Building GIGO-Free Response Models (download presentation)
2002 PMSA Conference, New Orleans, April 28 May 1, 2002.
Establishing how your drugs respond to promotion is not necessarily
confined to the realm of guesswork. While Delphi sessions can help guide
the construction of response curves, they are not the only option. This
article describes how to build response curves by aggregating responses
of individual physicians. The ingredients to build the promotional
response of individual physicians are readily available: IMS Xponent or
NDC Source Prescriber on the one hand and the company call file on the
other. The build-by-aggregation approach described here has three key
advantages. First, the resulting promotion response is far more accurate
than the best educated guess concocted by a group of experts. For one
thing, it is anchored in reality. Second, response curves can easily be
derived for specific territories, districts, areas, managed care
organizations, or other physician segments. This is next to impossible
if you only have a nationwide response curve to start with. Third,
adaptive targeting is a reality. This means you can adjust in real time
the number of details that go towards each physician individually, based
on the promotion response feedback you get from the physicians.
Why is it then that build-by-aggregation is not the industrys gold standard? It will be. Up to recently, two factors have been holding things back. First, insufficient computing power to handle 800K+ physicians at one go. This is no longer true thanks to increased desktop computing power (Moores law) and the ongoing price war in the PC industry. Second, technical difficulties associated with build-by-aggregation. For starters, how do we add two response curves of different lengths, say one that spans two details and another that spans twelve? The obvious [f +g](x) = f (x) + g(x) does not reflect reality since it amounts to assuming the physician that receives the smaller number of details does not respond past a few details. Here is the larger issue: In what order should we add the individual response curves? If the order were not important, that would mean which physician gets detailed first is immaterial and that would fly in the face of targeting. The obvious schemes for adding curves, as we will see, do not work. Lastly, it is imperative that we be able to answer questions such as: What would have happened had we delivered an additional 250K details last year? Somehow, the response curve should overshoot the actual number of details delivered. This article addresses these technical issues head-on.
2. Response Curves
Promotion response curves describe how sales increase with promotional
effort. Promotion is usually taken to mean detailing, but can also refer
to sampling, meetings and events, journal ads, DTC, etc. Promotion
response models play a pivotal role in sales and marketing since they
allow us to establish the ideal promotional effort to maximize sales,
long-term gross profit, or more importantly, any combination of the two.
Promotional response models help us play out implications of what-if
scenarios such as increases in standard costs, reductions in promotional
budget, changes in promotional budget mix, etc. Some people even call
promotion response the holy grail of the pharmaceutical industry!
we ensure the promotion response is an accurate depiction of reality?
Making the right decision based on the wrong model may cost tens of
million of dollars. Indeed, GIGO (Garbage In Garbage Out) may befall
the promotion response model. Delphi sessions are a great forecasting
technique (invented by the Rand Corporation during WWII to harness the
wisdom of international experts) but are oftentimes misused and abused,
probably for want of better alternatives. To that end, we developed a
novel response building technique that takes the guesswork away and
builds the aggregate response by combining promotion responses of actual
Build-By-Aggregation Approach: Issues and Solutions
focus on the top two technical issues associated with building promotion
responses by aggregation. First, the promotion response of some
physicians (actually a lot) may be too short, i.e., the physician
received only one or two details. If we simply add a short promotion
response to a long one, we would be making the tacit assumption that the
short response physician no longer responds past a few details, which is
clearly incorrect. Second, in what order should we add the promotion
responses of the individual physicians when aggregating the responses?
The catch here is different ordering schemes lead to different aggregate
we address insufficient response information associated with
under-detailed physicians? By leveraging the fact that we have detailed
more profusely other physicians with similar characteristics. The key
here is to assume the response of the under-detailed physician is a
blending of the observed responses of comparable physicians: other
physicians with similar specialties, managed care plan affiliations, or
geographic locations. Indeed, we combine observed responses that we
graft to the short response. That graft is represented not as a single
segment of a response curve but as a cone, to capture uncertainty. The
lower portion of the cone represents the low response scenario, the
higher portion the high response scenario, and the distance between the
two portions the magnitude of the uncertainty. This surgical grafting
actually does two things. For one, it allows us to be more assertive
regarding the response of individual physicians we barely detailed. For
another, it allows us to construct an aggregate response that goes
beyond the number of details actually delivered. This allows us to
address questions such as What would sales have been last year had we
delivered an additional 250K details?.
second issue is how to combine the individual responses to generate the
aggregate response. Consider the most-responsive-first approach. This
scheme sorts the physicians by decreasing responsiveness and connects
their response curves starting with the most responsive one first.
Because the physicians are sorted, the first half of the aggregate
response will contain only half of the physicians detailed. This means
reps will be granted the luxury of not detailing less responsive
physicians even when they have free time on their hands. Rep
compensation being sunk cost, this scheme is clearly unrealistic.
Consider the round-robin approach instead. Under this scheme, it is only
when all of the physicians have received their first detail that a
second detail may be delivered, and so on. The first half of the
aggregate response will contain all the physicians. Unlike the
most-responsive-first approach, reach is preserved. Frequency, however,
is butchered. The problem with the round-robin scheme is it treats every
physician equally. It does not discriminate between very responsive and
less responsive physicians, and discrimination is precisely what
targeting is about. Those two schemes just made two things very clear.
First, the ordering scheme we are after must preserve both reach and
frequency. Second, each response curve corresponds to an implicit
targeting strategy. The ordering scheme we recommend is random
summation. Like the round robin, random summation adds up segments of
each individual response curve. Unlike the round robin, it picks
physicians in a random manner. As a result, Dr. John Smith may get his
third detail before Dr. Jane Doe gets her first, although large
differences in details are most unlikely. The probability with which a
physician is chosen is not inversely proportional to the number of
physicians, but to the number of details yet to be delivered. This
increases the odds of detailing a heavily detailed physician early on in
the targeting process. As a result, both reach and frequency are
preserved. In a variation of the random summation, effective details are
distinguished from ineffective details (those delivered past saturation)
and the probability is based on effective details only. Ineffective
details are thereby deferred to the very end of the targeting process.
To implement the build-by-aggregation approach above, we developed a physician promotion response database in Access and promotion response matching algorithms in VB. These tools allow us to retrieve all physicians whose promotion response starts with, ends with or contains a specific user-defined promotion response pattern. Those promotion responses are then combined and grafted to promotion responses of physicians that have not been adequately detailed.
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