We score every contact in your next campaign with a gradient-boosted model - so your direct mail and outreach budget only goes to the audience most likely to respond. Same revenue, a fraction of the spend.
Existing customer or donor records - purchase history, engagement signals, demographics - cleaned and assembled into a feature set we can train on. SFTP, CSV, or warehouse export.
An XGBoost model fits on your historical conversions. Output: a probability score for every contact in your next campaign audience. We share feature importance so the model isn't a black box.
Pick a threshold based on your CPM and expected conversion. Mail or serve ads only above the cut-off. Same fundraising or revenue, fraction of the print, postage, or media spend.
If you're paying per recipient - postage, print, ad impressions, calls - and most of them don't convert, scoring before you spend pays for itself in the first send.
Donor mail-outs, newsletters, recurring-gift upgrades. Cut volume without cutting yield.
Catalogues, win-back coupons, churn campaigns. Predict the customers worth reactivating.
Door-knock priorities, persuadable-voter mail, GOTV cohorts. Same field budget, more contacts that matter.
Renewal letters, retention offers, free-trial conversions. Spend on the segment that's actually on the fence.
By scoring their donor base before each send, we identify which contacts are unlikely to donate in a given cycle - reducing print and postage costs without reducing fundraising yield. Same approach generalises to any organisation running volume campaigns.
Pilot is in flight. Results coming end of 2025.
CS student at the University of Copenhagen. Builds the data pipelines, the training infrastructure, and the deployment side of the product.
CS student at the University of Copenhagen. Owns the modelling and applied statistics - feature engineering, calibration, and evaluation.
Tell us about your campaign volume and the data you already have. We'll scope a pilot in one call.
info@cphml.com