Market Segmentation, n.
Sorting individuals into market segments with others who share similar traits.
What distinguishes one individual from another? It’s a complicated question, but when it comes to marketing, segmentation is the answer.
Sorting individuals into market segments with others who share similar traits.
Segmentation allows nonprofits and membership-based organizations to better understand the people that matter most, leveraging donor and supporter data to establish a comprehensive, nuanced view of who people are, where they live, what they like, how they think, and what moves them.
These segments are crucial to any data-driven campaign effort: whether it’s direct marketing, fundraising, relationship building, or a product launch, segmentation ensures that the right messages get to the right individuals, and in just the right way.
Many nonprofits already understand the importance of segmentation. But what they often don’t realize is there are cutting-edge approaches available that significantly upgrade their existing segmentation strategies.
The difference maker is data science, an interdisciplinary field that covers a wide range of methods to approach and derive meaning from large amounts of data.
Segmentation requires marketers to understand an individual’s motivations and preferences in relation to other aspects of their identity. Generally, users are sorted into a segment based on the following attributes:
Once audience members have been grouped, organizations can engage them more directly by crafting individualized approaches to drive desired results. For instance, an organization that advertises broadly to undifferentiated audience members is likely to waste resources and produce unmeasurable results, but the right segmentation strategy — say, sending membership upgrade appeals to affluent individuals with a history of one-off donations to your organization — can make the difference between a fundraising appeal getting ignored and a prospect converting into an annually renewing donor.
An effective segmentation strategy is essential for high-quality supporter engagement, but many successful organizations opt to further enhance their capabilities with data science.
Among its many applications, data science can be used to both augment and automate an organization’s segmentation strategy, which it does by amassing the largest dataset possible — often by combining first- and third-party constituent data — and by leveraging machine learning to search for patterns within that data that drive more efficient and effective outreach.
But while data science can help organizations reach even the most ambitious marketing goals, many still have yet to take advantage of the tools and technologies available. What’s holding them back? Marketing sophistication.
Generally speaking, organizations fall into three tiers:
Getting to the 300-level tier is critical for any organization that truly wants to stand out in today’s marketplace. For instance, they can harness the power of machine learning and artificial intelligence to process large amounts of data, and simulate (or even surpass) human learning curves in terms of completing complex tasks. This allows them to cluster their audiences for more direct engagement and segmentation. And as data science tools continue to advance and evolve, these organizations will remain on the leading edge of what is possible.
Any organization can get to the 300 level. In fact, getting there is easier than ever before, especially with Civis Analytics’ assistance.
A segmentation approach augmented by data science might look like this:
Segmentation on its own isn’t a solution. In order to select and shape the segmentation and data science strategies that will work best for a given organization, organizations first need to establish a hierarchy of goals, as well as KPIs to measure them. That means formulating and answering some key questions like:
Next up: considering who actually makes decisions for the organization. This allows us to decide which tools should be employed and, based on the decision maker, customize how they are delivered and presented, such as:
Too many organizations remain awash in a sea of data from a variety of sources, including email marketing, events, and social media, as well as information they may have already stored in a customer relationship management (CRM) tool. This data is often scattered, fragmented, and siloed, making it impossible to determine what is important, what is duplicative or inaccurate, and what may be missing.
Civis pulls all of an organization’s data into one central location, where it can be properly processed, cleaned, stored, and used in production. This is accomplished by creating a data warehouse or constituent data platform.
Data science unlocks and enhances the potential of any organization’s existing audience. Depending upon program goals and budget, a data science-led approach can include some of the following components:
After these steps are complete, it’s time to put your organization’s strategy into production. This might look like operationalizing your data science approach to build and train predictive models that are used for campaigns on an ongoing basis. This approach offers organizations the advantage of becoming more efficient by updating their approach as the underlying data changes over time.
Alternatively, embracing strategy grounded in data science can be as simple as compiling this research within a report for decision makers containing written analyses, visualizations, and engagement recommendations broken out by audience segment, channel, and geography. This report can help those decision makers determine the right strategy and tactics required to meet their organization’s goals.
For example, Civis worked with one nonprofit to complete an enterprise-wide segmentation study of its American constituent base, representing about 100 million individuals who previously engaged with the organization in some capacity. We determined these supporters:
Civis identified six distinct supporter segments in all, with the organization’s highest-value donors concentrated in three segments:
We also found that smaller supporter segments like Less Engaged Southerners and Hispanic Americans represent demographic bases that most nonprofits do not draw from at all — an asset and growth opportunity.
Using predictive modeling, Civis identified individuals who look like High Value Households but are not presently supporters of the organization — prospects of potential value to all lines of its business. We also determined that:
The organization now has segment labels available for all 100 million constituents, which it uses for optimizing person- and geographic-level marketing, as well as courting high-value prospects within each segment and acquiring new supporters based on look-alike modeling.
There’s one final consideration for every organization to bear in mind. Data science is a growing and evolving field: Supporter behaviors and needs will change over time. People can and will move between segments. Goals and decision makers will likely be updated. New barriers may also arise. That’s why it’s so important to embrace the mantras of constant experimentation, repeated iteration, and continued improvement. Long-term success means planning from the start in order to keep data fresh and insights relevant.
Civis is ready to help any organization ready to become a high-end, 300-level performer. We work with nonprofits, industry associations, and other groups to help personalize outreach and deepen relationships with their most valuable supporters. Together, we can help your organization achieve its most ambitious goals with segmentation and data science strategies — the most proven, most powerful ways an organization can set itself apart in today’s highly competitive, data-driven marketplace.
