Blog post

The Personification of Digital Marketing

By Andrew Frank | March 20, 2015 | 1 Comment

MarketingData-Driven MarketingBig DataAdvertising

Digital marketing has been a wellspring of new and re-purposed vocabulary. From “1st-party data” through “engagement,” and “programmatic” to “verification” this industry has not been shy about injecting words with new meanings.

But over the past few months it’s become clear to me that there’s a broad category of practices at the center of digital marketing that suffers from a bad case of mislabeling. Marketers call it “personalization.” For a long time ad tech and marketing tech providers have been extolling the benefits of “personalization” and its close cousin, 1-to-1 marketing, but much of what goes under this heading isn’t really “personalized” at all – it’s targeted at an audience segment rather than an individual who may prefer to remain anonymous.

Although this mislabeling may seem benign, it’s not. A leading marketing service provider recently told me of the problems this term was causing with regulators and privacy advocates who take the term at face-value as a claim that web sites and ads using “personalization” can personally identify a user, even if they have not consented to be recognized. Such confusion undermines the efforts of these providers to explicitly de-identify users and recognize them only as non-unique members of a broad demographic or behavioral category. We need a bright line between these two forms of recognition, both to assure that consumers can make informed choices about how their information is used online, and to help marketers clearly distinguish their efforts between customers who opt into true individual personalization and those who are targeted anonymously by group membership. Marketers need to respect this line and never cross it.

So we propose to co-opt yet another word. Gartner will define “persōnification” as “the delivery of relevant digital experiences to individuals based on their inferred membership in a defined customer segment, rather than their personal identity.” A key element here is the notion of a “persona” which we take to be a data-driven audience segment that has been irreversibly de-identified (in other words, all personally identifiable information removed) and can be dynamically activated in a digital campaign (advertising, email, or on-site).  Personification bridges the gap between broad-reach branding efforts like content marketing and true 1-to-1 interactions, which should always be under control of the customer. Consumers should also have the power to correct any data about them that may lead to their inclusion in a persona segment – a capability that serves both the public and marketers – and should have assurances that inclusion in a persona segment can’t be traced back to them personally (although the technical issue of re-identification requires further refinement of permissible techniques).

Although personas and anonymous targeting are not exactly new concepts in digital marketing, they have lacked a clear label that can enable us to establish rules and best practices even as data-driven marketing techniques like programmatic buying and dynamic creative optimization have substantially refined and accelerated their application. We’ll be talking more about persōnification in the coming weeks and months, but early feedback from marketers and providers has been enthusiastic. What do you think?

The Gartner Blog Network provides an opportunity for Gartner analysts to test ideas and move research forward. Because the content posted by Gartner analysts on this site does not undergo our standard editorial review, all comments or opinions expressed hereunder are those of the individual contributors and do not represent the views of Gartner, Inc. or its management.

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1 Comment

  • Grant Wagner says:

    Hi Andrew,

    I really liked this post. The term “personalization” has been hijacked by many doing nothing more than A/B Testing. Not very personal. I also liked your mention of personalization segments. These segments can be derived analyzing the string and patterns of events leading to a particular outcome (ie – conversion or purchase). Using a lot of anonymous visitor data and machine learning capabilities. Providers selling third party data or some of the “uber cookie” providers are walking a thin line.