The Two Deadly Sins of Sales Compensation Planning
As we race towards the end of Q1, it’s time to reflect on our salesforce productivity and determine whether our new comp plans have made an impact.
But be warned: the comp plan often isn’t to blame for poor sales performance.
If your plan isn’t meeting expectations, it’s important not to assume that the design, commission rates, or bonuses are to blame.
Most organizations commit two grave mistakes so early in the sales comp planning process that their plan is doomed to failure before reps even see it.
Here we discuss why those errors hold you back and detail the method we use to resolve this issue and boost revenue for multiple clients.
Why is your sales compensation plan failing?
If you’re like most sales organizations (even in the Fortune 500), new sales comp planning and communication normally go something like this:
- All effort goes into designing the comp plans — aligning incentives to corporate goals, making payout tables, setting the right commission rate.
- Next, you rely on “best guesses” and gut instinct to balance territories and set individual quotas (or continue using last years).
- You hold a training session to educate reps on the new plan and send them back out into the wild with a basic (but inadequate) understanding of what they need to do to maximize their earnings.
This approach is not uncommon, but it can be detrimental to reps and disastrous for business outcomes.
Consider these common culprits of diminished sales force effectiveness before blaming your comp plan and messing around with commission tranches and SPIFs.
Reason #1: Unbalanced Territories
When we don’t take the time to balance territories properly, we’re bound to upset a few sales reps and we will inevitably leave money on the table.
Reps in high workload territories won’t have enough time to cover all the accounts and opportunities available.
At best, they will score a few key accounts but leave the rest for competitors to swoop up. At worst, they will be spread so thin that they can’t give the level of attention necessary to impact revenue.Neglecting to take the time to balance territories properly is a great way to upset your sales team and leave a lot of potential revenue on the table. #sales #salescomp Click To Tweet
Meanwhile, reps in light workload territories will spend far too much time with their accounts to the point of diminishing returns. While they’re expending energy that could be directed elsewhere, reps with a high workload are left drowning.
To address unbalanced territories, use data to understand the true potential of the total addressable market, the resource-load to value-created ratio of existing accounts, and divide opportunity and workload between reps.
Here’s what you need to do:
- Identify factors that truly reflect the workload of a particular account.
- Balance workload across reps to optimize coverage of valuable accounts and untapped potential to maximize revenue with the resources you have.
- Calculate a workload based on relevant factors and use it as an input to rebalance territories, moving accounts or geographies from one region to the other.
We will go into more detail about what that looks like in practice in our case study below.
Reason #2: Unfair Quotas
Unattainable or unfair targets will undermine the power of the entire incentive plan. Even if the comp plan structure is well-designed and effectively aligns behavior to corporate goals, it won’t have much impact if reps do not believe they can hit their quota.
Reps will feel there is “no point” in working hard since the quota is completely out of reach. They will fail to hit their target payout, the organization won’t hit targets, and soon enough, the strain of impossible quotas will reverberate throughout the business. Higher rep attrition, fractured leadership, decremental revenue, and declining brand perception are just a few of the lasting impacts of poor quota setting.
As with physical strength or endurance training, quotas should be calibrated to stretch the rep’s output to help build their productivity threshold over time. Quotas should not be set based on some grand vision, standing rule, or ego but on proper market potential analysis and a nuanced understanding of the team’s output potential.
Here’s what you need to do to create equitable territories and quotas:
- Gather data that tells you more about your customers’ addressable spend or potential.
- Conduct simulations to test for bias in external factors beyond the rep’s control.
- Make final adjustments by considering factors outside of your analysis, such as experience, equity, and fairness.
That probably may seem overly simple, but that’s because there are so many personal variables at play. So much of getting quota and territory setting right depends on Step 1: Gathering the right data in a format we can manipulate easily.
Case Study: Territory Balancing Using Existing Customer Data
Quotas and territories areas of sale compensation planning are often separate, but they are inextricably linked. We must consider and calculate them together. We advise all our customers to take that approach.
When a software engineering firm recently engaged Forma.ai, its territories were created ad-hoc. No real strategy or data was used to determine the regions or the number of reps assigned to each. As part of their ongoing growth, they planned to increase salesperson headcount but were unsure of where and how to assign them between the territories for the most growth.
Defining Territory Potential Value
Most organizations can create basic target account lists and assess the potential market using third-party or publicly available data. But that is rarely an accurate reflection of the real potential in a region, especially for B2B, tech or service products.
Instead, we used ‘Max Sales’ as a proxy for value. We then ranked accounts from highest max sales to lowest max sales and divided them into quintiles where the sum of max sales for each quintile is approximately equal. Those quintiles looked something like this:
Defining Territory Potential Workload
As discussed above, employee workload is a crucial factor when forecasting incentive impact. In high-workload states, salesperson time is often split too thin between accounts and prospects for them to realize the true value in that territory. A high workload score is a warning sign of increased customer churn, limited account growth, and decremental employee and customer satisfaction.
To understand the full potential workload and resources required to service each customer, we classified customer accounts by complexity.
Each product had a different workload attached to it, and the approximate value (revenue) generated. Some products were loss-leaders in that they required significant resources to maintain but produced little income. Others demanded little work but brought in considerable revenue.
Most of the deal value was delivered on the initial transaction for this client, with ongoing support provided over the product’s life. More products would often mean exponential workload, so we assigned a “workload score” based on the number of unique products an account had purchased and their quintile based on max sales. That table looked something like this:
Creating a Territory Scoring Index
The next step was to use these quintiles to score each territory and create an index that we could use to map out balanced territories. The score was weighted as follows:
- Calculated workload = 50%
- 2021 sales = 25%
- 2020 sales = 25%
One thousand points in this system equalled about one full-time employee’s worth of work. A score between 750 and 1,250 points was regarded as well-balanced.
Once we calculated the new scores for each territory using this index, it was immediately obvious that several of their key territories were overburdened. Incorporating pipeline and forecasted growth into the model allowed us to demonstrate the impact of predicted growth on each territory and the consequent workload created. That allowed the customer to easily identify areas most in need of new hires or where they could reassign resources from less productive or more saturated areas.
The lasting value of that basic scoring model for accounts and territories is astronomical. It’s a foundation we can continually add transactional and intent-signal data, too, constantly evolving and enhancing the model.
The index enables us to
- identify the potential for existing account growth
- estimate the workload new customers or markets create
- incorporate buying signals to calculate the probability that new customers meet or exceed estimated LTV
- nudge sales reps towards certain products or actions more likely to increase account growth or decrease customer attrition.
- show management how those impact top-line numbers in real-time.
It’s a powerful tool for decision-making at the leadership level, but it also helped communicate the rationale behind territory assignments to reps, improving employee morale and quota attainment.
The Good, the Bad, and the Ugly
At Forma.ai, we do more than administer comp plans — we automate, analyze, and optimize your plans to meet your revenue goals. We can do that because our platform is designed from the ground up to help you better understand your sales performance data and the real impact of the behavior your incentive plan produces.
We’ve seen some great comp plans, along with some truly awful plans.
Most of the time, decisions made upstream, such as a lack of market or customer analysis, not the tactical details of the comp plan like payout tranches and percentages, prevent organizations from unleashing the revenue-driving potential of sales incentives.
Before you overhaul your plans again, we implore you to do the following:
- Get to know your addressable market so you can balance your territories.
- Get to know your customers so you can set fairer quotas.
- If the stated above seems implausible, embrace the wave of data and AI tools available to you — they’ll make your job a lot easier.
When we look back on the worst comp plans, the biggest crime wasn’t poor incentives. It was overcomplicating the program to account for conflicting upstream decisions.
Ironically, the comp plan was never the problem — their priorities were.