This is the second post in a three-part series on commercial resource planning.
As the pharma industry’s customers evolve with the shifts in the healthcare landscape, pharma companies can no longer take a static, semiannual, one-size-fits-all approach to commercial resource planning. They need to take an agile, localized, customized approach to aligning commercial resources to better meet their customers’ evolving needs—and they need the right analytics capability to mine the customer and market insights they need to get it done.
I recently spoke with my colleague Jude Konzelmann, a ZS managing principal and leader of the firm’s resource planning and deployment practice, about the role that data and analytics should play in reinventing pharma’s decades-old approach to commercial resource planning, and how technologies like artificial intelligence can help mine actionable insights to enable near-real-time agility to allow sales and marketing teams to be more responsive and event-driven.
Q: We’ve talked about the need for pharma companies to adopt a local mindset in order to tailor their commercial approach. How can they address any organizational fear of pushing decision-making power outside of HQ?
A: Yes, I think the local aspect of it is certainly scary for pharma companies because there’s an element there of ceding control. In an environment where everything is decided centrally and then pushed down, in theory, I’d have a lot of control over what’s happening because I could control the plan, to an extent.
Now, I say in theory because if that plan doesn’t work in front of customers, people will end up changing it. In other words, control already has been ceded to the field, in a sense. Most smart sales professionals will understand that they’ve got to do something different than what they were told to try to be successful. But what they may not understand is whether they’re necessarily using the best option.
As companies start to do this local customization, there’s a way to try to better distribute models that might work for a particular market, and now they’re putting trust in the local field leader to make sure that they’re making the right trade-off decisions and picking the right model that’s appropriate for their customers. I could see that being a hang-up that pharma companies now need to put a lot more trust in their local commercial leaders to make some of these decisions.
What I think some organizations like about the traditional batch process is that they don’t get too caught in the minutia on a day-to-day basis. They wait for a quarterly or a “semesterly” process to sweep up all of that stuff. As they start to think about all of these provider mergers and try to respond quickly, naturally they also need to think about all of the asynchronous change across the country. The initial feeling would be the shock of how many different things companies need to respond to.
Without the right technology in place, I can see that being an issue. But there’s certainly a technological path that can help to deal with a lot of that, and not overwhelm the analytics organization as they take it on.
Q: On both of those counts, the fear is a loss of control, meaning that somebody loses control to the local people, or marketing loses control to the salespeople. What can be done to help overcome these challenges?
A: When you first introduce the idea of customized structures in the local field situation, people think immediately of the regionalization approaches that many companies tried 20 to 25 years ago. Everybody had free rein to do whatever they wanted, and the duplication of effort and lack of consistency and measurability on what was working and what wasn’t created all kinds of problems.
Let’s imagine a different environment where, rather than going all the way down that path, you’d use analytics to identify four or five flavors of market influence models and then think about role and resource combinations that could be used to operate in those markets successfully. Imagine, rather than giving some carte blanche, you use analytics to help characterize the local market operation and learn that it looks like market X, and also provide some pretty good choices for the field leaders to make. You can now pull these down and customize them a little bit.
You’ve still maintained some of the strategic input from headquarters, but you've also allowed the decision to be implemented and customized locally. Hopefully, you get a little bit of the best of both worlds: centralized strategic thinking and decision-making combined with the local execution and operation of those decisions.
Q: In many senses, it seems a lot like other industries. They have a franchise model and they can change the inventory in a particular store very quickly and react to local needs without giving up the integrity of the commercial model, right?
A: Correct. And in such models, they’re often backed by a very solid technical backbone that’s doing the analytics for them on how and when to change inventory. And sure, they have the local customization piece, but they’re not necessarily all inventing their own methods to figure out at what point a natural disaster is large enough, for example, to stock certain supplies in a particular store. They’re relying, instead, on a smart system that’s helping them figure that out, and the local people are sort of running it and keeping abreast of it.
The other key is continuing to evaluate the models you’ve put in the market. Are they working or not? You need to be continuously evaluating what really is the best thing to do in each one of these different markets and now you’ve got many more comparators that you can use to start to assess what’s working.
Q: That goes back to the other point that you were making that when headquarters makes a decision, local people will ignore it or modify it. Today, that happens, but companies have no way of knowing whether those modifications are making the plan work better. There is no closing of the loop.
A: And I’d say that even if there was a closing of the loop, it’s way too late. Maybe I’ve learned after a year has now elapsed that somebody was doing something entirely different. I may learn a lot faster what people seem to want, what’s working and what’s not working.
As pharma firms begin to determine what it takes to customize their approach and allocate their resources accordingly, sophisticated analytics capabilities can assume some of the burden. Newer data management processes and analytics methodologies like predictive analytics and machine learning will become table stakes—and a way to ensure that companies can adapt their approach over time as local markets change.
Check back for the final post in this three-part series, which will touch on how companies can manage the expectations and timelines of a commercial resource planning redesign.
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