How Analytics Is Reshaping Key Account Management in Pharma | ZS

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How Analytics Is Reshaping Key Account Management in Pharma


Bill Coyle, Principal


Why has developing analytics for key accounts in pharma become so important?


So in the past, we could win with many physicians while losing with others and still achieve our market share of goals. In the future, however, we may end up seeing more of a win-or-lose dynamic with large accounts.

The U.S. pharma industry has been able to be very successful without really understanding how accounts make decisions, because it hasn’t been as important to their business. So unfortunately, a lot of what we need to do in the future to be successful with research and analytics and key account management is not what we’ve been doing in the past. A key gap really exists into understanding accounts.

As we think about conducting research with more B to B-like customers that pharma is becoming more involved with—payers, accountable care organizations, large practices and the like—the research is a bit harder to do, and the analytics and the data available are a little less complete. Therefore, it puts more pressure on us to do a better job at designing our research and using the data we do have to understand how they make decisions.

How can pharmaceutical companies integrate analytics into key account management?


What we first really need to do is understand how accounts make decisions. So that really requires us to do research into understanding what drives their decision making. When we talk about decision-making processes, we think of a number of different things.

So how do they make decisions related to protocols or formularies? What are the criteria by which they decide what a good decision is? What are the criteria that shape ultimately the decisions that they make? Also, we really need to know, if we’re key account managers, who’s involved in the decision-making process—who are the key stakeholders and decision makers, as well as those that influence?

One of the things we see that’s becoming more important for both our pharma clients at headquarters as well as key account managers in the field is becoming more adept at business case-style analytics. And that is thinking about what I’m trying to achieve, and what will “good” look like.

What are the types of metrics that, if this program is successful, that I’m going to see coming out the other end? Is it an uptick in utilization of my product with that account? Is it helping that account achieve better outcomes for a particular patient group or achieve a particular cost level that they’re trying to get down to?

We’re accustomed in pharma to having great information to do really strong analytics. And as we think about moving to the key account management space, we’re going to have to make do probably with a little bit less information and data, but really use that to the greatest impact that we can.

Which leads into the second best practice, which is really about taking a hypothesis-driven approach to analytics. For example, if we believe a particular large group practice is very effective at driving protocols, how would we investigate that hypothesis? We could, for example, look at the shared distribution of the physicians within the practice to see if they all have similar share patterns in a therapeutic area, which suggests they’re all doing the same thing.

Another important factor is making sure that we realize we can’t give key account managers the same level of direction that we give our normal reps from a detailing perspective. The information is less available to us in terms of the individuals that should be targeted within an account than it would be if we were just looking at physician prescribers.

Pharma companies can really better hone their research and analytics to serve KAMs (key account managers). And that is helping them [in] understanding how customers make decisions and really what their needs are associated with the selection of pharmaceutical products.