Supporter Engagement: Campaigns (tactical)

Sequoia can also help with reviewing individual campaigns. This can either be on an ad-hoc basis where we feedback results in a report to allow you to gain a deep understanding of the campaign’s impact and derive valuable insights, or we can build automated PBI reports to allow the in-house team to review each campaign for continued internal reviews and updates.  Both options ensure that thorough reviews take place after each campaign giving all the available learnings to take forward into future campaigns.

Our aim is to provide you all the information, in a format that suits you, to enable you to make informed decisions for future campaigns. By using our Post Campaign Analysis services, you can continuously optimise your fundraising strategies, boost donor engagement, and maximise your impact.

Testing should be at the heart of any communication programme to help improve the engagement and responsiveness from your supporters. This can cover many different aspects of your programme including audience selection, content, timing, ask levels and much more. Sequoia can help you make the most of these tests to ensure you get the correct learnings, including breaking the analysis down to segment level to identify high or low performing audiences within each test.

Structure and analyse your testing programme to ensure continual optimisation and more targeted and appropriate communications.

A good segmentation will help you understand more about the behaviour, demographics, attitudes or motivations of your supporters and optimise your stewardship of different audiences. What type of segmentation will depend on your overall aims and in our experience no single segmentation will answer all your questions, but we can help you develop segmentations for appropriate uses or source the data needed to provide the extra layers that you need. For example, a behavioural segmentation will help you understand who best to target with specific offers, or where your opportunities for cross sell are, whilst a motivational segmentation will tell you more about why people support your charity and help you develop targeted messaging for different groups.

Break down your supporters into robust and actionable segments to optimise engagement. Through a deeper understanding of different groups you can plan strategies and activities to provide a more personalised experience.

Predictive models can help you understand how likely supporters are to respond to particular types of campaign and can be used to improve targeting. Based on engagement and previous giving behaviour each supporter can be scored to show their likelihood of response to a campaign. This, along with a segmentation can allow a more granular level of selection.

Depending on discussions and review, we may recommend an improved framework for targeting your campaigns including a segmentation and a predictive model. The model would score each supporter to show their likelihood of response to allow selection down to a more granular level.

Using machine learning algorithms will ensure you are always selecting the best supporters for any given campaign and making sure they get the right ask at the right time.

As well as understanding who to include in appeals, it is vitally important to get the level of ask right. We would review how asks have been assigned to supporters in past appeals and compare against what they have responded to ensure supporters aren’t being asked too much or too little. In addition to this, we can then apply modelling techniques to help identify the optimal ask level for each supporter.

Getting the ask level right (which may be higher or lower than the current strategy) can increase response levels and ultimately lead to increasing supporter files and income.

Identifying current rules that define Mid or High value supporters. Profile of Mid or High Value supporters compared to the rest of the supporter base, do they differ, how do they perform in current campaigns.  Are there “bubbling under” supporters – not quite giving at current threshold levels but higher than the typical Standard Value supporter.

Using profiling, segmentation or modelling to help identify best prospects with the propensity to give at higher levels for further nurturing in mid or high value programmes.