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How to Make and Use a Proper Sales Bookings ...
kellblog.com
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[5] Many salesops people do it the reverse way -- I think because they see the problem as allocating quota whereas I see the the problem as building an achievable operating plan. Starting with quota poses several problems, from the semantic (lopping 20% off quota is not 20% over-assignment, it’s actually 25% because over-assignment is relative to the smaller number) to the mathematical (first-quarter reps get assigned quota but we can realistically expect a 0% yield) to the procedural (quotas should be custom-tailored based on known state of the territory and this cannot really be built into a productivity model). “One advantages of having those percentages here is they are placed front-and-center in the company's bookings model which will force discussion and agreement.”
[5] Many salesops people do it the reverse way -- I think because they see the problem as allocating quota whereas I see the the problem as building an achievable operating plan. Starting with quota poses several problems, from the semantic (lopping 20% off quota is not 20% over-assignment, it’s actually 25% because over-assignment is relative to the smaller number) to the mathematical (first-quarter reps get assigned quota but we can realistically expect a 0% yield) to the procedural (quotas should be custom-tailored based on known state of the territory and this cannot really be built into a productivity model). “One advantages of having those percentages here is they are placed front-and-center in the company's bookings model which will force discussion and agreement.”
I’ve seen numerous startups try numerous ways to calculate their sales capacity. Most are too back-of-the-envelope and too top-down for my taste. Such models are, in my humble opinion, dangerous because the combination of relatively small errors in ramping, sales productivity, and sales turnover (with associated ramp resets) can result in a relatively big mistake in setting an operating plan. Building off quota, instead of productivity, is another mistake for many reasons [1]. “Thus, to me, everything needs to begin with a sales productivity model that is Einsteinian in the sense that it is as simple as possible but no simpler.” What does such a model need to take into account?
What does such a model need to take into account? “Sales productivity, measured in ARR/rep, and at steady state (i.e., after a rep is fully ramped).” Rep hiring plans, measured by new hires per quarter, which should be realistic in terms of your ability to recruit and close new reps.
What does such a model need to take into account? “This is not quota (what you ask them to sell), this is productivity (what you actually expect them to sell) and it should be based on historical reality, with perhaps incremental, well justified, annual improvement.” Rep hiring plans, measured by new hires per quarter, which should be realistic in terms of your ability to recruit and close new reps.
Rep hiring plans, measured by new hires per quarter, which should be realistic in terms of your ability to recruit and close new reps. “Rep ramping, typically a vector that has percentage of steady-state productivity in the rep’s first, second, third, and fourth quarters [2].” Rep turnover, the annual rate at which sales reps leave the company for either voluntary or involuntary reasons.
Rep hiring plans, measured by new hires per quarter, which should be realistic in terms of your ability to recruit and close new reps. “This should be based in historical data as well.” Rep turnover, the annual rate at which sales reps leave the company for either voluntary or involuntary reasons.
Sales productivity, measured in ARR/rep, and at steady state (i.e., after a rep is fully ramped). This is not quota (what you ask them to sell), this is productivity (what you actually expect them to sell) and it should be based on historical reality, with perhaps incremental, well justified, annual improvement. “Rep hiring plans, measured by new hires per quarter, which should be realistic in terms of your ability to recruit and close new reps.” Rep ramping, typically a vector that has percentage of steady-state productivity in the rep’s first, second, third, and fourth quarters [2]. This should be based in historical data as well.
“Building off quota, instead of productivity, is another mistake for many reasons [1].” Thus, to me, everything needs to begin with a sales productivity model that is Einsteinian in the sense that it is as simple as possible but no simpler.
“Such models are, in my humble opinion, dangerous because the combination of relatively small errors in ramping, sales productivity, and sales turnover (with associated ramp resets) can result in a relatively big mistake in setting an operating plan.” Thus, to me, everything needs to begin with a sales productivity model that is Einsteinian in the sense that it is as simple as possible but no simpler.
Quota over-assignment, the extent to which you assign more quota at the “street” level (i.e., sum of the reps) beyond the operating plan targets “For extra credit and to help maintain organizational alignment -- while you’re making a bookings model, with a little bit of extra math you can set pipeline goals for the company’s core pipeline generation sources [4], so I recommend doing so.” If your company is large or complex you will probably need to create an overall bookings model that aggregates models for the various pieces of your business. For example, inside sales reps tend to have lower quotas and faster ramps than their external counterparts, so you’d want to make one model for inside sales, another for field sales, and then sum them together for the company model.
The next row shows quota capacity, which uplifts productivity capacity by the over-assignment percentage assumption higher up in the model. This represents the minimum quota the Sales VP should assign at street level to have the assumed level of over-assignment. Ideally this figure dovetails into a quota-assignment model. “Therefore, when we’re looking at coverage, we really mean to say coverage of the newbiz ARR target (i.e., new ARR from new customers).” Building the next-level models to support pipeline generation goals is beyond the scope of this post, but I have a few relevant posts on the subject including this three-part series, here, here, and here.
The next row shows quota capacity, which uplifts productivity capacity by the over-assignment percentage assumption higher up in the model. This represents the minimum quota the Sales VP should assign at street level to have the assumed level of over-assignment. Ideally this figure dovetails into a quota-assignment model. “So, we first reduce the ARR goal by a percentage and then multiple it by the desired pipeline coverage ratio and then allocate the result across the pipeline-sources by presumably agreed-to percentages [6].” Building the next-level models to support pipeline generation goals is beyond the scope of this post, but I have a few relevant posts on the subject including this three-part series, here, here, and here.
What does such a model need to take into account? “Sales productivity, measured in ARR/rep, and at steady state (i.e., after a rep is fully ramped).” Rep hiring plans, measured by new hires per quarter, which should be realistic in terms of your ability to recruit and close new reps.
What does such a model need to take into account? “This is not quota (what you ask them to sell), this is productivity (what you actually expect them to sell) and it should be based on historical reality, with perhaps incremental, well justified, annual improvement.” Rep hiring plans, measured by new hires per quarter, which should be realistic in terms of your ability to recruit and close new reps.
The next row shows quota capacity, which uplifts productivity capacity by the over-assignment percentage assumption higher up in the model. This represents the minimum quota the Sales VP should assign at street level to have the assumed level of over-assignment. Ideally this figure dovetails into a quota-assignment model. “Therefore, when we’re looking at coverage, we really mean to say coverage of the newbiz ARR target (i.e., new ARR from new customers).” Building the next-level models to support pipeline generation goals is beyond the scope of this post, but I have a few relevant posts on the subject including this three-part series, here, here, and here.
The next row shows quota capacity, which uplifts productivity capacity by the over-assignment percentage assumption higher up in the model. This represents the minimum quota the Sales VP should assign at street level to have the assumed level of over-assignment. Ideally this figure dovetails into a quota-assignment model. “So, we first reduce the ARR goal by a percentage and then multiple it by the desired pipeline coverage ratio and then allocate the result across the pipeline-sources by presumably agreed-to percentages [6].” Building the next-level models to support pipeline generation goals is beyond the scope of this post, but I have a few relevant posts on the subject including this three-part series, here, here, and here.
Rep hiring plans, measured by new hires per quarter, which should be realistic in terms of your ability to recruit and close new reps. “Rep ramping, typically a vector that has percentage of steady-state productivity in the rep’s first, second, third, and fourth quarters [2].” Rep turnover, the annual rate at which sales reps leave the company for either voluntary or involuntary reasons.
Rep hiring plans, measured by new hires per quarter, which should be realistic in terms of your ability to recruit and close new reps. “Rep ramping, typically a vector that has percentage of steady-state productivity in the rep’s first, second, third, and fourth quarters [2].” Rep turnover, the annual rate at which sales reps leave the company for either voluntary or involuntary reasons.
Let’s take a quick walk through the model. Cells in Excel “input” format (orange and blue) are either data or drivers that need to be entered; uncolored cells are either working calculations or outputs of the model. “Next to the block ramp assumption, which expresses, as a percentage of steady-state productivity, how much we expect a rep to sell as their tenure increases with the company.” To the right of that we have more assumptions:
Let’s take a quick walk through the model. Cells in Excel “input” format (orange and blue) are either data or drivers that need to be entered; uncolored cells are either working calculations or outputs of the model. “The “first/hired quarter” row represents our hiring plans for the year.” To the right of that we have more assumptions:
Sales productivity, measured in ARR/rep, and at steady state (i.e., after a rep is fully ramped). This is not quota (what you ask them to sell), this is productivity (what you actually expect them to sell) and it should be based on historical reality, with perhaps incremental, well justified, annual improvement. “Rep hiring plans, measured by new hires per quarter, which should be realistic in terms of your ability to recruit and close new reps.” Rep ramping, typically a vector that has percentage of steady-state productivity in the rep’s first, second, third, and fourth quarters [2]. This should be based in historical data as well.
Let’s take a quick walk through the model. Cells in Excel “input” format (orange and blue) are either data or drivers that need to be entered; uncolored cells are either working calculations or outputs of the model. “Next to the block ramp assumption, which expresses, as a percentage of steady-state productivity, how much we expect a rep to sell as their tenure increases with the company.” To the right of that we have more assumptions:
The section in italics, linearity and growth analysis, is there to help the Sales VP analyze the results of using the judgment row. After changing targets, he/she can quickly see how the target is spread out across quarters and halves, and how any modifications affect both sequential and quarterly growth rates. I have spent many hours tweaking an operating plan using this part of the sheet, before presenting it to the board. “Ideally this figure dovetails into a quota-assignment model.” Finally, while we’re at it, we’re only a few clicks away from generating the day-one pipeline coverage / contribution goals from our major pipeline sources: marketing, alliances, and outbound SDRs. In this model, I start by assuming that sales or customer success managers (CSMs) generate the pipeline for upsell (i.e., sales to existing customers). Therefore, when we’re looking at coverage, we really mean to say coverage of the newbiz ARR target (i.e., new ARR from new customers). So, we first reduce the ARR goal by a percentage and then multiple it by the desired pipeline coverage ratio and then allocate the result across the pipeline-sources by presumably agreed-to percentages [6].