By Ryan Bremer, Data Analyst
Chapman Cubine Adams + Hussey
Have you ever walked into a crowded store and thought, “Who are all these people?!”
If you are a direct marketer, you should ask that question about your target audience. Indeed, who are the people who donate to your organization? What motivates them? Do they live in similar regions? Do they earn similar incomes?
Many of these questions can be answered through segmentation, and subsequently, data overlays. Marketing segmentation, by definition, is identifying customers or donors with similar needs that demonstrate similar behavior. Segmentation is typically applied in one of the following three forms:
Reductive segmentation incorporates common demographic traits often asked in surveys. Useful demographic data ranges from the very broad (age groups) to the very specific (number of computers in the house), depending on what the researcher is trying to deduce. Geographic data can be important too, if an organization is looking to reach donors in a certain region of the country. For example, ocean conservation organizations tend to have more donors in states with coastlines.
Attitudinal segmentation is often employed when marketers want to segment donors into groups based on their perception of the product or brand. Attitudinal data is typically obtained through surveys, and is often used in conjunction with reductive segmentation to generate specific segments. For example, donors who identify themselves as cause-driven activists may exhibit different giving patterns than passive donors.
Behavioral segmentation is focused on how the customer has behaved since they have been on file. It aims to discover the giving patterns that emerge from a donor’s history with the organization. This kind of segmentation discovers groups of donors that are more likely to give around certain holidays, or which donors are more likely to respond to a monthly giving invite.
RFM (recency, frequency, and monetary value) is behavioral segmentation that evaluates customers based on how recently they purchased, how many purchases were made, and how much was spent. RFM is employed by retail companies to identify their best customers. It is also used to determine pricing, inventory levels, and product positioning.
RFM is the segmentation at the forefront of the mind for fundraising organizations. This is because RFM is so useful when conducting a cursory analysis of a donor file. Year on File, Most Recent Contribution, and Gift Frequency are all valuable RFM measures used to compare donors within an organization.
By using data overlays that incorporate segmentation methods, a direct response fundraiser uncovers valuable characteristics of the donors.
Data overlays can be obtained through a third party survey, purchased data appends, or donor data collected internally. Here are some examples of data overlays that can assist in segmenting a donor file:
Age: An age overlay is helpful when you have a message that can apply to certain age groups differently, or when you want to test a specific age group of your donor base.
Income: Several data firms offer income overlays. They are often segmented in creative categories that incorporate factors like savings rate, home ownership, and disposable income.
Gender: Sometimes organizations want to know if their donors behave differently by gender.
Some combination of the above overlays and segmentation can help you get to know who all these wonderful people donating to my organization are!
