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My former boss used to say: ‘Data analysis is nothing more than gut feeling validated with data‘. I really like this point-of-view. It actually says that it isn’t all about the data, but with gut feeling alone you’re not getting anywhere either. The magic of making better decisions is in combining the power of data with experience and common sense.

People who go by gut alone are wrong. Time and money are your scarcest resources. You want to make sure you’re allocating them in highest-impact areas. Data reveals impact, and with data, you can bring more science to your decisions.

Stuart McDonald (Freshbooks)

People are smart. Really smart. Of course machines can beat us in almost any specific task thanks to artificial intelligence. Machines are smarter in a game of chess, they know more answers in a quiz game and they solve a Rubik’s Cube in 0.38 seconds. But no machine is capable of doing such a broad range of tasks as human can. If we ever get to this point it is called artificial general intelligence, i.e. a machine that could successfully perform any intellectual task that a human being can. We could potentially get there one day (and thereby make ourselves superfluous), but before that the mind shouldn’t be underestimated.I

The mind is capable of processing and storing huge amounts of data. And your internal data analyst called ‘the mind’ is able to translate this data into all sorts of thoughts and feelings. Combined with a body, these can be translated in very complex physical actions. The drawback however, is that the mind is not logic by nature. Ok, some people are more ‘logic’ than others. But no person is able to tell exactly what the chance is he will catch the train when running for it. Nobody can predict with what certainty next year sales targets will be met. And for certain there is no person ever able to learn a complete library of books by heart.

Which brings me back to data vs. mind. You probably know after reading my earlier blogs that I like data. And I’m lucky, because data is everywhere. However, as for a pair of running shoes, I need intelligence and stamina to do something useful with it. Only by activating the data and interpreting the insights it reveals I can make real impact with it. This is data intelligence. In other words: the power of machines, combined with the cleverness of a person to turn data into impactful business results. Let me give you some examples:

Increase marketing effectiveness

Long-term results can not be achieved by pilling short-term results on short-term results.

Peter Drucker

Is there a company without marketing? Probably not. Every company has a need to let its target audience know what it is capable off. The challenge however is to do the right marketing. I for sure have the challenge about where to spend my precious time. Which people in my network should I invite for a cup of coffee, at what hour should I post my blog to get the most views and at what channels should I spend my time to get a result? All questions are common for every business, small or large. And data is there to help make the right decisions. For example, a customer segmentation can give insight in your most valuable customer group, a marketing dashboard can visualize the performance of the purchase funnel and a recommendation model can make sure every prospect will be target with the most conversion-likely message or ad. All these techniques will increase marketing effectiveness in the long-term and make sure you get more ‘bang for the buck’.

Running a faster 10k

I’m a runner. Preferably I run hard. However, running as hard as I could every single day wouldn’t get me anywhere. I would be overtrained within no time, if I didn’t have an injury already. Therefore I need to train according to a plan. And to make a plan I need data. Therefore I use Garmin and Strava to keep track of all my training stats and I have been rating my training on a scale of 1-10 for a couple of years now. This I combine with insights from literature and my subjective view on what is working and what isn’t. This results into my own personal running plan which I constantly improve based on the target goal, the feelings of the day and the progress I experience. This data driven approach on a personal running plan could also be brought to the next level. That’s exactly what To-getthere.com offers. They are working on an algorithm that predicts the most optimal training plan based on the historical running data of every runner. Now that is using data to run things better 🙂

Predicting customer churn

Your customers are your everything. Right? They are willing to pay you for your service, which provides you your bread and butter. So you treat them the best way possible. But still, some customers might be thinking about going to shop somewhere else. That ‘customer churn’ could be due to a lack of good service or a competitor persuading them to come to them. And when they’re gone, they’re probably gone for good. So if you could give them a little bit extra attention right when they are thinking about making a move you could perhaps convince them to stay. The thing is, you have no idea which customers have a high likelihood of leaving. Or do you?

Start the retention process when the person is still open to staying and not after they’ve already told you they’re leaving. 

Jeff Weiner (LinkedIn)

With predictive analytics your are able to predict beforehand the probability of every single customer churning. Based on customer characteristics and a historical dataset with customer behavior (churn or stay) it is possible to tell which subset of your customer base should be given some more attention. Common characteristics for predicting churn are non-activity for a long-term or checking the contract cancellation page on your website. Of course if you know which group is about to churn you can try to save them by giving them, for example, a phone call, an additional discount or whatever works best. This helps you save your precious customers effectively and in the end increase overall customer loyalty.

Those examples are only a tip of the iceberg in how data is able to impact a (business) process. With data available anywhere and increasing (artificial) intelligence power we’ll see things like price optimization, demand forecasting, propensity models, customer/market segmentation, attribution modeling, predictive maintenance and other ‘smart’ solutions entering daily business. Hence, to keep up companies need to be educated for the age of machines and become data-driven from the core. Are you ready?

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Is your business data-driven yet? IntelliCrew offers a free data-scan to check at which of the 5 levels of data-driven decision making you are. Send me an e-mail to get more info.