Market segmentation is a pillar of consumer marketing. P&G, Kraft, Unilever and other package goods giants practice market segmentation at the most sophisticated levels. These companies are recognized as the world’s premier marketers, so other industries have automatically adopted their practices.
Market segmentation was developed by package goods marketers to deal with two problems: consumer demographic and purchasing information that’s available for groups rather than individuals, and advertising media that can target groups reasonably well but individuals almost not at all. Market segmentation is not the end all and be all; it is a way to deal with sketchy information and scatter-gun media.
For the package goods giants, market segmentation is obviously superior to spreading marketing resources at random. It enables them to spend less money advertising to low potential consumer groups and to heavy up against groups that appear to have the greatest potential to respond. However, there is still enormous unavoidable waste and missed opportunities.
Things are different for pharmaceutical companies. They know almost exactly how much prescribing each doctor has been doing month by month. Their sales reps can choose to call on individual doctors with specific frequencies, or not at all.
Pharmaceutical companies could treat each doctor as a unique market but instead they deal with individuals as members of market segments. By emulating the package goods giants, pharmaceutical companies behave as if they don’t know as much as they do and cannot target marketing resources any better than P&G can target TV commercials.
The arrival of doctor-level prescribing data years ago should have changed pharmaceutical marketing much 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. This means that marketing resources can potentially be allocated with great financial precision.
Instead, the data are used as just another segmentation tool. It’s like limiting a great race horse to Sunday rides around the park.
Within the pharmaceutical industry, segmentation results in valuable sales calls going to doctors who are extremely unlikely to write more scripts as a result. PBE’s analysis shows that this is the case for about 40% of sales calls. Although some of this can be blamed on sales reps going off plan, PBE has 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 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.
Market segmentation dramatically reduces sales force productivity in comparison to the 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 market segments are typically constructed. Doctors are usually targeted according to their decile of past script writing. Each decile is assigned a call frequency with heavier writing deciles being targeted for more calls than lighter writers.
Past script writing is a very 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 incremental scripts a doctor will write in response to future detailing and sampling.
This conclusion may be hard for some to believe. However, PBE reached this conclusion after forecasting scores of prescription brands. In fact, our analyses confirm the truth of the old industry joke that prescriptions generate details more than the other way around.
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 the expected value of each call opportunity.
Call Value Targeting can be far easier for reps to implement than 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 is the only way to truly maximize profits.
Here is an outline of how Call Value Targeting works:
1.) Build accurate mathematico/statistical models to forecast how many incremental scripts each individual doctor will write in response to any number of details and samples over a defined period of time. This is done for each promoted brand.
2.) The accuracy of the method used to build these models is validated before they are put to work in the real world. How this is done is summarized later.
3.) Models for individual brands are integrated to find the most profitable sequence of brand details and numbers of samples on each potential sales call for each doctor.
4.) The expected value of each potential sales call is quantified in terms of sales or, better yet, gross profit. (This way, knowing which doctor to call on next becomes crystal clear!)
5.) It’s now possible for representatives to almost instantly access this information to plan their activities with profit maximizing precision.
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 mess with the sales force and produce no improvements in the business. If the models are accurate and put to use, PBE’s analysis indicates that most companies could dramatically increase the amount of incremental business their sales force generates in a given year.
There is a quick and easy way to validate the model-building methodology for in-line brands as long as the model builder does not have access to historic prescribing data for the brands. First, 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 the corresponding doctor-level prescribing data. Then, tell them to forecast how many scripts 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–a naive model. To create the naive model, one simply assumes that each doctor’s prescribing during the previous period will remain unchanged into the period being forecast.
These naive forecasts assume that detailing and sampling have no impact. If the model builder’s forecasts aren’t significantly closer to actual scripts than the naive model, models have no value.
It’s important to mention 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 always highly correlated with total script writing, but not highly correlated with incremental script writing. Thus the need for naive 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 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.
However, when it comes to marketing prescription drugs to doctors, market segmentation based on raw historical data is not cutting edge, even if it mimics the way P&G and Unilever do things. It’s dumbing down the data.