Introducing data driven Internal Communications

Hester Gras

If you don’t measure, you don’t matter. A pretty strong statement for sure but, nothing could be less true. In the world of Internal Communications, we mostly trust on our gut feeling. But what if you could make better decisions when you prove them by using data. In the long term you could optimize the way you work and create better reach than ever before!

Data science in an alpha world is a non-explored world for many of you out there. In External Communications and Marketing is the use of data already broadly integrated in the daily way of work. We believe that the time is now to start using data in order to optimize your Internal Communications and we want to guide you by exploring the first steps. Especially for this occasion we talked with IT specialist and one of the founders of our platform, Wilbert Smits.

Why is Data Science a must in the Internal Communications field nowadays?

“As we speak there is a big movement going on in the world of (Internal) Communications. It’s no longer about trusting the gut feeling but more and more about proving a certain strategy is the right way to go. In order to do so one must be able to show numbers and statistics. If you ask me why I’d say there are multiple reasons why you should want to use Data Science in the communications workplace:

  • Measure reach of your Internal Communications efforts;
  • Being able to personalize Internal Communications;
  • And in the end: work more efficient.”

The basics of data driven Internal Communications

I think you can define 3 stages of using Data Science at the Internal Communications department. Many organizations are still in phase 1; implementing a Beta approach of an Alpha profession. The basic KPI’s one has to measure are:

  • Reach of the news (How many employees actually read organizational news?)
  • Reading time (How long are they engaged to an article?)
  • Interaction (How many comments, likes and shares do you gain per published
    article

Taking the next steps of using data

Stages 2 and 3 are more complicated to get on to. In the end we are moving towards an automated content publishing system. One that knows your target group by learning from their reading behavior. One that can suggest the ideal time to publish a certain article. And one that is able to predict trends. Of course, content creation itself can never be done by computers. But, publishing, increasing your reach and sharing the right content on the right time via the right channel is something we can optimize with data.

What are you waiting for? Start tracking the reach of your organizational news as fast as you can! Need help or are you keen on learning more about data science? Don’t hesitate to give us a call or send a contact request!

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