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June 25, 2018
GDPR is affecting data-driven marketing in far-reaching ways to promote transparency and integrity, affecting key business models
Editor’s Intro: George Pappachen is one of the most far-seeing media research executives I know. His article discusses some important, developing implications of GDPR for the digital advertising business that go beyond much of the discussion I’ve read or heard. His views are definitely worth paying attention to and debating.
You’ve heard this before: in exchange for free content, the ad-supported internet gets to track and measure consumer activities – and do it better than other media.
I would argue this axiom has transformed at least two markets: capital and marketing. Data, as in access and control of it, being key for tailwinds in both cases. To illustrate with capital markets, two of the world’s ten largest companies by market cap derive the vast majority of their revenue by exchanging digital footprints for money. And judging by adoption rates and time spent, consumers, for the most part, have been delighted to play their part.
End of story. Not quite.
The exploding market value of observing consumer activities spawned myriad business models and supporting technologies to do the same, restrained only by imagination.
Speed bumps emerged. For instance, in the early part of this decade, the Wall Street Journal examined online tracking. In a series of 2011 articles, the paper delved into the implicit ‘free means the consumer is the product’ arrangement and its boundaries. The investigation found that as a group, the top 50 websites placed 3,180 tracking files in total on the paper’s test computer. Over two-thirds were installed by 131 companies, many of which are in the business of observing web users to create extensive consumer profiles.
Also in 2011, the Federal Trade Commission (the main regulatory watchdog in the US) delivered its much-awaited guidelines for collecting consumer data online. Around the same time, several federal privacy bills were introduced in the US Congress, though none made much progress converting to law.
The following year, a seminal event: the European Commission proposed a comprehensive reform of EU’s 1995 data protection rules to strengthen online privacy rights and boost Europe’s digital economy. The proposed regulations (GDPR) hold that observing online behaviors is processing personal data which requires explicit consent.
Some other events along the way and we arrived at May 25, 2018, when General Data Protection Regulations went into effect.
GDPR imposes more than express consent for data collection and processing. GDPR opposes black boxes. In natural or physical science, a black box is a system or object where inputs and output can be seen but without any knowledge of the internal workings. I am using the term more expansively to mean a process where the inputs are difficult to ascertain and the inner workings are usually proprietary to the designer. As such, the illuminating light is reflected rather than absorbed by these black boxes, ironically.
For the sake of progress and innovation, marketing (measurement functions included) has accepted black boxes for the better part of the last decade, in particular black boxes with regard to the mix of data that collate into audience segments.
Cambridge Analytica is top of mind where both the contributing data (gathered from social media) and the science (psychographic microtargeting) were suspect. And yet the lure of a new revolutionary approach was hard to resist. Though they are the latest, high visibility example of our proclivity for shiny black boxes, they are not alone.
The marketplace adjusted to these new entrants bucketing them as data aggregators, making Data Management Platforms (DMPs) that much more necessary.
GDPR is a pause moment for the industry. To be clear, data-driven marketing is here to stay but now is the opportunity to re-introduce rigor in regard to data provenance and data science. Truly randomized control experiments, for the purpose of examining novel marketing treatments, are difficult to implement in the present day splintered media environment but I am certain we can be more sound with reviews than we have been.
That’s the past; I see a future where a confluence of factors (some covered above) signal transparency and as a consequence, greater integrity.
With AI/machine learning packages such as deep neural networks, random forests about to take center stage, the requirement for light comes at an opportune time.
Given the cross-dynamics in play, here’s what I see for our industry:
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