Category Archives: SaaS

A Ten-Point Sales Management Framework for Enterprise SaaS Startups

In this post, I’ll present what I view as the minimum sales management framework for an enterprise SaaS startup — i.e., the basics you should have covered as you seek to build and scale your sales organization [1].

  1. Weekly sheet
  2. Pipeline management rules, with an optional stage matrix
  3. Forecasting rules
  4. Weekly forecast calls
  5. Thrice-quarterly pipeline scrubs
  6. Deal reviews
  7. Hiring profiles
  8. Onboarding program
  9. Quarterly metrics
  10. Gong

Weekly Sheet
A weekly sheet, such as the one used here, that allows you to track, communicate, and intelligently converse about the forecast and its evolution.  Note this is the sheet I’d use for the CEO’s weekly staff meeting.  The CRO will have their own, different one for the sales team’s weekly forecast call.

Pipeline Management Rules with Optional Stage Matrix
This is a 2-3 page document that defines a sales opportunity and the key fields associated with one, including:

  • Close date (e.g., natural vs. pulled-forward)
  • Value (e.g., socialized, placeholder, aspiration, upside)
  • Stage (e.g., solution fit, deep dive, demo, vendor of choice)
  • Forecast category (e.g., upside, forecast, commit)

Without these definitions in place and actively enforced, all the numbers in the weekly sheet are gobbledygook.  Some sales managers additionally create a one-page stage matrix that typically has the following rows:

  • Stage name (I like including numbers in stage names to accelerate conversations, e.g., s2+ pipeline or s4 conversion rate)
  • Definition
  • Mandatory actions (i.e., you can be fired for not doing these)
  • Recommended actions (i.e., to win deals we think you should be doing these)
  • Exit criteria

If your stage definitions are sufficiently simple and clear you may not need a stage matrix.  If you choose to create one, avoid these traps:  not enforcing mandatory actions (just downgrade them to recommended) and multiple and/or confusing exit criteria.  I’ve seen stage matrices where you could win the deal before completing all six of the stage-three exit criteria!

Forecasting Rules
A one-page document that defines how the company expects reps to forecast.  For example, I’d include:

  • Confidence level (i.e., the percent of the time you are expected to hit your forecast)
  • Cut rules (e.g., if you cut your forecast, cut it enough so the next move is up — aka, the always-be-upsloping rule.)
  • Timing rules (e.g., if you can forecast next-quarter deals in this quarter’s forecast)
  • Management rules (e.g., whether managers should bludgeon reps into increasing their forecast)

Weekly Forecast Calls
A weekly call with the salesreps to discuss their forecasts.  Much to my horror, I often need to remind sales managers that these calls should be focused on the numbers — because many salespeople seem to love to talk about everything but.

For accountability reasons, I like people saying things that are already in Salesforce and that I could theoretically just read myself.  Thus, I think these calls should sound like:

Manager:  Kelly, what are you calling for the quarter?
Kelly:  $450K
Manager:  What’s that composed of?
Kelly:  Three deals.  A at $150K, B at $200K, and C at $100K.
Manager:  Do you have any upside?
Kelly:  $150K.  I might be able to pull deal D forward.

I dislike storytelling on forecast calls (e.g., stories about what happened at the account last week).  If you want to focus on how to win a given deal, let’s do that in a deal review.  If we want to examine the state of a rep’s pipeline, let’s do that in a pipeline scrub.  On a forecast call, let’s forecast.

I cannot overstate the importance of separating these three types of meetings. Pipeline scrubs are about scrubbing, deal reviews are about winning, and forecast calls are about forecasting.  Blend them at your peril.

Thrice-Quarterly Pipeline Scrubs
A call focused solely on reviewing all the opportunities in the sales pipeline.  The focus should be on verification:

  • Are all the opportunities actually valid in accordance with our definition of a sales opportunity?
  • Are the four key fields (close date, value, stage, forecast category) properly and accurately completed?
  • All means all.  While we can put more focus on this-quarter and next-quarter pipeline, we need to review the entire thing to ensure that reps aren’t dumping losses in out-quarters or using fake oppties to squat on accountants.

I like when these calls are done in small groups (e.g., regions) with each rep taking their turn in the hot seat.  Too large a group wastes everyone’s time.  Too small forgoes a learning opportunity, where reps can learn by watching the scrubs of other reps.

As a non-believer in alleged continuous scrubbing, I like doing these scrubs in weeks 2, 5, and 8 so the data presented to the executive staff is clean in weeks 3, 6, and 9.  See this threepart series for more.

Deal Reviews
As a huge fan of Selling Through Curiosity, I believe a salesperson’s job is to ask great questions that both reveal what’s happening in the account and lead the customer in our direction.  Accordingly, I believe that a sales manager’s job is to ask great questions that help salesreps win deals.  That is the role of deal review.

A deal review is a separate meeting from a pipeline scrub or a forecast call, and focused on one thing:  winning.  What do we need to learn or do to win a given deal?  As such,

  • It’s a typically a two-hour meeting
  • Run by sales management, but in a peer-to-peer format (meaning multiple reps attend and reps ask each other questions)
  • Where a handful of reps volunteer to present their deals and be questioned about them
  • And the focus is on asking reps (open-ended) questions that will help them win their deals

Examples:

  • What questions can you ask that will reveal more about the evaluation process?
  • Why do you think we are vendor of choice?
  • What are the top reasons the customer wouldn’t select us and how are we proactively addressing them?
  • How would we know if we were actually in first place in the evaluation process?

Hiring Profiles
A key part of building an enterprise SaaS company is proving the repeatability of your sales process.  While I have also written a threepost series on that topic, the TLDR summary is that proving repeatability begins with answering this question:

Can you hire a standard rep and onboard them in a standard way to reliably produce a standard result?

The first step is defining a hiring profile, a one-page document that outlines what we’re looking for when we hire new salesreps.  While I like this expressed in a specific form, the key points are that:

  • It’s specific and clear — so we can know when we’ve found one and can tell recruiters if they’re producing pears when we asked for apples.
  • There’s a big enough “TAM” so we can scale — e.g., if the ideal salesrep worked at some niche firm that only had 10 salespeople, then we’re going to have trouble scaling our organization.

Onboarding Program
The second key element of repeatability is onboarding.  Startups should invest early in building and refining a standard onboarding program that ideally includes:

  • Pre-work (e.g., a reading list, videos)
  • Class time (e.g., a 3-5 day live program with a mix of speakers)
  • Homework (e.g., exercises to reinforce learnings)
  • Assessment (e.g., a final exam, group exercise)
  • Mentoring (e.g., an assigned mentor for 3-6 months)
  • Reinforcement (e.g., quarterly update training)

In determining whether all this demonstrates a standard result, this chart can be helpful.

Quarterly Metrics
Like all functions, sales should participate in an estaff-level quarterly business review (QBR), presenting an update with a high-quality metrics section, presented in a consistent format.  Those metrics should typically include:

  • Performance by segment (e.g., region, market)
  • Average sales cycle (ASC) and average sales price (ASP) analysis
  • Pipeline conversion analysis, by segment
  • Next-quarter pipeline analysis, by segment
  • Customer expansion analysis
  • Win/loss analysis off the CRM system, often complemented by a separate quarterly third-party study of won and lost deals
  • Rep ramping and productivity-capacity analysis (e.g., RREs)

Gong
As someone who prides himself on never giving blanket advice: everybody should use Gong.

I think it’s an effective and surprisingly broad tool that helps companies in ways both tactical and strategic from note-taking to coaching to messaging to sales enablement to alerting to management to forecasting to generally just connecting the executive staff to what actually happens in the trenches — Gong is an amazing tool that I think can benefit literally every SaaS sales organization.

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Notes
[1] This post assumes the existence of functioning upstream work and processes, including (a) an agreement about goals for percentage of pipeline from the four pipeline sources (marketing, SDR/out, sales/out, and partners), (b) a philosophically aligned marketing department, (c) good marketing planning, such as the use of an inverted funnel model, (d) good sales planning, such as the use of a bookings capacity model, and (e) proper pipeline management as discussed in this threepart series.

The Product Superpowers That Few Flex: Join Special Guest Brett Queener on the SaaS Product Power Breakfast

Please join Thomas Otter and me this Thursday, May 6th at 8:00 am Pacific for the SaaS Product Power Breakfast on Clubhouse with special guest Brett Queener, partner at Bonfire Ventures, former President & COO at SmartRecruiters, product-line general manager at both Salesforce.com and Siebel, and member of the board of directors at Aforza, Atrium, ClearedIn, Cube, Invoca, Lytics, Pendo, SmartRecruiters, and Spekit.

Our topic will be The Product Superpowers That Few Flex:  Intention and Conviction.

We aim to cover the following questions:

  • What was it like running product for Marc Benioff?  (Or, for that matter, Tom Siebel?)
  • What do you look for when evaluating products for seed-stage investments?
  • Cadence:  daily / monthly / quarterly releases — which is best and why?
  • What in your mind is a world-class product manager?
  • How is the role of product decisioning changing?
  • In a product-led growth (PLG) world, does product own growth?
  • What’s a feature and not a company?

With Brett, the action is sure to be cutting, frank, insightful, fast-paced — and funny.  Content warning:  when Brett and I get together, the errant F-bomb has been known to drop, so this may be our first R-rated episode.

Bring a friend — it should be a crackling session.  If you need a Clubhouse invite, ask.  And for those who can’t make it live, the SaaS Product Power Breakfast is now available in podcast form, so it will be recorded and you can always listen to it later.

See you there!

Using This/Next/All-Quarter Analysis To Understand Your Pipeline

This is the third in a three-post series focused on forecasting and pipeline.  Part I examined triangulation forecasts to improve forecast accuracy and enable better conversations about the forecast.  After a review of pipeline management fundamentals, part II discussed the use of to-go pipeline coverage to provide clarity on how your pipeline is evolving across the weeks of the quarter.  In this, part III, we’ll introduce what I call this/next/all-quarter pipeline analysis as a way of looking at the entire pipeline that is superior to annual or rolling four-quarter pipeline analysis.

Let’s start by unveiling the last block on the sheet we’ve been using the previous two posts.  Here’s the whole thing:

You’ll see two new sections added:  next-quarter pipeline and all-quarters [1] pipeline.  Here’s what we can do when we see all three of them, taken together:

  • We can see slips.  For example, in week 3 while this-quarter pipeline dropped by $3,275K, next-quarter pipeline increased by $2,000K and all-quarters only dropped by $500K.  While there are many moving parts [2], this says to me that pipeline is likely sloshing around between quarters and not being lost.
  • We can see losses.  Similarly, when this-quarter drops, next-quarter is flat, and all-quarters drop, we are probably looking at deals lost from the pipeline [3].
  • We can see wins.  When you add a row at the bottom with quarter-to-date booked new ARR, if that increases, this-quarter pipeline decreases, next-quarter pipeline stays flat, and all-quarters pipeline decreases, we are likely looking at the best way of reducing pipeline:  by winning deals!
  • We can see how we’re building next-quarter’s pipeline.  This keeps us focused on what matters [4].  If you start every quarter with 3.0x coverage you will be fine in the long run without the risk of a tantalizing four-quarter rolling pipeline where overall coverage looks sufficient, but all the closeable deals are always two to four quarters out [5].

Tantalus and his pipeline where all the closeable deals are always two quarters out

  • We can develop a sense how next-quarter pipeline coverage develops over time and get better at forecasting day-1 next-quarter pipeline coverage, which I believe marketing should habitually do [6].
  • We can look at whether we have enough total pipeline to keep our salesreps busy by not just looking at the total dollar volume, but the total count of oppties.  I think this is the simplest and most intuitive way to answer that question.  Typically 15 to 20 all-quarters oppties is the maximum any salesrep can possibly juggle.
  • Finally, there’s nowhere to hide.  Companies that only examine annual or rolling four-quarter pipeline inadvertently turn their 5+ quarter pipeline into a dumping ground full of fake deals, losses positioned as slips, long-term rolling hairballs [7], and oppties used for account squatting.

I hope you’ve enjoyed this three-part series on forecasting and pipeline.  The spreadsheet used in the examples is available here.

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Notes

[1] Apologies for inconsistences in calling this all-quarter vs. all-quarters pipeline.  I may fix it at some point, but first things first.  Ditto for the inconsistency on this-quarter vs. current-quarter.

[2] You can and should have your salesops leader do the deeper analysis of inflows (including new pipegen) and outflows, but I love the first-order simplicity of saying, “this-quarter dropped by $800K, next-quarter increased by $800K and all-quarters was flat, ergo we are probably sloshing” or “this-quarter dropped by $1M, next-quarter was flat, and all-quarters dropped by $1M, so we probably lost $1M worth of deals.”

[3] Lost here in the broad sense meaning deal lost or no decision (aka, derail).  In the former case, someone else wins the deal; in the latter case, no one does.

[4] How do you make 32 quarters in row?  One at a time.

[5] Tantalus was a figure in Greek mythology, famous for his punishment:  standing for eternity in a pool of water below a fruit tree where each time he ducked to drink the water it would recede and each time he reached for a fruit it was just beyond his grasp.

[6] Even though most companies have four different pipeline sources (marketing/inbound, SDR/outbound, sales/outbound, and partners), marketing should, by default, consider themselves the quarterback of the pipeline as they are usually the majority pipeline source and the most able to take corrective actions.

[7] By my definition a normal rolling hairball always sits in this quarter’s pipeline and slips one quarter every quarter.  A long-term rolling hairball is thus one that sits just beyond your pipeline opportunity scrutiny window (e.g., 5 quarters out) and slips one quarter every quarter.

 

Using To-Go Coverage to Better Understand Pipeline and Improve Forecasting

This is the second in a three-part series focused on forecasting and pipeline.  In part I, we examined triangulation forecasts with a detailed example.  In this, part II, we’ll discuss to-go pipeline coverage, specifically using it in conjunction with what we covered in part I.  In part III, we’ll look at this/next/all-quarter pipeline analysis as a simple way to see what’s happening overall with your pipeline.

Pipeline coverage is a simple enough notion:  take the pipeline in play and divide it by the target and get a coverage ratio.  Most folks say it should be around 3.0, which isn’t a bad rule of thumb.

Before diving in further, let’s quickly remind ourselves of the definition of pipeline:

Pipeline for a period is the sum of the value of all opportunities with a close date in that period.

This begs questions around definitions for opportunity, value, and close date which I won’t review here, but you can find discussed here.  The most common mistakes I see thinking about the pipeline are:

  • Turning 3.0x into a self-fulfilling prophecy by bludgeoning reps until they have 3.0x coverage, instead of using coverage as an unmanaged indicator
  • Not periodically scrubbing the pipeline according to a defined process and rules, deluding yourself into thinking “we’re always scrubbing the pipeline” (which usually means you never are).
  • Applying hidden filters to the pipeline, such as “oh, sorry, when we say pipeline around here we mean stage-4+ pipeline.”  Thus executives often don’t even understand what they’re analyzing and upstream stages turn into pipeline landfills full of junk opportunities that are left unmanaged.
  • Pausing sales hiring until the pipeline builds, effectively confusing cause and effect in how the pipeline gets built [1].
  • Creating opportunities with placeholder values that pollute the pipeline with fake news [1A], instead of creating them with $0 value until a salesrep socializes price with the customer [2].
  • Conflating milestone-based and cohort-based conversion rates in analyzing the pipeline.
  • Doing analysis primarily on either an annual or rolling four-quarter pipeline, instead of focusing first on this-quarter and next-quarter pipeline.
  • Judging the size of the all-quarter pipeline by looking at dollar value instead of opportunity count and the distribution of oppties across reps [2A].

In this post, I’ll discuss another common mistake, which is not analyzing pipeline on a to-go basis within a quarter.

The idea is simple:

  • Many folks run around thinking, “we need 3.0x pipeline coverage at all times!”  This is ambiguous and begs the questions “of what?” and “when?” [3]
  • With a bit more rigor you can get people thinking, “we need to start the quarter with 3.0x pipeline coverage” which is not a bad rule of thumb.
  • With even a bit more rigor that you can get people thinking, “at all times during the quarter I’d like to have 3.0x coverage of what I have left to sell to hit plan.” [4]

And that is the concept of to-go pipeline coverage [5].  Let’s look at the spreadsheet in the prior post with a new to-go coverage block and see what else we can glean.

Looking at this, I observe:

  • We started this quarter with $12,500 in pipeline and a pretty healthy 3.2x coverage ratio.
  • We started last quarter in a tighter position at 2.8x and we are running behind plan on the year [6].
  • We have been bleeding off pipeline faster than we have been closing business.  To-go coverage has dropped from 3.2x to 2.2x during the first 9 weeks of the quarter.  Not good.  [7]
  • I can easily reverse engineer that we’ve sold only $750K in New ARR to date [8], which is also not good.
  • There was a big drop in the pipeline in week 3 which makes me start to wonder what the gray shading means.

The gray shading is there to remind us that sales management is supposed to scrub the pipeline in weeks 2, 5, and 8 so that the pipeline data presented in weeks 3, 6, and 9 is scrubbed.  The benefits of this are:

  • It eliminates the “always scrubbing means never scrubbing” problem.
  • It draws a deadline for how long sales has to clean up after the end of a quarter:  the end of week 2.  That’s enough time to close out the quarter, take a few days rest, and then get back at it.
  • It provides a basis for snapshotting analytics.  Because pipeline conversion rates vary by week things can get confusing fast.  Thus, to keep it simple I base a lot of my pipeline metrics on week 3 snapshots (e.g., week 3 pipeline conversion rate) [9]
  • It provides an easy way to see if the scrub was actually done.  If the pipeline is flat in weeks 3, 6, and 9, I’m wondering if anyone is scrubbing anything.
  • It lets you see how dirty things got.  In this example, things were pretty dirty:  we bled off $3,275K in pipeline during the week 2 scrub which I would not be happy about.

Thus far, while this quarter is not looking good for SaaSCo, I can’t tell what happened to all that pipeline and what that means for the future.  That’s the subject of the last post in this three-part series.

A link to the spreadsheet I used in the example is here.

# # #

Notes

[1]  In enterprise SaaS at least, you should look at it the other way around:  you don’t build pipeline and then hire reps to sell it, you hire reps and then they build the pipeline, as the linked post discusses.

[1A]  The same is true of close dates.  For example, if you create opportunities with a close date that is 18+ months out, they can always be moved into the more current pipeline.  If you create them 9 months out and automatically assign a $150K value to each, you can end up with a lot air (or fake news/data) in your pipeline.

[2]  For benchmarking purposes, this creates the need for “implied pipeline” which replaces the $0 with a segment-appropriate average sales price (ASP) as most people tend to create oppties with placeholder values.  I’d rather see the “real” pipeline and then inflate it to “implied pipeline” — plus it’s hard to know if $150K is assigned to an oppty as a placeholder that hasn’t been changed or if that’s the real value assigned by the salesrep.

[2A] If you create oppties with a placeholder value then dollar pipeline is a proxy for the oppty count, but a far less intuitive one — e.g., how much dollar volume of pipeline can a rep handle?  Dunno.  How many oppties can they work on effectively at one time?  Maybe 15-20, tops.

[3] “Of what” meaning of what number?  If you’re looking at all-quarters pipeline you may have oppties that are 4, 6, or 8+ quarters out (depending on your rules) and you most certainly don’t have an operating plan number that you’re trying to cover, nor is coverage even meaningful so far in advance.  “When” means when in the quarter?  3.0x plan coverage makes sense on day 1; it makes no sense on day 50.

[4] As it turns out, 3.0x to-go coverage is likely an excessively high bar as you get further into the quarter.  For example, by week 12, the only deals still forecast within the quarter should be very high quality.  So the rule of thumb is always 3.0x, but you can and should watch how it evolves at your firm as you get close to quarter’s end.

[5]  In times when the forecast is materially different from the plan, separating the concepts of to-go to forecast and to-go to plan can be useful.  But, by default, to-go should mean to-go to plan.

[6] I know this from the extra columns presented in the screenshot from the same sheet in the previous post.  We started this quarter at 96% of the ARR plan and while the never explicitly lists our prior-quarter plan performance, it seems a safe guess.

[7]  If to-go coverage increases, we are closing business faster than we are losing it.  If to-go coverage decreases we are “losing” (broadly defined as slip, lost, no decision) business faster than we are closing it.  If the ratio remains constant we are closing business at the same ratio as we started the quarter at.

[8]  A good sheet will list this explicitly, but you can calculate it pretty fast.  If you have a pipeline of $7,000, a plan of $3,900, and coverage of 2.2x then:  7,000/2.2 (rounded) = 3,150 to go, with a plan of 3,900 means you have sold 750.

[9] An important metric that can be used as an additional triangulation forecast and is New ARR / Week 3 Pipeline.

 

Using Triangulation Forecasts For Improved Forecast Accuracy and Better Conversations

Ever been in this meeting?

CEO:  What’s the forecast?
CRO:  Same as before, $3,400K.
Director 1:  How do you feel about it?
CRO:  Good.
Director 2:  Where will we really land?
CRO:  $3,400K.  That’s why that’s the forecast.
Director 1:  But best case, where do we land?
CRO:  Best case, $3,800K.
Director 2:  How do you define best case?
CRO:  If the stars align.

Not very productive, is it?

I’ve already blogged about one way to solve this problem:  encouraging your CRO think probabilistically about the forecast.  But that’s a big ask.  It’s not easy to change how sales leaders think, and it’s not always the right time to ask.  So, somewhat independent of that, in this series I’ll introduce three concepts that help ensure that we have better conversations about the forecast and ultimately forecast better as a result:  triangulation forecasts, to-go pipeline coverage, and this/next/all-quarter pipeline analysis.  In this post, we’ll cover triangulation forecasts.

Triangulation Forecasts

The simplest way to have better conversations about the forecast is to have more than one forecast to discuss.  Towards that end, much as we might take three or four bearings to triangulate our position when we’re lost in the backcountry, let’s look at three or four bearings to triangulate our position on the new annual recurring revenue (ARR) forecast for the quarter.

In this example [1] we track the forecast and its evolution along with some important context such as the plan and our actuals from the previous and year-ago quarters.  We’ve placed the New ARR forecast in its leaky bucket context [2], in bold so it stands out.  Just scanning across the New ARR row, we can see a few things:

  • We sold $3,000K in New ARR last quarter, $2,850K last year, and the plan for this quarter is $3,900K.
  • The CRO is currently forecasting $3,400K, or 87% of the New ARR plan.  This is not great.
  • The CRO’s forecast has been on a steady decline since week 3, from its high of $3,800K.  This makes me nervous.
  • The CRO is likely pressuring the VP of Customer Success to cut the churn forecast to protect Net New ARR [3].
  • Our growth is well below planned growth of 37% and decelerating [4].

I’m always impressed with how much information you can extract from that top block alone if you’re used to looking at it.  But can we make it better?  Can we enable much more interesting conversations?  Yes.  Look at the second block, which includes four rows:

  • The sum of the sales reps’ forecasts [5]
  • The sum of the sales managers’ forecasts [6]
  • The stage-weighted expected value (EV) of the pipeline [7] [8]
  • The forecast category-weighted expected value of the pipeline [9]

Each of these tells you something different.

  • The rep-level forecast tells you what you’d sell if every rep hit their current forecast.  It tends to be optimistic, as reps tend to be optimistic.
  • The manager-level forecast tells you how much we’d sell if every CRO direct report hit their forecast.  This tends to be the most accurate [10] in my experience.
  • The stage-weighted expected value tells you the value of pipeline when weighted by probabilities assigned to each stage. A $1M pipeline consisting of 10 stage 2 $100K oppties has a much lower EV than a $1M pipeline with 10 stage 5 $100K oppties — even though they are both “$1M pipelines.”
  • The forecast category-weighted expected value tells you the value of pipeline when weighted by probabilities assigned to each forecast category, such as commit, forecast, or upside.

These triangulation forecasts provide different bearings that can help you understand your pipeline better, know where to focus your efforts, and improve the accuracy of predicting where you’ll land.

For example, if the rep- and manager-level forecasts are well below the CRO’s, it’s often because the CRO knows about some big deal they can pull forward to make up any gap.  Or, more sinisterly, because the CRO’s expense budget is automatically cut to preserve a target operating margin and thus they are choosing to be “upside down” rather face an immediate expense cut [11].

If the stage-weighted forecast is much lower than the others, it indicates that while we may have the right volume of pipeline that it’s not far enough along in its evolution, and ergo we should focus on velocity.

Now, looking at our sample data, let’s make some observations about the state of the quarter at SaaSCo.

  • The reps are calling $3,400K vs. a $3,900K plan and their aggregate forecast has been fairly consistently deteriorating.  Not good.
  • The managers, who we might notice called last quarter nearly perfectly ($2,975K vs. $3,000K) have pretty consistently been calling $3,000K, or $900K below plan.  Worrisome.
  • The stage-weighted EV was pessimistic last quarter ($2,500K vs. $3,000K) and may need updated probabilities.  That said, it’s been consistently predicting around $2,600K which, if it’s 20% low (like it was last quarter), it suggests a result of $3,240K [12].
  • The forecast category-weighted expected value, which was a perfect predictor last quarter, is calling $2,950K.  Note that it’s jumped up from earlier in the quarter, which we’ll get to later.

Just by these numbers, if I were running SaaSCo I’d be thinking that we’re going to land between $2,800K and $3,200K [13].  But remember our goal here:  to have better conversations about the forecast.  What questions might I ask the CRO looking at this data?

  • Why are you upside-down relative to your manager’s forecast?
  • In other quarters was the manager-level forecast the most accurate, and if so, why you are not heeding it better now?
  • Why is the stage-weighted forecast calling such a low number?
  • What’s happened since week 5 such that the reps have dropped their aggregate forecast by over $600K?
  • Why is the churn forecast going down?  Was it too high to begin with, are we getting positive information on deals, or are we pressuring Customer Success to help close the gap?
  • What big/lumpy deals are in these numbers that could lead to large positive or negative surprises?
  • Why has your forecast been moving so much across the quarter?  Just 5 weeks ago you were calling $3,800K and how you’re calling $3,400K and headed in the wrong direction?
  • Have you cut your forecast sufficiently to handle additional bad news, or should I expect it to go down again next week?
  • If so, why are you not following the fairly standard rule that when you must cut your forecast you cut it deeply enough so your next move is up?  You’ve broken that rule four times this quarter.

In our next post in the series we’ll discuss to-go pipeline coverage.  A link to the spreadsheet used to the example is here.

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Notes

[1] This is the top of the weekly sheet I recommend CEOs to start their weekly staff meeting.

[2] A SaaS company is conceptualized as a leaky bucket of ARR.

[3] I cheated and look one row down to see the churn forecast was $500K in weeks 1-6 and only started coming down (i.e., improving) as the CRO continued to cut their New ARR forecast.  This makes me suspicious, particularly if the VP of Customer Success reports to the CRO.

[4] I cheated and looked one row up to see starting ARR growing at 58% which is not going to sustain if New ARR is only growing at ~20%.  I also had to calculate planned growth (3900/2850 = 1.37) as it’s not done for me on the sheet.

[5] Assumes a world where managers do not forecast for their reps and/or otherwise cajole reps into forecasting what the manager thinks is appropriate, instead preferring for managers to make their own forecast, loosely coupling rep-level and the manager-level forecasts.

[6]  Typically, the sum of the forecasts from the CRO’s direct reports.  An equally, if perhaps not more, interesting measure would be the sum of the first-line managers’ forecasts.

[7] Expected value is math-speak for probability * value.  For example, if we had one $100K oppty with a 20% close probability, then its expected value would be $100K * 0.2 = $20K.

[8] A stage-weighted expected value of the (current quarter) pipeline is calculated by summing the expected value of each opportunity in the pipeline, using probabilities assigned to each stage.  For example, if we had only three stages (e.g., prospect, short-list, and vendor of choice) and assigned a probability to each (e.g., 10%, 30%, 70%) and then multiplied the new ARR value of each oppty by its corresponding probability and summed them, then we would have the stage-weighted expected value of the pipeline.  Note that in a more advanced world those probabilities are week-specific (and, due to quarterly seasonality, maybe week-within-quarter specific) but we’ll ignore that here for now.  Typically, one way I sidestep some of that hassle is to focus my quarterly analytics by snapshotting week 3, creating in effect week 3 conversion rates which I know will work better earlier in the quarter than later.  In the real world, these are often eyeballed initially and then calculated from regressions later on — i.e., in the last 8 quarters, what % of week 3, stage 2 oppties closed?

[9]  The forecast category-weighted expected value of the pipeline is the same the stage-weighted one, except instead of using stage we use forecast category as the basis for the calculation.  For example, if we have forecast categories of upside, forecast, commit we might assign probabilities of 0.3, 0.7, and 0.9 to each oppty in that respective category.

[10] Sometimes embarrassingly so for the CRO whose forecast thus ends up a mathematical negative value-add!

[11] This is not a great practice IMHO and thus CEOs should not inadvertently incent inflated forecasts by hard-coding expense cuts to the forecast.

[12] The point being there are two ways to fix this problem.  One is to revise the probabilities via regression.  The other is to apply a correction factor to the calculated result.  (Methods with consistent errors are good predictors that are just miscalibrated.)

[13]  In what I’d consider a 80% confidence interval — i.e., 10% chance we’re below $2,800K and 10% chance we’re above $3,200K.

The SaaS Product Power Breakfast Now Available in Podcast Form

As many of you know, Thomas Otter and I have been hosting a weekly Clubhouse room on Thursdays at 8:00 am Pacific which we’re calling The SaaS Product Power Breakfast (TSPPB).

After struggling to record them [1], we (or I should say the Thomas part of “we” who did all the work) succeeded on our most recent episode [2], which features a dazzling interview with entrepreneur, former Salesforce executive, and now venture capitalist (VC) Scott Beechuk of Norwest Venture Partners.

Thus, we are now starting to release the recordings as a podcast, The SaaS Product Power Breakfast podcast.  Think of it like a TV show filmed in front of live studio audience.  You can also find it on podbean here.

Or, for your convenience, I’ve embedded the episode with Scott below.

 

See you Thursdays at 8:00 am Pacific for our future episodes.  Block your calendar, please.  And tell a friend!

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Notes

[1] Clubhouse, like Snapchat, is built around a philosophy of ephemerality so recording is deliberately not a feature and the app seems to disable the iPhone’s recording ability as well.  (The app also has other quirks, like refusing to connect to and play over my Bluetooth speaker.)  That said, some people record rooms on Clubhouse and the protocol, which we follow, is to both put a red ball in the room title and announce that the room is being recorded.

[2] Ask Thomas about the gear he had to buy and how he’s now setup as a world-class podcaster.  He’s got a pretty cool setup.