Working in the (data) heart of KPN
The main question appears to be a simple one: What makes it so interesting to work as a Data Analyst at KPN? Do you have a minute, Ruben Timmermans asks with a smile. What follows is a talk about concretising Xs and Ys, the urge to innovate and true team spirit.
After earning his degree in Econometrics, Ruben Timmermans started working as a Data Analyst at KPN in 2016. Econometrics teaches you all about the theoretical application of mathematical models. At KPN, these Xs and Ys are translated into concrete business with the help of statistics. There is a mathematical solution for every wish or problem. It truly makes you realise how much value data analysis can add for the customer, Ruben explains excitedly.
Is a degree in Econometrics a requirement for working as a Data Analyst at KPN?
Ruben: We have a lot of econometricians in our Data & Analytics department, but this is also an interesting professional environment for people with a different scientific and statistical background. KPN is an organisation with millions of customers who are each on their own customer journey. It is our job to discover and optimise these journeys using data analysis. The challenge is to make the right connections, so you can have a real impact on the customer’s experience. It goes without saying that data security and privacy are our primary concerns: the customer is and always will be the owner of their own personal information and they are free to decide what information we can use to further optimise their customer journey. This quantity of (customer) information makes it interesting to create models that can make accurate predictions for every target audience. It is both challenging and an enormous responsibility.
Can you give an example of one of these predictions?
Ruben: “Suppose we adjust our proposition for all our subscriptions, which doubles the contents of every data plan. That affects what our customers do when their contract is about to expire. Some will opt for a smaller data plan, while others want a bigger plan. The question is whether we can predict this behaviour, so we can send the customer a new proposal that is tailored to their needs even before their previous contract ends.”
How can you predict a customer’s needs?
Ruben: “We start by properly formulating the customer’s question or expectation together with the marketing department. To do so, we study the data made available by customers and make the right connections to determine whether the question or expectation is correct. Then comes the best part: the modelling, which for me usually boils down to programming in R. I either write my own models or use existing codes. Next, you sit down with the marketers to discuss what the model’s results mean and what appropriate actions we should take. I deliver the opportunities that add value for our customers and for KPN. As a Data Analyst, you act as an adviser to the business. The Analytics team is the beating heart of KPN, so you can add a lot of value for the decision-making processes. It is awesome to see that my analyses have an immediate impact!”
You mentioned modelling in R as a tool that lets you create your own new models. To what extent is Data Analytics focused on innovation?
Ruben: In our department, we are constantly looking for the latest research methods. I am very proud of the model factory that I have been collaborating on for the past few months. In this model factory, we monitor our existing models on a 24/7 basis, so they are always up to date. This guarantees that we have access to the latest models at all times, so we can create the best opportunities for both our customers and for KPN itself.
What made you decide to apply for this position?
Ruben: Can I give you three reasons? First of all, this is a fantastic place to work for a Data Analyst, because there is so much to learn. We work with enormous quantities of data and we use innovative methods and techniques. KPN invests in our data expertise with e.g. a Data & Analytics Learning Lab. Furthermore, I saw a new department that was growing rapidly and consisted of many different divisions, so there are plenty of directions in which to advance your career. Everything we do requires data, so I can develop myself in many different ways. The main reason, however, is the great atmosphere here. Our department is made up of all kinds of people: from scripters to seniors and from Commercial Analysts to Data Science Lab employees. We work together on every project. This is simply a fantastic team of people who bring out the best in each other every single time!
This article is sponsored by KPN.
