As you might already be aware, our Marketing Plan's Persona model reveals who your target audience is, what interests them, and what they do online.

By adding up all this relevant information, we can target prospective parents that share similar attributes - in other words: Lookalikes.

Facebook Lookalike audiences are comprised of people who share the same basic characteristics of your current customers.

According to Facebook, a Lookalike Audience is a way to reach new people who are likely to be interested in your business because they're similar to your best existing customers.

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Following this logic, when creating a Lookalike, we can further infer two things in optimizing our model:

  • The quality of each individual sample
  • The sum of all those individuals, in order to have a wide, healthy sample pool

By combining these two important considerations, we get the highest quality leads to provide to your school.

Although our Persona is based on data that we collect from you during our onboarding process, there are two further, and better grades of quality leads to consider:

  1. Parents who have scheduled a call with your school.
  2. Parents who have already submitted applications to your school, from any and all years.

Naturally, creating a model based on the first set means that in addition to the information that you initially provide to us, we can further infer the common attributes of everyone who has scheduled a call with you. This means that the Lookalikes that we aim for are much more likely to schedule a call with you.

Furthermore, what we've observed is that by using data from actual applicants, the quality of the filter rises even higher.

You see, the more data points we have, the higher the resolution of our statistical map once we process the data.

Having actual applicant samples means that we're modeling our Lookalike filter according to the surest type of parent there is - the one that has already applied for your school.

Just imagine what this could mean for your enrollments.

In respect to privacy and confidentiality:

  • We accept to collect your information strictly for the purpose of research and development, with a view of benefitting you, the client, with the results.
  • We pledge that the information you provide (names, emails, etc.) is treated as confidential and is strictly used once, for anonymous statistical modeling. This information will not be disclosed to third parties and will be anonymized before processing. We will not monetize, sell, or otherwise benefit financially from being privy to this information.