Arjan de Ruijter de Wildt

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.

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.

• 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.

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.

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?

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

• 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.

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

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

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.

• 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.

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 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