As Data Director at ToucanTech, I oversee data migrations for customers who want to amalgamate and transfer years of data from their previous systems and, as such, I see lots of alumni and development data, stored in a multitude of formats and data structures.
When I first started managing fundraising databases and researching potential donors in the education sector I was a big believer in collecting as much data on individuals as possible, from whatever sources I could find, and storing each snippet of information in a designated area of my database. This, I thought, gave me the most power and flexibility to run any filter/query I liked.
As the years went on and the different sources of information and number of campaigns grew, I realised that in order to stay in control of the data not only did we need a comprehensive understanding of the database structure, we also needed to remember all the corners where we’d hidden data, often requiring the ability to run SQL queries or write complex reports to extract the information we needed.
As big databases grow, they can, over time, become unwieldly leviathans - especially if there’s no database expert in your fundraising office.
So, I decided to streamline and to empower others in the office to take back control of the data!
There are two ways to use a database:
- To look up an individual and read what’s written on their record (to do this you might want as much information as possible, personalised and written in an easy to read format)
- To filter or report on groups of individuals defined by sharing the same data (to do this effectively you need the data to be quantised and consistent)
Streamlining should take into consideration both approaches.
Let me give you an example:
Here are Four High-Net-Worth potential donors and how their data might sometimes be captured in a database:
- Jim – He appeared in the Sunday Times Rich List worth £700m. He has never been in touch with the School – you make a note on his record with a note type of ‘prospect notes’
- Helen – She is an elderly widow who lives in an expensive road in Hampstead and has indicated in a letter to you that she is considering a gift to the School. You upload a pdf of the letter into her communication history.
- Richard – the email signature in his latest correspondence says he is a Managing Director at Barclays Investment Bank. You create a work record for him detailing his position
- Anita – she was identified by a third-party wealth mining company you used to use (perhaps before GDRP!) in a wealth band of £10m+. This information was provided to you in a separate database provided by the wealth mining company. You give her a category or attribute saying ‘Wealth-Mining Research 2016’. Her children left the School 5 years ago.
All this information is useful to look at when you visit their records individually but to extract these individuals together as a unit along with many other potential donors in similar situations would require querying over several tables with a multitude of different criteria. Even if you group them all as a category called Potential Major Donor (PMD) then you will package them all together and still have to analyse each individual record to decide who are your most relevant PMDs.
When we’re gathering this information, we need to think about why we’re collecting it and how we’ll need to sift through the data in the future.
The truth is, as fundraisers, we are thinking about two things: a person’s ability to give a lot of money (their wealth) and their likelihood to give that money (their warmth towards the School).
So, I would recommend storing the prospect data using a series of simple tags:
Jim – Major Donor Prospect Level 10, Affinity Level 0
Helen – Major Donor Prospect Level 4, Affinity Level 9, Legacy Prospect
Richard – Major Donor Prospect Level 6, Affinity Level 6
Anita – Major Donor Prospect Level 9, Affinity Level 1 (but for Anita you might even decide not to keep this information at all)
Running a query to find your most relevant prospects becomes a lot easier when using these tags. For this particular capital campaign, we want to approach all Major Donor Prospects level 6 and above who have an affinity level of 5 and above but who aren’t candidates for the upcoming legacy campaign. Hello Richard!
It doesn’t just apply to categorising prospects. If Richard played the flute in the School Orchestra and the capital project you are trying to fund is a new music School. Make sure that Richard has the general tag ‘Music’ on his record rather than, or in addition to, the category ‘Flute’, ‘Orchestra’ or a note on his record. It is very unlikely your strategy will ever require a segment of flautists.
We would still add Richard’s work record – this is very useful information. However, we wouldn’t just record ‘Managing Director’ and ‘Barclays’ we would also define a distinct picklist
of industries and mark him down in ‘Investment and Finance’ or similar. This will then allow him to be easily extracted when you are holding a ‘Finance Drink event’ or asking people to provide careers advice.
This is streamlining your data! Trimming off the fat and summarising your data capture based on the purposes (the marketing or fundraising strategy) for which you will be using that data. If you want your fundraising to have the biggest impact
in the future you need a streamlined database
to help you plan, segment and execute your campaigns.
In addition to using tags and pick-lists, my final two recommendations would be to use a donor pipeline
, or series of campaign stages, to help you manage your communications and to use relevant automated data capture
(e.g. email open rates, content views etc) to save you admin time on data entry. But these are interesting topics for another discussion!
If you’d like to discuss streamlining your data message us with any questions at firstname.lastname@example.org