Attrition is generally high in sales function which makes it extremely important to have a good ramp-up strategy in place to attract top talent. To ensure motivation when newly-hired reps are still adjusting to their role, they are generally provided extra incentives or are put on special compensation plans with lower sales targets. The type of acceleration provided during the initial months depends heavily on the type of role and the industry. Some popular incentive plan designs that are used for new hire reps are:
The primary struggle in defining such new hire ramp plans lies in gauging correct parameter values such as duration of the ramp, quota relief given each month, guaranteed amounts paid each month, etc. These are often set based on ‘gut feel’ of compensation managers and might not necessarily correspond to ground realities. Simple analytics can be used to design the optimum plan for new-hire sales reps which helps them achieve their target incentive while motivating them to sell as much as possible, thereby increasing their sales productivity.
For such analysis, let's assume we've decided to utilize the reduced quota/goals strategy for the new hire ramp period. In order to design this plan we need to answer 2 questions:
To answer these questions, we categorize reps on the basis of their tenure and plot their average revenue achievement against the full rep quota (See figure 1). This shows the profile of rep performance improvement as they mature. It is clear from the graph that sales achievement in initial ramp months is very low and increases progressively until it reaches 80% in the 4th month after hire. Thereafter, the rate of increase slows as rep matures further and achievement eventually plateaus off. This makes a good case for a ramp duration of 4 months.
Figure 1 :Reps are categorized on the basis of their tenure and average revenue achievement of reps in a specific tenure category is plotted. For example, the graph shows that employees who have been in the company for 1 month achieve, on an average, 20% of the revenue quota.
In addition, we also plot the distribution of sales achievement of all reps within a specific month since hire and identify the quartile markers (see figure 2). This helps us determine if particular reps are overachieving compared to others. The graph shows that the median (50th percentile) is steadily increasing over the initial months and the values are similar to the averages we plotted in figure 1. 80% target achievement for a rep with 4-months of tenure passes the 'gut-feel' test as well. The rep can be expected to mature further without earning substantially lower than his target incentive. Had this number been around 60%, we might have further extended the ramp duration as it suggests that reps require more time to adjust to their role.
Figure 2 : Plot of achievement of all reps categorized on the basis of their tenure. The 50th percentile shows an increase with the increase in maturity level of reps.
Having decided the ramp duration, the next step is setting progressively increasing quotas for each month. For this, we need to see how sales reps' achievement ramps as their tenure progress (see Figure 3). Quota expectations should be in line with average sales ramp up. In this case, for example, we can set quota values for months 1, 2, 3, and 4 as 20%, 60%, 70%, and 80% of the full rep quota.
Figure 3: Reps are categorized on the basis of their tenure and their sales revenue is plotted. For example, the graph shows that employees who have been in the company for 1 month make sales of $2004 on average
While these numbers are analytically determined on the basis of data, company sales management would probably prefer to tweak these numbers based on their business judgment. Nevertheless, these analyses help create a great first pass and help company sales leaders to make a more informed decision.
If you got this far, we think you’d like our future blog content, too. Please subscribe on the right side.
Why choose Python for your startup?
If you are confused about which programming language should be used for your startup, then give this blog a read as it will tell you about Python and its benefits.
The role of Data lakes
What is a data lake? How is it beneficial to startups? Read this blog to know all about data lakes.
FinTech impact on Financial Services
UPI, Paytm, Online Banking, just think about how often you use these services. Life has been made easier since we were introduced to online banking. Read this blog to know about how FinTech has had an impact on Financial Services.