Incentive compensation is an art as much as it is an analytical exercise. There’s no one blueprint to help an organization create a flawless and motivating incentive compensation plan. And, there’s inherent tension in the plan design process. When crafting its incentive compensation plan, an organization must contend with two often-competing priorities:
- Motivate reps to maximize sales results by allowing them to earn significant amounts of money; and
- Ensure the organization remains fiscally responsible and therefore doesn’t pay out too much money to reps.
Threading this needle requires organizations to adopt an analytical mindset and gain more granular insights about customers and territories. Then they can set accurate goals and create sustainable incentives that motivate. What’s the best way to do this? Aggressively deploy advanced analytics.
Across industries, the amount of data organizations have at their disposal — and the computing and analytical horsepower that accompanies it — is rapidly increasing. Therefore, organizations can deploy advanced analytical techniques more easily than ever before to improve incentive compensation plan precision and effectiveness. But there’s reason to believe many organizations still underuse advanced analytics. In Beghou Consulting’s recent survey of emerging pharmaceutical companies, most respondents said analytics and modeling play only a “moderate” role in their incentive compensation plan design efforts. Indeed, nearly 60% said they use advanced analytics only “somewhat” or “to a limited extent” to generate commercial insights.
Without advanced analytics as a core component of incentive compensation plan design, an organization must paint with a broad brush and will be limited in the level of sophistication it can incorporate into its plan. For instance, it might be limited to using a commission-based plan for too long, which can easily lead to skyrocketing costs or hamper motivation as the organization lowers the commission rate to rein in costs.
An organization that undervalues advanced analytics also puts itself at risk for big, costly mistakes. For instance, if a group of reps earns an unsustainably high amount of money in a single quarter, the organization will need to react by increasing goals the following quarter, which can frustrate high-performing reps. Advanced analytics can help any data-rich B2B company avoid these sharp course corrections and build more sustainable and effective plans.
Clear Away the Fog
B2B sales and marketing can sometimes feel like driving in dense fog. Street signs are hard to read and potential obstacles can appear out of nowhere. In a fast-moving, ever-changing global marketplace, organizations often struggle to gain predictive insights, no matter their situation. For instance, if an organization is launching a new, innovative product, it will have little relevant data it can draw on to predict how the product will perform. In other cases, an organization may be launching an upgraded version of an existing product and have previous product sales data. However, new products from competitors have changed the market landscape significantly.
This lack of insight can lead organizations to create flawed incentive compensation plans, which can hamper commercial efforts across the board. We’ve discussed the problems that can result from reps reaping a windfall. In other situations, payouts may be too low, leading many reps to head for the exits. Similarly, if not enough reps hit their goals, demoralization can quickly follow. Poorly designed incentive compensation plans can create confusing, misaligned incentives that demotivate reps and encourage activities that can limit sales (e.g., “saving” a sale for the next quarter after meeting the current quarter’s quota).
Often, these problems eventually result in increased turnover, which leaves territories vacant, hurts the organization’s reputation among prospective sales rep hires and hinders its ability to maximize sales. But the proper use of data and advanced analytics can help companies clear away the fog. We see three ways organizations can deploy advanced analytics to improve plan design and refinement efforts.
1. Deploy Simulation Models to Cut Down on Surprises
The challenges facing organizations as they build and refine incentive compensation plans differ greatly depending on the product’s position vis-à-vis competitors and its position in the organization’s portfolio. In the pharmaceutical industry, for example, situations can range from an organizations launching its 10th product and the fourth in an established class of drugs to an emerging company launching a first-of-its-kind product to cure a rare disease that only a small number of doctors across the country treat. One organization may be focused on selling a single product, whereas another may be adding a new product to reps’ already crowded bags.
In these situations — and every other one in between — incentive compensation plan design and refinement is complex and riddled with uncertainty. But organizations can turn to simulation modeling to quantify their uncertainty.
Simulation modeling allows an organization to assign probabilities to various market outcomes. With this knowledge, the company can set better goals and quotas and understand its financial liability, including the unwanted scenarios in which it may undershoot its forecast, yet still overshoot its target payout.
Take, for example, an emerging pharmaceutical company with a hematology drug. Under a proposed revised incentive compensation plan, the organization may determine that if the New England territory performs at 110% of target, while the Southeast territory performs at 80% of target, the organization will be below its national target attainment but still over its plan payout budget. Simulation modeling can help the organization understand the likelihood of this scenario and whether it is an acceptable outcome.
This emerging pharmaceutical company may also want to know the likelihood of situations in which it surpasses its sales goals but pays even more over target to achieve that outcome. On the heels of this analysis, an organization can do additional financial modeling to determine its level of comfort with overpaying for results. After all, if an organization pays 130% of its total target payout to achieve 110% of its national goal, it may still benefit enough financially to justify the “overpayment.”
When designing a simulation model, it’s important to appreciate the variance in performance between territories. An organization can’t use a single probability distribution across all its territories. Instead, it must closely analyze historical sales data and build custom probability distributions for each territory. These efforts will help the organization maximize the accuracy of its simulation model.
2. Appreciate Territorial Differences
No two territories are alike, and, in many situations, territories can produce vastly different sales results. Therefore, territory-level differences pose a vexing challenge for commercial leaders seeking to construct motivating and fair incentive compensation plans. After all, if an organization adopts a one-size-fits-all approach to goal setting, a rep in a fertile sales territory will have a much easier time maxing out payout than a rep who’s stuck in a barren area.
The key to territory-level customization is knowledge. At a high level, organizations must assess market access, market potential and perform customer segmentation analysis. The level of analytical rigor associated with this knowledge-building varies. Segmentation analysis, in which a company identifies segments of customers with characteristics that are favorable for its salespeople, requires advanced techniques. This analysis goes beyond a strict counting of the types of prospective customers the organization wants to target. Instead, it incorporates a mix of promotion response statistics and analysis of customer characteristics.
Market assessments may be based on a mix of primary research (interviews with potential customers), experiential findings (insights from sales reps who have worked the territories) and logic (The upper Midwest will have a different market opportunity for aesthetic drugs than Beverly Hills), in addition to customer-level sales data.
It’s important to note that not every territory needs a custom approach. However, an organization must recognize the most significant territory-level differences and account for them in the construction of its incentive compensation plan.
Territory-level complexity grows when an organization's reps must sell multiple products. One product may depend greatly on rep promotion for sales, whereas another sells itself. Some territories’ customers may count on sales reps to educate them. But another territory may be filled with customers who do their own research. One product may perform well in California, but not in New York, during the summer months. And, of course, market events can impact product performance in significant ways. In the pharmaceutical industry, a payer’s formulary change can make or break a drug’s sales prospects in that payer’s region. Organizations must therefore analyze historical sales data and model the impact of various market events to account for product- and territory-level distinctions in their incentive plans.
3. Properly Weight New vs. Recurring Business
Should a rep who has built a strong book of business be rewarded year in and year out for ongoing success with existing clients? Or, should the reps only be awarded for the acquisition of new customers? And with recurring business, how crucial are the rep’s continued efforts? The answers to these questions depend on the organization’s product.
For “sticky” products that enjoy significant repeat business, an organization should put more emphasis on new customers. For easily replaceable products, an organization should put more emphasis on customer retention. For products with relatively few customers (e.g., rare disease drugs), it may be difficult to attribute a new sale solely to a rep’s efforts, so an organization needs to ensure it also rewards reps for “refills,” or recurring business.
Sales response analysis can help an organization more accurately predict the probability of sales and customer retention — and understand the extent to which the company’s efforts lead to sales. While some recurring sales can be attributed to reps’ efforts and should be rewarded, some carryover sales will occur without promotion from the salesforce. It’s important to understand the breakdown between salesforce-generated results and naturally recurring sales to set more accurate goals and more efficiently deploy payout dollars.
Avoid Communication Breakdowns
Explaining incentive compensation plans — and the advanced analytical models that underlie them — can be difficult. But it’s crucial to get communications right. After all, if field sales representatives don’t understand how to maximize their pay, they are likely to grow frustrated and lose interest in driving sales for the company.
Commercial leaders must explain to reps how plans are designed without letting these discussions turn into graduate school-level analytics courses. One useful tactic is to empower the reps with tools like payout calculators to test different sales outcomes and understand how they’ll be paid under different market scenarios.
Leaders can then complement this self-service approach with clear, qualitative explanations of their plans. For example, explain based on knowledge of the market and customer universe the logical reasons for different goals in different territories. Leading with this qualitative information over analytical models will increase the chance of rep buy-in.
Further, defend the plan and the analytics that went into creating it, but acknowledge the inherent uncertainty in incentive compensation plan modeling, and explain the organization’s plan for ongoing review and revisions.
Finally, commercial leaders should have a multiyear trajectory for the incentive compensation plan in mind. They don’t need to communicate the full, detailed trajectory to reps. But they should at least show reps the short-term plan for incentive compensation and provide a rough timeline for any anticipated structural changes, such as a move from a commission- to a quota-based plan.
An organization that effectively explains its incentive compensation plan puts the goalposts firmly in place and reduces the chance of rep frustration and demotivation. B2B firms should chart a clear course for their incentive compensation plans and share that plan with the salesforce.
To Motivate Reps in a Changing Market, Build a Culture of Analytics
To successfully integrate advanced analytics into incentive compensation plan design and refinement, an organization must embrace a culture of analytics. To do so, it needs commercial leaders to understand the value of advanced analytics and commit wholeheartedly to its use across the commercial organization. Then, it needs resources. Acquire data science professionals — either through hiring or by engaging outside experts — to do the hard work required to execute analytics projects, from organizing and cleaning data to building simulation models.
With commitment from leadership and resources in place, an organization can produce valuable analytics-fueled insights. These insights can help the organization gain a clearer vision of the future and develop better commercial tactics, including incentive compensation plans.
The organizations that execute this culture shift and make advanced analytics a core part of their incentive compensation plan design and refinement processes will be able to create plans that are:
- Motivating so that reps perform to the best of their abilities.
- More responsive to different geographies’ nuances.
- Sustainable so they continue to spur success throughout a product’s lifecycle.
Using advanced analytics to improve the effectiveness of incentive compensation plans is achievable for most B2B companies. It’s time to act.