Experienced marketing growth leader skilled at aligning strategy, analytics, insights, data engineering, and machine learning. Focus on experimentation and understanding consumer behaviour and motivations to go beyond statistics.

Arjan de Ruijter de Wildt


I have been building and managing teams since 2011

From being responsible for a small database marketing team, to managing and coaching managers and advising C-level executives.

I am a facilitative manager

I make sure objectives and priorities are clear, and that team members have the right skills, tools, resources and connections. And I am there to train and coach my team members through formal evaluations, weekly development and priority meetings, and work sessions.

My focus is on complimentary individual growth within my team

Knowing your team members strengths and weaknesses. Helping them become aware, build and overcome and ask for help when needed, allowing the team as a whole to be able to meet the organization’s challenges.


Since 2011 I have been leading data driven strategic projects

Including developing a full marketing data & analytics strategy and customer-centric practices for one of the largest pan-European online health and wellbeing groups. Expertise in marketing & product optimisation, multiplatform tracking, integrating third-party data, defining KPIs, and implementing robust budget cycles.

Example projects were:

  • Developing and rolling out a full European marketing data & analytics strategy.
  • Launch a new fully insights based media brand strategy together with brand and programming specialists
  • Shifting from an order centric to a customer centric marketing practice together with data engineering and the BI team.
  • Unifying web and app tracking and making it future proof by creating one central tracking plan and coaching analysts in the process.
  • Integrating relevant 3rd party marketing data, e.g. integrating data from Google Ads, GSC, Facebook Ads, Affiliate platforms like AWIN and Social media data.
  • Defining KPIs and creating a performance tracking framework and translating that to dashboards and actions with tools like Looker Studio and PowerBI.
  • Implementing a more robust budget cycle focusing on customer behaviour, data quality, advanced forecasting, and integrating customer, operational and finance data.

Analytics & Experimentation

I've been an analytics & experimentation expert since 2007

From KPI & topical tracking, annual planning & forecasting, to setting up A/B tests and growth hacking.

Analytics & experimentation is most effective when aligned with business strategy and objectives

It should answer the most common questions in an automated way, and fuel growth using an experimentation and knowledge framework. Experimentation and analytics should live within the organization supporting day to day decision making within all business critical teams.

Analytics & experimentation starts with a conversation about needs and it ends with a conversation about actions

What information do you need to make better decisions and how can data based experiments support that? What does the data tell us, what is the implication and recommended action?

Data Engineering

Since 2009 I have been working on data engineering projects

Together with data engineers/BI specialists, working on reporting views, database design, migrations, customer analytics layer designs & implementations, and batch & streaming data for tracking, reporting and modeling.

Data engineering is about creating a data environment that reflects all operational processes and customer behavior

It is about a well documented and auditable integrated environment representing data throughout the marketing and product funnel. An environment and design that is robust to changes and can cater to the entire organization

The data environment should:

  • Allow for structured data like orders, semi structured data like event tracking, and unstructured data like open feedback or attached documents.
  • Be flexible to be queried using multiple popular querying languages (at least Python and SQL or similar).
  • Should allow self service for different levels of expertise. From access to raw data for analytics engineering and data science to analytics layers and views that allow building topical visuals for non-expert users to answer their day to day questions.
  • Contain formats and structures that are tool agnostic allowing for migrations when business requires that.

Data Science

Data science should automate trend, outlier and relevant segment and event detection and prediction, and make it easier to process and structure large amounts of unstructured data like text to facilitate a personalized customer experience

The data, plus models should be well documented and chosen based on transparency

The train, test and validation data, plus models including parameters and hyper parameter tuning should be well documented in a centralized data science environment.

The models should not only be chosen based on performance, but when performance is similar also based on transparency and ability to explain the impact of different variables and help explain business outcomes

Since 2012 I have been working on data science projects including:

Automation of trend and outlier detection (visually and statistically)

  • e.g. just using rolling averages and SD, or algorithms like Mann-Kendall Test and Random Cut Forest from Amazon for streaming data

Lifecycle modeling & segmentation like churn risk modeling and customer segmentation

  • e.g. simply using regression and RFM, or clustering like K-means, decision trees, or Neural Network models based on K-means scores like Deep Embedded Clustering

Media mix modeling

  • e.g. using simple regression or bayesian MMM modeling

Data Governance

Since 2011 I have been involved in and leading data governance projects

Ranging from questions like how to handle personal information of customers to designing and auditing a new setup of audience tracking.

    Data governance starts with the objective and fit within the organization

    Why do you need the data, how does it support the strategy. How does the governance fit within the organization, how is it documented and used. And what tools, policies and processes are in place to ensure proper governance.

    It is about:

    • Defining and documenting an overview of needed and used data, purposes and use cases.
    • Setting data protection and access levels
    • Defining data quality levels.
    • Documenting data collection, processing, storage, analysis, modeling and visualization.
    • Having data owners for each source, pipeline, table, view, report and model.
    • Having the required policies in place and updating them with changes within the organization.
    • Making sure all access and use of data is being audited and monitored by IT.

    Data Culture

    I have been promoting data culture since 2007

    As a data enthusiast I cannot help to promote the use of data in decision making. I always make sure stakeholders and end users know what data is available, how it can be used and how it supports their decisions.

      To create a data based culture a whole set of measures is needed that is tailored to the existing knowledge sharing culture

      E.g. I have promoted one-on-one conversations between data experts and end users, organized educational sessions about the data landscape, hosted best practise presentations, set up organization wide data skill trainings, and created cross functional growth teams.

      Data culture is about data literacy throughout the organization, availability of data and insights, and people within the organization knowing how to follow up on it

      Data literacy is built, presenting, training, and coaching.

      It is about:

        • Knowing about the relevant data sources.
        • Knowing about the collection and organization of data, methods to analyze, visualize and predict
        • Using the insights gained from the data to inform decision-making
        • Understanding the limitations of data, including its biases and inaccuracies, and being able to communicate these limitations effectively
        • Being able to access the required data and insights fitting to your skill level