The three skills you need for fact-based marketing
By: Arjan Haring
Tags: Organizational Culture, Big Data, Data Science

The three skills you need for fact-based marketing

And how good coffee can make all the difference for your organisation

Companies often talk about creating a data-driven culture. Some organizations want to implement evidence-based management, others are engaged in business experiments. For marketers I would say it’s all about fact-based marketing.

These are all related movements that want to change organizations in a way they are no longer steered by opinions. It’s no longer about the person who gets paid the most money (the HiPPO) pushing her opinion through.

In this article I share my first experiences with building such organizations and I name three skills that marketers should develop (further) if they want to be part of this new movement.

When I worked at Booking.com, one day a colleague of mine complained the coffee tasted bad in our department and in his opinion the coffee machine had to be replaced. My mentor and senior product owner datascience, Mats Einarsen, took the initiative and he would fix the coffee problem. A few days later the coffee machine guy came, along with a new coffee machine.

But instead of replacing the old, Mats organized a blind taste test. Which of the two coffees was better? After a large number of colleagues had done the test, Mats shared the results; the coffee machine guy was allowed to pack up and take back the new device. The evidence was clear, the old coffee was better. The view that the old coffee machine had to be replaced, was refuted by the facts.

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Booking.com is – by far – the most data-driven company that I know. Data seems to be a religion and this belief runs through all layers of management. They are also the most successful company I know. (With probably a revenue of over 5 billion dollars last year only) The data-driveness of Booking.com seems a major reason for their success. It is difficult to prove, but at least more and more managers realize that is makes total sense to run .

The desire to get the ROI of marketing more clearly, so that we can make clear what our impact, obviously playing much longer.

Half the money I spend on advertising is wasted; the trouble is I do not know-which half. — John Wanamaker

But optimizing online marketing this awareness has only grown. Offline and above the line must be the same. We now have more experience. Building a data-driven culture is a desire to have more and more marketers.

For another project I am talking with Colin McFarland on how to design a data-driven organisation. Colin works for a hardcore data-driven company but he wants to get even better at sticking to facts and staying away from opinions. The company he works for is also very successful. It makes you wonder whether there is a link between having a data-driven culture and having commercial success…

Nowadays more organizations are warming up to the idea of evidence-based management, and I’d love to share my personal experiences with building a data-driven culture. You can’t create such a culture on the drawing board. That’s something you have to learn by doing. I train people in conducting experiments, making decisions based on facts and learning from the data.

There are three aspects that I focus on when an organization asks me how they can become more evidence-based. These are the skills that you need if you want to run more business experiments or if you want practice fact-based marketing.

1. Critical thinking

A marketing fallacy I identified already years ago is that marketers make informed and rational decisions. For what I have seen marketers normally base their actions on their intuition. An intuition that often turns out wrong. What I wanted to change already then, and still want to improve is what I now define as “critical thinking.” For me this means that you can formulate your opinion or idea in one or more testable statements.

In my trainings I force people to develop their ideas as testable hypotheses. The whole point is that marketers formulate their views in a formal manner. This requires training, but it is the start of any data-driven approach. The start that is often forgotten.

A critical thinker doesn’t just posit a statement, but bases it on sound reasons trustworthy sources.

2. Designing an experiment

If you have a testable idea, a workable hypothesis, you want that test it. In an ideal experiment you gather insights into the relationship between cause and effect of your actions.

Decisions in non-data-driven organizations often lean on opinions from outside. In a data-driven culture the opposite is true. I completely agree with my former colleague Stuart Frisby when he wrote about a hierarchy of reliable data sources for decision-making purposes:

Your own experiment data Your opinion Someone else’s opinion Someone else’s experiment data

Learning by experimentation with your customers and your own propositions. That is something no one else can do for you. And that’s why you shouldn’t value an outsider’s view much, and even less so copy the “best practices” in the industry.

Stuart phrased it probably best when he said:

“Test your own damn hypothesis”

3. Statistics

I spent a lot of time working side by side with a statistics professor, so I would never dare to say that statistics is less important for a data-driven organisation. But I will say this: many people I have worked with could learn the first two skills relatively easily. This in stark contrast to becoming significantly better at statistics. In university this is a stumbling block, but in business and more specific in the field of marketing this is simply not feasible.

Statistics is in my opinion important when you have already begun to run experiments. Your questions will be more clear. And suddenly you would like to know, given the data that you have from an unique customer, what the best choice of proposition is to show right now and how confident you are that the customer will act on the given proposition. In that case it is very handy to have someone on the team that speaks Bayesian in addition to the usual business lingo.

Marketers who like statistics have the future. But until then data scientists will work in their own specialized teams while they enjoy the power of statistics. I believe it is about time that marketers should like to test their opinions, think critically about their actions, learn based on facts and thus make sure they contribute to the success of their organization.

I’m curious what your experiences are!

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