How Data Analytics Can Protect Reputations in the Digital Ad Industry

    by Ian Gibbs
    March 30, 2017
    Photo from Unsplash

    Another day, another wave of bad news for digital advertising… well from a reputational point of view, that is. You may have heard that the U.K. government suspended advertising on Google and YouTube after its ads appeared next to extremist content, and U.K businesses began to follow suit. U.S. companies, according to Bloomberg, have also halted their ads, including top spenders Verizon and AT&T.

    It’s unlikely that Google is too concerned about revenues as of yet, and money will keep pouring in to the U.K. digital ad industry as a whole until it reaches a predicted £9.8 billion ($12.3 billion USD) by the end of 2017, according to forecasts from eMarketer.

    More accurate audience data can feed more accurate machine learning algorithms.

    Nonetheless, the question of how we got to this point still needs to be answered, and as always the root cause can be traced back to a fundamental deficiency in how we measure digital ad effectiveness: the perpetual preoccupation with the click. Too many publishers and advertisers still measure CTRs because they can; malicious bots defraud the ad industry out of billions of pounds because it’s easy; the long tail of low-quality content sites opens up another ad slot because the supply of ad inventory is virtually infinite; and consumers download and install ad blockers because they’re getting sick to the back teeth of the whole thing.


    The status quo is good for no one, except maybe Google.

    And what do marketers do when they discover that the people who they want to click on their ads are blocking them or worse, aren’t real people at all? They demand more scale to compensate, irrespective of quality or cost to reputation. Or they do that until a week like last week happens, then they find themselves in hot water appearing next to unsavory or despicable content.

    A better ad market through data analytics

    It’s going to be interesting to see how analytics and measurement in data-driven marketing evolve to tackle the issues outlined above. Sure, clients have a responsibility in asking the right effectiveness questions beyond simple behavioral metrics, but don’t underestimate the role of data analytics and research in providing a solution.


    For example, you can’t avoid articles about the impact of artificial intelligence on marketing at the moment, while Ad Week Europe is stuffed full of sessions on the impact of machine learning in sifting through big data sets. New and powerful machine-learning algorithms allow digital campaigns to be delivered with an unerring efficiency against specific ROI-oriented goals; real-time personalized experiences are being created for consumers through advanced UX techniques; while at the same time, AI is being used to detect ad fraud more accurately than ever before.

    While AI transforms how we analyze vast quantities of big data, it’s at the other end of the spectrum where data analytics advances will make a difference. Machine learning transforms predictive analytics, making ever-more accurate assumptions about who we all are, but it won’t totally de-anonymize big data sets (and nor should it). Creating a richer single-customer view about a known audience member complete with accurate contact details and marketing permissions will be the preserve of advanced CRM analytics. Richer CRM systems that stitch together multiple data sets for known users will enable marketers to focus on micro-level customer insight rather than chasing the shiny promise of seemingly limitless digital scale.

    Backing away from scale to protect reputations

    Is it contradictory to advocate big data techniques on one hand and “small” CRM data techniques on the other?

    I think the two can work in sync. More accurate audience data can feed more accurate machine learning algorithms. If this in turn helps us measure and optimize ad impact in terms of actual long term value and ROI rather than simple clicks, it might just help pull digital measurement, and as a consequence digital as a whole, back from the reputational brink. If we stop rewarding those behaviors that are causing brands, publishers and consumers alike so much harm, and start to measure what brings them real value, then chasing scale at the cost of reputation will no longer seem like a gamble worth taking.

    Ian Gibbs is the founder of Data Stories, an independent data consultancy focused on all things digital, advertising, media and publishing, and former head of commercial insight at Guardian News and Media.

    Tagged: ad fraud artificial intelligence digital advertising digital revenue publishing

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