Predictive Lead Generation Model

Wolters Kluwer Legal & Regulatory is a global leading provider of legal and compliance solutions that enable professionals to improve productivity and performance, mitigate risk and achieve better outcomes. The division has operations in Europe and the U.S. with over 4,000 employees.

Description of the Internship

Wolters Kluwer has various product for lawyers and tax professionals. One successful product for law professionals in Belgium would be Jura, which is an elaborate online database that allows easy navigation of the Belgian law, jurisprudence and articles about the Belgian law system. Wolters Kluwer also helps tax and finance professionals by offering them monKEY: an online database about anything related to tax, finance and law related to tax and finance.

With the aim of improving brand recognition across Belgium and the Netherlands, Wolters Kluwer is going to launch a new product. This product aims to help law professionals on the one hand, and tax and finance professionals on the other hand. The goal of this launch is to improve on the existing products of Wolters Kluwer, with the aim of providing a better user experience with more readily accessible content.

Wolters Kluwer would like to know which customers are most likely to buy or migrate to this new product. Your job would be to help Wolters Kluwer answer this business question by creating a model that can predict for any kind of customer how likely it is that they will purchase the new product. You will do this not only by analysing who currently has Jura or monKEY, but also by analysing who did not want to buy Jura or monKEY. This gives you insights into people who do not buy a specific product, which allows you to create a model that can give a probability of a successful purchase.

It is your job to create this model that can create, for any kind of customer, a probability of a purchase. You will do this in Python, where you use appropriate statistical methods and models. You will show statistical significance to motivate your model(s) and approach. It is of the utmost importance of the analytics department of Wolters Kluwer to make the insights and results insightful and actionable for the different business units. Therefore, together with data science team within analytics, you will ensure that the results can be interpreted in a straightforward and easy-to-understand manner.

Pre-requisites

• The student has experience with the programming language Python, or has similar experience with another programming language that allows them to use Python
• The student is familiar with models that can create probabilities, such as logit and probit models, random forest, SVM, and so on
• The student is familiar with statistical concepts and is able to assess the capabilities of their created (mixed) model
• The student has affinity with AI and machine learning
• The student can communicate effectively
• Tableau experience is a plus

What the student will learn

• The student will learn how to apply statistical models effectively to real world data
• The student will learn how to leverage the capabilities of Python to create a dynamic and good deliverable that provides business value
• The student will learn how to translate the needs of the business into a technical implementation

Team and environment at the company

The student will be part of the Analytics Team of Wolters Kluwer, which consists of 16 members and three different units: business intelligence, data management and data science. The analytics team helps Wolters Kluwer with their strategic planning and decision making, by providing insights into actual and future data. The analytics team is in frequent contact with business and helps them with various projects.

The student will work closely together with the data science unit, where a data scientist will act as an internal coach who will guide the student to create a good (mixed) model and to ensure that the project comes to a good end.

Application procedure

Send an email to Lien Mertens.