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Marketers need to target the right people, with the right message, at the right time, in the right place. We need to gauge how they might change day-to-day and frame our messaging accordingly.

At Rapp, we call this Adaptive Persuasion, the ability to adapt our marketing approach on the fly according to the context of our customer, their personal make-up right down to their personality profile and their personal heuristics at that moment.

Many of the ground-breaking techniques in AI that drive Adaptive Persuasion can be traced way back to Alan Turing’s “Intelligent Machinery” of 1948 – but now we have the computer processing power to bring these algorithms to life. Not only that, but we can do it at affordable prices.

Unfortunately, we’re not doing it anywhere near enough: while 69% of consumers want personalised experiences, only 40%1 of brands are attempting to provide them.

We can persuade to a level and at a scale never before possible

Using a combination of psychology and big data, we can predict consumer personality profiles from their language use, transactional spend – even their social network interactions or clicked links.

Employing a combination of techniques to interpret personality profiles helps brands better understand their consumers, targeting to segments of 1 – which has been proven to work. In fact, a recent paper titled “Psychological targeting as an effective approach to digital mass persuasion”demonstrated that aligning ads to people’s personalities led to an incremental 54% of sales.

Psychographic targeting works, but the Cambridge Analytica’s abuse of this power has created scepticism around these techniques. Whistle-blower, Christopher Whylie told The Observer:“We exploited Facebook to harvest millions of people’s profiles. And built models to exploit what we knew about them…we were playing with the psychology of the entire nation…” The ethics of their actions can be debated endlessly, but the science behind them is still valid: different personality profiles engage with different content in different ways.

Netflix uses similar scientific approaches for its recommendation engines. However, it uses the science responsibly. The streaming service enhances customer experience by making it easier to find and choose films and television. How? By creating hundreds of different creative variants for recommended content. Take the film Good Will Hunting. Romcom lovers might see an image of Matt Damon kissing Minnie Driver, while action lovers could be served with one of Robin Williams as ‘angsty psychologist.’

Marketers like Netflix have a responsibility to use data for good — not exploitation. RAPP’s research shows that 70% of customers don’t see value in exchange for their data and in this post-GDPR world, 53%1 of consumers will selectively opt out of brands they distrust. It is therefore imperative marketers restore faith with consumers around how we use their data. We also need to demonstrate the benefits of customer experiences created with Adaptive Persuasion far outweigh the risks of the few who will exploit the data.

There are 3 steps to implement Adaptive Persuasion:

Contextual Clarity

Consumer Data Platforms (CDPs) – contextual hubs that gather consumer signals (transactional, behavioural, preference centre, social handles, feedback etc) and the context within which these signals occur (i.e., economic trend data, political trend data, weather data, traffic data). This information is collated into a central ‘ecosystem’ for data scientists to analyse as it continuously accumulates data at little cost or hassle.

CDPs would clarify, for example, that it wasn’t your new subject line and email creative about chilled drinks that drove purchases, but would indicate the inflection point between hot and cold weather.

Create the engine to look at the consumer through multiple lenses

We need to be able to adapt our dialogue with consumers in real-time, based on context. This requires a multi-dimensional, adaptive, real-time view based on the latest signals they leave for us. Let’s take a hyper-personalised look at them through the following lenses:

  • Lifecycle – is your customer new? Established? Returning?
  • Value – once you know this, you can target/invest accordingly.
  • Engagement – which channels do they want you to communicate through and what makes them tick? What actions do you need to drive and by what point in time?
  • Utility – what customer need do you satisfy or could satisfy?
  • Personality – Is your customer an extroverts or introverts? What creative and calls to action resonate with them?

This will reveal new experiences your consumers need. Combining these vignettes with next-best action modelling will make it possible to adapt your message in real-time, based on in-the-moment interactions for each one.

Measure, measure and measure again

Until you test it, you won’t know if it’s working. Machine learning enables us to automatically optimise and demonstrate incremental gains; we now have the ability to do this at scale too.

Adaptive Persuasion is at our fingertips

We can provide hyper-relevant insights in real-time. However, we must remain ethically aware and have the customer’s interests at our heart. This way, we can positively disrupt the customer experience landscape on a grand scale. Otherwise, we head back to the world of one-size fits all, which can only take us so far.

Andy Rowe, head of marketing science, Rapp UK