Improving Engagement and Performance in Digital Advertising

Improving Engagement and Performance in Digital Advertising

          
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Improving Engagement and Performance in Digital Advertising

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    Frustrated by Fragmentation

    Most companies do not take full—or anywhere near full—advantage of the advanced technologies currently available to improve targeting, engagement, and performance. Often the biggest impediment is fragmentation. There’s plenty of fragmentation that advertisers cannot address—in audience and devices, for example, not to mention in the digital ecosystem itself. But the fragmentation that takes place in campaign development and execution—in strategy, teams, tools, and data—is within advertisers’ and agencies’ power to control. These forms of fragmentation too often result in conflicting goals and incentives, lack of transparency in performance, wasted effort, and sluggish execution—holding back big gains in performance.

    In most campaigns, the fragmentation starts as soon as the strategy is split between online and offline and multiplies as decisions are made with respect to digital-advertising channels (such as search, mobile, video, and display), buying approach (reservation versus programmatic), targeting criteria, and so forth. A major advertiser could not participate in our study because the company divides its digital-display-campaign activity across four separate agency teams—each addressing different parts of the consumer purchasing journey. Since the stages of the journey often overlap, teams working on the same campaign in a real-time bidding environment are bidding against each other to attract the same users. In most cases, the teams do not realize this because they are using different tools that don’t actively communicate with each other. During our study, a targeting technique used by one advertiser suddenly stopped functioning. An investigation revealed that another campaign—for the same advertiser but run by a different team at the same agency—had gone live and was targeting audiences based on precisely the same data but with a higher bid price. There was no process in place to prevent this from occurring. In many such cases, advertisers risk deteriorating performance over time as the price of targeting users rises when competing agency teams attempt to outbid one another.

    A much-heard complaint is that it takes an army to execute a campaign. Team fragmentation leads to inefficiency, misalignment of resources, lack of accountability, and sometimes conflicting incentives, among other problems. “You spend so much time getting people to work together [that] it’s dysfunctional,” a vice president of marketing told us. Said another: “Incentives are misaligned across agencies, publishers, [and] tech providers. Digital’s fragmentation forces you to make decisions on where to focus.”

    Using unified tools can tackle inefficiencies and enable the design of campaigns based on advanced techniques. As we found in an earlier study of digital-campaign processes, the setup of even standard techniques is often highly inefficient, with only about 20 percent of the activity actually creating value. The balance is taken up in repetitive tasks, waiting time, and approvals. For advanced techniques, this process can be even more complex and inefficient. To address efficiency issues and take full advantage of advanced behavioral targeting techniques, advertisers need to leverage unified tools to connect their media, workflow, and data strategies.

    The use of data to optimize targeting is often cut short by a fragmented campaign setup and approach. An ad hoc use of data results in missed opportunities. On several occasions, we came across advertisers that had purchased prime advertising inventory—for example, on the home page or masthead of a major publisher—without properly setting up the mechanisms in advance to collect data from the consumers who were exposed to the ads, missing out on the opportunity to reengage with them. Given the value of consumer insights, it is surprising how many of these opportunities are missed and how many consumer interactions go ignored.

    Advertisers also find themselves stuck in a rut of insignificant metrics. A global advertiser requested its agency optimize to a conversion metric that was providing only a handful of data points each day. This made it impossible for the agency to prove that their techniques were driving improvements in performance since the variations were too small to be statistically significant—they could have been random noise. As part of the study, we worked with the agency to identify a proxy metric that was both statistically significant and correlated to the advertiser’s original performance goals.