# Category Archives: Forecasting

## What Do “Pipeline Coverage” and “Forecast” Mean When Your Sales Cycle is 30 Days?

I grew up in enterprise.  I have already written a post on the tricky problem of mapping one’s mindset from enterprise to velocity SaaS, meaning smaller deals, shorter contract durations (e.g., month-to-month), and/or monthly-varying pricing [1].  That post was focused on what, if anything, “annual recurring revenue” (ARR) means such an environment, and how that impacts metrics that rely on ARR as part of their definition (e.g., CAC ratio).

In this post, I’ll continue in the velocity SaaS direction by exploring short average sales cycles (ASC), as opposed to short contracts.  Specifically, what does it mean in short ASC companies when you discuss common concepts like pipeline coverage and the sales forecast?

Let’s demonstrate the problem.

In enterprise, quarterly pipeline (defined as the sum of the values of opportunities with a close date in the quarter) is somewhat intertwined the notion of long sales cycles.  Meaning that in a company with 9–12-month sales cycles, virtually every deal that has a chance of closing within the quarter is already in the pipeline at the start of the quarter.  Thus, you can meaningfully calculate “coverage” for the quarter by dividing the quarterly starting pipeline by the quarterly sales target.  Most sales VPs like a 3x ratio [2].

Thus, the concept of pipeline coverage implicitly assumes a sales cycle (significantly) longer than the coverage period.  That’s why most companies don’t look at out-quarter pipeline coverage much (though they should) and if they do, they expect a much lower coverage ratio.

Now, let’s imagine an average sales cycle of 30 days and — rather than futzing with cohorts, statistics, and distributions [3] — let’s assume that all oppties are won or lost in exactly 30 days [4].

In this scenario, at the start of the quarter, what is the pipeline coverage ratio? It’s 1.0x.  Why?  We have zero pipeline for months 2 and 3 of the quarter.  If we assume that we have 3.0x coverage for month one and that the quarterly goal is evenly distributed across months, then we’d have 3.0x, 0.0x, and 0.0x for the three months of the quarter, or 1.0x overall [5].

In this example, quarterly pipeline coverage is basically meaningless because two-thirds of the pipeline you need to close during the quarter hasn’t been created yet.  Assuming a 30-day MQL-to-opportunity lag, one-third is working its way through the high funnel and the other third is still a wink in marketing’s eye.

If quarterly pipeline coverage is basically meaningless in short ASC companies, then what is meaningful?

• Examining monthly pipeline coverage. Instead of week-3 quarterly pipeline coverage [6], we should look at day-3 monthly pipeline coverage — dividing the starting monthly pipeline by the monthly sales target. (After that, you can use to-go pipeline coverage to get continuous insight.)
• Treating months 2 and 3 the way you’d treat next-quarter and the quarter thereafter in enterprise. Using a pipeline progression chart to see how the out-month pipeline is shaping up.
• Getting marketing to forecast starting pipeline for month 2 and month 3, based on what they have already generated in the high funnel and their current pipeline generation plans for month 2.

Inherent in my point of view is that the definition of “coverage” is based on opportunities that already exist in the pipeline. Call me untrusting, but somehow I can’t feel covered by something that hasn’t been created yet.  Some might define quarterly coverage in this environment using month 1 pipeline plus month 2 pipeline forecast and month 3 pipeline plan.  But to me, that’s not coverage.  And it’s objectively not the same thing as pipeline coverage when we use the term in enterprise.

Now, let’s zip back to reality for a minute.  In the velocity companies that I work with, ASC is closer to 60 days and with a pretty broad distribution where maybe 90% of the deals close within 30 and 120 days.  Happily, this means you will have month 2 and month 3 opportunities in the starting quarter pipeline, but it nevertheless also means you will be increasingly reliant on to-be-generated opportunities across the months of the quarter.

In this case, I would make a three-layer forecast:

• Sales (from existing opportunities). Forecast month 1, 2, and 3 sales using the normal sales forecasting process.
• Marketing, from the high funnel. Use existing MQLs and your standard conversion rates, ideally time-based time-based (not just the total rate, but the rate split by time period)
• Marketing, from planned demandgen. Forecast responses, then use standard conversion rates and ideally time-based. (Ideally you can start with your inverted funnel model.)

This approach is preferable to looking only at pipeline generation (pipegen) because a pipegen approach:

• Tends to ignore the oppties that are already there
• Almost always ignores that time-based nature of close rates
• Uses an average sales price (ASP) as the proxy value for an opportunity [7].

In the example above you can clearly see how much of the forecast comes from existing opportunities (51%), how much from the existing high funnel (36%), and how much from planned demandgen activities (13%).

Finally, I have the same problem with the word “forecast” as I do with “coverage” in the short ASC world. They’re not quite the same thing as they are in enteprise. First, let me define “forecast,” along with its cousins, “plan” and “model.”

• The plan is about accountability. It’s what we signed up for and accountable to. Budget is a synonym [8].
• The model is a driver-based model of the business. It’s a calculated output (e.g., opportunities generated) given assumptions for a number of inputs and the way they interact (e.g., demandgen spend, MQLs generated, conversion rates).
• The forecast is about prediction. It’s someone’s latest prediction for an output (e.g., bookings) given all available information at the time it’s made.

The plan is what we were willing to sign up for last December (when we received board approval). The forecast is what we think is going to happen now.  We used models to help build the original plan and we can certainly re-run those models today using actuals as inputs to see what they produce.

In enterprise, the sales forecast is all about the deals in play.  What if Mike closes deals A, B, and either C or D.  The buyer at deal E promised me they’d give us the order.  Given everything we know about Sally’s deal F, what value do we think it will close at?  Sales VPs spend hours in Excel (or a modern forecasting tool like Clari) running scenarios to arrive a number.  It’s usually more about different combinations of deals than it is about probabilities and expected values.

In the velocity world, as discussed above, the forecast cannot be only about existing deals. If you want to forecast a quarter, you’ll need to include results from the high-funnel and planned demangen. I’d still call it a forecast, but I’d know that it’s not quite the same thing as a forecast in enterprise. And by presenting in the three layers above, you can remind everyone of that.

# # #

Notes

[1] Monthly-varying SaaS is a different concept, which I used in that post, featuring short contracts (e.g., month-to-month) where the spend can vary every month, usually as the result of a flexible user-based pricing model, a consumption-based pricing model, or a hybrid pricing model (e.g., base + overage).  In such environments, simple SaaS concepts like ARR can quickly lose meaning, as do the metrics that rely on them (e.g., CAC ratio).

[2] Which I think had its ancient origins in the idea that you win 33%, lose 33%, and 33% slip. (Thus assuming a 50% competitive win rate.) Regardless of its roots, 3x (starting) coverage is a widely accepted norm, so much so that I fear it’s often a self-fufilling prophecy.

[3] We’re ignoring the distribution of average sales cycle length for closed/won deals, its standard deviation, and the fact the three different outcomes (i.e., win, loss, slip) will likely have three different average opportunity cycle lengths (e.g., you usually lose faster than you win), each with its own distribution.

[4] And, most unrealistically, that deals never slip to a subsequent period. We’re also assuming that all opportunities are generated on the first day of month, an exactly 30-day lag from MQL to opportunity, and that all MQLs are generated on the first day of month, and convert in exactly 30 days. (And, for the detail-oriented, that every month is 30 days.) Overall, with these simplifying assumptions, you start every month with only the opportunities generated from MQLs generated the prior month and only those opportunities. There is no leftover pipeline sloshing around to confuse things.

[5] The reality is likely somewhat less than 1.0x because we’d normally expected to some backloading (“linearity”) of the quarterly target across the months of the quarter.  In enterprise, that backloading is severe (e.g., most enterprise cash models assume a 10/20/70 distribution). In velocity SaaS, I’ve seen from 30/30/40 (i.e., pretty flat) to 10/20/70 (i.e., as backloaded as enterprise), typically reflecting a quarterly (as opposed to a monthly) sales cadence which is usually a mistake in a velocity model.

[6] To intelligently compare pipeline across quarters we need to fix a point in time to snapshot it. In enterprise, I prefer day one of week three because it’s early enough to take actions (e.g., reducing expenses), but late enough so sales can no longer credibly claim they need more time for pipeline cleanup (aka, scrubbing).

[7] In enterprise, this is a major sin because deal sizes vary significantly and values should be inserted only after discovery and price-point socialization (e.g., “you do know that this costs \$150K?”)  In velocity, it’s a lesser sin because the deal sizes tend to be more similar.  Either way, if all we’re doing is counting opportunities and multiplying by a constant, then why not just admit it and count opportunities directly? The more sophisticated the proxy, the more I like it (e.g., using \$10K for SMB, \$25K for MM, and \$75K for ENT).

[8] Technically, I’d say budget is a synonym for the financial part of the plan. That is, a budget is only one part of a plan. A plan would also include strategic goals, objectives for attaining them, and organization structure.

## 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.

# # #

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.

# # #

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.

## Next-Generation Planning and Finance, A Broader and Slightly Deeper Look

This post was prompted by feedback to the last prediction in my 2021 annual predictions post, The Rebirth of Planning and Enterprise Performance Management.  Excerpt:

EPM 1.0 was Hyperion, Arbor, and TM1. EPM 2.0 was Adaptive Insights, Anaplan, and Planful (nee Host Analytics).  EPM 3.0 is being born today.  If you’ve not been tracking this, here a list of next-generation planning startups …

Since that post, I’ve received feedback with several more startups to add to the list and a request for a little more color on each one.  That’s what I’ll cover in this post.  I can say right now this got bigger, and took way longer, than I thought it would at the outset.  That means two things:  there may be more mistakes and omissions than usual and wow if I thought the space was being reborn before, I really think it now.  Look at how many of these firms were founded in the past two years!

Order is alphabetical.  Links are to sources.  All numbers are best I could find as of publication date (and I have no intent to update).  I have added and/or removed companies from the prior post based on feedback and my subjective perception as to whether I think they qualify as “next generation” planning.  Note that I have several and varied relationships with some of these companies (see prior post and disclaimers).  List is surely not inclusive of all relevant companies.

• Allocadia.  Founded in Vancouver in 2010 by friends from Business Objects / Crystal Reports, this is a marketing performance management company that has raised \$24M in capital and has 125 employees.  Marketing planning is a real problem and they’re taking, last I checked, the enterprise approach to it.  They have 93 reviews and 4.1 stars on G2.
• Causal.  Founded in 2019 in London.  I can’t find them in Crunchbase, but their site shows they have seed capital from Coatue and Passion Capital.  They promise, among other things, to “make finance beautiful” and the whole thing strikes me as a product-led growth strategy for a new tool to build financial models outside of traditional spreadsheets.
• Decipad.  Co-founded in late 2020 in the UK by friend, former MarkLogic consultant, and serial entrepreneur Nuno Job, Decipad is a seed-stage, currently fewer than 10 employee, startup that, last I checked, was working on a low-code product for planning and modeling for early-stage companies.
• Finmark.  Raleigh-based, and founded in 2020, this company has raised \$5M in seed capital from a bevy of investors including Y Combinator, IDEA Fund, Draper, and Bessemer.  The company has about 50 employees, a product in early access mode, and is a product built “by founders, for founders” to provide integrated finance for startups.
• Grid.  This company offers a web-based tool that appears to layer atop spreadsheets, using them as a data source to build reports, dashboards and apps.  The company was founded in 2018, has around 20 people, and is based in Reykjavik.  The founder/CEO previously served as head of product management at Qlik and is a “proud data nerd.”  Love it.
• LiveFlow was founded in 2021, based in Redwood City, has raised about \$500K in pre-seed capital from Y Combinator and Seedcamp.  The company offers a spreadsheet that connects to your real-time data, supporting the creation of timely reports and dashboards.  Connectivity appears to be the special sauce here, and it’s definitely a problem that needs to be solved better.
• OnPlan.  Founded in 2106 in San Francisco by serial entrepreneur and new friend, David Greenbaum, OnPlan is a financial modeling, scenario analysis, and forecasting tool.  The company has raised an undisclosed amount of angel financing and has over 30 employees.  Notably, they are building atop Google Sheets which allows them “stand on the shoulders of giants” and provide a rare option that is, I think, Google-first as opposed to Excel-first or Excel-replacement.
• PlaceCPM.  Founded in 2018 in Austin, this company takes a focused approach, offering forecasting and planning for SaaS and professional services businesses, built on the Salesforce platform, and with pricing suggestive of an SMB/MM focus. The company has raised \$4M in pre- and seed financing.  The product gets 4.9 stars on G2 across 13 reviews.
• Plannuh.  Pronounced with a wicked Southie accent, Plannuh is Boston for Planner, and a marketing planning package that helps marketers create and manage plans and budgets.  Founded by (a fellow) former \$1B company CMO, Peter Mahoney, the company has raised \$4M and has over 30 employees.  As mentioned, I think marketing planning is a real problem and these guys are taking a velocity approach to it.  They have 5.0 stars on G2 across five reviews.  I’m an advisor and wrote the foreword to their The Next CMO book.
• Pry.  Founded in San Francisco in 2019 by two startup-experienced Cal grads (Go Bears!), with investment from pre-seed fund Nomo Ventures, Pry has fewer than 10 employees, and a vision to make it simple for early-stage companies to manage their budget, hiring plan, financial models, and cash.
• Runway.  This company is backed with a \$4.5M seed round from the big guns at A16Z.  I can’t find them on Crunchbase and their website has the expected “big thinking but no detail” for a company that’s still in stealth.  Currently at about 10 people.
• Stratify.  Founded in 2020 in Seattle, this company has raised \$5.0M to pursue real-time and collaborative budgeting and forecasting to support “continuous planning” (which is reminiscent of Planful’s messaging).  Both the founder and the lead investor have enterprise roots (with SAP / Concur) and plenty of startup experience.  The company has fewer than 10 employees today.
• TruePlan.  Founded in 2020, with three employees, and seemingly bootstrapped I may have found these guys on the early side.  While the product appears still in development, the vision looks clear:  dynamic headcount management, that ties together the departmental (budget owner) manager, finance, recruiting, and people ops.  Workforce planning is a real problem, let’s see what they do with it.
• VaretoFounded in 2020 in Mountain View, with fewer than 10 employees and some pretty well pedigreed founders, the company seeks to help with strategic finance, reporting, and planning.  The website is pretty tight-lipped beyond that and I can’t find any public financing information.

Thanks to Ron Baden, Nuno Job, and Bill Rausch for helping me track down so many companies.