How will AI improve
your media mix?

Challenge

Fusion92 manages paid search marketing for nearly 1,500 dental practices, each with unique goals and budgets. The programs combine brand and generic campaigns across traditional search and local campaigns. The team aims to spend the entirety of the monthly budget allotted for each office to maximize conversion volume. However, they must refund their client if they spend more than the allotted amount. They also want to get the most efficient CPAs possible.

Background

One of our customers is a digital advertising and marketing agency that delivers highly targeted campaigns custom-tailored to their partner’s needs and goals. For their client, a senior living facility company, their goal was to generate leads for people looking for senior care. 

The client operates dozens of communities – each with its own target budget for lead generation campaigns. The agency’s job is to maximize leads for the allocated budget and any unspent budget represents missed opportunities. Before using Marin, the agency team would have to monitor budgets manually, a time-consuming and error-prone process. In the month before enabling Marin’s automatic pacing, only 50% of folders were within 10% of the target budget.

Challenge

Alumni Ventures targets alumni from specific colleges and universities, with advertising spend spread across LinkedIn and Meta. Monthly budgets are allocated to each alumni network, and then the marketing team splits that investment across publishers to maximize lead volume for the sales team.

They found that although the alumni audiences targeted on both Meta and LinkedIn were similar, there was no consistency in how each channel performed across networks. While their audiences were demographically similar, they experienced inconsistent engagement. Some audiences strongly outperformed on LinkedIn and others on Meta – making it difficult for them to optimize using traditional attributes. Manually allocating budgets by alumni audience and channel over a defined period was quite tricky and time-consuming. They needed a more adaptive approach that could quickly adjust for both spend pacing and channel performance trends automatically.