How To Successfully Weaponize Your Data

What’s the key to Google and Facebook’s revenue success? We all know the answer: data. The reason for Amazon’s retail takeover 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 many users — their search habits, the posts they share, the products they buy, the music they listen to — into sizeable 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 Google and Facebook), it’s also that they have managed to effectively use 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. Most artificial intelligence involves some type of machine learning, which essentially means giving an algorithm data and “training” it to perform a certain task. Deep learning is itself a form of machine learning, albeit one that relies on layers of artificial neurons in an approximation of the human brain in order to come up with more sophisticated results.

However, you don’t have to be Facebook or Google to compete successfully in the data war. As artificial intelligence becomes increasingly advanced and more widely adopted, we’ll start to see a lot of companies — big and small — coming up with better data strategies to win customer adoption and better compete against their competition.

The key to beating your competition is having better data — not necessarily more of it, but the data your competitors do not have. In theory, every brand is capable of developing its 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’s, which means they have a unique angle that they can use. Every piece of data you get about 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 — more sales, higher foot traffic in stores, higher awareness of a product, etc. — and then look at the data to see if it is in the right format for use with deep learning. This is something that’s hard to explain simply, but fundamentally, data has to be in a disaggregated state. That means you don’t really need to know how many people visited a store, but instead when exactly each person visited. You no longer need to understand how many sales you’ve made, but what each sale was and to whom. This is important because it allows you to break down the impact that various decisions or strategies have, and evaluate whether or not it’s something you want to continue in the long term. It also allows you to see how trends emerge and follow their trajectory.

You need to know what touchpoints you had with a customer before a sale, what ads were shown and when and where all the interactions occurred. If you do not gather this discrete type of data yet, that should be your first mission. It means you will have a lot more data to store than you are used to, but storage is cheap, and without that information, you will not be able to take advantage of the power of deep learning and compete in this new world.

The need for deep learning solutions that can weaponize data is great. Ruben Sigala, the chief analytics officer for Caesars Entertainment, says that what he and others in his position find challenging when it comes to data analytics is “finding the set of tools that enable organizations to efficiently generate value through the process,” as well as tools that can be easily integrated into existing ecosystems.

A study conducted in 2016 amongst Fortune 1000 executives showed that only 48.4% of those surveyed reported measurable results from 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.

As the CEO and co-founder of a deep-learning platform for marketers, I might be biased, but I feel that deep learning is that essential ingredient missing from most data initiatives. Its 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. Without deep-learning capabilities, companies might struggle to unlock the full potential of all of their information, as it’s only with the computing power and analytical capabilities of machine learning that all of that data can be put to good use. 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.

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.