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Blog / Why Data-driven Marketing Should Start with a Data Strategy

Why Data-driven Marketing Should Start with a Data Strategy

As online users, we all produce large amounts of data. If you are on Facebook, Amazon or any other online network, your behaviour is saved as data - and this data generates value for those platforms: they know what you like, what you do, what you click. They even know if you’re pregnant before you do.

There is this well-known saying: 'If you're not paying for it; you are the product'. In this context this just means: the owners of the products sell your behavioural data to advertisers who in turn try to sell stuff to you: because you clicked, liked, interacted with similar products.


'If you're not paying for it, you are the product'

 In short, data creates value because it consists of information about users. The tricky thing here is: the biggest chunk of the data is not really helping. Believe me, when I say: data is good. More data is not. We know from our daily work with marketers and agencies: it is easy to drown in data if you don’t know what you are looking for. Remember the needle in the haystack that you are desperately looking for? Well, in this case, you don’t even know if it’s a needle or any other pointy object. At the end of the day, data is a product as any other product: either it delivers value or it doesn’t. One of the biggest challenges marketers are facing is having a data strategy - a plan what to do with your data and how to create value from it.


A data strategy is not just collecting data

It starts with a definition of your goals and continues with asking the right questions: what data do you need? What is the best place to store it? What kind of software do you need to analyse it? Who takes care of all this?

In the beginning, it needs patience. It hurts to tell you something so obvious, but getting the data you need and creating value from it is an inherently difficult process. We know from our research that 70% of CEOs see analytics as a key for their success - but only 25% agree on the fact that they are in good position to extract insights from their data. This gap shows that ambition and business reality are unaligned. By a lot.

And it’s too easy to say it’s their fault. In our countless pitches, we keep hearing possible clients say that they want actionable insights quickly, extensively and in real-time, without ever thinking of the implications. We see it as our job to manage their expectations, too. Without realistic goals and the right strategy, data is just a clunky mess. But it really does not need to stay that way.


7 points to draft a data strategy

To create actionable insights from your marketing data, you need to keep your eyes on these 7 points:

  1. When building your data strategy think of the right questions you want to be answered. Aim for questions that not only confirm your hypothesis. Re-elevating old assumptions can help you generate great insights.
  2. Know your business goals inside out and connect them to your insights. This will help you drive actions quickly because you know why you’re doing it.
  3. Take your time and find the right software for your business. It’s critical to have right tools at hand that translate your needs into actions. What’s the point in having a magic wand without knowing how to cast a spell
  4. Don’t settle with your beloved KPIs. Explore new ways to evaluate the success of your marketing campaigns.
  5. Always look for references. Your insights are only valuable if you compare them with other time frames or put them in context. This will quickly give you confidence when assessing your campaigns.
  6. As with any other discipline in the business world, leadership trumps everything. Be an example when it comes to thorough analysis, set realistic goals and give your team confidence to experiment, learn and optimise their analytics routine. Your team will thank you with new methods of data exploration and fresh results.
  7. Last but not least: not all your insights are actionable. This might seem like a trivial counter-argument to the author’s promise, but it’s actually a highly relevant reminder that there will be findings in your data that will not be able to tackle. Stop wasting time on things you cannot change.


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