It’s a topic that will only become more pressing as artificial intelligence (AI) solidifies its status as a “must-have” tool for sales teams across industries. From financial services to life sciences, companies are working to not only define new compensation structures, but also overcome operational hurdles and drive internal adoption.
These trends mean reps may have less direct control over their ability to achieve a sales quota. Classic pharma incentive compensation design suggests that when a pharma rep’s control over the outcome goes down, the percentage of their variable, or incentive, compensation should decrease accordingly. As pharma companies move to a lower percentage of variable pay and more team-based vs. individual incentives, we’re seeing this outcome play out in real time.
I recently had the privilege of participating in an interesting panel discussion on this topic at World@Work’s Sales Compensation 2019 conference in my hometown of Chicago, where I was surprised to learn that pharma is ahead of many industries in leveraging AI to help direct and optimize sales and marketing efforts.
With the experience fresh in my mind, I’d like to share some key takeaways from the discussion, consider some of the implications for our clients, and think about what’s next.
AI will certainly impact how we compensate the sales force, but it’s not the only variable to consider
Before we can think about how to pay AI-directed pharma sales reps, it’s important to consider how their job has already become more complex in recent years:
- The number and breadth of stakeholders and influencers in the buying process has increased significantly, in many cases now including patients and caregivers
- The number of team members a rep must coordinate with has also increased
- The channel mix that a rep must understand and manage has exploded
- As therapy costs rise for increasingly rare diseases, the need to demonstrate both clinical and economic advantage heightens the skills a rep must possess
Two critical starting points shape the AI-directed rep discussion
1. What is the role of change management in this performance evolution?
Panelists from other industries often cited an unwillingness to aggressively pursue new selling approaches as a barrier to adopting AI. At Aktana, we’ve found that involving the rep and first-line manager in the AI implementation process is one of the best ways to overcome rep resistance.
For example, we actively engage champion reps, managers and brand leadership throughout the upfront Business Requirements Gathering (BRG) process. Their input not only helps us understand what distinguishes top performers, but also provides insight into the issues all reps routinely face. Armed with this firsthand information, we can codify optimal behaviors to help lift the performance of average reps and alleviate the challenges to a rep’s daily work life.
But the benefits of rep involvement go beyond initial design. In our experience, it’s just as important to encourage reps to provide ongoing feedback to improve the ecosystem.
Two key tenets drive this goal:
- The AI system is constantly learning
- The AI system will NEVER know everything, and in fact, there is a bunch of data that the rep knows and the AI engine does not (e.g. when the HCP is on vacation, when the HCP is wanting fewer visits because they are too busy, etc). The role of the AI system is in decision support, not decision replacement.
Each time we provide a rep with a suggested next step, we know there’s a chance that the rep will disagree with our recommendation. In fact, we urge reps to dismiss suggestions that run counter to their on-the-ground insights.
But we also prompt them to provide a reason for the dismissal, which not only drives rep engagement but also helps the system learn. Over time, suggestions start to incorporate this local knowledge and begin to “feel right” to the rep, the importance of which cannot be overstated.
This combined BRG and AI-driven process lays a solid foundation and creates a system for continuous learning and improvement.
2. Do you reward the AI-directed rep for behaviors or outcomes (i.e., sales results)?
There are three basic ways to use incentive compensation in pharma to drive desired selling behaviors:
- A: Direct incentive compensation (IC) element—Designate a certain percentage of IC directly to attainment of a behavioral goal. Example: 15% of rep’s IC target is tied to seeing at least 80% of tier-1 HCPs at the target frequency this quarter.
- B: IC Gatekeeper—Incent behaviors by including an indirect linkage. Example: for a rep to achieve 100% of target IC payout, he/she must hit at least 80% of a T1 frequency goal. For every frequency attainment percentage shortfall, IC payout is reduced by a percentage.
- C: Exclude from IC plan—Behaviors are managed completely outside the IC plan, typically as part of the performance management process.
The current approach life sciences leans on are options B and C, both of which still have validity. If your sales force activities and objectives are informed by any of the following, you’ve already thought about this question:
- HCPs are segmented based on behaviors or attitudes, and reps are provided with key message guidance based on this segmentation
- Frequency targets are provided based on promotional response modeling
- Changes in payer status, such as formulary listing in a hospital, prompt changes in suggested tactics
Now, the key source of guidance is no longer an analyst sitting in the home office, but increasingly, machine-driven, augmented by the powerful learning that takes place from rep feedback.
As such, it should be the case that the analysis is more robust, and the guidance is actually better as a result. Generally, I believe options B and C are still the best path to success.
One word of caution: Offer incentives on the level of engagement with suggestions, not on the level of acceptance. The difference is subtle but critical.
We define engagement as a suggestion that is either accepted or dismissed (again, with the reason for dismissal required). Incentives based solely on acceptance rates can significantly decrease the number of dismissals—a critical part of the feedback loop that drives continuous system learning.
As leaders in the application of AI and machine learning for life sciences commercial teams, we’re excited to see this field continue to progress and rise in prominence. The rep channel is just one facet of the commercial process that we address, but an important one. It’s our mission to enable customers to transform their entire commercial process to become customer-centric by using intelligent tools.
Bruce Carlson is VP Strategic Accounts & CS Effectiveness at Aktana. In his 25+ year career in pharma, he has advised many pharmaceutical companies on sales compensation design, including as a Principal with Mercer HR Consulting.