Thursday, February 23, 2012

The Grand Theory of Pharma Marketing

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Today's guest post comes from at closerlook, inc. He blogs at digital-pharma.tumblr.com and pretty much lives on Twitter (@digital_pharma) if you'd like to reach out.

Einstein’s last project before he died was the search for the grand unified theory, proving how all the major forces in the universe are related: gravity to the speed of light to the attraction between electrons and everything else. It remains the “holy grail” of physics, something huge, important, and possibly unknowable.

HCP marketing has a grand unified theory that marketers are trying to understand. A unified theory would tell us that a married male gastroenterologist in Alabama with a degree from a public school who works in a small private practice needs X touches before he will change their prescription behavior, but an unmarked female Obstetrician in a large hospital in Seattle will need Y touches.
Our unified theory shows us how tactics are related to each other. If an HCP has seen a complete eDetail, for example, maybe they need five fewer touches than one who has never seen one. What about rep visits? Or conference attendances? How do they all interrelate, and how much work do they do in encouraging behavior, by themselves or in tandem with the other tactics?

It's far easier to think in smaller terms. Here’s a hypothetical campaign: Send a million emails and see how many someone has to get before they’ll go to a web site. How many web visits do they need before they register? How many registrations turn into an eDetail viewing or sample request? How many samples (both with and without a rep visit) will lead to more prescription writing?

We look at these questions in isolation because looking at them in the larger picture is... messy at best, requiring very complex mathematical models. To be fair, if any of us were better at math, we’d probably be engineers instead of marketers. And the kind of math we’re talking about goes beyond the pre-calculus class we took freshman year. And if I had a nickel for every marketer with an MBA who said that they barely passed their stats class, I’d be able to retire. To an island. That I owned.
That’s why companies exist that are full of math nerds who can run modeling data and tell us what our deciles are. They tell us who should be prescribing and aren’t, and who needs a little push based on what data they have available to them.

But again, this is small ball. What if we started thinking big?

What if we took all the data we had... But, we don't have enough data. We need a lot more data, and it's gonna take a little work to get it.

For the next year, every banner ad you put in the field needs to get a dynamic ID number. In places where an HCP has to login, correlate that ID number to the HCP ID. You already know what emails are being sent to that target in your brand, so figure out a way to get all your brands to collect the same data so that you can put it into one big pool. Take all the data the reps send back (oh yeah, you might need to teach reps how to collect info so they can tag and input it into your CRM). Take all your eDetail data. All those business cards you collect at the conference to win a free iPad. All the mailers. All the web traffic (cookies, people). Every giveaway, every teleconference or teledetail, every meal, every pen left behind. All these things are collectible and able to be connected to a given target. Then add everything else you can find out about your target: specialty, school, practice type, practice size, gender, age, geography, marital status, family status, psychographic data about their parents and how they were raised (what, you think that’s not available?), everything. Think like Amazon or Target. Collect every coupon that’s been redeemed, every prescription filled, every visit, everything. We’re playing for the majors now, so stop thinking small.

What could all that data in the hands of serious math nerds tell us? Here’s a sample of the questions they can answer: are reps effective (broken down by specialty, brand, location, practice type)? How many emails does it take before a target prescribes (and how many before they will opt out completely)? Do nurse practitioners ever click on banner ads? Is there value in seeing your brand name again and again on an ad even if it doesn't lead to a click? Which is better: email then a rep visit, or a rep visit and then email? Which works better at a conference: a lot of little trinkets or a chance to win a big prize?

The days when a brand didn't know which half of their marketing was effective is long long gone. These days, we aren't far from knowing if a PCP in New Jersey wants your brand emails every 21 days or every 18 days, and how many they need to receive before a rep shows up to make the visit worth the trip.

But it all starts with thinking big and collecting the data. What data are you collecting? How are you planning on using it?

Maybe you need better answers to those questions.
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