Analyzing the Analytics

For some time now it has struck me that we need to do a better job looking at the data that we view to be so foundational in the pharmaceutical industry.  Looking at the failure of so many launches and the way generics have totally dominated the solid oral pharmaceutical world, it just seems that the incredible amount of data we use has not served us very well.  Is it because the data has gotten less reliable or is it because we have not analyzed it properly?  Is it because we refuse to re-consider the tried and true decile concept or targeting or call frequency or less than reliable ROI analyses or what?  I am not sure what the answer is, but I think it might have to do with our inflexibility and inability to understand the limitations of data and the potential of other possibilities.

I was intrigued with last week’s New York Times opinion piece by David Brooks where he talks about data-ism as the philosophy of today and tells us he is going to dedicate the next year looking into the various issues that surround this area.  He raises a number of very interesting concepts in his piece that should be studied closely.  Brooks points out that he thinks perhaps we may be wrong in trying to quantify everything and basically make all decisions based on some type of formula.  He points out a couple key points about data.  First, it protects us when our intuition is wrong as it provides counterbalancing evidence that could perhaps keep us from making mistakes.  Secondly, data can let us see patterns of behavior before we realize it.  These are both really good points but I think we may need to dig deeper to see where we are perhaps misusing data.

In our industry we have become so data dependent that I think it may be blocking success in some way.  We only call on high decile doctors without really understanding why they are high decile.  Often this is because they have just been around a long time and have many patients continuing therapy. We often fail to look at who is writing more or fewer new prescriptions and concentrate only on total volume.  We don’t consider the role managed markets really play on the decile situation.  Physician may practice one way or another not based on any personal belief but rather because they are just following a formulary.  We do market research with doctors to hear their opinions when often times they have no power or desire to do anything other than what the payers say.  Yet, in the research we treat their opinions as if they matter.  We do flawed ROI work and then treat it as perfect data.  We look at prescribing data and come to conclusions on behavior without actually doing the research to determine if there really is a cause/effect relationship.

The concern is that the more data we get the less smart we seem to become.  Everything has become more regimented with less room for business-growing experiments.  We fail to recognize that everyone has the same data and is concentrating on the same targets and perceived areas of potential.  There is very little “Blue Ocean” strategy work going on that involves going to areas where the competition is avoiding.  Would it be better to call on a decile 2 doctor who is open minded, loves scientific breakthroughs and tries new therapies if she thinks they would help her patients or a decile 10 who rigorously follows a formulary where your product is in a poor position.  The answer to me is obvious and that is to go where you have a chance of success.  Where do you think your sales plan demands your reps go?  What if the lower decile physician worked in an outpatient clinic or another setting where prescriptions are not tracked well and she may actually have more potential than the decile 10.  Your reps would still be directed to the decile 10, and so would all your marketing spend as well, right?

Perhaps like David Brooks we ought to take the time to really look at the data we are using to run the huge enterprises we are charged with managing.  We need to thoroughly understand what the data means, how it was derived, it’s strength and limitations.  Then we need to decide when it makes sense to follow the data and when you use the data to actually go in a different direction.  We need to control the data rather than letting it control us.  I wouldn’t even be questioning this if things were going smoothly. It does seem like as we get more and more data the industry is doing less and less well.  Generic companies and smaller companies that have less data and perhaps use it in a less controlling manner seem to be beating the bigger more regimented companies.  It is worth considering.

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