Kellblog covers topics related to starting, managing, leading, and scaling enterprise software startups. My favorite topics include strategy, marketing, sales, SaaS metrics, and management. I also provide commentary on Silicon Valley, venture capital, and the business of software.
This is part II in this series. Part I is here and covers the basics of management education, employee communications, and simple steps to help slow virus transmission while keeping the business moving forward.
In this part, we’ll provide:
A short list of links to what other companies are doing, largely when it comes to travel and in-office work policies.
A discussion of financial planning and scenario analysis to help you financially navigate these tricky waters.
I have broken out the list of useful information links and resources (that was formerly in this post) to a separate, part III of this series.
What Other Companies are Saying and Doing
Relatively few companies have made public statements about their response policies. Here are a few of the ones who have:
Financial Planning and Scenario Analysis: Extending the Runway
It’s also time to break out your driver-based financial model, and if you don’t have one, then it’s time to have your head of finance (or financial planning & analysis) build one.
Cash is oxygen for startups and if there are going to be some rough times before this threat clears, your job is to make absolutely sure you have the cash to get through it. Remember one of my favorite all-time startup quotes from Sequoia founder Don Valentine: “all companies go out of business for the same reason. They run out of money.”
In my opinion you should model three scenarios for three years, that look roughly like:
No impact. You execute your current 2020 operating plan. Then think about the odds of that happening. They’re probably pretty low unless you’re in a counter-cyclical business like videoconferencing (in which case you probably increase targets) or a semi-counter-cyclical one like analytics/BI (in which case maybe you hold them flat).
20% bookings impact in 2020. You miss plan bookings targets by 20%. Decide if you should apply this 20% miss to new bookings (from new customers), expansion bookings (new sales to existing customers), renewal bookings — or all three. Or model a different percent miss on each of those targets as it makes sense for your business. The point here is to take a moderately severe scenario and then determine how much shorter this makes your cash runway. Then think about steps you can take to get that lost runway back, such as holding costs flat, reducing costs, raising debt, or — if you’re lucky and/or have strong insiders — raising equity.
40% bookings impact in 2020. Do the same analysis as in the prior paragraph but with a truly major bookings miss. Again, decide whether and to what extent that miss hits new bookings, expansion bookings, and renewal bookings. Then go look at your cash runway. If you have debt make sure you have all covenant compliance tests built into your model that display green/red — you shouldn’t have to notice a broken covenant, it should light up in big letters (YES/NO) in a good model. Then, as in the prior step, think about how to get that lost runway back.
Once you have looked at and internalized these models, it’s time for you and your CFO to call your lead investors to discuss your findings. And then schedule a discussion of the scenario analysis at your next board meeting.
Please note that it’s not lost on me that accelerating out of the turn when things improve can be an excellent way to grab share in your market. But in order to so, you need to have lots of cash ready to spend in, say, 6-12 months when that happens. Coming out of the corner on fumes isn’t going to let you do that. And, as many once-prodigal, now-thrifty founders have told me: “the shitty thing is that once you’ve spent the money you can’t get it back.” Without dilution. With debt. Maybe without undesirable structure and terms.
Now is the time to think realistically about how much fuel you have in the tank, if you can get more, how long should it last, and how much you want in the tank 6-12 months out.
I just reached out to the CEOs I work with with on this topic and figured I should also do a quick post to speak to the CEOs who follow Kellblog as well.
The primary purpose of this post is to remind busy startup CEOs that an important part of your job is to be out ahead of things. Usually that means customer needs, market trends, and competitors. I’d argue it also includes potential epidemics, such as the one threatened by COVID-19.
Nobody wants to work for a CEO who’s panicking. But nobody wants to work for a CEO without a plan, either. You owe it to your employees, customers, and (yes) shareholders to start thinking about the impact of the Coronavirus on your business. That starts with your first action item: having a conversation about it at your next weekly e-staff meeting, if you’ve not done so already.
My thinking is based largely on this Scientific American article about what individuals should do to prepare for an eventual outbreak. On the theory that most startup employees are relatively young and healthy, the reality appears to be that the lives you save may not be your own — but instead those of the sick, elderly, weak, or otherwise vulnerable around you .
The driving principle behind the article is the best thing people can do to slow the spread of a virus is to stay away from each other for a few weeks. That’s not easy for a business to do, but at least in software we rarely rely on physical supply chains so we have one less major factor to consider in our planning.
So, with that warm up, let’s jump into a list of things you should consider:
Researching how other companies are responding to help inform your own response. Call a few of the CEOs or Chief People Officers in your portfolio peer group. Or go online and read documents like Coinbase’s four-tier response framework .
Sending a strong message telling people not be a hero and stay home when they’re sick. Startups are full of people who give it their all, so it’s not uncommon for folks who are not feeling well to come into the office for that big presentation or meeting .
Placing restrictions on travel, including not only guidelines for travel to affected areas but also guidelines for what you should do if you have recently traveled to one .
Changing the format of regular, periodic meetings. Most startups have some form of quarterly business review (QBR), typically a live two- or three-day meeting. Now is a great time not only to try it as a videoconference but to re-invent it while you’re at it  .
Encouraging customers and prospects to do videoconferences, particularly if they are uncomfortable with a live meeting. While salespeople love live meetings (and so do I), a videoconference is far superior to no meeting at all. We need to keep deals moving through the pipeline, so if someone suggests delaying a few weeks, I’d counter with a videoconference every time. For both the customer’s business and our own, the show must go on.
And, while some folks will probably trash me for saying this, if you have a natural, non-contrived marketing angle that can keep your business moving, don’t be afraid to gently say it . Examples: (1) it’s more important now than ever to have real-time supply chain information, (2) in times like these business analytics have never been more important, (3) we all have an obligation to our employees, customers, and shareholders to keep business moving ahead.
Let me end by providing links to some other excellent thoughts on this and related subjects:
 Yes, it appears that infected people who are asymptomatic can also communicate the virus so this may not solve as much as we hope, but it’s certainly a start.
 Coinbase’s framework dives pretty deep here.
 There’s a reason Zoom stock was up 6% yesterday in a market down 5%.
 On the theory that you should almost certainly get a better result if you re-invent the agenda based on the format, rather than simply video-conferencing the existing meeting and format. Something about paving cow paths comes to mind.
 And how you say it makes all the difference. I can think of genuine, sincere, intelligent ways to do so and I can think of absolutely stone-handed ways of doing so as well. If you’re considering this, bounce the idea off lots people within your company and with your family and friends for a sniff test.
Enterprise SaaS and retailers have more in common than you might think.
Let’s think about retailers for a minute. Retailers drive growth in two ways:
They open new stores
They increase sales at existing stores
Opening new stores is great, but it’s an expensive way to drive new sales and requires a lot of up-front investment. It’s also risky because, despite having a small army of MBAs working to determine the right locations, sometimes new locations just don’t work out. Blending the results of these two different activities can blur what’s really happening. For example, consider this company:
Things look reasonable overall, the company is growing at 17%. But when you dig deeper you see that virtually all of the growth is coming from new stores. Revenue from existing stores is virtually flat at 2%.
It’s for this reason that retailers routinely publish same-store sales in their financial results. So you can see not only overall, blended growth but also understand how much of that growth is coming from new store openings vs. increasing sales at existing stores.
Now, let’s think about enterprise software.
Enterprise software vendors drive growth in two ways:
They hire new salesreps
They increase productivity of existing salesreps
Hiring new salesreps is great, but it’s an expensive way to drive new sales and requires a lot of up-front investment. It’s also risky because, despite having a small army of MBAs working to determine the right territories, hiring profiles and interviewing process, sometimes new salesreps just don’t work out. Blending the results of these two different activities can blur what’s really happening. For example, consider this company:
If you’re feeling a certain déjà vu, you’re right. I simply copy-and-pasted the text, substituting “enterprise software vendor” for “retailer” and “salesrep” for “store.” It’s exactly the same concept.
The problem is that we, as an industry, have basically no metric that addresses it.
Revenue, bookings, and billings growth are all blended metrics that mix results from existing and new salespeople 
Retention and expansion rates are about cohorts, but cohorts of customers, not cohorts of salespeople 
Sales productivity is typically measured as ARR/salesrep which blends new and existing salesreps 
Sales per ramped rep, measured as ARR/ramped-rep, starts to get close, but it’s not cohort-based, few companies measure it, and those that do often calculate it wrong 
So what we need is a cohort-based metric that compares the productivity of reps here today with those here a year ago . Unlike retail, where stores don’t really ramp , we need to consider ramping in defining the cohort, and thus define the year-ago cohort to include only fully-ramped reps .
So here’s how I define same-rep sales: sales from reps who were fully ramped a year ago and still here.
Here’s an example of presenting it:
The above table shows same-rep sales via an example where overall sales growth is good at 48%, driven by a 17% increase in same-rep sales and an 89% increase in new-rep sales. Note that enterprise software is a business largely built on the back of sales force expansion so — absent an acquisition or new product launch to put something new in sale’s proverbial bag — I view a 17% increase in same-rep sales as pretty good.
Let’s conclude by sharing a table of sales productivity metrics discussed in this post that I think provides a nice view of sales productivity as related to hiring and ramping.
The spreadsheet I used for this post is available for download, here.
# # #
 Billings is a public company SaaS metric and typically a proxy for bookings.
 Public companies never release this but most public and private companies track it.
 By taking overall new ARR (i.e., from all reps) and dividing it by the number of ramped reps, thus blending contribution from both new and existing reps in the numerator. Plus, these are usually calculated on a snapshot (not a cohort) basis.
 This is not survivor-biased in my mind because I am trying to get a productivity metric. By analogy, I believe stores that closed in the interim are not included in same-store sales calculations.
 Or to the extent they do, it takes weeks or months, not quarters. Thus you can simply include all stores open in the year-ago cohort, even if they just opened.
 I am trying to avoid seeing an increase in same-rep sales due to ramping — e.g., someone who just started in the year-ago cohort will have year sales, but should increase to full productivity simply by virtue of ramping.
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.
# # #
 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 work life from time to time you may be accused of not listening. It may not be fair. You may not like it. But you’d be shocked how many people completely flub their reaction when the boss, a coworker, or a customer says, “you’re not listening.”
Here’s my three-part formula for what to do when someone says you’re not listening.
Keep and use a mental ledger going forward
Shut Up, Immediately
If someone says you’re not listening the first thing to do is immediately begin the demonstration that you can. Acceptable responses:
Keep talking, simply ignoring the comment. Recall the First Rule of Holes: when you’re in one, stop digging.
Get defensive. “Of course, I’m listening to you.” “Most people tell me I’m a great listener.” “I pride myself on my listening skills.” Recall Kellblog’s Second Rule of Feedback: defensiveness kills communications.
Make pedantic distinctions between listening and hearing. “I’m listening to you, but perhaps I’m not hearing you.” “I’m hearing you just fine — my ears work perfectly — I just don’t agree with you.”
The second part of your listening demonstration is to use active listening. This boils down to showing that you’re listening and confirming understanding using these techniques:
Focus on the speaker. Look at him/her. Make eye contact. Don’t engage in any common distractions like looking at your phone or screen.
Take notes, even if you have an amazing memory and don’t need them. Taking notes shows that you are engaged and listening.
Don’t interrupt. If the speaker says something you disagree with, write it down. I put it in triangle I’ve pre-marked at the bottom of the page. Doing this gives you a third option other than conceding the point or interrupting to dispute it. I’m amazed by how infrequently I come back to these points that, in the heated moment, seemed worthy of interrupting someone.
Confirm back. “OK Charlie, I want to make sure I understood what you just said. I’m hearing that you  tried to set up the review meeting on Monday,  that everyone initially indicated they could come, and  that … Did I get that right?”
Keep and Use a Mental Ledger
The first two steps help eliminate basic communication problems. But say it’s deeper. You’re communicating just fine, you just happen to disagree with a lot of the feedback. Examples:
You disagree with almost every piece of directive feedback a board member gives you — and he gives you about ten pieces of it  every board meeting  .
You are a consultant and you disagree with most of the feedback your client gives you on a draft survey that you’re running.
You are a manager and you disagree with most of the messaging in a presentation one of your subordinates is creating.
These are not easy situations and nobody wants to lose on every point, so you need to step back and make a mental ledger of credits (I took your input) and debits (I did not), so you can both ensure you’re somewhat balanced and to get a big picture sense of the score. This will prepare for you for a “you never listen to anything I say” attack, because you have kept some tally of accepts and rejects.
“Well, in fact, I took about 40% of your ideas and rejected about 60% and while I know that might not feel good, it’s simply not true that ‘I never listen to anything you say.’ Now, let’s go discuss the important points on the merits.” 
You may think I’m reducing feedback to game theory, and I suppose I am. The three key points are:
People do keep some mental tally and it’s almost always biased, so why not actually keep some rough score to inform the conversation.
You must keep the power balance in mind when playing the feedback/input game. If you’re a consultant servicing a customer, you want the customer winning. If you’re a manager challenging a senior vice president, you should be hoping to score a few points.
More than anything it says choose your battles, keeping the power balance in mind when you do so.
The last point leads to a corollary I love: when you are in the position of inferior power you should never argue about small matters. Why? Because the mental tally is, in my opinion, unweighted, so the smart way to get what you want and let the person with superior power win, is to let them win on issue-count while you win on importance-weighting. Put differently, if it’s a small matter it definitionally isn’t that important, so so why take a mental debit to win? Concede, instead.
Finally, when responding to input, it’s always useful to start not with the numerical tally  but with a summary. “Well, Sarah, I agreed with your on these points and I disagreed with you on those.” That starts the conversation in a balanced place which should keep everyone most open for feedback.
# # #
 Directive feedback = “You guys should do X.”
 The best solution here, if relationship allows, is to ask the board member not to give directive feedback. However, that’s not always possible.
 I have a theory that board members should never give CEOs directive feedback. Here’s the proof. Case 1: the CEO wants to do the idea, in which case it will be done anyway. Case 2: the CEO doesn’t want to do the idea and does it only because they were so directed. Thus the only result from directive feedback is to make CEOs do ideas they don’t want to do, which is a terrible practice. QED.
 For spouses I recommend an entirely different methodology. Say, “you’re right.” Repeat as necessary.
 Which you can keep in your pocket for later if challenged.
Knowing that CFO transformation is one of my favorite subjects (having run a planning company for six years) the folks at Sage invited me to moderate a set of panels at their San Francisco and New York media events this week. Sage is launching the results of a research study where they surveyed over 500 CFOs of primarily medium-size businesses about the ongoing transformation of the CFO role, and their outlook on topics ranging from the evolution of finance to advanced automation (robotic process automation, or RPA), artificial intelligence (AI), and machine-learning.
98% of CFOs say their job has changed in the past 5 years. Fortunately, none of our panelists were in the other 2%.
94% believe that financial management tools success increase productivity in the department.
46% of finance professionals reported an increasing demand for business counsel beyond just basic reporting and analytics. Finding more time to offer such counsel is an ongoing theme in CFO transformation.
92% are hopeful that AI/ML can further increase automation in finance and help create more such time for strategic matters.
Yet 83% say that their organizations may be yet be culturally ready to adopt more automation technology.
While only 25% saw themselves as change agents, nearly 75% reported that they were leading digital transformation efforts at their organizations. Humble people, those CFOs.
In addition to a great joke (question: “how do you know when you’re talking to an extroverted accountant?” answer: “they’re the one looking at your shoes when they’re talking.”) we heard a few colorful stories as well, my favorite from Jack who at age 19 was hauled into the CFO’s office for questioning as to why Jack referred to him as the “CFNo.” Expecting to be fired from his first job, Jack was instead thanked, “that’s quite a compliment,” said the CFO 1.0, “I’m pleased to hear you’ve been calling me the CFKnow.” Jack dodged a bullet on that one, for sure.
Thanks to Sage for inviting me and best of luck on the continuing journey to transform finance. You can get a copy of the full Sage CFO 3.0 study here.
I’m working with more early-stage companies these days (e.g., pre-seed, seed, seed-plus ) and one of the things I’ve noticed is that many founders cannot clearly, succinctly, and confidently answer some basic questions about their businesses. I decided to write this post to help entrepreneurs ensure they have their bases are covered when speaking to angel investors, seed firms, or venture capitalists.
Note that Silicon Valley is the land of strong convictions, weakly held so it’s better in most cases to be clear, confident, and wrong than it is to waffle, equivocate, and be right. I often have to remind people of this — particularly founders recently out of PhD programs — because Sand Hill Road is about the dead opposite of graduate school when it comes to this philosophy .
Here are ten questions that early-stage founder/CEOs should be able to answer clearly, succinctly, and confidently — along with a few tips on how to best answer them.
1. Who is the target customer? Be precise, ideally right down to a specific job title in an organization. It’s great if the answer will broaden over time as the company grows and its strategy naturally expands, but up-front I’d name the people you are targeting today. Wrong: “The Office of the CIO in IT organizations in F5000 enterprises around the world.” Right: “VPs of financial planning and analysis in 250-1000 employee Services firms in North America.”
I’m admittedly fanatical about this, but I want to know what it says on the target buyer’s business card  . I can’t tell you the number of times that I’ve heard “we sell to the CIO,” only to be introduced to someone whose business card said “director of data warehousing.” If you don’t know who you’re selling to, you’re going to have trouble targeting them.
2. What problem do you solve for them? When you meet one of these people, what do you tell them? Right: “We sell a solution that prevents spear phishing.” Wrong: “We sell a way to improve security culture at your organization” . The latter answer is wrong because while an improvement in security culture may be a by-product of using your solution, it is not the primary benefit.
First-order benefit: our solution stops spear phishing. Second-order benefit: that means you avoid data breaches and/or save millions in ransomware and other breach-related costs. Third-order benefit: that means you protect your company’s reputation and your valuable brand. Fourth-order benefit: using our solution ends up increasing security culture and awareness. People generally go shopping for the first-order benefit — they may buy into higher-order benefits, they may say they like your company’s approach and/or vision — but budgets and shopping lists get made on the first-order. Don’t be selling security culture when customers are buying anti-spear-phishing.
3. How do they solve that problem today? The majority of startups solve a problem that is already being solved in some way today. Be realistic about this. Unless you are solving a brand-new problem (e.g., orchestrating containers at the dawn of the container revolution), then somehow the problem is either being solved today (e.g., in Excel, a legacy app, a homegrown system) or the buyer has deliberately decided not to solve it, likely because they think it’s unsolvable (e.g., baldness cures ).
If they are already solving the problem in some way, your new solution more likely represents an optimization than a breakthrough. And even breakthrough companies, such as VMware , solved very practical problems early on (e.g., providing multiple environments on a laptop without having to physically change hard drives).
As another example: even if you’re using advanced machine learning technology to automate trouble ticket resolution and — technically speaking, customers aren’t doing that today — they certainly are handling trouble tickets and the alternative to automatic resolution is generally a combination of human work and case deflection.
4. Why is your solution superior to the status quo? Once you can clearly describe how customers solve the problem today, then you should be able to clearly answer why your solution is superior to the status quo. Note that I’m not asking how your technology works or why it’s superior — I’m asking why it provides a better solution for the customer. Sticking with the trouble ticket example: “our solution is superior to human resolution because it’s faster (often by days if not hours), cuts ticket resolution cost by 90%, and results in greatly superior end-user satisfaction ratings.” That’s a benefits-driven explanation of why it’s superior.
5. Why is your technology different from that offered by other suppliers? Marketers call this differentiation and it’s not really just about why your technology is different from alternatives, it’s about why it’s better. The important part here is not to deep dive into how the technology works. That’s not the question; the question is why is your technology is better than the alternatives. The most common incorrect answer to this question is a long speech about how the technology works. (See this post for tips on how to build a feature, function, benefit marketing message.)
Example 1: traditional databases were built for and work well at storing structured data, but they have little or no capability for handling unstructured data. Unlike traditional databases, our technology is built using a hybrid of database and search engine technology and thus provides excellent capabilities for storing, indexing, and rapidly querying both structured and unstructured data.
Example 2: many planning systems require you to throw out the tool that most people use for planning today — Excel. Unlike those systems, our product integrates and leverages Excel as part of the solution; we use Excel formula language, Excel formatting conventions, and provide an Excel add-in interface that preserves and leverages your existing Excel knowledge. We don’t throw the baby out with the bathwater.
6. How many target customers have you spoken to — and what was their reaction to your presentation? First, you means you, the founder/CEO. It doesn’t mean your salesperson or co-founder. The answer to the first part of the question is best measured in scores; investors want to know that you are in the market, talking with customers, and listening to their feedback. They assume that you can sell the technology , the strategic question for later is the transferability of that skill. They also want to know how target customers react to your presentation and how many of them convert into trials or purchases.
7. Who’s using your product and why did they select it? It’s not hard to sell government labs and commercial advanced research divisions one of pretty much anything. It’s also not hard, in brand new categories, to sell your software to people who probably shouldn’t have purchased it — i.e., people not knowing all their options in the nascent market picked the wrong one. And that’s not to mention the other customers you can get for the wrong reason — because a board member had a friend on the executive staff, because someone was a big donor, etc. Customers “buy” (and I use air quotes become sometimes these early “customers” didn’t pay anything at all) the wrong software all the time, particularly in the early days of a market.
So the question isn’t who downloaded or tried your product, the question is who’s using it — and when they selected it did they know all their options and still choose you? Put differently, the question is “who’s not an accidental customer” and why did that set of non-accidental customers pick you over the alternative? So don’t give a list of company brand names who may or may not be active users. Instead tell a few deep stories of active customers (who they could ask to call), why they picked the software, and how it’s benefiting them.
8. What is the TAM for solving this problem? There are a lot ofgreat posts about how to build a total available market (TAM) analysis, so I won’t explain how to do it here. I will say you should have a model that calculates an answer and be able to explain the hopefully simple assumptions behind that model. While I’m sure in b-school every VC undoubtedly said that “getting 1% of a $10B market is a bad strategy,” when they got into the workplace something changed. They all love big TAMs . Telling a VC you’re aiming for 50% of an $800M TAM will not get you very far. Your TAM better be in the billions if not the tens of them.
9. Why are you and your team the best people to invest in? Most interesting ideas attract several startups so, odds are, you have fairly direct competitors pretty much from inception. And, particularly if you’re talking with a VC at a larger firm, they have probably researched every company in the nascent space and met most of them . So the question here is: (of all the teams I’ve met in this space) why you are the folks who are going to win?
I’d expect most startups in your space have smart people with strong educations, with great backgrounds at the right companies. That’s become the table stakes. The real question is thus why is your team of smart, well educated, and appropriately experienced people better than the others :
A lot of this is confidence: “of course, we’re the right folks, because we’re the ones who are going to win.” Some people feel like they’re doing a homework assignment while others feel like they’re building a winning company. Be the latter. We know the stakes, we know the second prize is a set of steak knives, and we are going to win or die trying. #swagger
Drivers vs. passengers. Big successful enterprise software companies have definitionally employed a lot of people. So if you’re doing a sales-related category it’s not hard to companies full of ex-Siebel and ex-Salesforce people. The real question thus becomes: what did your people do at those prior companies? Were they drivers (who drove what) or were they passengers just along for the ride. If they drove, emphasize the amazing things they did, not just the brand names of where they worked.
Completeness. Some startups have relatively complete teams while others have only a CEO and CTO and a few functional directors. The best answer is a fairly complete team that’s worked together before. That takes a lot of hiring and on-boarding risk off the table. Think: give us money and we can start executing right away.
Prior exactly-relevant experience. Saying Mary was VP of ProductX Sales carrying a $500M number at BigCo is quite different from saying Mary just scaled sales at her last startup from $10M to $100M and is ready to do the exact same thing here. The smaller the gap between what people just did and what you’re asking them to do, the better.
Finally, and this is somewhat tongue in cheek, remember my concentric circles of fundraising from this post. How VCs see founders and entrepreneurs:
10. If I give you money what are going to do with it? The quantitative part of this answer should already be in the three-year financial model you’ve built so don’t be afraid to reference that to remind people that your plan and financial model are aligned . But then drill down and give the detail on where the money is planned to be spent. For extra credit, talk about milestone- or ARR-based spend triggers instead of dates. For example, say once we have 3 sales reps hitting their numbers we will go out and hire two more. The financial plan has that happening in July, but if July comes and we haven’t passed that milestone we won’t pull the trigger. Ditto for most hiring across the company. And ditto for marketing: e.g., we’ve got a big increase in programs budget in the second half of next year but we won’t release that money until we’re sure we’ve correctly identified the right marketing programs in which to invest.
It’s also very important that demonstrate knowledge of a key truth of VC-backed startups: each round is about teeing-up the next one. So the key goal of the Series A round should be to put the company in a position to successfully raise a Series B. And so on. Discuss the milestones you’re aiming to achieve that should support that tee-up process. And don’t forget the SaaStr napkin for getting a rough idea of what typical rounds look like by series.
Bonus: origin story. If I were to add one question it would be: tell me how you came to found your company? Or, using the more modern vernacular: tell me about your origin story? If yours is good and your founders are personable and videogenic, then I’d even make it into a short video, like the founders of Hashicorp did. You’re going to get asked this question a lot, so why not work on building the optimal answer and then videoing it.
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 My, how things have changed. The net result is that the new choke-point is series A (prediction 9). Seed and angel money seems pretty easy to raise; A-rounds seem pretty hard — if you’ve already raised and spent $2M in seed capital then you should have something to show for it.
 Most of the graduate student types I meet tend to be quite circumspect in their replies. “Well, it could be this, but we don’t really know so it could be that. Here are some arguments in favor of this and some against.” In business, it’s better to be seen as decisive and take a clear stand. As long as you are also perceived as open-minded and responsive to data, you can always change your mind later. But you don’t want to be seen as fence-sitter, endlessly equivocating, and waiting for more data before making a decision.
 Or the more modern equivalent: an email footer or LinkedIn profile.
 Unless a company is shopping for training to improve security culture. In which case, it’s a first-order benefit.
 Reminder that I have moral authority to talk about this :-). This type of problem is often called “latent pain” in sales, because it’s a pain the buyer is unaware they have because they don’t believe there is a solution. Ergo, they just get used to it. Thus, the first job of sales and marketing is to awaken the buyer to this latent pain.
 Yes, I know that virtual machines predate VMware considerably, particularly IBM’s VM/CMS operating system, so it wasn’t the creation of the virtual machine that I’d call a breakthrough, but using it to virtualize Microsoft and later Linux servers.
 If you can’t, it’s hard to assume that someone else will be able to. Perhaps you’re not a natural-born seller, but if you were passionate enough about your idea to quit your job and found a company that should generally compensate. Authenticity works.
 Most probably on the logic that they don’t want 1% of a $5B market, they want 40%. That is, they want both: big share and big TAM. And, if you mess up, there’s probably a safer landing net in the $5B market than the $500M one. Quoting the VC adage: great markets make great companies.
 This is the big difference between angels and funds. Angels typically meet one team with one idea, evaluate both and make a decision. Early-stage funds meet a company then research every company in the space and then pick a winner.
 I’m doing this in the abstract; it’s much easier in the concrete if you make a table and line up some key attributes of your team members vs. those of the competition. You use that table to come up with the arguments, but you don’t ever use that table externally with investors and others.
 I’m surprised how many folks dive into answer this question completely ignoring the fact that you’ve likely already put a three-year financial model in front of them that provides the high-level allocation of spend already. While it doesn’t seem to slow down some entrepreneurs, I think it far better to be a founder who refers to his plan a bit too much than a founder acts as if the financial plan doesn’t even exist.
I’m Dave Kellogg, consultant, independent director, advisor, and blogger focused on enterprise software startups.
I bring a unique 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 enterprise performance management 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 Service Cloud at Salesforce and CEO at NoSQL database provider MarkLogic, which we grew from zero to $80M in run-rate revenues during my tenure. Before that, I was CMO at Business Objects for nearly a decade as we grew from $30M to over $1B. 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 Cyral, FloQast, GainSight, Kelda, MongoDB, Plannuh, Recorded Future, and Tableau. I currently sit on the boards of Alation (data catalogs), Nuxeo (content management) and Profisee (master data management). I previously sat on the boards of agtech leader Granular (acquired by DuPont for $300M) and big data leader Aster Data (acquired by Teradata for $325M).
I periodically speak to strategy and entrepreneurship classes at the Haas School of Business (UC Berkeley) and Hautes Études Commerciales de Paris (HEC).