Step 2: (part 1): Defining the types of data to drive your decisions
It would be naïve to try and list out every type of data that can be used to drive your decision-making, mainly because the number of fields and their combinations can produce near-infinite possibilities. Instead, this blog will deal with the types of data that have, generally, produced great results for me and my team over our many years working in not-for-profits (NFPs) and corporations. I include the latter as we have incorporated much of our corporate experience to finding innovative solutions for NFPs.
For starters, you’ll need all the necessary contact information about your donors. Mailing address, email, phone number. You’ll also need to make sure that this information is current and has been validated through some form of address correction and NCOA (National Change of Address).
Communication “preferences” are also important. I’m “quoting” them here because, in the past, communication preferences were considered a way of being respectful of donors’ wishes in receiving messages from you. Examples such as “once a year”, “no mail”, “do not contact”, etc helped you determine who could receive a campaign and who should be removed. They are still important to the donor relationship today. However, as of July 2014, bill C-28 (anti-spam law) greatly affected one major part of communication preferences: opting-in for emails. NFPs must now ensure that they have the opt-in of all their donors for any emails that are NOT of a fundraising nature. IF NFPs are sending emails to solicit funds, these communications are excluded from the law. Any other emails are subject to the law. For more information on this law: http://fightspam.gc.ca/eic/site/030.nsf/eng/00285.html .
Once your basic data is in order, you need a way of grouping your donors into manageable cohorts (or buckets, as I like to call them). Depending on your segmentation methods, the resulting buckets will allow you a little (or a lot) of flexibility in how you develop your content, packaging and ask amounts to address each group’s behaviours.
For instance, you can split your donor base by province or region, allowing you to create campaigns according to the physical location of your donors. This would require two fields in your database: province (which you already have in your Basic data) and region, which would assign a region to each province.
Or you can segment your donors according to the type of donor they are: monthly, non-monthly, active, lapsed. This type of data is based on the application of data rules. Data rules define the criteria for being part of a group. For instance: How would you determine someone is a monthly donor? One way is by selecting any donor with a monthly gift in the past XX months. When is a donor considered active? When they have given a gift in the past 24 months.
Data rules can be applied when targeting for a campaign (using sql) or they can be programmed as indicator fields in your database to be updated at regular intervals (more on this further in the blog). So, in the same way that you have a field that contains the donor’s postal code, you can have a field for donor status (active or lapsed) and donor type (monthly or non-monthly). This simplifies the selection and segmentation of donors by status and type for strategy and targeting.
Some more sophisticated methods of segmentation look at donor behaviours, socio-demographic variable, giving trends, life cycle stage and/or RFM (recency, frequency, monetary) to carve out specific behavioural segments. The combinations of these elements are distilled into codes that represent very specific behaviours. For example, iNFORMED Communications Group’s “Lens segmentation” has segment ‘GM_1307’ that defines the following group of donors: ‘Monthly donors displaying upgrade readiness behaviour’. This code can be appended to your donor database in a specific field called “segment” that can be used for selecting donors for a campaign. (For more information about the Lens visit our Topics page at: https://www.informedcg.com/topics/).
Summary of segmentation data:
- Geography (province, region)
- RFM methodology
- Sociodemographic segments
- Behavioural segmentation
Indicator fields, much like segmentation data, “tag” donors according to observed data rules. They can represent a closed series of values (such as ‘donor status’) or can contain a calculated value (such as average gift). In certain cases, indicators are used to assess the performance of a group of donors or of a campaign (called KPIs: Key Performance Indicators – more on this later). They can also be used to support and enhance data segmentation and targeting as they can further segment a group. For instance, using the example in the previous section, you could select all donors in segment ‘GM_1307’ and use an average gift indicator field to further select all of the donors in this segment who ALSO have an average gift value greater than $35.
Most donor segmentation methodologies start out as indicators that keep getting combined and refined.
Common indicator fields for fundraising would be:
- donor type (monthly, non-monthly)
- status (active, lapsed)
- Recency (nb of months since last gift, grouped into categories):
- 0-12 months
- 13-24 months
- 25-36 months
- Frequency (nb of lifetime gifts to date):
- Monetary (amount of last gift):
- >=$10 and <$20
- Major donor indicator (true or false)
- New donor indicator
- Issues (this would indicate which of your organization’s issues is most important to the donor)
- Prospect indicator (set to “true” where the individual has never donated, set to “false” where the individual is a donor)