Pipeline Model: A Companion to Sales Capacity Planning
As you plan for growth, you’re already working with your anticipated sales capacity. But how do you ensure your pipeline can support those growth targets? This is where a detailed Pipeline Model comes in, helping you identify the key assumptions necessary to achieve your growth goals. It also serves as a check against your Total Addressable Market (TAM) to ensure you have enough available accounts to support those targets.
Key Components of the Pipeline Model
Pipeline Sources
Understanding where opportunities originate from is essential, as conversion rates will differ by source—whether from outbound efforts, inbound marketing, partners, or SDR/BDR teams.
I love the work that CS2 has put out around tipping point conversion tracking. I recommend you check out their approach and how to implement within CRM. Yes, assigning one team attribution to pipeline/bookings misses a lot of nuance, but the purpose of this is to be sure we’re setting ourselves up to achieve our targets.
Source 1: Marketing
Marketing that is focused on pipeline generation should have a bookings sourced target assigned to them. Full stop. This avoids the common scenario where marketing KPIs appear healthy (e.g., high MQLs) but the company misses the mark on actual bookings.
An easy to understand approach from a pipeline modeling standpoint is to use the program spend and divide that by the number of MQLs they produced from that spend. This is not to be confused with CAC. Marketing leads will come from various tactics: webinars, emails, SEO/SEM, events, etc. Take all of your spend and the number of MQLs produced in the same period. While this is not an exact science, it gives us something to start from. As a respected CMO once said to me “Fuzzy numbers over time show trends.” Your planned number may change from year to year if the underlying tactics shift dramatically.
With our spend/MQL value, we then take our budgeted marketing program spend per month (will vary based on seasonality and when key events happen – events being tradeshows or new product launches). This will show peaks and valleys of MQLs produced in our model. Allocate those MQLs across your teams based on historical distributions. If you know the majority of your leads come in the mid-market, use that.
Your conversion rates through the funnel (MQL>SAL>SQL) likely vary based on segment. Use what is most natural for your business. use your assumed conversion rates and share what the SAL and SQL results will be by month (you can also build in a delay if you think MQLs in one month are more likely to show up as SQLs in future months – if this is the case, be sure to include your prior year’s final month’s MQL numbers so month 1 of the new year isn’t 0). The ending result is a number of SQLs/Opps created by month. Multiply that by the segment’s ASP and you have how much pipeline is expected to be created from marketing-driven activity.
Source 2: SDRs/BDRs
This group should have a similar census as described in our sales capacity discussion on sellers and ramps. Note that different roles within your SDR org may have different ramp schedules. Those roles likely have different assumptions around:
Meetings scheduled
Meetings completed (Quota)
ASP
Conversion rate of Meeting completed:Opp created
Productivity / Quota attainment of the meetings completed
Build out the funnel for each of your reps starting out with their ramping/ramped quota and the expected conversion rates by step. The outcome will be a certain number of Opps created by month (you can also layer in a delay from meetings completed to opps generated).
The final piece here is an assumption about how much of their activity is from outbound activity vs what is sourced by marketing. This may vary by role/team and may change throughout the year if you are anticipating any procedural changes (lead scoring changes, routing adjustments, etc). Comp /productivity attainment doesn’t need to account for the split, but we need to make sure we aren’t double counting opps that are sourced by marketing and followed up on by our SDRs.
Source 3: Sellers
There are 2 paths you can take on the seller side. We’ll get to the numbers to use by rep in a moment, but let’s show the remainder of the flow first.
We have our ramped headcount over time thanks to our Capacity Model. Included in that was our effective headcount (if someone is considered 50% productive in a month, they are .5 of a ramped head). We should reduce that by any attrition assumed or planned (see the prior article for more detail). Now that we have our headcount/capacity, we need to know how many opps each person will create on their own.
Path 1
Understand historical trends to see how many opps are generated per ramped seller per month. These are purely sales generated, not marketing provided leads (again, don’t want to double count). This number likely varies by segment so allow that variability in your assumptions. Are there any changes occurring that would change this up or down in the following year?
Path 2
Instead of taking actuals for this assumption, you could back into the required opps created per seller. To do this, review the other inputs (marketing/SDR/partner) and determine how many additional opps need to be added to the pipeline to achieve the growth targets being set. Compare to historicals to see how realistic the number is. You may decide to invest more in other areas (ex: marketing programs) to reduce the sales requirement.
Source 4: Partners (if applicable)
Depending on the makeup of your GTM team, you could either determine this by the number of Partner Account Managers or historical opps provided through deal reg.
Consolidated pipeline creation
Now that we have all of our pipeline created by source, consolidate those values into a single table where you have columns for:
Segment
Source
Month created
12 or 24 columns of bookings resulting from those pipeline dollars. We will calculate this shortly.
Existing pipeline
We have pipeline we will carry over into the year that we need to incorporate. If you have a longer sales cycle, you should know your Q1 number will likely come from opps generated in advance and not assume the capacity approach will work.
Take your existing pipeline data by source and when it was created. Assuming you’re planning early, you may need to make assumptions for pipeline created in the final months of the current fiscal year (be sure to include seasonality in your business; I’ve seen lower pipe create in the final quarter as reps turn their focus to closing deals and hitting accelerators). Any misses in those numbers will impact Q1 performance and should be flagged quickly. For longer sales cycles, you should know any risk in Q1 well before month 3 of the quarter.
Make this data set easily refreshable but also monitor how frequently it’s changed during planning.
Distribution curve
Just because a business will say they have a 6 month sales cycle, doesn’t mean all deals close after 6 months. Some close faster and others take longer. The average may get to the 6 month number, but your road to get there will vary.
Sample chart showing conversion % of pipeline created into Won bookings over time (by month)
Transactional businesses will see high percentages in month+0 and month+1 and begin to level off toward their win rate. Mid-Market motions will see a steeper climb up through months 3-6 before leveling off. Enterprise motions with 12 month cycles will see a slower path in the early months before reaching the win rate around 12-15 months after creation.
Note that these rates may vary by source (ie: sales source pipeline converts better than SDR outbound sourced). Get as granular as you want but don’t sacrifice statistical significance (if you cut by too many segments, you may run into very small counts which will vary conversions wildly)
Question – what observations have you seen in your business?
Pulling it all together
Now that we have our projected pipeline creation by source and we have visibility into our existing pipeline along with how our pipeline typically converts to bookings, let’s pull it all together.
Take your existing pipeline and pass it through the distribution curve to produce your bookings from existing. For the planned pipeline creation, you will take the projections we built earlier and pass those through your conversion table.
Note: if you are expecting any improvements to conversions (ie: from enablement efforts, etc) you can use a 2nd set of distributions. Be sure to document those assumptions.
The result will show how much bookings will come from existing + pipe creation and produce a total booking output. You can slice it by segment, type, etc.
Now that you have your result, compare that to the top-down projections that were initially created. Are they in line? Do they differ? By how much? If they are lower, what types of adjustments would need to be made? Increases in marketing spend? Increases in conversion rates? Higher ASP (from pricing/packaging)? More sales capacity or better productivity?
Understanding where the numbers land can help ground the discussion in numbers vs just a headcount and cost perspective. If the costs are constrained, what would need to be true to allow us to feel confident in our ability to achieve the targets?
Conclusion
Capacity models and pipeline models are very helpful during planning season. They each have their own benefits and should be looked at to confirm how realistic your target setting is. They will also allow all key stakeholders to have inputs into what the plan looks like. The end result gives you targets for pipeline creation by source (for sales, SDR, marketing, partner), conversion rate expectations and any improvements needed (for enablement) and bookings (for finance, sales, marketing, partner). Be sure to measure the actual performance of each key step throughout the year and make adjustments as needed.
Taking a data-driven approach to your planning will give everyone in your organization more confidence in the numbers and plan.
If you would like help building out your model or just want some feedback on your existing models, reach out!