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Case Studies

Discover how we are helping organizations use data analytics for evidence-based decision making to improve marketing, guide their high-level strategic planning and drive operational improvements.

 

 

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Boasting seven Nobel Prize winners and two Canadian prime ministers as alumni, the University of British Columbia (UBC) is consistently ranked among the top 40 universities in the world. With more than 60,000 students, UBC's commitment to research is evident in its investment of $580 million in 8,808 research projects.


  • “Some of our findings blew away the fundraisers. We showed that we could accurately identify the major giving potential of nine out of 10 individuals who had already given us a major gift based solely on where they live and data recorded within our CRM system.”

    —Tracey Carmichael, Director of Research and Data Analytics | UBC's Development and Alumni Engagement Department (UBCDAE)

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Challenge

Finding new donors to financially support a university’s burgeoning academic programs is always a difficult task. If an advancement office relied solely on previous donors for contributions, it would miss out on many promising prospects. The challenge for UBC’s Development and Alumni Engagement Department was that because of strict privacy laws, they did not have any way to identify individuals who had the wealth or ability to make a major gift (more than $25,000), beyond looking at census data.


Solution

 

Step one is gathering data


The first step in aiding UBC in its donor research was to help them understand Canada’s financial landscape. By using the WealthScapes financial dataset, UBC was able to gather neighbourhood-level information on Canadian’s incomes, investments, liquid assets and net worth.

  • Market Ranking
  • Data Collection
  • Market Studies and Area Reporting


 

Step two is applying analytics


The AgeByIncome and LiquidAssets databases were used to narrow the search results to identify prospective target groups. The university was able to identify older candidates, as well as those who could give at a major gift level, by isolating neighbourhoods according to desired liquid asset holdings and age and income classifications. 

  • Financial Profiling
  • Custom Segmentation
  • Visualization and Reporting


 

Step three is actionable outcomes


To turn this in-depth financial neighbourhood information into actionable insights, an innovative model was used to rank postal codes based on wealth indicators. This model identified the households who had the financial wherewithal and propensity to give generously to the university.

  • Data Mining
  • Custom Modelling
  • Predictive Analytics
  • Visualization of Segments


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Results

By using data-driven profiles and visualizations of the segments identified by the model, UBC’s Development and Alumni Engagement Department’s fundraising staff recognize the potential major gift donors faster and more reliably. One insight indicated that 27 percent of non-donor alumni were both willing and wealthy enough to become major donors.