--- title: Not for Profit Case Studies | Datamine description: Data analytics for not for profit charities improves donor engagement, donor return ROI, strategy. Case studies for data science success. image: https://www.datamine.com/hubfs/Blog%20images/Not%20For%20Profit%2010.jpg --- [![Datamine](https://www.datamine.com/hs-fs/hubfs/Imported_Blog_Media/Logo-for-website-white-01.png?width=417&name=Logo-for-website-white-01.png "Datamine")](https://www.datamine.com/) - [Working together](https://www.datamine.com/how-to-drive-commercial-growth-with-data-analytics-and-ai-datamine) - [How we work together](https://www.datamine.com/how-to-drive-commercial-growth-with-data-analytics-and-ai-datamine) - [Our Workshops](https://www.datamine.com/envisaging-and-development-workshops) - [Our Pricing](https://www.datamine.com/how-much-does-data-analytics-and-ai-cost) - [Solutions](https://www.datamine.com/oursolutions) - [Case Studies](https://www.datamine.com/industryexpertise/case-studies) - [Retail](https://www.datamine.com/industryexpertise/case-studies/retail) - [FMCG](https://www.datamine.com/industryexpertise/case-studies/fmcg) - [Travel](https://www.datamine.com/industryexpertise/case-studies/travel-tourism) - [Not for Profit](https://www.datamine.com/industryexpertise/case-studies/not-for-profit) - [Media & Entertainment](https://www.datamine.com/industryexpertise/case-studies/media-and-entertainment) - [Telco](https://www.datamine.com/industryexpertise/case-studies/telco-and-technology) - [Utilities](https://www.datamine.com/industryexpertise/case-studies/energy-and-utilities) - [Health & Wellness](https://www.datamine.com/industryexpertise/case-studies/health-and-wellness) - [Government](https://www.datamine.com/industryexpertise/case-studies/government) - [Finance](https://www.datamine.com/industryexpertise/case-studies/banking-and-finance) - [Insurance](https://www.datamine.com/industryexpertise/case-studies/insurance) - [Real Estate](https://www.datamine.com/industryexpertise/case-studies/real-estate) - [Industries](https://www.datamine.com/industry) - [Retail](https://www.datamine.com/industry/retail) - [FMCG](https://www.datamine.com/industry/fmcg) - [Travel](https://www.datamine.com/industry/travel-and-tourism) - [Health & Wellness](https://www.datamine.com/industry/health-and-wellness) - [Not for Profit](https://www.datamine.com/industry/not-for-profit) - [Media & Entertainment](https://www.datamine.com/industry/media-and-entertainment) - [Telco](https://www.datamine.com/industry/telco) - [Utilities](https://www.datamine.com/industry/utilities) - [Government](https://www.datamine.com/industry/government-and-public-sector) - [Finance](https://www.datamine.com/industry/banking-and-finance) - [Insurance](https://www.datamine.com/industry/insurance) - [Discover](https://www.datamine.com/resources) - [Products](https://www.datamine.com/what-we-do/our-products) - [WeekAhead](https://www.datamine.com/weekahead) - [Retailwatch](https://www.datamine.com/ourservices/retailwatch) - [Site Spotter](https://www.datamine.com/ourservices/sitespotter) - [NZ Fuel Prices](https://www.datamine.com/data/fuel-price-nz) - Insights - [Executive Insights](https://www.datamine.com/executive-insights) - [The Boardroom Table](https://www.datamine.com/theboardroomtable) - [Retailwatch Dashboard](https://www.datamine.com/reports/retailwatch-dashboard) - [Telcowatch Report](https://www.datamine.com/telcowatch) - [Downloadable Guides](https://www.datamine.com/resources/datamine-guides) - [Datafix Blog](https://www.datamine.com/datafix) - [The Boardroom Table](https://www.datamine.com/theboardroomtable) - [About us](https://www.datamine.com/about-us) - [How we work together](https://www.datamine.com/how-to-drive-commercial-growth-with-data-analytics-and-ai-datamine) - [The Datamine Story](https://www.datamine.com/the-datamine-story) - [Our People](https://www.datamine.com/being-a-dataminer/our-people) - [Our Values](https://www.datamine.com/en-au/about-us/our-values) - [Our Partners](https://www.datamine.com/company-we-keep-tech-partners) - [Join Us](https://www.datamine.com/being-a-dataminer/join-us) - [Contact us](https://www.datamine.com/contact-us) # not for profit case studies ## NOT FOR PROFIT ![Not For Profit-1](https://www.datamine.com/hs-fs/hubfs/Iconography/Black/Not%20For%20Profit-1.png?width=45&name=Not%20For%20Profit-1.png)   Datamine is dedicated to providing the Not For Profit sector with state-of-the-art predictive tools and enhanced analytics.  As part of our philanthropic values, we've done pro bono work across multiple major charities, such as Child Cancer Foundation, IHC, ChildFund and Cancer Society, in order to help these incredible organisations improve their donor return rates and maximise the impact of donations. Scroll down to find out how Datamine has helped a number of leading charitable trusts and non-profits respond to industry challenges.     # CHILD CANCER FOUNDATION GETS GREATER DONOR VISIBILITY "We really enjoyed having the opportunity to work with the team at Datamine.  They come with sound knowledge, clear understanding and impressive expertise with analytics, and they delivered a very thorough and client-friendly report that provided in-depth insights into our donors as well as future recommendations." **Di Senior - ****Marketing Manager at Child Cancer foundation** **THE CHALLENGE** Child Cancer Foundation is a non-profit organisation dedicated to helping Kiwi families through the challenges of a childhood cancer diagnosis.  They have been collecting donor data for decades, but they haven’t had the internal capabilities to analyse it.  As part of Datamine’s sponsorship of the Haier Big Hoot 2018, we worked with Child Cancer Foundation to help them learn enough from their data to be able to connect on a more meaningful level with donors. **THE SOLUTION** Our objectives were to [clean and organise Child Cancer Foundation’s data](https://www.datamine.com/ourservices/datacleaning) to remove duplicates, inaccuracies and other issues, as well as provide them with information about their donors to enable [better targeted marketing campaigns](https://www.datamine.com/ourservices/customer-segmentation) moving forward.  With this knowledge, Child Cancer Foundation employees will know where their precious, limited time and resources are best spent - this in turn will facilitate cost reduction and more relevant spend, freeing up more resources for the children and families themselves. **THE RESULT** After segmenting Child Cancer Foundation’s donor base into groups based on location, gender, donation amount and year of last donation, Datamine was able to determine a number of average donor profiles, as well as which marketing campaigns were most successful.  Child Cancer Foundation is thrilled with the results, and the team is planning to implement a targeted outreach system, a solid data management system and repeat campaigns to better aid the families of those struggling with child cancer. # DATAMINE ANALYSIS REVEALS FLAW IN CHILDFUND DONOR STRATEGY **THE CHALLENGE ** ChildFund had experienced a dramatic increase in new child sponsors year on year - and expected a corresponding lift in donations as a result.  However, donor revenue didn’t reflect this.  New sponsors were not staying and the number of children in need of a sponsor was growing.  ChildFund asked for Datamine’s help to discover why the organisation was experiencing a high rate of donor churn - and why the new donors weren’t contributing as expected. **THE SOLUTION** Datamine took the following approach: - Quantified churn over time and identified the specific time period when the churn rate at ChildFund increased - Investigated revenue changes and correlated that data with ChildFund’s known recruitment channels - Considered the effect of the acquisition channel on donor churn - Used Census data to provide a profile of donors in good revenue years and compared that with donor profiles during the downturn **THE RESULT ** Datamine determined that revenues didn’t match the increase in sponsors for two critical reasons: 1. The profile of the new sponsors showed that they were from a lower socio-economic background 2. These sponsors had been acquired through direct recruitment (approached on the street) In essence, prior to the downturn ChildFund had made a strategic shift in its recruitment strategy and had decided to invest more resource into direct ‘on the street’ sponsor acquisition.  The problem was, however, that these sponsors either couldn’t afford the sponsorship or simply couldn’t refuse signing up when approached in a shopping centre or supermarket. The findings resulted in a total revision of ChildFund’s acquisition strategy with advertising spend being allocated more effectively due to ChildFund’s better understanding of its target audience. # DATA-DRIVEN DONOR PROFILING BOOSTS CANCER SOCIETY'S ROI **THE CHALLENGE** Charities, like businesses in general, have limited resources – and in the same way that businesses are accountable to their owners, charities are accountable to their donors.  To this end, the Wellington Cancer Society needed to ensure that it spent donor’s contributions wisely and approached Datamine for assistance in ensuring its marketing spend delivered a positive return on investment (ROI). **THE SOLUTION** To help the Cancer Society increase its campaign ROI Datamine profiled the Society’s existing donors - establishing what they ‘looked like’ and how they behaved.  Using a combination of Cancer Society donor data, census data, Quotable Value NZ data, and the Deprivation Index, Datamine created profiles for five unique donor groups – from very high through to very low.   **THE RESULT** Using variables such as age, frequency of donations, occupation and property values, Datamine compared each of the five donor groups to the Society’s entire donor base (i.e. the average), and also to the greater NZ population.  In addition, maps of the Wellington region showing the penetration and value of donors by geographic location were produced.  Equipped with accurate donor profiles, the Cancer Society was then able to make strategic shifts in its donor acquisition campaigns, much improving their targeting, relevance and effectiveness. # IHC SMILE CLUB GETS AN ACQUISITION MODEL **THE CHALLENGE** As a provider of services to people with intellectual disabilities and their families, IHC depends on its Smile Club members to donate a regular amount every month by direct debit or credit card.  Administratively the Club is easy for IHC to manage and from an operations perspective, having Club members contributing automatically is the most efficient way to receive donations.  With this in mind, IHC and Datamine embarked on a project together to more effectively target the prospects that were most likely to become donors and join the Smile Club. **THE SOLUTION** Datamine constructed a predictive model to identify which meshblock’s (the smallest defined geographic area for which statistical data is collected by Statistics NZ) contained the most residents who were likely to become donors and long term members of the Smile Club.  The model correlated IHC donor base data with Statistics NZ census info and ranked prospects based on their propensity to donate.   **THE RESULT** The Datamine model has enabled IHC to more efficiently target its most likely Smile Club prospects, resulting in more successful donor acquisition campaigns and a reduction of wasted campaign spend in areas with a low to moderate likelihood of conversion. 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