Marketers have always faced challenges connecting cause and effect in their actions and business outcomes. In  a pre-internet world, there were challenges in performing such tasks given the difficulties and expenses associated with identifying consumer interactions across media or accounting for in-store activities.  But  the internet was meant to provide a whole other level of measurability, especially for marketers whose businesses are e-commerce focused. As it turned out, to paraphrase a comment from a comScore executive made in 2007, the most measurable medium instead became the medium with the most measures.

And this was before the rise of the smartphone, let alone the tablet or other internet-connected things that have begun to emerge. We can imagine a consumer using a PC with Google’s Chrome browser, an iPhone with the Safari browser, a mobile app and another mobile browsing session on a tablet followed by a PC with Firefox in the course of a day. Marketers want to know that these five potentially “unique” targets are actually the same person for purposes of frequency capping campaigns, measuring audience reach, retargeting and improving overall campaign effectiveness. They also want to know if these visitors are the same person to better track activity on their own websites or improve a consumer’s overall experience on their sites.

In an effort to solve for the digital media aspect of this problem, numerous vendors have developed a wide range of solutions over the years, beginning with a “device graph” (mapping devices and browsers to individuals). Building on the device graph, the notion of “People-based marketing” – popularized with Facebook’s re-launch of the Atlas ad server late in 2014 – has become a common way to characterize what marketers want to do.

Marketers have a few different, sometimes overlapping and often incomplete approaches to consider, and some will be more or less appropriate for different kinds of marketers. For buying media, one approach is to work with a media owner who has login data, such as Facebook or Google, which can choose to track users based on known logins across devices (this is often called a “deterministic” approach to cross-device audience identification). Another approach involves statistical models which infer whether or not an individual is the same person across devices. This is called a “probabilistic” approach. Companies primarily focused on this space include privately- held Drawbridge, AdTruth (owned by Experian) and Tapad (which Telenor agreed to buy in February). Advertisers may buy media directly from these data providers, from media owners who license their targeting capabilities or  by buying only the data which can then be activated by a demand side platform (DSP). Many other DSPs and ad networks also incorporate proprietary device graphs into their targeting capabilities.

Alternately, a marketer may use an ad server such as Facebook’s Atlas or Google’s Doubleclick Campaign Manager (DCM). From our understanding, Atlas can facilitate both targeting and measurement across devices (and not including Google’s YouTube), while DCM can only facilitate measurement at this time (and not within Facebook or Instagram). Other ad servers such as Sizmek support targeting and tracking with partnerships. Attribution tools and econometric models, such as Google’s Adometry, AOL’s Convertro, NeuStar’s MarketShare Partners and agency services such as Publicis’ Ninah and WPP’s Gain Theory provide yet another way to  connect spending by channel with business outcomes.

With respect to marketers’ own properties, analytics services can support measurement of consumers across devices, although typically only those who are logged in. For some categories of marketers this may capture the bulk of relevant consumers; for others this limitation is more problematic.

And then there is the data co-op concept. Data co-ops exist in different industries, and in marketing have mostly established themselves in the catalog business. They and are generally intended to help participants broaden  their insights without sacrificing the confidentiality of the data or hampering members’ competitive positions. The first one we are aware of for digital marketing was Acerno, which was founded in 2005 as a co-op of online shopping and purchase data. That business was bought by Akamai in 2008 and then by Mediamath in 2013. Mediamath is best known for its leading DSP, but also operates a service called Helix, which built upon Acerno, Adroit and other assets. Helix aggregates anonymized site visit and transactional data from participating  marketers for the purposes of better targeting media and producing audience insights. It then allows its clients to target new customers and consumers those clients do not already know. Mediamath has reported that Helix’  users can access 500mm profiles, that it works with one third of the 100 largest internet retailers and that more than 300 brands share transactional data with Helix.

Although other digital co-ops likely exist (we have heard of agency trading desks establishing such capabilities), none are likely to be as significant as the one that Adobe is now launching, the “Adobe Marketing Cloud Device Co-op.” Trade press reports had indicated Adobe was pursuing such an initiative as early as last summer. The company has now confirmed the product and its applicability to Adobe Analytics (web analytics), Adobe Target (site testing and personalization), Adobe Audience Manager (a data management platform) and Adobe Media Optimizer (the company’s DSP and buying platform). The Co-op is scheduled to launch in the second half of this year.

As Adobe has described it:

“The Co-op will enable member brands to provide their consumers with a better, more consistent content experience as they migrate across devices by establishing links between a group of devices used by unknown consumers or households. With this new capability, marketers will be able to better understand and respond to consumer behaviors across devices. The result will be more accurate website engagement metrics, more personalized content, and more targeted advertising experiences across search, display and social.”

It seems clear that an offering of this nature should facilitate better quality digital marketing for those customers of Adobe who embrace these products. Overall we would expect that the Data Co-op should contribute to revenue expansion by way of increased revenues from like-for-like customers, churn reduction and new customers alike.

Presuming it all works as intended and – most critically – that privacy and competitive concerns are managed well, other questions will relate to whether or not this product is a net improvement on other approaches. For example, competitors’ DSP or DMP solutions may be superior for a given marketer on a stand-alone basis  relative to the DSP or DMP offerings that Adobe provides. A marketer might value what the Adobe Co-op can bring to digital targeting, but not as much as they value their incumbent DMP / DSP solution. For customers of Adobe’s Analytics product, it seems more likely to be a cost-benefit trade-off, and one which must be considered against product improvements that competitors such as Google, IBM and WebTrends bring to the market. Google is the most significant “sleeping giant” in this space given the potential to combine its already strong analytics business with deterministic data associated with email logins. To some degree Google has already pushed forward on a people-based marketing concept with its Customer Match offering, although to the best of our knowledge this product does not integrate with Google Analytics and does not access Google Display Network inventory. Arguably Google could push harder in integrating more of its software and more of its inventory, but concerns related to the perception of Google’s management of customer privacy probably restrains their efforts.

Whether or not people-based marketing leads to shifts of media or software budgets in a significant way is probably dependent on the advantages of a given product relative to a marketer’s alternatives. But the broader trend towards focusing on people as something distinct from “visitors” will probably continue, and drive much of the focus going forward from companies across the advertising and marketing technology industries.

RISKS. Three core risks for all web publishers companies relate to: 1) high degree of rivalry given an absence of barriers preventing new competition from emerging 2) overly high and increasing capital needs to remain competitive and 3) government regulations and consumer pushback related to management of consumer data and respect for privacy. Adobe’s risks include the potential availability of better and/or cheaper software for core Creative Cloud solutions, risks associated with Marketing Cloud’s expansion into a market with specific characteristics which are still evolving and risks around external shocks from Adobe’s value chain/eco-system.

FULL REPORT INCLUDING RISKS AND DISCLOSURES CAN BE FOUND HERE: People Based Marketing 3-28-16.pdf

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