The frameworks come out of the thinking. This is the thinking.
I analyzed nearly 8,000 enrollment emails with an LLM. Before I trusted a single rating, I had to answer a harder question than 'can it do this?' — I had to figure out how I'd know if it couldn't.
Exact Match: 35 search terms. Phrase Match: 136. Broad Match: 3,062. That's more than 22x the matches — and a crash course in why the old advice about long-tail keywords no longer holds.
Google's Search ads might feel like a black box, but behind the curtain is really just a big pile of math. If you want to understand how search works today, we need to talk about what's been happening behind the scenes.
Conversions are a foundational element of digital marketing. Having said that, they can also be exceptionally confusing. Given this, let’s start at the very beginning.
"Opportunity" appeared in 536 emails across my dataset. I expected to find it used in a wide range of contexts. What I actually found was far more uniform — and more telling.
"Apply" appears in 51% of RFI emails and was used nearly 3,000 times across my dataset. Turns out, the same word is doing very different things depending on where it shows up.
Phrase Match went from 35 to 136 matched search terms — almost 4x the volume. Nearly a third of those were for competitor schools, and not a single one resulted in a click.
Keywords are what you tell Google you want to target. Search terms are what Google actually decides to show your ads on. These are not the same thing — and the gap between them is where campaigns get expensive.
Colleges are closing at a pace of one per week. The pandemic and the enrollment cliff are part of the story — but they're not the whole story.
Three ways social listening aligns athletics and central marcom strategies.
When we segment earned conversation, we do so to capture opinions and perspectives and to understand trends over time that relate to our two main types: audience and theme.
When it comes to social listening, your data set is the elephant, and your segments are the bites we’re taking as we eat our way through the data. Segmentation is all about taking an unwieldy full dataset and breaking it down into more manageable, usable parts.