Is pharma serious about customer relationship management?

Kevin Dolgin

In our marketing excellence focus month, Kevin Dolgin compares the role of a sales rep in the financial industry with the pharmaceutical industry. In particular, Kevin looks at how customer-facing personnel build and maintain relationships and how the introduction of predictive analysis could affect pharma.

Years ago, the job of a pharmaceutical sales representation was to visit doctors as much as possible. During the visit, the rep was supposed to deliver the same pre-determined message as every other rep – consistency was key and the same message was delivered to all the reps’ physicians. Reps were also expected to contribute substantially to the question of who, exactly to see, they could re-assign doctors to different segments, within certain limitations.

Of course, these days, everything is different. Most reps are expected to make as many calls as possible to physicians, taking care to present the same thing to everyone while providing significant input into the constitution of their target lists.

Oh, wait, nothing much seems to have changed. Strange, really, since direct sales are an extraordinarily expensive channel of promotion and physicians themselves are increasingly limited in their prescription discretion. Of course, that’s one of the principle reasons there are fewer reps, but it’s rather striking that the underlying nature of the job hasn’t really changed much at all.

I can hear you protesting… it’s totally different being a rep now than it was ten or fifteen years ago. There are far more restrictions on what can be done with physicians, for one thing. True enough, but this is just restricting the industry in its movements, not redefining the rep’s role, and for the most part, the reaction has been to try to figure out how to maintain the old model despite these restrictions. Not much of a change, that.

But wait, back then our systems weren’t as sophisticated. Now, reps have computers, some have iPads. We spent millions with Siebel and Cegedim to buy ourselves some customer relationship management, you’ll say. But let’s face it, you can’t buy customer relationship management any more than you can buy respect, all you can do is buy the systems that should make it easier, and in the end, most of those systems were primarily used to generate key performance indicators (KPIs) about reach and frequency and coverage at frequency, or whatever KPIs you use. That’s not CRM, CRM is a way of doing business, and it involves managing the relationship between the customer and the company.


“…you can’t buy customer relationship management any more than you can buy respect…”


A few years ago, I wrote an article about the future of pharmaceutical sales, in which I predicted that if pharma was serious about customer relationship management and forward-thinking enough given the changes that were beginning to appear, then the traditional job of the rep would shift toward one of “customer relationship manager”. This hasn’t happened (I tried to get a refund on my crystal ball, but I haven’t had any luck yet).

Today, the industry is on the verge of another systems revolution, closed-loop marketing. CLM can allow the pharma industry finally to start redefining the role of the rep. Of course, this could have been done long ago, before CLM, but it wasn’t and despite the manufacturer’s flaws in my crystal ball, I think it will happen eventually, so why not prod a bit today? After all, these customer-focused systems are supposed to make it easier, and they do. I’ll hold on to my predictions: reps will be much more focused on building and creating relationships and much less focused on making decisions about who to see or what to say. Decisions about targeting, segmentation, and messaging will be facilitated automatically by the system itself. This will be done via predictive analytics.

Whenever I use words like predictive analytics, pharmaceutical executives IMMEDIATELY back off. Either they say that it’s just silly, that computers will never be as good as reps at making these decisions, or they say that it may happen one day, but it’s far too complicated to envision now. They’ll take it step by long, cautious, drawn-out step. For the moment, they’ll creep up on CLM by taking their paper-based detail aids and slapping them onto an iPad. Maybe they’ll even include a couple of animated graphs, but they certainly won’t let the systems decide anything.

I find this frustrating. I think it’s like saying that driving a car is complicated so you’ll start by buying one and pushing it around the parking lot for a few years. That should help. And then when you realize you’ve been wasting your efforts, you’ll proclaim that cars are indeed too complicated and you’ll go back to riding a horse.

Let me trade in my crystal ball for a moment and whip out my time machine. Let’s go back to the 1980’s and visit a parallel universe… that of financial services.

Once upon a time, when people wanted credit, financial services institutions would have these customers sit with a customer-facing employee (loan officer, advisor, whatever the title might have been) and, armed with all kinds of documents like tax returns and so on, they would explain why they wanted the credit and why they were a good risk. The loan officer would consider these documents, mix in any outside data available, such as credit agency reports (depending on the country) and then make an informed decision as to whether or not this potential customer was indeed a good risk. He or she would then decide how to deal with the customer, either approving or denying the credit and determining what other products to try to sell.


“Instead of blindly approaching all customers with the same marketing message, they could customize.”


And then came the statisticians. They began to develop automated systems, known as credit scoring, which could take all this data and run it through sophisticated predictive analytics to predict who was actually a good risk, without any human involvement in the decision-making process.

At first, the banks said that no computer could possibly make a better decision than a well-trained employee. It wasn’t just the numbers, you have to take into account, but the look in the customer’s eyes, the sincerity of their voice, etc.

Eventually, rather quickly, in fact, financial services companies accepted the fact that on the whole, the predictive analytics were much better than their human employees at determining risk. Today, if a financial services employee seems to be making an actual decision about granting you credit, it’s extremely likely that he or she is misleading you… the computer is making that decision.

Very quickly, companies like American Express came to realize that if these models could predict who wouldn’t pay, they could also predict who would pay and for what. The same approaches could help to determine exactly who was interested in what kind of product or service. Instead of blindly approaching all customers with the same marketing message, they could customize. A Polish restaurant has opened in Kensington? Amex can send a mail with a special offer to people who live or work within a mile of the restaurant and who have eaten at other Polish restaurants, or perhaps recently been to Poland. This was the birth of CRM.

Customer-facing personnel in financial services are no longer tasked with analyzing and segmenting their customers, nor are they obliged to present the same thing to everyone. The system decides which customers should receive more or less attention, and which products, services and approaches are most appropriate for each of them. The customer-facing person can concentrate on building relationships, communicating differential messages effectively, responding to questions and providing information and support services. His or her opinions about the customer are often important inputs into the system, but they do not override it.


“While modern CLM systems are indeed complex to build, they will make the reps’ jobs easier…”


In pharma, the rep plays and should continue to play a bigger role than in financial services. Reps have more contact with their customers and they have valuable insights into their customers’ behavior and needs. This makes for even better input into the system, but just as in financial services, they should be allowed to concentrate on managing relationships, letting systems based on predictive modeling guide them in the analytical tasks. You must keep in mind the difference between complex systems and complex processes. While modern CLM systems are indeed complex to build, they will make the reps’ jobs easier and allow them to focus on what is truly important… building and managing relationships.

Industries such as financial services have uniformly moved to greater systems automation and less reliance on the discretion of contact personnel, but pharma has not. I think the only reason is that it is glaringly obvious to those in financial services when they have made a mistake about how to treat a given customer… they lose whatever money they lent, or the customer doesn’t respond to marketing. In the first case, there’s a big, glaring, red number indicating a loss. When pharma gets it wrong, there’s no big red number, there’s just a waste of effort that will probably never be noticed, while the company continues to generate profits. I firmly believe that losses are there, that many pharma reps are wasting their time, but the absence of big red numbers means you need to dig a little deeper and create the sense of urgency yourself.

Whatever system you use, I strongly suggest you consider how to take advantage of it and add sophisticated, automated analytics, closing that marketing loop. Let the system analyze your customers, let it refine your targeting and determine how best to approach each individual, allowing the rep to focus on actual, genuine, undiluted customer relationship management. Stop pushing that car around… get behind the wheel!


About the author:

Kevin Dolgin is a consultant, entrepreneur and Associate Professor of marketing at the University of Paris. He has worked with more than twenty-five pharmaceutical companies in more than thirty countries and has published numerous articles on pharmaceutical sales and marketing in Europe, North America and Asia. He was one of the two founders of Areks and is currently the president of Observia, a French company providing patient compliance services in France. He can be reached at

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