Contemporary Sales operations is evolving to become more strategic by the day. Balancing incentive compensation and sales quota management on one hand and maximizing bookings leading to improved earnings on the other is ever more critical now!
In this example I’m going to play the role of a Sales Performance Manager working in the Sales Operations organization. I’ve been given the job to analyze our increasing overall commission costs and to look for insights to bolster our earnings.
I’ll use IBM Watson Analytics to help me on this.
It all begins with a dataset!
I begin with the Opportunity Payment dataset with details of 35,000 closed opportunities. I want to know how much of the bookings is being paid out in the form of commissions or incentives across all plan participants.
Once I upload this dataset, I click on it to begin my analysis. While I can use one of the ‘unbiased’ cognitive starting points, I go ahead and type my question in natural language to look at the spread of Compensation Cost of Sales or CCOS across our operating Geos.
What on earth is CCOS?
Expressed as a percentage, CCOS for an opportunity is essentially total bookings divided by total incentives paid for that opportunity. Hence, it’s the cost incurred on incentivizing a sale.
I drill down to look into the American regions to find the Western region having the highest CCOS of over 10%. Further drilling down into the Districts under the Western region, we see that Pacific district has the highest average CCOS of more than 13%.
The question is whether such high costs of making a sale is leading to proportionate high bookings that eventually lead to earnings?
Is the high cost of making a sale actually leading to overall high bookings?
I open a new tab and this time create my own visualization – a Tree Map. I quickly fill the data slots with columns from the data tray to focus on credited Bookings and CCOS across Regions within AMER & EMEA geographies.
While the West American region carries the highest CCOS at over 10%, it does not yield the highest proportion of Bookings.
Similarly, while we found Pacific district with the highest CCOS earlier, we now know it’s the worst performing district in terms of bookings!
Digging deeper into the Pacific district lets us hone in on the area of concern – Named Accounts in the Bay Area.
At this point, I realize we need to revisit our Account Planning and Forecasting strategy that could be contributing to inaccurate quota-setting for this territory.
Understanding the WHY behind the WHAT –
I continue my analysis by typing my next question as though I were talking to an expert – “Tell me more about CCOS” and select the most relevant spiral visualization.
This easy to understand spiral visualization shows me the factors that drive CCOS and ranks them in order of their predictive strengths. I can view each of the associated detailed insights to delve deeper.
I see that the combination of SF_Type or Sales force type and Region has a 31% bearing on CCOS and click on the associated detailed insight. Analyzing their interplay in this heat map, I can see specific SF_Types having disproportionately high costs of making a sale across specific regions.
However, I want to further customize this view based on some of the insights I gleaned earlier. So I multiply this heat map over quarters and filter on the Pacific which we found to have the highest CCOS in the American Western region. While I see high CCOS for Resellers during Q1, Q3 and Q4, it’s surprising to see that during Q2 it’s the Wholesalers that have the highest average Compensation Cost of Sales.
The mystery behind sales quota attainment levels –
I continue my analysis by bringing in quota Attainment details of all our sales employees or Payees. Essentially, I want to check if our quotas are distributed accurately and fairly across our sales territories and if attainment of quota is leading to proportionate bookings.
Note that if too many Payees are over-attaining, then we are paying too much in incentives (hence high CCOS) and our quotas were not accurately aligned with the market and if too few are meeting quota, then that means the targets were too high. Both of these situations are not ideal for business and our sellers!
First I take a look at our average Earnings and credited Bookings for the last six quarters. Note that credited or validated bookings and the speed with which they are pursued towards making a sale define the quality of our sales pipeline.
Filtering on America, I see this steady decline in both since the last three quarters – not a good sign at all!
The distinction between over-attaining and over-achieving –
Next, looking at the quota Attainment levels – it’s concerning to note that the largest number of commissioned employees is falling into the over 150% category, while the highest average for credited Bookings was found in the 100-105% category. The implication is that we are paying bulk of our commissions for significantly lower revenue results.
Also note the cognitive insights bar on the right that dynamically generates insights based on the visualization being discussed here.
Next, this view indicates the Territories that exceeded the quota by 50% or more over the last three quarters in America. Ideally, more sellers ought to lead to more Bookings at such high attainment levels.
Given that’s not the case here points to a serious under-estimation of forecast business and the need to reassess both the coverage model and individual team quota setting to make sure they align with the sources of revenue.
So to recap, I was able to analyze my raw datasets and quickly noticed the disparity between Bookings & Compensation Cost of Sales and was able to zero in on one of the problem sales territories. I also found that the actual Bookings and quota Attainment levels of our sellers need to be realigned across regions, roles, sales force types and quarters to drive Bookings. With my recommendations, I look forward to optimizing our compensation cost of sales, streamlining our quota attainment levels and improving our earnings!
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