With companies like Kraft and Kellogg’s starting to leverage the programmatic pipes for equity advertising, we are starting to hear a lot of buzz about the potential for “programmatic branding,” or leveraging ad tech pipes to drive upper-funnel consumer engagement. It makes sense. Combine 20 years in online infrastructure investment with rapidly shifting consumer attention from linear to digital channels, and you have the perfect environment to test whether or not digital advertising can create “awareness” and “interest,” the first two pieces of the age old “AIDA” funnel.
The answer, put simply, is yes.
Online reach is considerably less expensive than linear reach, and we are starting to have the ability to reliably measure how that brand engagement is generated. Marketers want an “always-on” stream of equity advertising that comes with measurement—but they also need it. With attention rapidly shifting from traditional channels, investments in linear television are starting to return fewer sales. But most marketers are just starting to gain the digital competency to make programmatic branding a reality.
That competency is called data management—the ability to segment, activate, and analyze consumer audiences in a reliable way at scale. Why is that so?
The most fundamental problem with digital branding is that it is truly a one-to-one marketing exercise. If we dream of the “right message, right person, right time,” then matching a user with her devices is table stakes for programmatic branding. How do I know that Sally Smith on desktop is the same as Sally Smith on tablet? Cross-device identity management (CDIM or, alternatively, CDUI) is the key. Device IDs must be mapped to cookies, other mobile identifiers, and Safari browser signals in order to get a sense of who’s who. Once you unlock user identity, many amazing things become possible.
Global Frequency Capping
One of the reasons programmatic branding has yet to gain serious ground with marketers is because of waste. This is both real (lots of wasted impressions due to invisible ads or robotic traffic) and perceived (how many impressions are ineffective due to frequency issues). The former problem is getting solved by smart technology, and already somewhat mitigated by market pricing. But the latter problem is solvable with data management. Assuming the marketer understands the ideal effective frequency of impressions per channel, or on a global basis, a DMP can manage how many impressions an individual sees by controlling segment membership in various platforms. Let’s say the ideal frequency for cereal advertising aimed at Moms is 30 per day across channels. The advertiser knows less than 30 impressions lessens effectiveness—and over 30 impressions has negligible impact. Advertisers using multiple channels (direct-to-publisher, plus a mobile, video, and display DSPs) are likely over serving impressions in each channel, and maybe underserving in key channels like video. Connecting user identity helps control global frequency, and can save literally millions of dollars, while optimizing the effectiveness of cross-channel advertising.
If “right person” technology is enabled as above, then it makes sense to try and get to “right place and right time.” Data management can enable this Holy Grail of branding, helping marketers create relevance for consumers as they embark on the customer journey. What brand marketers have dreamed of is now possible, and starting to happen. Dad, in the auto-intender bucket, gets exposed to a 15 second pre-roll ad before logging into his newspaper subscription on his tablet in the morning; gets the message reinforced by more equity display ads in the afternoon at work; and, while checking messages on his mobile phone on the way home, gets an offer for $500 off with a qualified test drive. After he hits the dealership and checks in via the CRM system, he receives an e-mail thanking him for his visit and reminding him of the $500 coupon he earned. These tactics are not possible without tying both user identity and systems together. Doing so not only enables sequential messaging, but also the ability to test and measure different approaches (A/B testing).
Cross Channel Attribution
How about attribution? It’s impossible to perform cross-channel attribution without knowing who saw what ad. At the end of the day, it’s really about the insights. Proctor and Gamble is famous for spending millions of dollars every year to understand the “moment of truth,” or why people choose Tide over Surf detergent. Although they know consumer segmentation and behavior better than anyone, even the biggest brand marketers struggle to gain quality insights from digital channels. Data management is starting to make a more reliable view possible. Brand advertising is just another form of investment. Money is the input, and the output is sales and—as important—data on what drove those sales. In the past, brand marketers were reliant upon panel-based measurement to judge campaign effectiveness. Now, data management helps brands understand which channels drove results—and how each contributed. It is early days for truly reliable cross-channel attribution modeling, but we are finally starting to see the death of the “last click” model. Smart marketers are using data to author their own flexible attribution models, making sure all channels involved receive variable credit for driving the final action. In the near future, machine learning will help drive dynamic models, which flex over time as new signals are acquired. We will then start to see just how effective (or not) tactics like standard display advertising are for driving upper funnel engagement.
So, is 2015 the year for programmatic branding? For a select group of marketers that are leveraging data management to enable the best practices outlines above, yes. The more accurately marketers can map online user identity and understand results, the more investment will flow from linear to addressable channels.