Can multiple data sources be integrated to understand the true cost per acquisition, from inquiry right through to enrollment?

A large, moderately selective, 4 year, public program in the Midwest was already using both Slate and Salesforce to facilitate their recruitment efforts, but a lot of the crucial information that would drive decision-making around advertising spend was sitting in separate places. This was leading to unnecessary guesswork about what campaigns on what channels were having the most impact on enrollment figures.

How we got involved:


Our experts recognized that by analyzing the advertising activity, then creating a direct connection between media activity and eventual enrollments, they could achieve zero-based budgeting (precise budgets calculated from true costs per acquisition).

This was the ultimate goal as, by eliminating guesswork, they could work out the exact advertising budget needed to achieve their desired number of enrollments. 

Not only would that minimize wastage, but it would also prove beneficial in the long-term by giving full clarity on what works best for their specific institution.


So how did we go about it? First, we implemented a two-way integration between Akero, Slate and Salesforce to create a connected picture between media activity and conversion activity for the first time.

Then, we mapped every movement a prospect made through their application journey, from the first advert that caught their eye, right through application stage and ultimate enrollment, which all automatically updated in Akero. This meant a game-changing level of information for the marketing and admissions teams, without adding to or disrupting their workload.


By using Akero to combine all siloed data and provide valuable insights into it as a complete picture, they could now see the true digital cost per acquisition (something notoriously difficult to achieve) across their advertising campaigns – from the first advert click right through to enrollment on past and present recruitment efforts. It ultimately enabled them to use the zero-based budgeting system as well.

The team now had much better and reliable advertising spend data to work out the exact advertising budget they were using, and would need to use to be financially viable. It also allowed the team to filter at a granular level (e.g. level of study) to further reduce cost per acquisition in future.

By identifying their best performing campaigns, tactics, copy, search words and advertising channels, they can filter at an even deeper and granular level such as:

  • Program
  • Level of study
  • Campus
  • Year/term of entry
  • Location
  • Motivations to Study

This deeper level of data analysis enables them to identify areas where they can further reduce the cost per digital acquisition in the future.