach and frequency of their campaigns. They can analyze how different ads and marketing tactics perform across channels, and optimize toward what works and suppress what doesn’t, reducing wasted ad spend and over-saturation.

With customers demanding immediate, seamless and contextually relevant brand experiences as they move across touchpoints, marketers must get up-to-speed on ID graphs. Working with a unified data set, brands can understand and relate to customers with authentic and meaningful experiences in the moment, driving better marketing outcomes in the future to enhance brand loyalty.


Originally published October 04, 2016, updated June 11, 2020

Todd Schoenherr was formerly SVP of Product Strategy at Signal. Prior to Signal he was a VP at Guild Capital, a venture capital firm focused on media and technology companies. He also led product and marketing teams at Orbitz Worldwide and consulted for Fortune 500 clients at Accenture.Todd received a B.S. in Engineering from Purdue University and an M.B.A. from the University of Chicago Booth School of Business.

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