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Starting Machine Learning in a Charity - the data, features, insights and predictions to begin

Want to start machine learning but not sure how? Think about what you have, what you can learn and the predictions it can inform.

Data and features

  • Website data- User demographics, page views, click-through rates, user interactions.

  • Charity Advocacy system data- Advocacy campaigns, user engagement, success rates.

  • Donations system data- Donor information, donation amounts, donation frequency, donor demographics.

  • Raw text messages- Message content, sender/receiver information, timestamps, sentiment analysis.

  • Events system data- Event details, attendee demographics, participation rates, event success metrics.

  • Mass email system data- Email open rates, click rates, unsubscribe rates, recipient demographics.

  • Postcode data- Geographical information, demographic data, socio-economic indicators.

  • Image data- Image metadata, image categories, image analysis results (e.g., object detection, sentiment analysis).

  • Online advertising data- Ad impressions, click-through rates, conversion rates, ad targeting.

  • Open data sets- Publicly available datasets related to relevant topics (e.g., demographics, health, education).

  • Survey data sets- Responses to surveys conducted by the charity, respondent demographics, survey questions, ratings.

  • CRM data- Donor and supporter contact information, contact history, communication preferences, donor lifetime value, legacy giving.



New insights that become available in the Data Model

Start to compare and contrast datasets that were once separate. These are just a few ideas to spark conversation of what you feel will be of the most value!


  • Analyzing website data and mass email system data together can provide insights into the effectiveness of email campaigns in driving website traffic and user engagement.

  • Combining donations system data with CRM data can help identify patterns and characteristics of high-value donors, enabling targeted fundraising strategies.

  • Integrating raw text messages with sentiment analysis and CRM data can help identify trends in donor sentiment and preferences, informing personalized communication strategies.

  • Linking events system data with CRM data can reveal correlations between event attendance and subsequent donor engagement or retention.

  • Cross-referencing postcode data with CRM data can provide insights into geographical patterns of donor support and enable targeted fundraising efforts in specific areas.

  • Analyzing image data and online advertising data can help evaluate the effectiveness of visual advertisements and identify the most engaging content for specific target audiences.

  • Combining survey data sets with CRM data can help identify common motivations and preferences among donors, informing campaign messaging and fundraising strategies.

  • Integrating website data, open data sets, and postcode data can provide a comprehensive understanding of the charity's online reach and its impact on specific communities.

  • Analyzing website data, online advertising data, and CRM data can reveal insights into user behavior and conversion rates, optimizing marketing campaigns and improving donor acquisition.

  • Linking advocacy system data, survey data sets, and CRM data can help identify correlations between advocacy engagement, donor preferences, and long-term support, informing targeted advocacy and fundraising efforts.


What we could predict

Some ideas that are interesting, will add value and are not too complex to train the model on.


  • Donor Lifetime Value Prediction- Using historical data from the donations system, CRM data, and other relevant factors, a predictive model can be created to estimate the potential lifetime value of individual donors. This model can help the charity prioritize its fundraising efforts and allocate resources effectively.

  • Event Success Prediction- By leveraging data from the events system, CRM data, website data, and other relevant features, a predictive model can be built to forecast the success of upcoming events. This model can help the charity make informed decisions regarding event planning, marketing strategies, and resource allocation.

  • Donor Churn Prediction- By analyzing donor behavior, engagement patterns, and other factors derived from CRM data, mass email system data, and website data, a predictive model can be developed to identify donors who are at risk of churning or ceasing their support. This model can enable proactive retention strategies, such as personalized outreach or targeted campaigns, to increase donor loyalty and minimize attrition.

  • Legacy Giver prediction- By looking for trends and insights we can design a model that explores existing trends for legacy givers, ways to extrapolate this to wider audiences and produces a target dataset for use with existing contacts and ideas for advertising to attract new potential donors.


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