Why GDPR could finally make online ads accountable

markAlmost daily, out comes a new GDPR nightmare, from the recent World Federation of Advertisers survey which revealed 70% of brand owners felt their marketers are still not fully aware of the implications to the Unicom Global study which claimed up to 10 million UK consumers are eyeing GDPR compensation. But while many businesses might be in the early stages of panic, we’re delighted.
Under the GDPR, cookies are only mentioned once, but it is a crucial change as they will now be considered personal data. Not all the cookies, just the ones that can be used to profile and identify a user. The cookies that advertisers use to target consumers with advertising.
We don’t see this as a negative. We welcome GDPR, as it will reduce the amount of mindless retargeting of irrelevant ads, in irrelevant moments for the consumer, at frequency levels that are both cannibalistic and wasteful for the advertiser.
You know, the shoes that follow you around even after you’ve bought them, telling you what an amazing purchase they are, but you already know because you have them. You might even be wearing them.
But brands don’t have to use cookies. By using data to build predictive models, the application of the approach does not require personally identifiable information in its execution.
Through connected, anonymous data, these predictive models are used to place a value on content, against audience attributes that are useable. Brands can target consumers based on engagement in that content and deliver it to new audiences using anonymised data in context – rather than delivering advertising messages back to individuals which, pending on permissions of the user, would constitute a flagrant breach of GDPR.
It’s about content, audience and timing. It’s about delivering communications that enhance consumer experience, in context, of the content they are seeking or reading, that are relevant to their needs.
A combination of old-school direct marketing and cutting-edge tech is the only way to effectively target consumers. This requires brands to start backwards – the last thing marketers would normally think of doing. It moves towards outcomes-based planning by looking at the behaviour brands are seeking and then finding it. It allows a connection between editorial content and the audience.
The content a user is reading or seeking is the most indicative predictive variable. By categorising editorial content and advertising copy, this data can be used to allow brands to place relevant offers to predictively profitable consumers.
Conceptually, it’s not rocket science. If I have a friend who wants to go on holiday, I might suggest a location that I think they would love. It might be a place that’s outside of their usual tastes. For example, instead of that package holiday in Corfu, I could suggest island hopping in Greece. I’m serving a need, based on information or signals that I have, geared toward the needs of that friend, ahead of another person.
These signals are not always as obvious as this ‘friend asking for holiday advice’, so brands can machine learning and AI against content and the needs of the consumer, which allows them to identify pre-sales triggers automatically. These machines read the content consumers are reading, anonymously identify what they post online and then predict how likely a signal is to turn into a sale.
It’s about having more efficient conversations with the customer. The approach may use AI and machine learning, but it’s a process that is rooted in marketing discipline.
The technology is doing no more than trying to understand the needs of consumers. Most of the answers are common sense and understandable – technology allows brands to create this at scale, and in real-time.
One example of this is our own work with The Army, which not only identified the barriers to joining the Forces but also the key moments of reconsideration in candidates’ lives. These include occasions such as birthdays, “I’m 21 and I’m still stacking shelves”, the back to school period, “My friends are going to college and I’m stuck here”, or the sense of belonging, “I spend my life supporting football team, I need my own team”.
After we have identified these signals and placed a value on them, we can listen for new signals from lookalike audiences and target them across digital channels, with the most appropriate message at the right time, while also understanding their barriers.
The approach delivers profitable advertising around relevant content in the context of the consumer’s wider, or predicted needs. If they are looking at an article on car safety, then serving an ad that majors on performance is probably a mistake. Again, it’s common sense.
Pre-targeting not retargeting. Making your conversion funnel better, and response funnel efficient, allows you to create and distribute content to reach consumers before the competition. This can build brands’ market share at that point, stopping them spending a disproportionate amount of money close to conversion, which cannibalises marketing.
The updated Data Protection Bill contains a new addition to the principles set out in the 1998 Data Protection Act. According to the minister of state for digital, Matthew Hancock, it’s the principle of accountability.
Brands should be accountable for their advertising and how it’s targeted. GDPR should not only signal a change in how brands use customer data, but the outdated tactics they use to engage consumers. Advertising should be relevant, it should serve a need, and only then will it be effective.

Mark Phillips is global head of data at Ignition D4

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