If only you could clone the best customers produced from your account-based marketing campaigns…
Well, you basically can, using a well-known and effective trick in the B2C toolbox that B2B types should be eager to adopt: lookalike audience targeting.
Instead of manually targeting prospects, lookalike modeling automatically produces ideal prospects based on the collective traits of converting customers.
Today we’re going over what lookalike audiences are, and how lookalike modeling can boost your marketing game — possibly to B2C marketing’s level.
What is a Lookalike Audience?
A lookalike audience is a kind of ideal representation of your best prospects and ideal customers. It is created using biographical and behavioral data collected on successful leads, giving you an indication of what kind of person or company is most likely to buy.
Casting a wide net may seem like a good idea for audience targeting, but you’ll have a better chance at converting if you seek out those with similar behaviors to successful customers.
After your marketing team creates ideal customer profiles, it’s time to gather first-party data and find the most common factors from current accounts. Lookalike targeting tools take that data, search third-party data sets, then return with your lookalike audience data.
Facebook Lookalike Audiences
Although it’s not the only solution for lookalike modeling, Facebook is certainly one of the most popular.
Hands down, Facebook has the most users with a gigantic pool of freely given personal information (jobs, groups, education, and interests). That data bank is enough to amass a vast ecosystem of contact information — all marketers have to do is pick out what they want.
Along with targeted ads, B2C marketers utilize Facebook for lookalike modeling. With its AI, B2B marketers can submit a data set of a “seed” audience, have the intelligence comb through its data, and find that coveted ideal audience.
How Does Lookalike Targeting Enhance B2B Marketing Campaigns?
B2B messaging poses a challenge to marketers because they typically sell to more niche markets. Consequently, a one-size-fits-all message just doesn’t cut it.
Lookalike targeting ensures your generated audience is both interested and qualified to buy your product. That ideal audience makes it easier for sales and marketing teams’ outreach strategies.
With machine learning and AI tools, lookalike modeling streamlines data-driven outreach, improving the customer experience. (It’s a huge win for ABM too!)
How Do You Create Lookalike Audiences? And How do They Work?
Finding your lookalike audience starts with creating your in-house data set, the “seed” audience. Seed audiences include segmented customer data by most common traits, ranging from location, job title, to buyer behavior.
Lookalike modeling tools analyze your seed audience and seek out potential prospects most similar to them.
Take these steps to find and create your lookalike audience:
- Gather your first-party data through pixels, tracking, and form fills.
- Analyze patterns and pinpoint recurring characteristics to form data for your seed audience.
- Export your seed data set into a compressed file (and store it to use for future engagement!)
- Feed the seed data into a digital tool that crawls its database and spits out your lookalike audience data.
This lookalike audience data is crucial for more precise targeting in your marketing campaigns.
B2B Data + Artificial Intelligence
Personalization is now a staple for B2B marketing, which increases the demand for AI solutions.
Unlike B2C data collection, users and audiences in the B2B realm don’t offer their information quite as freely. B2B marketers take extra precautions to collect and use contact information from potential buyers’ work contact information — which is more safeguarded than at-home personal information.
As a valuable tool in predictive analytics, AI solutions can also fill in contact information gaps missed in manual lead generation.
Grow Your Audience Size with Lookalike Targeting
Digital marketers have much to gain from lookalike modeling — especially saving resources consumed by acquisitions, lead generation, and engagement.
It’s essential to engage leads with valuable insights and specific messaging.
For instance, if your email campaign’s subject line is, “Increase your sales with us,” it might be too vague to get opens. However, a more specific, pain point-centered subject line like, “Find out which prospecting practices are getting in the way of a full pipeline,” could lead to better engagement and open rates.
There’s much to gain from advanced analytics and machine learning tools, but it all starts with a fully-cooked, data-driven strategy. For more, learn how ZoomInfo data can deliver targeted ads to prospects.