With customers continuing to seek personalized experiences, the vast majority of marketers have adopted data-driven marketing practices, using insights derived from customer data to help organizations better understand their customers and ultimately deliver a more tailored experience.
Yet, too often do marketers instead rely on data as a short-cut, for example by using it to glean insights from A/B testing tools that ‘optimize’ content by generating thousands of variants of a particular asset before pitting them against one another. Over time, this approach will eventually whittle the list down to a single Marketing asset that is expected to have the highest rate of engagement.
This method of selecting content sounds great in theory – but unfortunately, A/B testing does little in the way of explaining why a specific advert will resonate well with a particular audience. Ironically, by sacrificing insight for success in the short term, many marketers actually now have less understanding of their customers than they would without data-driven marketing – and the result can damage your brand’s reputation.
That’s not to suggest that we shouldn’t use data to inform our marketing campaigns. In today’s world, businesses simply have too many customers for marketers to understand each customer’s unique behavior, wants and needs. Marketing technology clearly has a massive role to play, but tools should be used to think about customers as individuals instead of focusing on clicks.
One example of how data-driven Marketing can go wrong is seen with hotel comparison sites, where a popular strategy includes using customer data to highlight offers and issue flashing warnings about how many others are viewing that particular offer and how many rooms are left available. This is clearly a great way of grabbing attention, and it might even do wonders for short term click-through rates; but for customers, it’s highly likely that this will cause “digital fatigue” and eventually they may go elsewhere.
A smarter way of using customer data is to match the right content to the right people and adapt the tone of voice, messaging and visuals to drive individual engagement. However, just as important as learning what content works, is understanding why.
Instead of automating away the creative aspects of Marketing through A/B testing, marketers should instead seek out collaborative AI tools that augment, rather than replace, the workflow of the user. These tools are designed to empower the user to make better decisions or accomplish more, faster, through AI. This also includes important features like interpretability. For example, perhaps a special offer should include an emphasis on how easy a product is to use, rather than how much it costs, because the AI has identified that the audience being targeted is time-poor rather than resource-poor.
This way, the AI handles the “heavy lifting” so to speak, while marketers are left to do what they do best: solve problems, be creative and understand customers on a much deeper level than automated testing tools ever could.
Ultimately, by equipping marketers with AI tools that empower them to make the final decision, the customer experience is intentional rather than just simply personalized. When handled correctly, the result is happier customers, better retention rates and higher quality – rather than quantity – click-throughs.