Closer’s Data Science Approach

Data Science Figures

Knowledge is not useful in the hands of people that explore data all day. It is useful in the hands of those that don’t have that role in the organizations but need fast access to what data brings.

More than knowing what happened (Descriptive Analytics), high-performing companies need to know what will happen (Predict Analytics) and what actions they should take to make it happen (Prescriptive Analytics).

Those companies are using Advanced Analytics Techniques such as Machine Learning, Optimization, Sentiment Analysis, Cluster Analysis or Predictive Modeling to collect, analyze and process data that generate insights to make better business decisions, improving operations, creating smarter products and developing personalized marketing campaigns.

At Closer, we develop algorithms and customized mathematical models based on our Data Science, Data Analytics and Data Engineering experience, enabling companies to use the data at their disposal and converting it into competitive advantages.

Closer Analytics

What are your business challenges?

Some examples that we address:

Customer acquisition

  • Should I approve this credit line?
  • Who is the best target for my campaign?

Customer usage and growth

  • Which campaign is the most promising one for which customer?
  • Which clients will buy a car and when? 
  • Whom do we approach with a differentiated offer?

Risk and profitability

  • How can we optimize our pricing model?
  • How can we optimize our scoring models further?
  • Apply PufferFish portfolio optimization


  • Who will leave us and when? What should I do to avoid it?


Stay up to date and keep yourself informed about Data Science trends, best practices and much more. We hope you find these scientific insights relevant for your business.

Why we should not build models like the statisticians

Why we should not build models like the statisticians

by João Pires da Cruz, October 2016

Why Econometrics stink?

Why Econometrics stink?

by João Pires da Cruz, June 2016

Quantitative Tragedy

Quantitative Tragedy

by João Pires da Cruz, March 2016