Empowering Non-Profits: Boosting Donations, Engagement, and Community Growth.

For non-profits, growing a community of active supporters while increasing donations and engagement, are amongst the most critical marketing objectives.

Accessible Intelligence improves the outcomes of these marketing objectives through the use of our proprietary Machine Learning (AI) models, developed and refined over several years of intense R&D.

What exactly does our technology do?

Accessible Intelligence improves the outcomes of non-profit marketing by defining the messaging that will most effectively convince a supporter to achieve a fundraising objective. For example, asking a one-time donor to become a monthly donor.

There are many models in production and in development: supporter re-activation, monthly donor retention, etc. The platform includes easy-to-use tools to understand what content has been specified by the algorithms, and also automates the email sending and reporting. New generative AI models launching in 2023 will create the email content.

Each model is built to address a specific use case:

convincing one-time donors to give monthly

convincing non-donor activists become donors

the re-activation of lapsed supporters

increasing the level of participation in activism

maintaining engagement (retention)

The Accessible Intelligence platform has been in use with several pilot clients of Engaging Networks for three years. Using objective benchmark metrics, the improved outcomes for all clients have been substantial (please download our most recent case studies).

How does it work?

All Machine Learning models leverage large amounts of data in order to predict outcomes. The effectiveness of the Accessible Intelligence platform relies on two critical sets of data:

Our models rely on two critical sets of data:

The ‘transactional history’ of each client’s supporters (e.g. making a donation, clicking an email, signing a petition, etc.)

A database that describes the marketing content sent to supporters via email
Because our models connect a supporter’s engagement to the marketing content that triggered the activity, algorithms can define the messaging that will most effectively convince a supporter to achieve the objective of the use case (e.g. convincing a one-time donor to give monthly).
The algorithms in our models generate a specification of the email content that they need to write to clusters of supporters (note: we are releasing later this year our first generative AI tools to create the content). The platform has easy-to-use tools to enter and update the email content, supporter emails are triggered and sent automatically, and sophisticated reporting is available to track progress.

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