Wood for Trees has a team of analysts skilled at modelling and presenting your data in enlightening ways to drive insights and actions
More than just providing numbers, we’ll help you understand what the numbers mean and what should be done about them. We do this using a range of tools and techniques including post-campaign analysis, segmentation, machine learning and forecasting, all underpinned with a keen eye for storytelling and data visualisation.
We offer improved insights based on better data, to help you:
- Monitor fundraising performance and strategy
- Monitor, evaluate and improve results across your campaigns and appeals
- Optimise and automate your communications and selections
- Target supporters with the rights asks and products
- Provide structure and strategy to your programmes
- Robustly plan and refine your strategies
Campaign analysis and performance reviews
Monitor, evaluate and improve results
The first step is to make sure you have a full understanding of how your activities are working and how supporters are responding to your individual campaigns. Wood for Trees can help you get under the skin of your campaigns and appeals and ensure you know where they are performing and where they are not. Our analysis will help you improve targeting, response rates, prompt levels and, ultimately, revenue.
We will also ensure you look at these results in the context of the bigger picture in terms of lifetime value, supporter engagement and cross-sell opportunities.
Segmentations and supporter understanding
Provide structure and strategy to your programmes
Segmentations help you truly get to grips with who your supporters are and how they support you. Wood for Trees has built many different segmentations for many different purposes and can help you determine the best data and approaches to use for your specific requirements.
Ranging from behavioural models to attitudinal models and using data from your CRM enhanced with external data or research data where appropriate, we will create segments that help engage with your supporters in more appropriate ways.
Machine learning and predictive modelling
Optimise and automate your communications and selections
We pride ourselves on creating models that make good business sense as well as being statistically accurate. We can do this because we understand the context of fundraising that we are observing the results in and we understand the language and ways of giving behaviours that are being evaluated. We will also work with you to ensure the model is fully understood, applied in the most appropriate way and monitored on a regular basis.
Wood for Trees uses a range of analytical data mining software, including R, Python, FastStats and SAS to create our models. We can produce a suite of models such as campaign response, prompt levels and recommendation engines to improve appeal programmes and produce optimised journeys for supporters. We also have a suite of survival-based models to look at predicting longer term supporter outcomes, such as, the likelihood of a donor to double their annual giving value or identifying donors most likely to hit mid- or major-value giving levels.
Forecasting tools
Robustly plan and refine your strategies
Big planning decisions become easier when you can rely on what your data is telling you, not just about what happened last week, last month, last year but about what will happen next year or even 10 years from now. A coherent approach to data understanding can reveal patterns that may have always been there, but which were previously obscured by the surrounding noise.
We work with our clients to ensure all fundraising insight is placed in the full context of an organisation’s goals, making sure the right questions are being asked and that any learnings from previous projects are referred to where relevant.
Pulling all this together into a suite of off-the-shelf forecasting models can help you make the right decisions on your future strategies.
We offer improved insights based on better data, including:
- Propensity models
- Prompt analysis and modelling
- Lifetime value analysis
- Segmentation
- Supporter engagement scoring
- Machine learning algorithms
- Marketing mix modelling
- Legacy audits
- Digital analysis and attribution
- Service delivery impact
