As marketers, our primary goal is to encourage people to buy our company’s products or services through the effective and targeted promotion.
What makes marketing content effective? For customers, creativity and relevance are what count the most. Particularly on digital channels where there is so much noise to cut through, people only have time for content that is:
- Beautiful and entertaining, as well as
- Personalised to meet a genuine and personal need
Creativity and relevance have long been the top focus for advertisers but it takes time to develop and roll out great content, and marketing teams are still stuck devoting much of their energy to retrospective admin tasks like reporting. Management is a crucial part of every marketer’s job, but if you’re constantly checking to ensure existing processes are running smoothly, that leaves little time for trying new things or pushing the envelope.
This is why the buzz around Artificial Intelligence (AI) has reached a fever pitch in recent months. The reason admin takes so long and many brands are adapting to digital platforms more slowly than they’d like is that they are simply collecting more data than they can manage. Just think – a digital campaign targeting tens of millions of people, in dozens of markets, creates more data in a day than an entire year’s worth of campaigns would have created just a decade ago. Where do you even begin looking for answers in these results, much less uncover new insights to inform your future activity?
Analytics is a process. Action is the goal
One of AI’s biggest selling points is that it allows brands to spend less time looking at the past and more time gaining a step on the future. Most analytics solutions (even recent ones) were only designed to collate and summarise facts. They combine data on customers, content performance, third-party sources, and so on into a more digestible format that serves a marketing team’s reporting needs. This is hugely valuable for digital advertisers, but they are still limited to asking the same questions they’ve always asked, just on a larger scale.
Real innovation happens when you uncover insights you didn’t even think to explore, and that’s what AI offers. It allows marketers to understand why a particular data set or combination of data sets is interesting, which can have much broader implications than what that data says on the surface. It can take months or sometimes years to uncover patterns that reveal it’s time for a change, whereas AI algorithms can help marketers do the job in seconds.
That’s why nearly 85% of executives believe AI will allow their company to develop or sustain a competitive advantage, according to one MIT Sloan study. Our own research reinforces this point – when asked what their biggest priority will be in the next three years, nearly 40% of marketers and agencies said it was to “deliver personalised experiences in real-time”, something that can only be achieved at scale with AI.
Of course, it’s not all just theory. At the risk of overdoing it on the facts and figures, Capgemini recently found that three-quarters of organisations currently using AI have already seen customer satisfaction rise by more than 10%.
A prescription for customer engagement
To understand how AI adds value to data, we first need to understand the different forms of analytics brands currently rely on, of which there are four:
- Descriptive analytics – This is basic data analysis allowing brands to report on what’s already happened, with no added value.
- Diagnostic analytics – Taking things a step further, this involves selecting and sorting data to understand what happened on a deeper level.
- Predictive analytics – Things are now getting more interesting, with brands starting to make educated guesses about the future based on historical patterns in their data. This is as far as most of today’s analytics programmes go.
- Prescriptive analytics – This is the holy grail, whereby users combine present, past and real-time data to help them make the best possible decisions about how to serve a specific customer in a specific scenario
The next question many marketers will ask themselves is “Well, I didn’t study data science, or any science for that matter, so how am I supposed to understand and use advanced data analytics technologies?”
It’s a valid question, but the beauty of AI is that the technology has matured and become more accessible. You don’t actually need to know how the technology works or be able to develop an algorithm from scratch. You just need a clear idea of what you want to achieve.
At Adobe, we’ve developed purpose-built AI solutions to help marketers with everything from intelligent targeting to creative content creation. We understand that digital advertisers need a leg up to stay on top of change, and believe that even novices or occasional users should be able to get useful, actionable insights from their data easily and on their own.
That’s when analytics is at its best, when everyone is empowered to find the information they need when they need it and act on it in time. It still falls to marketers to define clear objectives, whether it’s to raise awareness for a new product, support a sales campaign, or segment customers more precisely. From there, they can determine how AI can help them to do the job better.
We are still in the early days of AI-fuelled analytics, but companies from SKY UK to TimeOut are already starting to reap the benefits and they’re just getting started. Click here to see how we’re helping customers get more from their data with Adobe Sensei.
And read our report, AI-powered analytics: new insights, better business results, for a deeper dive on how Sensei can fuel better decision-making in your own organisation.