As I’m personally found of data and anything intelligence related, this is a fair question to ask. From what I’ve seen so far in this digital ecosystem, it’s kinda a chicken and egg problem.

Headline readers all know data has a potential value when used properly, but very few know how and what exactly they can do with data. It’s a very complex, yet compelling matter. And it beholds so many variations and aspects. Its tied to all sorts of verticals and stretches over a broad horizon of possibilities over time, if strategically planned and used.

On the other hand, its very confronting as well. It’s the black in the white space, creeping up on the gray area most of us tend to play in, in this thing called digital landscape.

Its hard to put data and analytics efforts on the roadmap in a marketing or business context. And its even harder to make people look and see beyond the traditional channel and funnel reports possible with Google Analytics.

So, this is an open letter towards anyone who’s doubting to put analytics and any form of intelligence on his roadmap for the next few years. If it is even to become or start to become even a little bit data-orientated. That would be lovely.


Why? Well, it’s a fairly good investment over time. It’s that building block that can support decision making or even trigger innovation. It can be the trigger to build something you can’t say what it will become over time. The only thing you do know is the more you keep putting time in this thing, it will return the favor by giving you numbers to steer upon.


No light without electricity. No electricity available, makes a lightbulb fairly useless. Like a website receiving any visitors is fairly useless, a marketing tactic or strategic paper without a stripe of data is fairly useless. Right?

Data is a bit like electricity. We take it for granted, yet when left away, it leaves an enormous GAP. Unlike the visible effect of light created by the help of elektricity, we don’t seem to be able to directly tie the effect of data to our outcomes.

It’s a bit of a taboo in the marketing sector, isn’t it? We all know it can effect our outcome and yet… we tend to avoid it and settle for less.

Compare it to sitting in a dark room with your mom and dad. You didn’t pay the bills. No electricity, so no lights. And yet you lie and come up with the excuse you want to test your new flashlight instead and how it remembers you how boy scouts was like …


No personalization without segmentation. No segmentation without product data or demographical user data. No e-mail marketing without e-mailaddresses.No bot without classification or taxonomization.

Everywhere, in every subject, numbers are part of it, at some point. To do effective marketing communication. From insights to automated actions. From flash sales to creative campaigning and hyperpersonalised e-mails. All of them have one thing in common: when build on a layer of insights, they perform at their best.

Those insights, those are available. Everywhere. Thank god! The only thing that stops us is? Us. We can’t see what is possible. We tend to stop dreaming. We tend to stop thinking. We tend to ignore our common sense. We tend to think this is expensive, as most of us can’t comprehend or imagine the possibilities. And yet… it’s not. It doesn’t need too be.


We tend to value analytics and the effort to do something with data as an economical value before even starting to taste the possibilities. Because, the hours that need to put into this thing, to get it to work, need to be accounted. Of course. Right?


Every dataset per company or segment is different. You can’t compare it to anything else. However, the structure of datasets (classification) kinda look all the same, the raw data can’t be compared to anything else. That’s the thing that makes it very hard to guess its value.

Do we need to know its value? How about looking at it as a foundation of the thing your building. The necessary thing to do, like when your building a career. Like, when your building a home. Never done. Right?

The current state of the digital landscape doesn’t help. Big or small, companies with ambition are facing, to some extent, all the same challenges when it comes to data. The only difference is the raw data itself. So every analytics or data strategy has a custom outcome, yet covers some basic overlapping challenges. At some point, when those basis challenges are covered, for instance classification, the custom data planning & analysis begins.


Of course, we all dream of automating the shit out of everything (less work, more play, hell yes!). We all dream of delivering the best message at the right time to the right person no matter what device or channel (shut up and take my money, booyah!). Eventually, we’ll get there. Or not?

Year over year, I’ve seen mass marketing move into a more one-to-one marketing direction putting the client at the center of everything. Which makes dataplanning interesting. And above all, more challenging than ever.


The same thing applies to personal data. Every transaction, every comment, every e-mail, every tweet or Facebook post, every phone call, every SMS, every WhatsApp message, every credit card balance, every click on a website, every key word query, yada yada yada … is a little piece of a certain behaviour or specific intention that indirectly links and reveals your needs, preferences and above all highlights your personal expressions as an individual.

Not only is that raw personal data interesting for behavioural analysis. The metadata of those transactions, products, segments and other classifications offer possibilities to steer upon. Giving personal data a value is quiet challenging.


Calculating the economical value of something happens most of the times based on the cost of the acquisition. So based on a historical handling. When it comes to data, that’s hard to do. Sure, you can calculate the costs of buying a list of e-mailaddresses. No problem. When prices change, you recalculate the cost. Easy.

But how about other datasources and the usage of those datasources? Allow me to give an example.

You want to start your first e-mail campaign. Yay, good for you! How to calculate that economical value? Hmmm… hard to tell. And what about the economical value of that new subscriber to your newsletter? What’s the value of that e-mailaddress you get by private message on Facebook, to send the client an update on this shipping? What’s the value of the possible segmentation you’ll be able to make when you get to at least a few hundred e-mailaddresses? Hard to tell, right?


What data has the most value? Net worth of that one datapoint? Can you even guess, I wonder? Who decides? On what ground? And that last visit yesterday by the same person that visited the store today? What’s that visit worth? And how will all those tools and that analytics framework help me? How can I even calculate the economical value out of those things?

Calculating the economical value of data is a waste of time. It doesn’t have to be calculated.

All set-ups, tools and outcomes due to those efforts to work with the data are more worth than the data itself. Data has an indirect value, which makes it valuable!


I think it makes sense to drop discussions about this topic, but rather encourage each other to talk about the possibilities of data and analytics. We should talk about things that matter. About valuable insights and how to capture the data needed to get them. How we could make data more valuable. What framework we need to build to distract the value out of the valuable data. And so on.

The cool thing about this? The investment grows with the ambition. Meaning, the companies with a strong vision will likely be more open to this talk than others. On the other hand, the profit out of this, can fuel the investment. So… you can start small and grow step by step. The only thing you need to do, is to focus on using the most valuable data at that time.

And yet… the question that need to be asked is this one: “How will you eventually get closer to your client and directly talk to him?”

An open question. Nothing more. Nothing less. At the table? A nice mix of stakeholders, management as well as the marketers themselves. Which makes it very interesting to cover all sides of the needs and wants.

Need any help? Feel free to contact me to have a chat.

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