This is the fifth installment of our “Customer Data Strategy” playbook.
Identity resolution is the process of attaching related identifiers, attributes, and behaviors from multiple systems to a unified profile. It establishes a grid that powers the full spectrum of marketing, advertising, customer support, and analytics activities so you’re never in the dark when it comes to identifying a user across platforms and screens. Creating unified customer profiles can significantly increases your odds of providing a relevant, fluid, and profitable customer experience.
Here are four steps to get started with Identity Resolution:
1) Collect Identity Information
In the multi-screen world, every new digital source and platform has its own unique way of identifying users, but you can broadly group them into two categories:
- Implicit characteristics: Mobile identifiers (IDFA, IDFV, Google Ad ID), IP address, web cookies
- Explicit characteristics: Email address, Facebook ID, Google Account ID, etc.
These are the characteristics that will be used to identify your customers whenever and wherever they take any action. When determining what information to collect there are a couple of things to keep in mind:
- Prioritize mobile identifiers as they go beyond other channels and media as the most personal of all consumer access points. Web based identifiers like cookies are linked to a web browser that may be shared by multiple people in a household, making it hard to identify the exact user.
- Don’t forget the identifiers you’ve already collected, many marketers capture email addresses and mobile device IDs without thinking through the long-term value they can provide. Email addresses, for example, are often siloed in CRM systems, just as cookies are left in ad targeting and personalization systems.
2) Build and Enrich User Profiles
Your users are of course interacting with you across multiple devices. Thus, the next step in the process is to use various matching techniques to create and enrich your user profiles across devices.
Whenever possible, you will want to rely on what is known as deterministic matching. A deterministic match is when you have been able to collect the same persistent, stable identifier for a user across devices. An example of making a deterministic match is when a user visits your website and logs into their account. Upon the user logging in, your data platform sets a cookie and collects their email address. You have now tied the user’s email address to their cookie. Then, at some point in the future, the same user downloads your mobile app and logs into the account in the mobile app. Again, upon the user logging in you collect their email address. Once that happens, you are now able to make a deterministic match – tying the user’s browser cookie, to their mobile device ID, based upon collecting the same email.
However, there are plenty of users (especially at the top of the funnel) who will never log in to either the site or the mobile app. This makes it impossible to do a deterministic match, in order to find those users across say web and app based marketing channels. It is here where probabilistic matching can help. Probabilistic relies on matching collects as many data points as possible, such as user agent information, IP addresses, and geographic data, and then algorithmically determining, with a level of certainty, that two devices are indeed the same user. Probabilistic matching can be a great way to target users who never log in, across various devices.
In addition to matching identifiers you can build even richer user profiles by:
- Take into account descriptive user attributes: Implicit and explicit identifiers only provide a two-dimensional view of users, making it hard to understand who they truly are and what they want. It’s the ability to weave in user attributes such as gender, referral source, reward program status, and preferences that delivers a more three-dimensional view of a user. There are countless descriptive attributes you can use to categorize users. The ones that are most important to your business will depend on your business objectives and data plan.
- Enrich profiles with 3rd party data: These additional insights can cover everything from demographic and behavioral data, to cross-device data. You can gain a much better understanding of who your users are by overlaying data collected in-app with data from third-party sources that cover areas of interest like brand affinities, household purchase data, and more.
3) Continuously Refresh User Profiles
The next step is to actively refresh user profiles so they remain up-to-date. Outdated user profiles can result in wasted media spend or worse a bad customer experience.
For example, users now have to give permission for everything from push tokens and email messages to location capturing. It’s important to keep track of those permissions so that you collect information and engage with users in accordance with privacy and security standards.
4) Activate User Profiles
Finally, user identities are only as valuable as how they’re used. The good news is, by following the first three steps, you should have a deep level of insight into who your customers are, what they want, and what actions they’ve taken along the entire customer journey. Build custom audiences and segment users based on real-time and historical data, as well as in-app activities. Then connect them to executional systems, such as ZenDesk and cloud data warehouses.
For example, you can use identities to segment users into different groups and personalize outreach based on preferences or location, and make predictive offers based on what similar users previously liked, or purchased. Specifically, you can use these segments to filter all users who fit mold X and took action Y (e.g. women in California who added products from a certain line to their shopping cart on your mobile app), so that you can engage with this group in a very personalized manner.
Orphaned data won’t provide any value. Managing user identity from the very beginning so that subsequent behavioral data, such as event and session data, gets properly linked to specific user will help create a customer-centric data model. It also ensures you’re not just thinking about user identities, but actually putting those identities into action and making them the center of your data model.
These identity resolution activities will go a long way to help standardize data practices, avoid issues, and squeeze the most value out of the data you collect.
As the final part of this series, learn How to Manage Privacy and Security in a Multi-Screen World.