Physician targeting for a product launch

Reach the right prescribers in a launch window that never comes twice

A launch has a narrow window. Your field team can reach only a share of the healthcare professionals in a therapeutic area in the first months, and the order matters.

Deciles built on last year's prescribing in a nearby class miss HCPs whose patients fit the new indication but who never wrote the old product. They also give your reps no reason to trust the list.

How physician targeting for a launch works

Step 1: Agree the data

Your compliance and privacy teams approve the sources: licensed prescription and claims data, specialty, practice setting, patient mix and past engagement with your approved materials.

Step 2: Frame the prediction

A classification model predicts which HCPs are likely to adopt a therapy for the approved indication, trained on past launches in comparable classes.

Step 3: Build the model

H2O Driverless AI runs feature engineering and model search, and documents the result with AutoDoc.

Step 4: Explain each rank

Shapley values show which factors drove each HCP's score, so a field lead and a compliance reviewer can check it.

Step 5: Plan and refresh

Your commercial team plans call sequences and approved content channels from the ranked list, then rescores as early prescribing data arrives.

Keeping targeting inside your compliance rules

A targeting score tells your team where approved information is most relevant. It does not decide what a rep says, and it is never tied to payments, samples, speaker fees or anything of value.

Keep the model to the approved indication and to data sources your compliance team has reviewed. Every score comes with its reasons, so your reviewers can audit how the list was built.