Farewell 2017, a year in which marketing technology really took off and where both consumers and marketers alike started to experience the potential of artificial intelligence in our daily personal and professional lives. Thanks to our new voice-operated companions Alexa and Siri, and advanced analytics tools based on machine learning becoming increasingly accessible, we all caught a glimpse of the exciting future driven by AI.
Or did we?
Certainly, interest in AI among marketers this past year was the highest it has ever been. It was difficult not to stumble upon some discussion about AI and marketing in every conference, blog, trade press article or pitch. That’s a very healthy interest, and we should encourage it into the new year. However, AI hasn’t yet lived up to all its promises (and hype). I don’t know about you, but there’s a 50-50 chance when I ask Siri something that I will get an intelligent response. Half the time it fails to understand what I’ve just said (which is deeply troubling to me given that there are no voice recognition issues when my three-year-old son says “Hey Siri, what’s the weather like?”). But voice recognition matters aside (which aren’t truly what AI is about anyway), we are still in the nascent stages. We’re not quite there yet, which is perfectly fine. This year we will see more advances and the computer science and software engineering will keep on getting better and better. That’s exciting, but from a professional marketing standpoint, there are a few things that we need to keep in mind as (hopefully sophisticated) users of AI, machine learning and advanced analytics tools.
1. Data quality is paramount
We all know “garbage in, garbage out” (GIGO). This needs to be a marketing mantra in the age of AI-enabled marketing. The learning that happens in AI, particularly in advanced systems based around deep learning and neural networks, requires a lot of data. So collecting a lot of data is important, that is, quantity matters. But the quality of the data needs to be taken into account (just like in the good old days of traditional “quant” market research). In fact, data quality is paramount. There’s nothing magical about the algorithms behind AI systems, it is just data crunching in sophisticated ways. And if the data are highly noisy or poorly measured, it is harder for the systems to identify meaningful patterns that allow it to make good predictions upon which your decisions can be informed. So focus on data quality more than ever. Collect as many data points as you desire (after all, storage is cheap these days), but make sure that every variable you capture is properly measured, understood by all the relevant people in your organization, thoroughly documented and actually used in business decisions. If you get the data capture, particularly measurement, right, then you stand a greater chance of getting more value out of your analytics and AI systems.
Fluency for all marketers in your organization is necessary. Fluency does not mean technical proficiency, however. It simply means that everyone needs to have a reasonable understanding of what they are talking about when it comes to these methods and tools and how they can be applied to marketing practice. This will likely require some training and “upskilling” but it is worth it. As these tools become more common it means that understanding what they can–and cannot–do is required of everyone on a marketing team. It also makes marketers less susceptible to relying on technical experts to explain everything, which can be wasteful of time as well as lead to confusion and misunderstandings that get in the way of great work. Ultimately, leaders need to push fluency in this respect, and need to realize that although not everyone needs to know how to use a hammer, they do all need to know what the hammer is, what it does, how it can be useful and when it shouldn’t be used.