As we fast forward to May 25th, there’s a big focus in the industry on the implications of the introduction of GDPR. In this, the second article in our GDPR series, I’ll be looking at the role I think GDPR will have in catalysing the use of AI in marketing.
Firstly, a quick recap. GDPR is designed to give consumers more control of their personal data; how it is stored and how it is used. The Facebook scandal serves as a timely reminder that this legislation has been a long time coming and the rights of the consumer really do need protecting.
A cultural shift
Facebook aside, we’re witnessing a cultural shift towards a renewed awareness around the issue of privacy and individual responsibility in managing our own digital footprints. This is currently evidenced in different industries. In fintech, JP Morgan Chase are enabling customers to transact cryptocurrencies anonymously through zk-SNARK (zero-knowledge succinct non-interactive argument of knowledge). This game changing technology allows customers to provide zero-knowledge proof of, for instance, age, financial status or eligibility. Your give them enough information to transact without revealing anything about yourself. In a similar way companies, such as Microsoft, are supporting the introduction of ‘self-sovereign identity systems’ that keep user data encrypted and secure unless a private key is shared.
However, these data-aware consumers still have high expectations of a non-spooky personal relationship with the brands that they choose to associate with. So how will organisations harness data in a trusted manner to create meaningful conversations with their consumers in the wake of recent scandals and GDPR?
Less personal data
First things first, data will still, one hundred percent, guide marketing decisions and remain central in driving customer journeys and experiences. One only need look at the number of data scientists currently being recruited and the ever-present need to quantify campaign success to be convinced. But the use of personal data will, and has to change.
Under the new legislation, consumers have to proactively opt-in and have the right to be forgotten. There is no doubt that this will immediately reduce the marketer’s active audience universe and the longevity of the data that they hold.
As a result, there will be a bigger percentage of an existing or potential target audience for which an organisation does not hold personal data. But these consumers will still demand a great customer experience. So how will marketers look to meet this need?
The role of AI?
In a February article on Big Data, Forbes tells us that 84% of marketing organisations are implementing or extending AI usage in 2018. Whilst AI, remains a mystery to some, it is already playing a significant role in marketing and delivering experiences. Adobe Sensei is helping with anomaly detection in Adobe analytics, ASOS is using deep learning to create a visual search that matches customer’s pictures of clothes to similar products, chatbots are using AI to answer questions and Amazon Alexa and Google Home use natural language processing algorithms to turn speech into intents and answer our questions.
So how will marketers use AI and machine learning to compensate for the loss of available personal data?
Currently personal data is predominately used in targeting, segmentation, personalising and tailoring the user experience. In the absence of personal data, marketers will look to real-time data that can be gleaned as consumers move through the customer journey. The focus will shift to behavioural data which, when quickly analysed and acted upon, gives experience makers replacement cues to segment, personalise and optimise. This analysis and activation is a key area of activity for machine learning, according to Forbes, with 57% of enterprise executives believing the most significant growth benefit of AI and machine learning will be in the area of improving customer experience and support.
How does it work?
What does a typical GDPR compliant process featuring machine learning look like? It is based on building up an aggregated picture of online customer journeys. The application of AI then is in the sifting through of the data looking for patterns, which allows the creation of a predictive model of behaviour that signals a useful business intent. To spot these patterns requires a lot of data and is time consuming for a human marketer. But once a model is built, the AI can quickly classify new users coming onto the site, pushing them into a certain bucket and updating the experience to drive them towards a valuable action. That could be sharing on social media, signing up to a newsletter or buying a certain product.
Is this the end of personalisation as we know it?
Consumers will still expect personalisation and it delivers results. The form of personalisation is likely to change for users that don’t wish to share their personal data since true one-to-one personalisation becomes much harder. One-to-many tailoring based on aggregated data is still very much a possibility and one that we think will become more commonplace.
We are already seeing this type of personalisation or customisation adopted by an automotive client, for whom we have introduced capabilities to update the user journey in real time, according to the model of car in which they show primary interest.
The role of the machine can be extended here, to increase the impact and results of marketing efforts, by testing all elements of the customer experience against user segments in an automated manner.
There are plentiful predictions that 2018 will be the year that AI slips into more mainstream use within the marketing community. But as marketers lean more heavily on behavioural data post-GDPR, the rise of AI and machine learning will be unstoppable and organisations will really start to reap the benefits of the reduction in effort and increased results.
For a quick overview of GDPR and what it will mean for experience makers, take a look at the first blog in our GDPR series.
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