Expanding the reach of your university or college communications and marketing materials is a continuous and ever-evolving process. Identifying high-value prospects beyond your base is essential for connecting with promising students, faculty, donors, and partners in your community and relevant industries.
Figuring out how to find and target those new audiences can be a significant challenge, but adding lookalike modeling to your marketing toolbox can help you maximize your reach.
What Is Lookalike Modeling?
Lookalike modeling is a way to identify new prospects who possess similar characteristics and behaviors to your most successful stakeholders, including current students, parents, faculty, staff, donors, and corporate and community partners, as well as alumni.
You start by analyzing the data you have on these existing or “seed” audiences, which identifies their defining characteristics and typical behaviors. Lookalike modeling then uses machine learning to find other individuals who fall into similar categories. Targeting ads, website content, social media posts, newsletters, microsites, and more based on that analysis will make it more likely that you’ll reach people ready to apply, connect, donate, or otherwise engage.
The difference between lookalike modeling and propensity or predictive modeling is that a propensity/predictive model looks at what variables drive an audience to take specific actions or formulate specific opinions, rather than modeling potential audiences based on the characteristics and behaviors of the seed audience. Lookalike modeling takes a smaller seed audience and expands upon it so you can engage new prospects. Propensity/predictive modeling helps you divide your larger audience into distinct personas, which can guide smaller targeted marketing campaigns.
How Can Lookalike Modeling Extend Your Campaigns and Help You Optimize Media Buys?
When you use a lookalike model, you can scale your marketing and communications campaigns to reach larger numbers of prospects and influential targets with confidence that they’re more likely to engage. If a specific campaign has performed well with your seed audience, these new data-defined audiences are more likely to respond in similar ways.
Lookalike modeling can take a lot of the guesswork out of a media buying strategy. That will likely save your team a great deal of time and effort, help you home in on your ideal marketing plan, and benefit your bottom line.
How Can You Build an Effective Lookalike Model?
When you’re ready to build a lookalike model, first define the types of audiences you’re trying to reach and what type of engagement you’re looking for from those audiences. Then, use existing data from your current audiences with a similar description to create the seeds for your expanded marketing strategy.
When you input that data into a lookalike modeling tool, machine learning will help you identify a broader audience ready for your targeted campaigns. A data management platform can help you aggregate data from all of your sources to create a clearer “seed” audience and use machine learning algorithms and techniques to illuminate matching prospect profiles.
Ready to get started with lookalike modeling, but need a little help honing your strategy and finding the right tools? LIGHTSTREAM can help. Contact us today.