Jean-Patrick Tsang, PhD & MBA (INSEAD)
Tel: (847)920-1000
Email: bayser@bayser.com

Igor Rudychev, PhD
Tel: (847) 679-8278
Email: igor@bayser.com

Sampling

Samples are a crucial component in the pharmaceutical marketer’s toolbox.  They not only encourage physicians to initiate patients on the therapy, but they also provide a convenient excuse for sales reps to get through to the physician, hence making detailing possible.  Yet tracking of samples stops with the reps dropping off the samples.  There is no way to know if the samples are actually used, and by which physician in the group practice, or for which patient profile – not to mention how the sample is actually consumed.

Patient-level data lifts that veil of obscurity.  It allows the marketer to track the sample beyond the sample closet by shedding light on the consumption behavior of individual patients.  This has far-reaching implications in terms of sample use pertaining to physician profile, patient profile, prescription-sample combination, initiation on new patients, cannibalization of paid prescriptions, missed therapy initiations, and sample response.

Six Insights of using patient-level data

  1. New patient starts of a physician.

    Promotion-response models may now be developed to capture the impact of samples, not on Rx volume, but on new patient starts and compliant days of therapy of continuing patients.

  2. Profile of patients who receive samples.

    Patients who can’t, or won’t, purchase prescriptions may not be worth sampling.  The same applies for patients who have a history of poor adherence.  If the bulk of the patient base of a physician has such a profile, aggressive sampling may be a waste of time and money.

  3. Indication for which the sample is used.

    Where a drug has multiple indications, analysis of the data will show for which indication the sample is used.  If the findings are at odds with the marketer’s expectations, custom messages may be crafted and delivered via sales reps in an attempt to get physicians on the right track.

  4. Correlation between Sample Days of Therapy and Paid RX Days of Therapy.

    Knowledge of this relationship may help the marketer steer clear of cannibalization by not sampling physicians beyond the point where Paid Rx Days of Therapy drop off.

  5. How a physician uses samples.

    Data might indicate whether samples given out to continuing patients taking a competitor’s drug leads the patient to switch to the marketer’s drug, or that poor market share correlates with a low number of patients initiated with a sample.

  6. How patients use samples.

    Data might indicate that patients switch to a competitor’s drug after using the marketer’s sample for a short period of time.  The patient could be concluding that the drug is not working or is not potent enough, so the marketer may have to increase strength of the sample and/or increase the pack size.

 

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