5 Reasons Why Brands Should Invest in a Deterministic Consumer Identity Graph

A consumer identity graph is a vital tool for brands to have in their marketing arsenal. It allows brands to deliver relevant, personalized customer experiences by linking customer data from a multitude of channels, devices, and touchpoints to create unique customer profiles. However, not all consumer identity graphs are equal. Many companies tout the ability for their identity graphs to make distinct deterministic connections to individuals with sizable scale, but are utilizing probabilistic algorithms to make those linkages. Unfortunately for marketers, this methodology leaves room for a great deal of error because probabilistic matching uses anonymized data signals (location, browsing history) and analysis, not 1-to-1 matching for the verification of identity.

Some identity resolution providers also claim to combine these methods of probabilistic and deterministic matching for the best possible reach and engagement, but don’t be fooled. Only deterministic, individual-based consumer identity graphs can accurately unify fragmented customer data to create a holistic view of each individual for better customer engagement, both offline and online.

So, the question remains, if a brand cannot confidently, accurately and consistently identify its customers across multiple devices, channels, and touchpoints, then is the consumer identity graph really doing its job?

Here are the top 5 reasons why brands should invest in a deterministic consumer identity graph.

1. Marketing Accuracy – Deterministic consumer identity graphs are data-centric. They use a wide array of data points, including email addresses, postal addresses, mobile numbers, device ids, etc., to validate individual identities consistently and in real-time. Since a 1-to-1 direct match is made, brands are ensured the highest level of accuracy for marketing and customer engagement.
2. Data Hygiene and Identity Management – Consumer identity graphs are complex, ever-changing entities. Unlike probabilistic consumer identity graphs, which use anonymized data points to make inferred connections, providers with deterministic consumer identity graphs carefully implement and maintain in-depth data hygiene processes to guarantee the accuracy of their 1-to-1 matches. Investing in a deterministic consumer identity graph means you are investing in the technology and data management necessary for effective marketing.
3. Higher Match Rates – Many brands and owners of data are under the impression that there is a trade-off between accuracy and reach, habitually opting for probabilistic (or combination) solutions because they believe they offer the highest match rates and scale for targeting. But that just isn’t the case! Since deterministic consumer identity graphs have access to a constant stream of consumer data sources (email addresses, device ids, cookies, etc.), they can actively validate both offline and online identities, allotting for better offline to online matching with no sacrifice on reach.
4. Enhanced Customer Engagement – When using probabilistic, household level matching, the ability to enhance customer engagement for consumers with personalized and relevant campaigns is lost. Deterministic consumer identity graphs enable marketers to understand each customer at the individual level, offering the demographic and behavioral knowledge necessary to determine the correct strategy for interacting with each person. This means that Mr. and Mrs. Smith can now be viewed as Jane Smith, 42, who drives a BMW and John Smith, 45, who loves his Ford pick-up truck, and each person can receive customized messaging that will better resonate with them as unique individuals.
5. Better Attribution and ROI – With deterministic consumer identity graphs, brands can resolve identities and personalize messaging to each unique individual, reducing marketing waste with generalized campaigns or campaigns delivered to the incorrect recipients (I.E. Female running shoe campaign displayed to males). Brands are also able to use the individual-based, deterministic matching to help connect the dots between purchases made and brand engagement, offering better attribution metrics.