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Is Pharmaceutical Marketing Really Mass Marketing?
March 22, 2007
By Kent Stephan
The pharmaceutical industry relies on planning principles people have drawn directly from the mass marketing practiced by the big package goods companies. P&G, General Foods, Unilever and the rest are generally recognized as the world’s premier marketers. It is not surprising that others would emulate them in hopes of achieving their success.
The question is—or at least should be—is the marketing of prescription pharmaceuticals really a mass marketing problem? After all, package goods companies are forced to deal with unknowns that do not confront the pharmaceutical industry.
Package goods companies lack precise information about their consumers' use of products and their exposure to advertising messages. Pharmaceutical companies, on the other hand, have precise information about doctors' script writing and their exposure to details and samples.
For example, consider a hypothetical household headed by a Dr. Jones who lives at some hypothetical address. P&G will not know how much Crest toothpaste the Jones household uses. P&G will also have no idea about their usage of other toothpastes. Finally, P&G will not know how many Crest commercials the family saw or how many coupons they redeemed.
However, sufficient data are gathered periodically so that P&G knows the toothpaste usage for the average household with the same demographics as the Jones’. P&G also knows in fine detail how often households within the same demographic segment as the Jones’ have been exposed to different media.
Unfortunately, the Jones household may differ greatly from the norm, but the Crest brand manager has no way of knowing this as the next year's marketing plans are being prepared. P&G and the other large package goods companies must market to audience segments. They have no choice.
They work hard to define market segments with as much specificity as possible. They want to target their promotions and messages as much as they can. Clearly, the large package goods companies would market to individuals, if they could.
Because mass marketers must direct their marketing efforts toward segments, they end up paying for a lot of individuals who aren't worth it. They are also unable to fully exploit the potential of individuals who would be highly responsive to even more promotion.
The purpose of mass-market segmentation is to reduce waste and direct extra resources against apparent high potential targets. Although it partly succeeds in this effort, segmentation still leaves an awful lot of money on the table.
Pharmaceutical marketers, on the other hand, have a wealth of information that mass marketers lack. They will have excellent data on how many prescriptions a Dr. Jones wrote for a particular brand. They will also know how many prescriptions a Dr. Jones wrote for its competitors. They will have all this data month by month.
Pharmaceutical marketers will also know how many details and samples the doctor received for the brand in question.
Finally, because pharmaceutical companies rely on representatives to deliver their marketing messages, they can call on whom they should as often as they should, as long as the doctors will see them. Pharmaceutical reps don't have to call on doctors who aren't worth it, assuming the reps know who they are. Reps can devote extra effort to doctors who will be the most responsive. Most importantly they can, if armed with the right information, call on each doctor according to his or her likelihood of responding to detailing and sampling at that point in time.
Having so much vital information about individual doctors, does it not seem strange that most pharmaceutical marketers keep behaving as if they were selling Crest toothpaste to mass market segments? Again, does anyone doubt that if P&G had an economical way of selling Crest to individual households, it would continue to market to the average household in mass-market segments?
The arrival years ago of doctor-level prescribing data should have changed pharmaceutical marketing more than it has. Breakthrough analytical tools make it possible to take this data and quantify each doctor’s unique responsiveness to details and samples for a brand. Instead, the data has been used as just another segmentation tool.
Within the pharmaceutical industry, segmentation results in valuable sales calls going to doctors who are extremely unlikely to write more prescriptions as a result. Our analysis shows that this is the case for about 40 percent of sales calls. Although some of this can be blamed on sales reps going off plan, we've seen many examples where reps have actually done better by cheating on the plan.
It's not as if the wasted calls couldn’t have been put to better use. Most, if not all wasted calls could have been used to address profitable opportunities that mass-market segmentation failed to identify. For example, one company found that the average doctor who was identified as highly responsive to detailing but was not included in targeted segments wrote three times as many incremental scripts per sales call as doctors who were targeted through segmentation but did not make the responsiveness cut.
Mass-market segmentation dramatically reduces sales force productivity in comparison to an individualized approach. This approach, "call value targeting," looks at each doctor as an individual market and each potential sales call as a unique investment opportunity with a definable value. It overcomes the flawed assumption in segmentation that all individuals in a segment are the same.
Call value targeting also addresses a subtle flaw in the way mass-market segments are typically constructed. Doctors are usually targeted according to their decile of past script writing. Each decile is then assigned a call frequency with heavier writing deciles targeted for more calls than lighter writers.
Past script writing is a good predictor of total script writing. In other words, a doctor who wrote one script last year is more likely to write one script next year than 100 scripts. A doctor who wrote 100 scripts last year is more likely to write 100 again than one. However, the volume of historic script writing is a very weak predictor of how many scripts a doctor will write in response to future detailing and sampling.
This may come as a shock. However, we reached this conclusion after examining scores of prescription brands. The call value targeting approach goes beyond simply targeting individual doctors rather than segments. It targets individual call opportunities (which happen to be attached to individual doctors) based on their expected value.
Call value targeting can be far easier for reps to implement than mass market segmentation because there are no judgment calls about whom to see next. Reps simply make the calls with the highest values. Profits are maximized when a representative’s inventory of sales calls is allocated to call opportunities with the greatest expected values.
Call value targeting works only if the doctor-level models accurately forecast incremental scripts. If the models don't work, the resulting program will needlessly meddle with the sales force and produce no improvements in the business. If the models are accurate and put to use, our analysis indicates that most companies could about double the amount of incremental business their sales force generates in a given year.
There is a reliable method for validating the models for in-line brands. First, deny the model builder access to historic prescribing data for the brands. Then give the model builder 24 months or so of detailing and sampling data at the doctor level. Also give them the first 18 months of doctor-level prescribing data. Tell them to forecast how many prescriptions each doctor wrote during the last 6 months for which the prescribing data were withheld.
In order to determine if the models accurately account for the impact of detailing and sampling, the doctor-level forecasts need a sort of placebo for comparison. To create the placebo, one simply assumes that each doctor's prescribing during the previous period will remain unchanged into the period being forecast.
These placebo forecasts assume that detailing and sampling have no impact. If the model builder's forecasts aren't significantly closer to actual scripts, than the "placebo forecasts," then the models have no value.
It is important to note that simply correlating forecasted Rx's with actual Rx's will frequently show a high correlation, even when the models that produce the forecasts fail to account for details and samples. This is because model builders will always take into account the volume of past script writing and past script writing is often highly correlated with future script writing. Thus the need for a placebo forecasts for comparison.
The ways of successful mass marketers offer the pharmaceutical industry a lot. Above all, those companies strive to avoid relying on judgment. They try to keep from doing anything in the real world without first knowing what the result will be. Pharmaceutical marketers should, too.
Mass marketers spend a lot of time testing. They also devote considerable effort to making sure they are testing the right things and using the best methods. One of the most respected companies took two years to experiment with different copy testing methods before choosing a winner. The same company takes far less time to select a new ad agency.
However, when it comes to marketing prescription drugs to doctors, mass-market segmentation based on raw historical data is not cutting edge. It is a misuse of the data.
Kent Stephan is the CEO of Princeton Brand Econometrics (PBE), a marketing engineering firm that simulates sales and marketing scenarios to provide clients with accurate forecasts and efficient marketing plans. He co-founded PBE in 1991 with Barry L. Tannenholz.
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