Donor Management: Importance of Predicting a Donor’s Giving Cycle

Many repeat donors have a well-defined giving cycle defined by frequency of donation and the timing of their donations. These cycles can be very well predicted using techniques available in data science. A fair question here would be, why should an organization incur the extra expenditure in putting analytics in place to detect a donor’s giving cycle, and not just reach out to its existing donor as per their regular campaign-based schedule? 

Here are some reasons for making this investment:

· Cost Savings – Knowledge of the donor’s giving cycle will allow an organization to reach out only when a donor is ready to contribute. This saves the expense of sending them costly solicitations at other times.

· Greater Response – By not saturating the donor with too many solicitations, the actual responsiveness of the donor increases.

· Detecting silent attrition – Acquiring a new donor is an expensive proposition, so an organization should do its best to retain existing donors.  Predicting the giving cycle, allows an organization to put in place an early warning system for donor attrition, and take remedial measures to prevent donor churn.

· More effective upsell – An important goal of donor management is to identify the giving potential of a donor, and solicit them accordingly for periodic contributions, planned giving and higher value contributions. Understanding the pattern for giving, might be important to determine the right approach for a donor. For example, for two donors with the same total contribution, one who gives it using a greater number of periodic gifts, might be more open to a periodic giving plan while the other donor, who contributes irregularly, might be more open to giving a larger gift around a designed event. 

Jigyasa’s predictive analytics automation modules, allows it to implement a very cost-effective Donor Cycle Management system for non-profit organizations, making it possible for fairly moderate sized organizations to adopt a data science driven approach to donor management.