First, there was the big data hype and now the AI boom engrosses marketing, although none on its own can create data on its own. Since big data gurus once told us that we would only need to collect as much information as possible about our customers to know everything about them, we lived the misconception, but it soon became clear that you can’t find the needle in the haystack by accumulating more hay. Neither does it get easier to find the relevant information about a customer by collecting more data about him.

If you want to know, for instance, when and why a customer is about to terminate a contract with your company, then you must ask yourself what the reasons for the termination are and how you can measure those reasons – to figure out what data you should collect.

Data or Algorithms?

While Big Data focuses on the data, Artificial Intelligence focuses on the algorithms. The awakening of AI is the result of deep learning - algorithms that use complex, "deep" neural networks to learn from past data (e.g. to predict the probability of a customer’s contract termination). However, AI is only as smart as the data with which it was trained, as seen in an analysis of the AI successes of the past few years (Figure 1) of KDnuggets.

dataFigure 1: How long does it take from the invention of an algorithm or the creation of a data set to successful application? Source: https://www.kdnuggets.com/2016/05/datasets-over-algorithms.html

What we see is that the algorithms have been around for decades, but the breakthrough, e.g. in image recognition, did not materialize until the data was available for training.

What is more important now? The data or the algorithm? Big Data or AI? Neither one. Decisive are the right data and the appropriate algorithm!

If you want to successfully run data-driven marketing, you must ask and answer three questions:

1. Why do I want to analyze our data? For example, because I would like to be able to predict potential terminations to make an improved offer early enough.

2. How do I have to process our data for this? For example, by developing a predictive model that not only calculates the probability of termination, but also the likelihood that the customer will respond positively to the offer.

3. What data do I need for this? Well, the contract and customer data from the CRM and ERP systems are not enough, since data from the outsourced call service center and social media department, information about competitor offerings, market data and many other data sources are needed, too. The more data you can incorporate into the model, the better your predictive model. However, the "more" refers not only to "more customers", but also to the period. The further you can investigate the past, the better the prediction of the future will be. So, you need to think long-term about the data you need to start collecting in time.

Incidentally, this shows the difference between a data alchemist and a data scientist. A data alchemist wonders: what data do I have to extract information from? A data scientist thinks: what data do I need to get the information I need - and how do I get that data? Successful data-driven marketing therefore clearly requires a data strategy!

Data strategy before data campaigning

You need to know what you want to do with your data so that you can decide what data you should collect. Because without strategy you might go anywhere - just not where you want to go.

Certainly, you know the phrase "data is the new oil", from the British mathematician and architect of Tesco's Clubcard, Clive Humby, which is kind of true and at the same time wrong. Yes, data drives businesses and increases their productivity - just as you can run your diesel or gasoline engine with oil - but you need to refine data for information before you can use it.

Consequently, a data strategy deals with the questions:

1. Why do I want to use the data?

2. How should I refine the data?
3. What kind of data sources do I have to tap for information?

But let’s get back again to the data oil analogy, since every analogy has its point of failure: A barrel of oil always has (approximately) the same composition and therefore a (nearly) constant (fuel) value. However, the (information) value of one gigabyte of data depends firstly on the concrete composition of the data and secondly on the respective utilization goal.

Because both your company goals and your company data are individual, you need an individual data strategy to create value for your company from your data. However, neither Big Data tools nor AI technology can help you develop an individual and successful data strategy.

As a tool you need first and foremost your head and that of your colleagues.

By Daniela La Marca