What's the key to Amazon and Google's revenue success? Everyone knows the answer: Data.
The reason for Facebook's social media empire and Spotify's upending of the music streaming business? Data.
All of these companies have managed to leverage the vast amounts of information they get from their multitude of users - whether it be their search habits, the posts they share, the products they buy, or the music they listen to - into major revenue streams. It's not just the fact that these companies have been able to gather data on millions (or billions, in the case of some of these companies); it's that those companies have managed to effectively utilize that data to better understand and market to their users. All of these companies are using artificial intelligence (or, more accurately, deep learning) to do this.
Of course, it's important to note that you don't have to be a dominating enterprise like Amazon or Google to turn data into a competitive advantage. As artificial intelligence becomes increasingly advanced and more widely adopted, we'll start to see a lot of companies - big and small - turning to AI in order to come up with better data strategies and win customer adoption, and to better compete against their competition.
The key to beating your competition, according to Jeremy Fain, of pioneering neural network technology Cognitiv, is having better data - not necessarily more of it, but the data that your competitors do not have. In theory, every brand is capable of developing their own unique data assets, because every brand has to be slightly different to compete. This means that a brand's customers are, at the very least, slightly different from those of their competition, which means that they have a unique angle that they can utilize. Every piece of data you get on your customer or potential customer is therefore another piece of information you can use to craft an effective marketing or advertising strategy.
In order to use this information effectively, you must first decide what your goal is. Are you looking for more sales? Are you trying to achieve higher foot traffic in stores? Is your goal to have higher market awareness of your product? Once you've done that, you can look at the data to see if it is in the right format for use with deep learning. This is something that is hard to explain simply, but fundamentally, data has to be in a disaggregated state - that is to say, it has to come from multiple sources so that more in-depth conclusions can be drawn from it. That means you don't really need to know only how many people visited a store, but instead when exactly each person visited. You no longer need to look at just how many sales you made, but also what each sale was and to whom. To get one step further, you must identify what touchpoints you had with a customer before they transacted with you, what ads they were shown, and when and where all the interactions occurred. Don't gather this type of data yet? Well, that's your first homework assignment.
This means you will have a lot more data to store than you have been used to, but the good news is that storage is cheap. Plus, without that information, you will not be able to take advantage of the power of deep learning and compete in this new world.
A 2016 study of Fortune 1000 executives uncovered that only 48.4% of those surveyed reported measurable results as a result of their data initiatives - but 80.7% felt the efforts were a success and essential. This means everyone knows they have to do better and do not see an alternative, but something more is needed before measurable benefits are achieved across the board.
Most data initiatives miss one simple ingredient: deep learning. It's an oft-misunderstood topic, defined by Cognitiv's Fain as "a more advanced type of machine learning that's capable of generating human-like insight." Deep learning's ability to get results from big data is now essential not only for competitive reasons, but also to make previous investments in big data pay off. Sadly, 39.3% of those surveyed still said that their organizations were lacking an enterprise Big Data strategy, or were otherwise unaware if one existed - these companies have a long hill to climb. In fact, most data-driven professionals has a steep climb ahead of us. "Part of the challenge is that the industry itself is immature around data. We'll look back 15 years from now at what we're doing and say, 'Wasn't that cute?'," said one Director of Programmatic Media for a global media agency interviewed for a recent Winterberry Group IAB study.
Big data, data analytics, and artificial intelligence go very much hand-in-hand. Artificial intelligence - and, by extension, deep learning - requires data, reams and reams of it. The only way that deep learning can be effective for your organization is if you have a steady stream of information to feed it." Armed with this information, deep learning and neural networks can create algorithms and strategies that are unique to your brand - thus ensuring that the brand remains competitive and innovative. As Fain points out, "The ability to more fully describe and understand a consumer's behavior is more complete than ever before, and that kind of data will make AI marketing tools even more effective over the next few years."
At this point, all brands need a strong data strategy. Just look at brands like Macy's and J.C. Penney's today, who are struggling as a result of the data-centric approaches of e-commerce giants like Amazon and eBay. Having the right strategy and, just as importantly, the right tools to get the most out of your data, is what will help keep your company competitive, and successful.