As a freelance Data Analyst, I translate data into meaningful insights to help teams make the right decisions. The goal is to save costs and improve results. Actionable KPI’s and automated dashboards will help you measure the success of your business initiatives and drive product development.

Please find below some examples of my work (section Portfolio).


I am available for: 

  • Data Analysis (CRO, Segmentations, Data Pipeline Management, Data Processing)
  • Data visualisation (Dashboards, Reporting, Presentations)
  • Data Science (Prediction models, Machine Learning models)
  • Training and workshops (Hands-on SQL training, Python training, Dashboarding & Data visualisation training)

My Skills


Portfolio - Data Analysis

Project 1: Increase sales for a subscription selling e-commerce start-up

Background: A start-up approached me, because for their investors they needed a solid strategic plan to grow sales within the next quarter.

Firstly, it was important to understand the start-up’s revenue numbers and growth rate.

Revenue and YoY

Finding 1: Despite the fact that the start-up was growing Month-over-Month, when looking at the Year-over-Year Growth Rate, we could see a declining trend. This means, the company growth was indeed slowing down compared to last year.

The next question to answer was: How many Website Visitors does the company have and how many Orders do they get per month?

Orders per Visitor’ is a great indicator for the businesses actual health. For example, if revenue is going up but Orders per Visitor is going down, it tells you that either you’re going to need a lot more customers to continue growing at the same pace or you need to improve the quality of your customer.

Visitors, orders, orders per visitor

Finding 2: Visitors were increasing, however the Number of monthly Orders did not increase at the same pace. Looking at the Orders per Visitor, we can see that it was on a declining trend.

Recommendation: In order to grow sales, it is most efficient to improve the quality of your customers, especially if one is on a tight budget. To do so, the company had to target their most valuable customer.  

So, the question was: Who is the company’s most valuable customer? 

Table conversion etc

Finding 3: The first segment of valuable customer identified is: Small Businesses (< 4 employees). This segment shows a high Conversion Rate, as well as a high Monthly Revenue, however most of these customers churn after one year.

Recommendation: The start-up is recommended to reduce the Churn Rate for this customer segment. They are advised to work out a new value proposition for this customer segment and approach them actively two month before their contract ends in order to decrease the Churn Rate.

Finding 4: The second group of valuable customer identified is: Private House Owner. This customer segment contributes to the biggest share of Visitors (35,5%), yet, do they show a low Monthly Revenue (57.834 €) and a very low Conversion Rate (0,4 %)

Recommendation: The start-up is advised to create tailored content to persuade this customer segment. A further deep dive analysis revealed the segment’s age group, the keywords that brought them to the website, and their interests. Using this knowledge, the start-up is recommended to create landing pages tailored to this segment in order to increase the Conversion Rate of this segment. 

Portfolio - Data Science

Project 2: Customer Segmentation

Background: The client contacted me, because they needed an effective method to target their customer. A good customer segmentation can help a company to develop more focused strategies. This improves customer retention-rates and leads to increased sales. Marketing campaigns can be run more effectively with the result of a higher ROI (return of investment).

I have used a clustering algorithm (k-means model) to group customers into different segments. Grouping customers into segments (cluster) makes it easier to understand each group’s collective behavior. This will allow the company to handle each cluster differently — i.e. offer exclusive discount coupons to some of them. 

The first step of the clustering algorithm is to select the customer characteristics that would help the algorithm categorising them into different clusters. Some characteristics might be:

  • Number of orders per month
  • Average order value
  • Average value per item
  • Number of items ordered
  • Number of employees
  • Customer Demographics
  • Customer location
  • Number of active membership months
  • Number of Customer service contact points


In our case, we have chosen the Customers Age and Yearly Order Value as input into the algorithm.

Customer age yearly order value

Plotting the data into a Scatter Plot allows us to get a rough understanding of the distribution of the data points.

Scatterplot customer segmentation

Next, we are using the Elbow Technique, to determine the optimal number of clusters for the algorithm. The Elbow Technique confirms that three clusters is the optimal number for our algorithm.

Determine optional k for k-means

After training a K-Means Clustering Model, the algorithm has grouped the customers as follows:

Clustered customer base

Every customer got assigned to a specific cluster (see column ‘Cluster’).

Clustered customer table

The machine learning model can now be used to group new customer instantly.

Result: The company has use this information to optimize their content strategy and create targeted marketing campaigns.


Project 3: Customer Churn Probability

Background: The client asked me to predict the churn probability of each of their customer. Knowing whether a customer is at a high risk of churn is a very valuable information for the company, as it enables them to pro-actively approach high-risk churn customer and thereby, potentially prevent a churn.

Work in progress. – To be continued soon – 

Portfolio - Dashboards

My Recent Works (in progress)

— To be added soon. —

Following you can find a collection of some of the dashboards that I have built for my clients (all numbers and designs have been anonymised).


Dashboard 1



Dashboard 2



Dashboard 3



Dashboard 4



Dashboard 5



Dashboard 6



Dashboard 7



Dashboard 8



Please don’t hesitate to leave me a message explaining your business situation and analytics needs. I will come back to you shortly. Based on your objectives, we will make a plan to get the insights and dashboards your team needs to make the right decisions.


Or use this form:


    • Data analysis, data science and visualisation projects: €120 per hour
    • Presentations, training and workshops starting from €800 (max 4 hours)
    • Full-day training (Python, SQL, Tableau, Power-Bi) starting from €1.250 per day

    Rates are excluding VAT.


    Project Duration

    • Project duration can vary from three month to up to two years