The Challenge of Retention

Accessible Intelligence launched in 2020 with models to help improve the outcomes of two specific marketing challenges:

  1. Convince one-time donors to become monthly donors
  2. Convince non-donor activists to become donor-activists

The plan was never to stop there. I can remember asking the team, in the very early days of work on the product concept, to come up with a list of marketing challenges that Machine Learning could help address. The list was so long it was a little bewildering if I’m honest. 

Now that we’re only a few months away from delivering Generative AI, and we have delivered other bigger picture technology needs, the team is turning back to the development of more use-cases for clients to leverage in the Accessible Intelligence ecosystem. The next two will be interesting.

Donor retention has always been a major priority for all non-profits. Spending considerable resource to acquire donors only to have them walk away is the sort of thing that keeps fundraisers up at night.

There are two connected components to the challenge of retention:

  1. How do you sustain regular engagement with your donors after they decide to support you (in other words, reducing the risk that supporters will lapse in the future while they are still engaged)?
  2. How do you re-engage donors that have wandered off while they remain opted-in to your marketing communication?

So this single challenge of ‘retention’ can actually be broken down into at least two separate models, or use-cases: increase retention, and re-engage lapsed supporters.

Preventing donors from lapsing in the first place is perhaps the easier challenge from a Machine Learning perspective. There are very definite markers that algorithms can use to predict outcomes and recommend the most effective content to keep donors engaged.

To re-engage supporters that have already walked away requires some creative thinking. Let me explain.

One critical question is whether there is enough training data for models to understand what might convince supporters to re-engage. To illustrate, let’s say you have 100,000 lapsed donors (a one-time or recurring donor that has not engaged with any marketing content for at least 12 months is a reasonable qualification). If they remain opted-in to your email list, and you keep sending them email, will enough of these 100,000 donors re-engage at some stage to allow AI to find the patterns they need to accurately make content recommendations?

If the answer to this first question is ‘no’, the second question is whether the transaction history of supporters, prior to disengaging, is sufficient for clustering and defining content that will convince them to re-engage. This is what we are working on at the moment as we believe this is the best route to successful prediction.

We are in the business of answering questions like these. There are of course plenty of other questions to answer: if you are looking at one-time donors, is this a separate cluster within a single use-case, or a separate use-case altogether when compared with a model for monthly donors, or activists? What role do some of the marketing content categories play in influencing content choices? How often should donors be targeted with AI-defined content?

We have a great team of data scientists, great client data sets, and innovative algorithms to help us find solutions.

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