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We’ve all heard the phrase ‘big data’, but when you see the numbers it’s clear that ‘big’ doesn’t really cut it. More than 188 million emails sent, 55,140 photos posted on Instagram and nearly 10,000 Uber rides taken. In 2019? No. In every minute of 2019. And each of those is a data source. So where does this leave the marketers who depend on data to create effective campaigns?
The ‘single view of the customer’ is the holy grail of marketing. And it finally seems within reach, given that customers are more willing than ever to exchange their data and their dollars for the best in customer experience (CX). Big data surely holds the key to providing the ultimate in personalised, connected CX.
But the sheer magnitude of the data available presents complication, and the speed at which in must be processed is staggering. Traditional, manual data mining and analysis is simply not up to the task.
In AI, however, big data meets its match. With its powerful models and algorithms, AI can help marketers and organisations of all sizes make sense of the petabytes upon petabytes of data they are now dealing with. And with entire sectors of business dedicated to capturing, storing and leveraging all this data, marketers can now choose from a plethora of vendors to help build data-driven strategies and tactics.
Here are three ways AI helps marketers build a single view of the customer.
It’s a well-worn refrain, but one worth remembering: marketers must first understand their audience before they can start a meaningful conversation — and that means the person, not their devices. After all, the 360-degree view applies to the person.
While conceptually determining a customer’s identity is as easy as matching two data points together (e.g. email address, phone number, loyalty ID), things get more challenging as consumers now interact with a brand in more channels than ever before. Add to that complexity the fact that nearly two-thirds of customers will use more than one device to complete a single transaction and the challenge looks positively daunting.
Fortunately, AI doesn’t get daunted. Its powerful algorithms can recognise patterns even when personally identifiable information (PPI) is not available for a direct, deterministic match – that is, a 100% accurate match. This probabilistic identity matching, or ‘fuzzy matching’, allows marketers to reach further and personalise more successfully.
For example, an individual who has been browsing similar websites and content, from the same IP location, but on different devices, like a phone and a tablet, would normally receive a stock version of a marketing message for each device since their profile is separated.
AI, however, can link the individual’s identity on both devices with a high degree of confidence, which means the customer – not simply their behaviour when using their tablet or phone – remains the focus of a message that can be personalised no matter where they are browsing.
If you are considering how best to resolve identity – deterministically or probabilistically – think about the following:
Do you need 100% accuracy? If the match were wrong, would there be any negative consequences? If so, you’ll need to use deterministic matching.
Are you okay with the match being accurate to a high degree of confidence, that you choose? If so, consider probabilistic matching or even a blend of the two approaches.
Segmentation based on customer profiles and behavioural attributes has traditionally been the next step for marketers.
But how can we be sure we are using all the data and insights available, especially given the immense quantities of data now at our disposal? Do we really know the customer well enough to start engaging with them on that all-important personalised level?
Again, AI can be a powerful tool by filling in the inevitable information gaps left by progressive profiling – a useful technique that has largely on customers providing explicit feedback, like ticking a box that says ‘I’m interested in X’.
Instead, AI can observe customer interactions and behaviour then apply models that help marketers infer and predict customer behaviours and preferences.
Consider this approach to filling in the gaps when it comes to knowing your customer:
Build a progressive profiling strategy incorporating messages designed to gather more data, not to sell products. Did they download the infographic on investment strategies or budgeting and saving advice?
Identify where engagement rates are low and use predicted attributes to fill in the gaps. For example, what type of content or products does a customer have a strong affinity for or what is their preferred channel of communication?
AI uses the most advanced tools and cluster analysis models to unlock the deepest level of customer data and make it actionable.
With AI, marketers aren’t just finding new attributes to refine existing segments. They are discovering entirely new personas within existing audiences – personas that were previously unimagined. Chances are, every broad audience segment could be whittled down to smaller groups who share some attributes of the larger audience but present the opportunity for a new angle on messaging – in the vein of those classic ‘homebuyers are holiday-buyers’, ‘new parents look for new health insurance’ links.
AI’s ability to micro-segment any given audience has powerful ramifications for any marketing campaign.
Big data can at first feel like a gift and a curse to marketing teams – one that leaves them wondering if it’s possible to have too much of a good thing. But with the advent of enterprise-grade AI tools, marketers can now gain deeper, more subtle and more actionable understanding of their customers from big data.
Find out more about what customers want from businesses. Download the State of the Connected Customer report.
