About Prior Predict
We build AI tools that help marketing and customer success teams predict outcomes, not just report on them after the fact.
Why we exist
Most marketing tools tell you what happened after a campaign has already gone out, or after a customer has already churned. By the time the report lands, the decision that mattered has already been made.
Prior Predict exists to move that decision earlier. Our two tools — the AI Email Campaign Predictor and the AI Customer Churn Predictor — are built around a single idea: give a team the prediction before the outcome is locked in, so a weak subject line can be rewritten before it goes to forty thousand inboxes, and an at-risk account can get a call before the cancellation request arrives.
What we do
How we approach this
We explain every prediction
Every score ships with SHAP-based factors showing exactly what drove it — not just a number with no way to question it.
Practical AI over hype
Both tools are built to answer one specific, useful question well, rather than promising to do everything.
Built for a decision, not a dashboard
Every prediction comes with plain-language next steps, because a number alone doesn't change what your team does today.
One account, no data re-entry
Both models run from the same platform, so switching between email and churn prediction doesn't mean switching tools.
Who's behind this
Prior Predict is built by a small team of engineers and marketers who got tired of finding out a campaign underperformed, or a customer churned, only after it was too late to do anything about it.
The technical approach behind both tools
The Email Campaign Predictor fuses tabular campaign data, subject line and body text, and an optional banner image into a single multimodal model, rather than treating a campaign as one flat spreadsheet row. The Churn Predictor uses a sequence model (LSTM) that reads a customer's behavior over time, since a single snapshot rarely tells the full story of who's about to leave.
Both models are paired with SHAP explainability, so every prediction can be traced back to the specific factors that produced it — the same standard of transparency we'd want if we were the ones reading the score.