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.
Vareto. Founded 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.
I admit that I’ve been more than a little slow to put out this post, but at least I’ve missed the late December (and early January) predictions rush. 2020 was the kind of year that would make anyone in the predictions business more than a little gun shy. I certainly didn’t have “global pandemic” on my 2020 bingo card and, even if I somehow did, I would never have coupled that with “booming stock market” and median SaaS price/revenue multiples in the 15x range.
2020 Predictions Review
Here a review of my 2020 predictions along with a self-graded and for this year, pretty charitable, hit/miss score.
Ongoing social unrest. No explanation necessary. HIT.
A desire for re-unification. We’ll score that one a whopping, if optimistic, MISS. Hopefully it becomes real in 2021.
Climate change becomes new moonshot. Swing and a MISS. I still believe that we will collectively rally behind slowing climate change but feel like I was early on this prediction, particularly because we got distracted with, shall we say, more urgent priorities. (Chamath, a little help here please.)
The strategic chief data officer (CDO). CDO’s are indeed becoming more strategic and they are increasingly worried about playing not only defense but also offense with data, so much so that the title is increasingly morphing into chief data & analytics officer (CDAO). HIT.
Database proliferation slows. While the text of this prediction talks about consolidation in the DBMS market, happily the prediction itself speaks of proliferation slowing and that inconsistency gives me enough wiggle room to declare HIT. DB-Engines ranking shows approximately the same number of DBMSs today (335) as one year ago (334). While proliferation seems to be slowing, the list is most definitely not shrinking.
A new, data-layer approach to data loss prevention. This prediction was inspired by meeting Cyral founder Manav Mital (I think first in 2018) after having a shared experience at Aster Data. I loved Manav’s vision for securing the set of cloud-based data services that we can collectively call the “data cloud.” In 2020, Cyral raised an $11M series A, led by Redpoint and I announced that I was advising them in March. It’s going well. HIT.
AI/ML success in focused applications. The keyword here was focus. There’s sometimes a tendency in tech to confuse technologies with categories. To me, AI/ML is very much the former; powerful stuff to build into now-smart applications that were formerly only automation. While data scientists may want an AI/ML workbench, there is no one enterprise AI/ML application – more a series of applications focused on specific problems, whether that be C3.AI in a public market context or Symphony.AI in private equity one. HIT.
Series A remains hard. Well, “hard” is an interesting term. The point of the prediction was the Series A is the new chokepoint – i.e., founders can be misled by easily raising $1-2M in seed, or nowadays even pre-seed money, and then be in for a shock when it comes time to raise an A. My general almost-oxymoronic sense is that money is available in ever-growing, bigger-than-ever bundles, but such bundles are harder to come by. There’s some “it factor” whereby if you have “it” then you can (and should) raise tons of money at great valuations, whereas, despite the flood of money out there, if you don’t have “it,” then tapping into that flood can be hard to impossible. Numbers wise, the average Series A was up 16% in size over 2019 at around $15M, but early-stage venture investment was down 11% over 2019. Since I’m being charitable today, HIT.
Autonomy CEO extradited. I mentioned this because proposed extraditions of tech billionaires are, well, rare and because I’ve kept an eye on Autonomy and Mike Lynch, ever since I competed with them back in the day at MarkLogic. Turns out Lynch did not get extradited in 2020, so MISS, but the good news (from a predictions viewpoint) is that his extradition hearing is currently slated for next month so it’s at least possible that it happens in 2021. Here’s Lynch’s website (now seemingly somewhat out of date) to hear his side of this story.
So, with that charitable scoring, I’m 7 and 3 on the year. We do this for fun anyway, not the score.
Kellblog’s Ten Prediction for 2021
1. US divisiveness decreases but unity remains elusive. Leadership matters. With a President now focused on unifying America, divisiveness will decrease. Unity will be difficult as some will argue that “moving on” will best promote healing while others argue that healing is not possible without first holding those to account accountable. If nothing else, the past four years have provided a clear demonstration of the power of propaganda, the perils of journalistic bothsidesism, and the power of “big tech” platforms that, if unchecked, can effectively be used for long-tail aggregation towards propagandist and conspiratorial ends.
The big tech argument leads to one of two paths: (1) they are private companies that can do what they want with their terms of service and face market consequences for such, or (2) they are monopolies (and/or, more tenuously, the Internet is a public resource) that must be regulated along the lines of the FCC Fairness Doctrine of 1949, but with a modern twist that speaks not only to the content itself but to the algorithms for amplifying and propagating it.
According to this McKinsey report, the “transition towards normalcy is likely during the second quarter in the US,” though, depending on a number of factors, it’s possible that, “there may be a smaller fall wave of disease in third to fourth quarter 2021.” In my estimation, the wildfire gets contained in 2Q21, with brush fires popping up with decreasing frequency throughout the year.
(Bear in mind, I went to the same school of armchair epidemiology as Dougall Merton, famous for his quote about spelling epidemiologist: “there are three i’s in there and I swear they’re moving all the time.”)
3. The new normal isn’t. Do you think we’ll ever go into the office sick again? Heck, do you think we’ll ever go into the office again, period? Will there even be an office? (Did they renew that lease?) Will shaking hands be an ongoing ritual? Or, in France, la bise? How about those redeyes to close that big deal? Will there still be 12-legged sales calls? Live conferences? Company kickoffs? Live three-day quarterly business reviews (QBRs)? Business dinners? And, by the way, do you think everyone – finally – understands the importance of digital transformation?
I won’t do detailed predictions on each of these questions, and I have as much Zoom fatigue as the next person, but I think it’s important to realize the question is not “when we are we going back to the pre-COVID way of doing things?” and instead “what is the new way of doing things that we should move towards?” COVID has challenged our assumptions and taught us a lot about how we do business. Those lessons will not be forgotten simply because they can be.
4.We start to value resilience, not just efficiency. For the past several decades we have worshipped efficiency in operations: just-in-time manufacturing, inventory reduction, real-time value chains, and heavy automation. That efficiency often came at a cost in terms of resilience and flexibility and as this Bain report discusses, nowhere was that felt more than in supply chain. From hand sanitizer to furniture to freezers to barbells – let alone toilet paper and N95 masks — we saw a huge number of businesses that couldn’t deal with demand spikes, forcing stock-outs for consumers, gray markets on eBay, and countless opportunities lost. It’s as if we forget the lessons of the beer game developed by MIT. The lesson: efficiency can have a cost in terms of resilience and agility and I believe, in an increasingly uncertain world, that businesses will seek both.
5. Work from home (WFH) sticks. Of the many changes COVID drove in the workplace, distributed organizations and WFH are the biggest. I was used to remote work for individual creative positions such as writer or software developer. And tools from Slack to Zoom were already helping us with collaboration. But some things were previously unimaginable to me, e.g., hiring someone who you’d never met in the flesh, running a purely digital user conference, or doing a QBR which I’d been trained (by the school of hard knocks) was a big, long, three-day meeting with a grueling agenda, with drinks and dinners thereafter. I’d note that we were collectively smart enough to avoid paving cow paths, instead reinventing such meetings with the same goals, but radically different agendas that reflected the new constraints. And we – or at least I in this case – learned that such reinvention was not only possible but, in many ways, produced a better, tighter meeting.
6. Tech flight happens, but with a positive effect. Much has been written about the flight from Silicon Valley because of the cost of living, California’s business-unfriendly policies, the mismanagement of San Francisco, and COVID. Many people now realize that if they can work from home, then why not do so from Park City, Atlanta, Raleigh, Madison, or Bend? Better yet, why not work from home in a place with no state income taxes at all — like Las Vegas, Austin, or Miami?
Remember, at the end of the OB (original bubble), B2C meant “back to Cleveland” – though, at the time, the implication was that your job didn’t go with you. This time it does.
The good news for those who leave:
Home affordability, for those who want the classic American dream (which now has a median price of $2.5M in Palo Alto).
Lower cost of living. I’ve had dinners in Myrtle Beach that cost less than breakfasts at the Rosewood.
Burgeoning tech scenes, so you don’t have go cold turkey from full immersion in the Bay Area. You can “step down,” into a burgeoning scene in a place like Miami, where Founder’s Fund partner Keith Rabois, joined by mayor Francis Suarez, is leading a crusade to turn Miami into the next hot tech hub.
But there also good news for those who stay: house prices should flatten, commutes should improve, things will get a little bit less crazy — and you’ll get to keep the diversity of great employment options that leavers may find lacking.
Having grown up in the New York City suburbs, been educated on Michael Porter, and worked both inside and outside of the industry hub in Silicon Valley, I feel like the answer here is kind of obvious: yes, there will be flight from the high cost hub, but the brain of system will remain in the hub. So it went with New York and financial services, it will go with Silicon Valley and tech. Yes, it will disperse. Yes, certainly, lower cost and/or more staffy functions will be moved out (to the benefit of both employers and employees). Yes, secondary hubs will emerge, particularly around great universities. But most of the VCs, the capital, the entrepreneurs, the executive staff, will still orbit around Silicon Valley for a long time.
7. Tech bubble relents. As an investor, I try to never bet against bubbles via shorts or puts because “being right long term” is too often a synonym for “being dead short term.” Seeing manias isn’t hard, but timing them is nearly impossible. Sometimes change is structural – e.g., you can easily convince me that if perpetual-license-based software companies were worth 3-5x revenues that SaaS companies, due to their recurring nature, should be worth twice that. The nature of the business changed, so why shouldn’t the multiple change with it?
But I also believe in reversion to the mean. See this chart by Jamin Ball, author of Clouded Judgement, that shows the median SaaS enterprise value (EV) to revenue ratio for the past six years. The median has more than tripled, from around 5x to around 18x. (And when I grew up 18x looked more like a price/earnings ratio than a price/revenue ratio.)
What accounts for this multiple expansion? In my opinion, these are several of the factors:
Some is structural: recurring businesses are worth more than non-recurring businesses so that should expand software multiples, as discussed above.
Some is the quality of companies: in the past few years some truly exceptional businesses have gone public (e.g., Zoom). If you argue that those high-quality businesses deserve higher multiples, having more of them in the basket will pull up the median. (And the IPO bar is as high as it’s ever been.)
Some is future expectations, and the argument that the market for these companies is far bigger than we used to think. SaaS and product-led growth (PLG) are not only better operating models, but they actually increase TAM in the category.
Some is a hot market: multiples expand in frothy markets and/or bubbles.
My issue: if you assume structure, quality, and expectations should rationally cause SaaS multiples to double (to 10), we are still trading at 80% above that level. Ergo, there is 44% downside to an adjusted median-reversion of 10. Who knows what’s going to happen and with what timing but, to quote Newton, what goes up (usually) must come down. I’m not being bear-ish; just mean reversion-ish.
8. Net dollar retention (NDR) becomes the top SaaS metric, driving companies towards consumption-based pricing and expansion-oriented contracts. While “it’s the annuity, stupid” has always been the core valuation driver for SaaS businesses, in recent years we’ve realized that there’s only one thing better than a stream of equal payments – a stream of increasing payments. Hence NDR has been replacing churn and CAC as the headline SaaS metric on the logic of, “who cares how much it cost (CAC) and who cares how much leaks out (churn) if the overall bucket level is increasing 20% anyway?” While that’s not bad shorthand for an investor, good operators should still watch CAC and gross churn carefully to understand the dynamics of the underlying business.
This is driving two changes in SaaS business, the first more obvious than the second:
Consumption-based pricing. As was passed down to me by the software elders, “always hook pricing to something that goes up.” In the days of Moore’s Law, that was MIPS. In the early days of SaaS, that was users (e.g., at Salesforce, number of salespeople). Today, that’s consumption pricing a la Twilio or Snowflake. The only catch in a pure consumption-based model is that consumption better go up, but smart salespeople can build in floors to protect against usage downturns.
Built-in expansion. SaaS companies who have historically executed with annual, fixed-fee contracts are increasingly building expansion into the initial contract. After all, if NDR is becoming a headline metric and what gets measured gets managed, then it shouldn’t be surprising that companies are increasingly signing multi-year contracts of size 100 in year 1, 120 in year 2, and 140 in year 3. (They need to be careful that usage rights are expanding accordingly, otherwise the auditors will flatten it back out to 120/year.) Measuring this is a new challenge. While it should get captured in remaining performance obligation (RPO), so do a lot of other things, so I’d personally break it out. One company I work with calls it “pre-sold expansion,” which is tracked in aggregate and broken out as a line item in the annual budget.
9. Data intelligence happens. I spent a lot of time with Alation in 2020, interim gigging as CMO for a few quarters. During that time, I not only had a lot of fun and worked with great customers and teammates, I also learned a lot about the evolving market space.
I’d been historically wary of all things metadata; my joke back in the day was that “meta-data presented the opportunity to make meta-money.” In the old days just getting the data was the problem — you didn’t have 10 sources to choose from, who cared where it came from or what happened to it along the way, and what rules (and there weren’t many back then) applied to it. Those days are no more.
I also confess I’ve always found the space confusing. Think:
Wait, does “MDM” stand for master data management or metadata management, and how does that relate to data lineage and data integration? Is master data management domain-specific or infrastructure, is it real-time or post hoc? What is data privacy again? Data quality? Data profiling? Data stewardship? Data preparation, and didn’t ETL already do that? And when did ETL become ELT? What’s data ops? And if that’s not all confusing enough, why do I hear like 5 different definitions of data governance and how does that relate to compliance and privacy?”
To quote Edward R. Murrow, “anyone who isn’t confused really doesn’t understand the situation.”
After angel investing in data catalog pioneer Alation in 2013, joining their board in 2016, and joining the board of master data management leader Profisee in 2019, I was determined to finally understand the space. In so doing, I’ve come to the conclusion that the vision of what IDC calls data intelligence is going to happen.
Conceptually, you can think of DI as the necessary underpinning for both business intelligence (BI) and artificial intelligence (AI). In fact, AI increases the need for DI. Why? Because BI is human-operated. An analyst using a reporting or visualization tool who sees bad or anomalous data is likely going to notice. An algorithm won’t. As we used to say with BI, “garbage in, garbage out.” That’s true with AI as well, even more so. Worse yet, AI also suffers from “bias in, bias out” but that’s a different conversation.
I think data intelligence will increasingly coalesce around platforms to bring some needed order to the space. I think data catalogs, while originally designed for search and discovery, serve as excellent user-first platforms for bringing together a wide variety of data intelligence use cases including data search and discovery, data literacy, and data governance. I look forward to watching Alation pursue, with a hat tip to Marshall McLuhan, their strategy of “the catalog is the platform.”
Independent of that transformation, I look forward to seeing Profisee continue to drive their multi-domain master data management strategy that ultimately results in cleaner upstream data in the first place for both operational and analytical systems.
It should be a great year for data.
10. Rebirth of Planning and Enterprise Performance Management (EPM). 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 that I know (and for transparency my relationship with them, if any.)
Planning is literally being reborn before our eyes, in most cases using modern infrastructure, product-led growth strategies, stronger end-user focus and design-orientation, and often with a functional, vertical, or departmental twist. 2021 will be a great year for this space as these companies grow and put down roots. (Also, see the follow-up post I did on this prediction.)
Well, that’s it for this year’s list. Thanks for reading this far and have a healthy, safe, and Rule-of-40-compliant 2021.
I’ve seen numerous startups try numerous ways to calculate their sales capacity. Most are too back-of-the-envelope and to 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 .
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 . 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.
Judgment, the model should have the built-in ability to let the CEO and/or sales VP manually adjust the output and provide analytical support for so doing .
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 , 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.
In this post, I’ll do two things: I’ll walk you through what I view as a simple-yet-comprehensive productivity model and then I’ll show you two important and arguably clever ways in which to use it.
Walking Through the Model
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.
You need to enter data into the model for 1Q20 (let’s pretend we’re making the model in December 2019) by entering what we expect to start the year with in terms of sales reps by tenure (column D). The “first/hired quarter” row represents our hiring plans for the year. The rest of this block is a waterfall that ages the rep downward as we move across quarters. 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. I’ve modeled a pretty slow ramp that takes five quarters to get to 100% productivity.
To the right of that we have more assumptions:
Annual turnover, the annual rate at which sales reps leave the company for any reason. This drives attriting reps in row 12 which silently assumes that every departing rep was at steady state, a tacit fairly conservative assumption in the model.
Steady-state productivity, how much we expect a rep to actually sell per year once they are fully ramped.
Quota over-assignment. I believe it’s best to start with a productivity model and uplift it to generate quotas .
The next block down calculates ramped rep equivalents (RREs), a very handy concept that far too few organizations use to convert the ramp-state to a single number equivalent to the number of fully ramped reps. The steady-state row shows the number of fully ramped reps, a row that board members and investors will frequently ask about, particularly if you’re not proactively showing them RREs.
After that we calculate “productivity capacity,” which is a mouthful, but I want to disambiguate it from quota capacity, so it’s worth the extra syllables. After that, I add a critical row called judgment, which allows the Sales VP or CEO to play with the model so that they’re not potentially signing up for targets that are straight model output, but instead also informed by their knowledge of the state of the deals and the pipeline. Judgment can be negative (reducing targets), positive (increasing targets) or zero-sum where you have the same annual target but allocate it differently across quarters.
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.
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.
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 .
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.
Two Clever Ways to Use the Model
The sad reality is that this kind of model gets a lot attention at the end of a fiscal year (while you’re making the plan for next year) and then typically gets thrown in the closet and ignored until it’s planning season again.
That’s too bad because this model can be used both as an evaluation tool and a predictive tool throughout the year.
Let’s show that via an all-too-common example. Let’s say we start 2020 with a new VP of Sales we just hired in November 2019 with hiring and performance targets in our original model (above) but with judgment set to zero so plan is equal to the capacity model.
Our “world-class” VP immediately proceeds to drive out a large number of salespeople. While he hires 3 “all-star” reps during 1Q20, all 5 reps hired by his predecessor in the past 6 months leave the company along with, worse yet, two fully ramped reps. Thus, instead of ending the quarter with 20 reps, we end with 12. Worse yet, the VP delivers new ARR of $2,000K vs. a target of $3,125K, 64% of plan. Realizing she has a disaster on her hands, the CEO “fails fast” and fires the newly hired VP of sales after 5 months. She then appoints the RVP of Central, Joe, to acting VP of Sales on 4/2. Joe proceeds to deliver 59%, 67%, and 75% of plan in 2Q20, 3Q20, and 4Q20.
Our question: is Joe doing a good job?
At first blush, he appears more zero than hero: 59%, 67%, and 75% of plan is no way to go through life.
But to really answer this question we cannot reasonably evaluate Joe relative to the original operating plan. He was handed a demoralized organization that was about 60% of its target size on 4/2. In order to evaluate Joe’s performance, we need to compare it not to the original operating plan, but to the capacity model re-run with the actual rep hiring and aging at the start of each quarter.
When you do this you see, for example, that while Joe is constantly underperforming plan, he is also constantly outperforming the capacity model, delivering 101%, 103%, and 109% of model capacity in 2Q through 4Q.
If you looked at Joe the way most companies look at key metrics, he’d be fired. But if you read this chart to the bottom you finally get the complete picture. Joe is running a significantly smaller sales organization at above-model efficiency. While Joe got handed an organization that was 8 heads under plan, he did more than double the organization to 26 heads and consistently outperformed the capacity model. Joe is a hero, not a zero. But you’d never know if you didn’t look at his performance relative to the actual sales capacity he was managing.
Second, I’ll say the other clever way to use a capacity model is as a forecasting tool. I have found a good capacity model, re-run at the start of the quarter with then-current sales hiring/aging is a very valuable predictive tool, often predicting the quarterly sales result better than my VP of Sales. Along with rep-level, manager-level, and VP-level forecasts and stage-weighted and forecast-category-weighted expected pipeline values, you can use the re-run sales capacity model as a great tool to triangulate on the sales forecast.
You can download the four-tab spreadsheet model I built for this post, here.
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 Starting with quota starts you in the wrong mental place — what you want people to do, as opposed to productivity (what they have historically done). Additionally, there are clear instances where quotas get assigned against which we have little to no actual productivity assumption (e.g., a second-quarter rep typically has zero productivity but will nevertheless be assigned some partial quota). Sales most certainly has a quota-allocation problem, but that should be a separate, second exercise after building a corporate sales productivity model on which to base the operating plan.
 A typically such vector might be (0%, 25%, 50%, 100%) or (0%, 33%, 66%, 100%) reflecting the percentage of steady-state productivity they are expected to achieve in their first, second, third, and fourth quarters of employment.
 Without such a row, the plan is either de-linked from the model or the plan is the pure output of the model without any human judgement attached. This row is typically used to re-balance the annual number across quarters and/or to either add or subtract cushion relative to the model.
 Back in the day at Salesforce, we called pipeline generation sources “horsemen” I think (in a rather bad joke) because there were four of them (marketing, alliances, sales, and SDRs/outbound). That term was later dropped probably both because of the apocalypse reference and its non gender-neutrality. However, I’ve never known what to call them since, other than the rather sterile, “pipeline sources.”
 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. Otherwise, if not documented centrally, they will end up in different models across the organization with no real idea of whether they either foot to the bookings model or even sum to 100% across sources.
In part I of this three-part series I introduced the idea of an inverted funnel whereby marketing can derive a required demand generation budget using the sales target and historical conversion rates. In order to focus on the funnel itself, I made the simplifying assumption that the company’s new ARR target was constant each quarter.
In part II, I made things more realistic both by quarterizing the model (with increasing quarterly targets) and accounting for the phase lag between opportunity generation and closing that’s more commonly known as “the sales cycle.” We modeled that phase lag using the average sales cycle length. For example, if your average sales cycle is 90 days, then opportunities generated in 1Q19 will be modeled as closing in 2Q19 .
There are two things I dislike about this approach:
Using the average sales cycle loses information contained in the underlying distribution. While deals on average may close in 90 days, some deals close in 30 while others may close in 180.
Focusing only on the average often leads marketing to a sense of helplessness. I can’t count the number of times I have heard, “well, it’s week 2 and the pipeline’s light but with a 90-day sales cycle there is nothing we can do to help.” That’s wrong. Some deals close more quickly than others (e.g., upsell) so what can we do to find more of them, fast .
As a reminder, time-based close rates come from doing a cohort analysis where we take opportunities created in a given quarter and then track not only what percentage of them eventually close, but when they close, by quarter after their creation.
This allows us to calculate average close rates for opportunities in different periods (e.g., in-quarter, in 2 quarters, or cumulative within 3 quarters) as well an overall (in this case, six-quarter) close rate, i.e., the cumulative sum. In this example, you can see an overall close rate of 18.7% meaning that, on average, within 6 quarters we close 18.7% of the opportunities that sales accepts. This is well within what I consider the standard range of 15 to 22%.
Previously, I argued this technique can be quite useful for forecasting; it can also be quite useful in planning. At the risk of over-engineering, let’s use the concept of time-based close rates to build an inverted funnel for our 2020 marketing demand generation plan.
To walk through the model, we start with our sales targets and average sales price (ASP) assumptions in order to calculate how many closed opportunities we will need per quarter. We then drop to the opportunity sourcing section where we use historical opportunity generation and historical time-based close rates to estimate how many closed opportunities we can expect from the existing (and aging) pipeline that we have already generated. Then we can plug our opportunity generation targets from our demand generation plan into the model (i.e., the orange cells). The model then calculates a surplus or (gap) between the number of closed opportunities we need and those the model predicts.
I didn’t do it in the spreadsheet, but to turn that opportunity creation gap into ARR dollars just multiply by the ASP. For example, in 2Q20 this model says we are 1.1 opportunities short, and thus we’d forecast coming in $137.5K (1.1 * $125K) short of the new ARR plan number. This helps you figure out if you have the right opportunity generation plan, not just overall, but with respect to timing and historical close rates.
When you discover a gap there are lots of ways to fix it. For example, in the above model, while we are generating enough opportunities in the early part of the year to largely achieve those targets, we are not generating enough opportunities to support the big uptick in 4Q20. The model shows us coming in 10.8 opportunities short in 4Q20 – i.e., anticipating a new ARR shortfall of more than $1.3M. That’s not good enough. In order to achieve the 4Q20 target we are going to need to generate more opportunities earlier in the year.
I played with the drivers above to do just that, generating an extra 275 opportunities across the year generating surpluses in 1Q20 and 3Q20 that more than offset the small gaps in 2Q20 and 4Q20. If everything happened exactly according to the model we’d get ahead of plan and 1Q20 and 3Q20 and then fall back to it in 2Q20 and 4Q20 though, in reality, the company would likely backlog deals in some way  if it found itself ahead of plan nearing the end of one quarter with a slightly light pipeline the next.
In concluding this three-part series, I should be clear that while I often refer to “the funnel” as if it’s the only one in the company, most companies don’t have just one inverted funnel. The VP of Americas marketing will be building and managing one funnel that may look quite different from the VP of EMEA marketing. Within the Americas, the VP may need to break sales into two funnels: one for inside/corporate sales (with faster cycles and smaller ASPs) and one for field sales with slower sales cycles, higher ASPS, and often higher close rates. In large companies, General Managers of product lines (e.g., the Service Cloud GM at Salesforce) will need to manage their own product-specific inverted funnel that cuts across geographies and channels. There’s a funnel for every key sales target in a company and they need to manage them all.
You can download the spreadsheet used in this post, here.
 Most would argue there are two phase lags: the one from new lead to opportunity and the one from opportunity (SQL) creation to close. The latter is the sales cycle.
 As another example, inside sales deals tend to close faster than field sales deals.
 Doing this could range from taking (e.g., co-signing) the deal one day late to, if policy allows, refusing to accept the order to, if policy enables, taking payment terms that require pushing the deal one quarter back. The only thing you don’t want to is to have the customer fail to sign the contract because you never know if your sponsor quits (or gets fired) on the first day of the next quarter. If a deal is on the table, take it. Work with sales and finance management to figure out how to book it.
I’m Dave Kellogg, advisor, director, consultant, angel investor, and blogger focused on enterprise software startups. I am an executive-in-residence (EIR) at Balderton Capital and principal of my own eponymous consulting business.
I bring an uncommon perspective to startup challenges having 10 years’ experience at each of the CEO, CMO, and independent director levels across 10+ companies ranging in size from zero to over $1B in revenues.
From 2012 to 2018, I was CEO of cloud EPM vendor Host Analytics, where we quintupled ARR while halving customer acquisition costs in a competitive market, ultimately selling the company in a private equity transaction.
Previously, I was SVP/GM of the $500M Service Cloud business at Salesforce; CEO of NoSQL database provider MarkLogic, which we grew from zero to $80M over 6 years; and CMO at Business Objects for nearly a decade as we grew from $30M to over $1B in revenues. I started my career in technical and product marketing positions at Ingres and Versant.
I love disruption, startups, and Silicon Valley and have had the pleasure of working in varied capacities with companies including Bluecore, Cyral, FloQast, GainSight, MongoDB, Recorded Future, and Tableau.
I previously sat on the boards of Granular (agtech, acquired by DuPont), Aster Data (big data, acquired by Teradata), and Nuxeo (content services, acquired by Hyland), and Profisee (MDM, exited to Pamlico).
I periodically speak to strategy and entrepreneurship classes at the Haas School of Business (UC Berkeley) and Hautes Études Commerciales de Paris (HEC).