For any organization, growing your customer base, recruiting new employees, finding new donors, or expanding your user base can be daunting tasks. If you already have a good sense of what types of people your current users are, you might focus acquisition efforts on finding others who “look most like” those people, which can be done with a look-alike modeling exercise that identifies consumers most similar to your current customer base. While I could talk about that process more in-depth, I’m going to focus this post on how you can apply data science to target a different population – those who have expressed interest in or are good potential fits for your organization, but who don’t necessarily “look like” your current user base.
Acquisition of interested prospects can be a more costly and risky undertaking than acquisition of “look-alikes.” However, these acquisition prospects often lead to greater gains in the long run, as they allow you to expand into new markets, both geographically and demographically, in order to find new, untapped sources of users.
|“Look-Alike” Targeting||Prospect Targeting|
|Data||Requires only data on current user base||Requires additional data on interest collected from a survey|
|Acquisition Methods||Requires mostly just messaging to increase awareness of the product/service||Might require more extensive persuasive messaging in addition to awareness messaging|
|Potential Gains||Will only increase market penetration among the population that is similar to your existing user base||Allows for market development, growing your userbase into new populations and regions|
Finding these people might not seem like a difficult problem, but there’s quite a bit that goes into identifying and reaching those who have expressed interest.
First, how do you define and measure interest in a product or service? Simply asking a person their level of interest for a product, service, or willingness to donate doesn’t quite pass the full test for a variety of reasons: people don’t always tell the truth, they may be unclear about what you are asking, or they might have biases based on past experiences, misinformation, or a host of other things. While some of these people might not express interest in a product or service when asked, you shouldn’t be too quick to discount them as potential customers as some of their misconceptions can be changed with marketing and other advocacy tactics.
At Civis, we solve this problem with a complete data science workflow, identifying those that would be good targets for acquisition by surveying a sample of people who are within your organization’s market, yet are not part of your current user base. In our surveys, we ask respondents a variety of questions – some of which are about expressed interest in your product, service, or organization – but others about their lifestyle, behaviors, and preferences that you believe would deem them a good fit as a user of your product.
Each of these individual questions are designed to capture slightly different populations, as they measure different concepts of prospective behavior. The challenge then becomes how do we combine these various measures into a single indicator of whether or not a person is a good acquisition target? We answer this question by using our data science platform to build models for all of these measures as predictors, creating an acquisition prospect score that is then a composite measure for whether or not a customer would be a good fit. This new composite measure takes into account not only stated interest and demographic information of the consumer, but also behaviors and preferences that would lead them to be an ideal prospect for acquisition.
By using this method of customer acquisition modeling you can unlock new opportunities for your business to expand and grow, rather than limiting marketing efforts to only consumers who look like your current user base or consumers who express interest in your product. Now, you can begin to reach past the low hanging fruit to tap new markets of consumers who are great fits – they just don’t know it yet.
Written by Akshaya Suresh