Not All Identity Graphs Are Created Equal

In today’s hyper-connected world, consumers move at an accelerated speed across devices, platforms, and touchpoints. Tracking consumers as they move through their frenetic routines – from home to car, desktop to cellphone – from emails to websites and apps. . .  represents a fundamental challenge for contemporary marketers. 

Increasing complexity notwithstanding, marketers have a mandate to deliver unified, 360-degree views of their audiences across an increasingly fragmented digital landscape. 

The Diminishing Relevance of Cookies in Determining Consumer Identity
There are misconceptions, –  perhaps misinterpretations, associated with the legitimate definition of consumer identity resolution.  According to Forrester, “consumer identity resolution stitches together information and behavior across interactions, devices, platforms, and channels to better understand personalize, target and measure marketing and advertising initiatives.”

Cookies were once a foundational element of identity resolution. They were used to stitch together device-related information, but their application is limited in today’s hyper-personalized, omnichannel ecosystem.  Among other things, cookies are browser-specific, they don’t work with cell phones, have short shelve lives and they are not supported by emerging platforms which will dominate the digital landscape.  (Online television is an example of an emerging digital platform where cookies are irrelevant.  Today online TV represents nearly 75% of the American television audience and cookies are not used to identify viewers.) 

In other words, cookies are a legacy digital identifier and their value will continue to diminish as digital migration intensifies and consumers become more reliant on their mobile devices to process transactions. 

Matching Matters – Dynamic, Mobile Interactions Require Accurate Consumer Identification

Accurate customer identification is a requirement for omnichannel customer engagement because frictionless customer interactions across platforms, channels, and devices drive consumption.  There are essentially two approaches to matching complex omnichannel interactions – deterministic matching and probabilistic matching. 

At Audience Acuity, our Super Identity Graph uses a deterministic matching methodology to consolidate and organize omnichannel data, at scale.  Deterministic matching refers to the use of first-party data that is independently verified, and explicit to the individual (i.e. associated with residential addresses, email addresses, hashed email addresses, cell phone numbers, etc.).  The data is obtained directly from privacy-compliant sources and matched to individuals and to their households.   

In contrast, probabilistic matching applies inferred, intent-based data gleaned from subjective predictive models.  Probabilistic matching has been associated with large scale applications, but the application of an inferential matching methodology has proven to be less reliable for our clients and applications.

What’s the Best Approach for Your Business?

There is no shortage of research on this subject.  We encourage our clients to educate themselves.

Ultimately it comes down to delivering results.  This means testing with the client’s data.  In our experience, clients weigh matched results and the costs along with their institutional capabilities to determine the best fit for their organization.

We welcome the opportunity to continue the discussion and to help improve the performance of your omnichannel marketing initiative.