Over the past year we’ve spoken with over 200 advancement services professionals. They all tell a similar story: we’re only using 8-12% of the data captured in our CRM (i.e. donor database). So what’s the problem? Well, from our vantage point we firmly believe this under utilized data represents an enormous opportunity for more effective fundraising.
To be clear, what we strive for here is information, that is, data with a context and a purpose. What is the “8-12%” currently being used? It comes down to just a few data points:
- Last gift date
- Last gift size
- Geographic location
Certainly some organizations are also making use of:
- Largest gift
- Lifetime giving
- Years of giving (consecutive or total)
(While commonly used, I did not include others like ask amount and capacity. Those last two are based on an organizational algorithm and your research team, generated from CRM data or additional sources.)
You may find the majority of your data is not useful, but for many there is plenty available. The key is to identify your priorities, assess all available data sources, and looking to see what information your data can provide within the context of those priorities.
For example, if your priority is donor retention, you may decide to focus on getting beyond segmentation (e.g. geographic data) with stronger personalization (e.g. giving purposes). How do we go beyond simply acknowledging someone as a LYBUNT or SYBUNT? What more could a donor identify with beyond their year of graduation? How do donor interests align with your philanthropic priorities? Potential overlooked data within your CRM include:
- Gift purposes
- School or college; academic department
- Favorite professor
- Extracurricular activities
- Alumni events attended
- Relevant content vis-a-vis email response data
There is a lot of room to improve when it comes to how we market and communicate with our constituencies. You may be surprised to discover what your CRM already knows. The trick is to take a step back and imagine how you could apply it.
Lastly, I should note that if we assume for a moment that most of us truly really are only using 8-12% of our data, it is possible that this is due to a lot of useless data being stored. It might suggest a need for better data to aid in higher-quality data-driven decisions. However, our experience leads us to stress the underlying reality, which is to be more effective with what we already have.