A look at key martech drivers in 2019: Customer-centricity, consent and collaboration
2018 has been a tumultuous year for technology, with growing concerns of privacy and high profile scandals shaking previously unassailable giants. These giants heralded a new era of capability for marketers, creating new ways of targeting, unprecedented insight and scale. But now that customers are increasingly sensitive to data usage, and turning away from walled ecosystems, what does the future hold for marketing technology in 2019?
Ethics and customer-centricity
It’s easy to pay lip service to customer-centricity by adjusting brand messaging or starting a segmentation initiative. However, consumers in 2019 will be much more aware of misalignments between their interests and those of the companies that use their data. Thinking about how to align these interests is important for ethical data use. Consumers are very happy to share personal data when there is a clear value alignment - for example, users are happy to provide access to detailed GPS driving data for cheaper car insurance premiums. Customers are happy to share data to get better digital services, but hyper-optimised clickbait for conversion (see the CMA takedown of hotels bookings sites) is highly unlikely to be representative of good digital service. Blind optimisation misses the point of customer-centricity, which requires clear customer consent and strategic intentional modelling of the customer’s needs by the marketer.
Consent and first party data
Aside from regulatory reasons imposed by GDPR, engaging data-driven experiences requires consent, because users express their buying intent with the first-party relationships they establish with brands. The best experiences are driven from the context that the user has with the brand itself, so companies must focus on creating stories from the multiple touchpoints it has achieved with the existing client base. Utilising first party data in this way can require more advanced technology - such as the semantic understanding of customer interactions, but it represents the most relevant clues for user preferences.
Often customers are represented as anything other than people. They’re clickstreams, or members of behavioural segment A or B, revenue generating units. Data-driven experiences are powerful when they are intentional. The marketer has identified needs through data and has crafted an experience to suit that need. Blunt multi-variate testing, dynamic content optimisation and other forms of brute force personalisation don’t share this intentionality.
We see a powerful and underutilised intersection between digital behaviour and established models of human behaviour, such as psychology and other behavioural science. By linking digital footprints and well-studied personality types, traits and personas, marketers can identify human needs and create powerful experiences. They can also bring in unseen preferences, e.g. extraverts preferring bright colours or them preferring marketing content that includes the word “exclusive”, as tools to build the message, look and feel.
2018 was the year that every company added the word AI to their board presentations, while consumer and regulatory opinion regarding data changed around them. AI and data are intimately interlinked, and 2019 will see the rise of new operating models which take into account the new factors of transparency and explainability.
One of these is the use of collaborative AI, an attempt to break past the troubling ‘black box’ barriers of current AI systems. Black box systems aim to make decisions without user input, and they form the bulk of industry research budgets today. But complex, creative jobs like marketing strategy require human input not only for regulatory reasons, but because good campaigns understand audience needs holistically. Collaborative AI aims to combine the power of machines, such as unlimited attention spans and access to terabytes of interaction data, with the creative power of people - in order to empower them to build better experiences.
The intelligent use of controlled automation, decision support tools and AI assistants, will help marketers move up the chain of abstraction, concentrating their skills at the decision level and away from tedious tasks, like fine-tuning copy or generating variants for A/B testing.
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