Barnardo's
The Mission:
Barnardo's wanted to reconnect with long-lapsed supporters who had previously demonstrated an affinity with the charity but had not donated for many years. The organisation believed these supporters represented a valuable opportunity, with the potential to deliver stronger loyalty and lifetime value than entirely new donor acquisition.
However, limited historical data, ageing records and complex fundraising rules made identifying the right audiences a challenge. Barnardo's partnered with Euler to determine which supporters were most likely to re-engage, uncover new prospect opportunities and improve the effectiveness of future fundraising campaigns.
Euler helped Barnardo's unlock value from its existing supporter data by combining data cleansing, enrichment and predictive modelling.
Working alongside trusted partners to enhance supporter records with additional demographic insight, Euler created a stronger foundation for identifying supporters most likely to re-engage with the charity.
Using this enriched dataset, Euler developed a propensity model to rank supporters according to their likelihood of responding to future campaigns. This enabled Barnardo's to focus marketing investment on the audiences with the greatest potential, while also uncovering new prospect opportunities and testing more targeted fundraising messages.
How did Barnardo's identify supporters most likely to re-engage?
Euler combined supporter history, demographic insight and behavioural indicators to identify the individuals most likely to respond to future fundraising campaigns.
Why was data enrichment important to the project?
Many supporter records contained limited historical information. Additional demographic and profiling data helped create a more complete picture of supporter characteristics and potential value.
How did predictive modelling improve campaign targeting?
Supporters were ranked according to their likelihood of responding, enabling Barnardo's to focus investment on the audiences most likely to deliver a positive return.
How did the project support future fundraising activity?
The insights generated from the modelling helped Barnardo's refine audience selection, improve prospect targeting and identify opportunities for more relevant fundraising communications.
Performance Highlights:
Here's what we achieved through a combination of data, technology and expertise.
Predictive modelling helped Barnardo's identify and reactivate supporters who were most likely to donate again.
More targeted audience selection and messaging significantly increased the value of supporter donations.
Modelled audiences delivered stronger fundraising performance than traditional campaign approaches.
Barnardo's:
As Barnardo’s have seen, modelling and profiling are two ways in which significant improvements can be made, but what became clear was how the right message, matched to the right audience, could transform results beyond the most optimistic hopes for a direct mail campaign.
CEO, Euler
Mechanica
Download the Full Case Study
See how Barnardo’s used data insight to reconnect with lapsed supporters:
Learn how predictive modelling improved campaign targeting
Discover how enriched data helped identify re-engagement opportunities
See how smarter audience selection increased donation value
