Daggy is a marketing mix modeling (MMM), causal inference and forecasting service for university marketing teams, and for other marketing teams too. Send exports from Slate or your CRM and from Google Ads, Meta, LinkedIn and your other ad platforms. Daggy works out what each application costs you by channel and program, finds where your cost per application is too high, and in about six weeks proposes next year's budget to bring it down.
Request a pilotCost per application is spend divided by applications, but a platform's own report can't tell you which applications its spend caused. Programs differ in size, season and budget, and a small program has too little data to judge on its own. Daggy's marketing mix model measures what each channel did for each program while pooling what programs share, accounts for the lag between an inquiry and an application, and proposes a budget by channel, program and month that moves money from where an application costs the most to where it costs the least, inside the limits you set, with ranges. Channels your data is too thin to judge are named, to test before money moves. Media mix modeling and budget optimization.
“We’re seeing great results from the budget reallocation (leads are up across most programs with lower CPLs so we’re getting a more efficient use of our spend).”
Too thin to judge. Test before money moves.
The campaign's effect, as a range.
Applications rise every year as deadlines near, so last year's number is the wrong comparison. Daggy models the season, the trend and the deadlines alongside your spend, and gives the campaign's effect as a range. Causal inference.
A forecast by program and month, with ranges.
An inquiry this month can become an application months later, and the lag differs by program. Daggy learns that lag from your own history and forecasts applications by program and month from the season, the trend and your spend plan, with ranges. Forecasting.
Daggy replays the change on your own history before you make it.
Scenario simulation.
Pages rewritten until they're cited in the simulation.
An application that comes from an AI answer costs no ad spend, which lowers your cost per application too. Daggy takes the questions your prospects actually ask, from your inquiries and search data, runs them against the pages you compete with, and rewrites your program pages until they are cited in the simulation. AI search simulation.
Is a part-time program worth it if I work full time?
One engagement, about six weeks from your exports. You send leads and applications by program from Slate or your CRM, and spend from Google Ads, Meta, LinkedIn and your other platforms. We clean and join them by hand, so there is nothing to install.
Slate or your CRM, and your ad platforms.
Your top program pages, tested and rewritten for the questions your prospects ask.
A proposed budget for next year by channel, program and month, with ranges, and the read behind it: what each channel did for each program with season and trend taken out, and the forecast.
The pilot counts toward a year-long program if you continue within 60 days.
Where is your cost per application too high?
Request a pilotFor developers: Setup and the MCP reference.