10 Questions to Ask Yourself Before Moving into Management

I went looking for a post to help someone decide if they should move into management, but couldn’t find one that I really loved.  These three posts aren’t bad.  Nor is this HBR article.  But since I couldn’t find a post that I thought nails the spirit of the question, I thought I’d write one myself.

So here are the ten questions you should consider before making a move into management.

 1. Do you genuinely care about people?  

Far and away this is the most important question because management is all about people.  If you don’t enjoy working with people, if you don’t enjoy helping people, or if you’d prefer to be left alone to work on tasks or projects, then do not go into management.  If you do not genuinely care about people, then do not go into management.

2. Are you organized?

While a small number of organizational leaders and founders can get away with being unstructured and disorganized, the rest of us in management need to be organized.  If you are naturally disorganized, management will be hard for you — and the people who work for you — because your job is to make the plan and coordinate work on it.

This is why one of my managment interview questions is:  “if I opened up your kitchen cabinets what would I see?”

3.  Are you willing to continuously overcommunicate?

In a world filled with information pollution, constant distractions, and employees who think that they can pay continuous partial attention, you’d be amazed how clearly you need to state things and how often you need to repeat them in order to minimize confusion.  A big part of management is communication, so if you don’t like communicating, aren’t good at it, or don’t relish the idea of deliberately and continuously overcommunicating, then don’t go into management.

4.  Can you say “No” when you need to do?

Everybody loves yes-people managers except, of course, the people who work for them.  While saying yes to the boss and internal customers feels good, you will run your team ragged if you lack the backbone to say no when you need to.  If you can’t say no to a bad idea or offer up reprioritization options when the team is red-lining, then don’t go into management.  Saying no is an important part of the job.

5. Are you conflict averse?

Several decades I read the book Tough-Minded Management:  A Guide for Managers Too Nice for Their Own Good, and it taught me the importance of toughness in management.  Management is a tough job.  You need to layout objectives and hold people accountable for achieving them.  You need to hold peers accountable for delivering on dependencies.  You need to give people feedback that they may not want to hear.  If you’re conflict averse and loathe the idea of doing these things, don’t go into management.  Sadly, conflict averse managers actually generate far more conflict than then non-conflict-averse peers.

6. Do you care more about being liked than being effective?

If you are someone who desperately needs to be liked, then don’t go into management.  Managers need to focus on effectiveness.  The best way to be liked in management is to not care about being liked.  Employees want to be on a winning team that is managed fairly and drives results.  Focus on that and your team will like you.  If you focus on being liked and want to be everyone’s buddy, you will fail as both buddy and manager.

7. Are you willing to let go?  

Everybody knows a micromanager who can’t let go.  Nobody likes working for one.  Good managers aim to specify what needs to be done without detailing precisely how to do it.  Bad managers either over-specify or simply jump in and do it themselves.  This causes two problems:  they anger the employee whose job it was to perform the task and they abdicate their responsibility to manage the team.  If the manager’s doing the employee’s job then whose doing the manager’s?  All too often, no one.

8.  Do you have thick skin?

Managers make mistakes and managers get criticized.  If you can’t handle either, then don’t go into management.  Put differently, how many times in your career have your run into your boss’s office and said, “I just want to thank you for the wonderful job you do managing me.”  For me, that answer is zero.  (I have,  however, years later thanked past managers for putting up with my flaws.)

People generally don’t complement their managers; they criticize them.  You probably have criticized most of yours.  Don’t expect things to be any different once you become the manager.

9.  Do you enjoy teaching and coaching?

A huge positive of management is the joy you get from helping people develop their skills and advance in their careers.  That joy results from your investment in them with teaching and coaching.  Great employees want to be mentored.  If you don’t enjoy teaching and coaching, you’ll be cheating your employees out of learning opportunities and cheating yourself out of a valuable part of the management experience.

10.  Are you willing to lead?

Managers need not just to manage, but to lead.  If stepping up, definining a plan, proposing a solution, or taking an unpopular position scares you, well, part of that is normal, but if you’re not willing to do it anyway, then don’t go into management.  Management requires the courage to lead.  Remember the Peter Drucker quote that differentiates leadership and management.

“Management is doing things right, leadership is doing the right things.”

As a good manager, you’ll need to do both.

The Dogshit Bar: A Memorable Market Research Concept

I can’t tell you the number of times I’ve seen market research that suffers from one key problem.  It goes something like this:

  • What do you think of PRODUCT’s user interface?
  • Do you think PRODUCT should be part of suite or a standalone module?
  • Is the value of PRODUCT best measured per-user or per-bite?
  • Is the PRODUCT’s functionality best delivered as a native application or via a browser?
  • Would you like PRODUCT priced per-user or per-consumption?
  • Rank the importance of features 1-4 in PRODUCT?

The problem is, of course, that you’ve never asked the one question that actually matters — would you buy this product — and are pre-supposing the need for the product and that someone would pay something to fulfill that need.

So try this:  substitute “Dogshit Bar” (i.e., a candy bar made of dog shit) for every instance of PRODUCT in one of your market research surveys and see what happens.  Very quickly, you’ll realize that you’re asking questions equivalent to:

  • Should the Dogshit Bar be delivered in a paper or plastic wrapper?
  • Would you prefer to buy the Dogshit Bar in a 3, 6, or 9 oz size?
  • Should the Dogshit Bar be priced by ounce or some other metric?

So before drilling into all the details that product management can obsess over, step back, and ask some fundamental questions first.

  • Does the product solve a problem faced by your organization?
  • How high a priority is that problem?  (Perhaps ranked against a list of high-level priorities for the buyer.  It’s not enough that it solves a problem, it needs to solve an important problem.)
  • What would be the economic value of solving that problem?  (That is, how much value can this product provide.)
  • Would you be willing to pay for it and, if so, how much?  (Which starts to factor in not just  value but the relative cost of alternative solutions.)

So why do people make this mistake?

I believe there’s some feeling that it’s heretical to ask the basic questions about the startup’s core product or the big company’s new strategic initiatiave that the execs dreamed up at an offsite.  While the execs can dream up new product ideas all day long, there’s one thing they can’t do:  force people to buy them.

That’s why you need to ask the most basic, fundamental questions in market research first, before proceeding on to analyzing packaging, interface, feature trade-offs, platforms, etc.  You can generate lots of data to go analyze about whether people prefer paper or plastic packaging or the 3, 6, or 9 ounce size.  But none of it will matter.  Because no one’s going to buy a Dogshit Bar.

Now, before wrapping this up, we need to be careful of the Bradley Effect in market research, an important phenomenom in live research (as opposed to anonymous polls) and one of several reasons why pollsters generally called Trump vs. Clinton incorrectly in the 2016 Presidential election.

I’ll apply the Bradley Effect to product research as follows:  while there are certain exception categories where people will say they won’t buy something that they will (e.g., pornography), in general:

  • If someone says they won’t buy something, then they won’t
  • If someone says they will buy something, then they might

Why?  Perhaps they’re trying to be nice.  Perhaps they do see some value, but just not enough.  Perhaps there is a social stigma associated with saying no.

I first learned about this phenomenom reading Ogivly on Advertising, a classic marketing text by the father of advertising David Ogilvy.  Early in his career Ogilvy got lucky and learned an important lesson.  While working for George Gallup he was assigned to do polling about a movie entitled Abe Lincoln in Illinois.  While the research determined the movie was going to be a roaring success, the film ended up a flop.  Why?  The participants lied.  After all, who wants to sound unpatriotic and tell a pollster that you won’t go see a movie about Abe Lincoln?  Here’s a picture of Ogilvy doing that research.  Always remember it.

ogilvy

The Evolution of Marketing Thanks to SaaS

I was talking with my friend Tracy Eiler, author of Aligned to Achieve, the other day and she showed me a chart that they were using at InsideView to segment customers.  The chart was a quadrant that mapped customers on two dimensions:  renewal rate and retention rate.  The idea was to use the chart to plot customers and then identify patterns (e.g., industries) so marketing could identify the best overall customers in terms of lifetime value as the mechanism for deciding marketing segmentation and targeting.

Here’s what it looked like:

saas-strategic-value

While I think it’s a great chart, what really struck me was the thinking behind it and how that thinking reflects a dramatic evolution in the role of marketing across my career.

  • Back two decades ago when marketing was measured by leads, they focused on how to cost-effectively generate leads, looking at response rates for various campaigns.
  • Back a decade ago when marketing was measured by opportunities (or pipeline), they focused on how to cost-effectively generate opportunities, looking at response and opportunity conversion rates.
  • Today, as more and more marketers are measured by marketing-sourced New ARR, they are focused on cost-effectively generating not just opportunities, but opportunities-that-close, looking all the way through the funnel to close rates.
  • Tomorrow, as more marketers will be measured on the health of the overall ARR pool, they will be focused on cost-effectively generating not just opportunities-that-close but opportunities that turn into the best long-term customers. (This quadrant helps you do just that.)

As a company makes this progression, marketing becomes increasingly strategic, evolving in mentality with each step.

  • Starting with, “what sign will attract the most people?” (Including “Free Beer Here” which has been used at more than one conference.)
  • To “what messages aimed at which targets will attract the kind of people who end up evaluating?”
  • To “who are we really looking to sell to — which people end up buying the most and the most easily – and what messages aimed at which targets will attract them?”
  • To “what are the characteristics of our most successful customers and how can we find more people like them?”

The whole pattern reminds me of the famous Hubspot story where the marketing team was a key part forcing the company to focus on either “Owner Ollie” (the owner of a <10 person business) or “Manager Mary” (a marketer at a 10 to 1000 person business).  For years they had been serving both masters poorly and by focusing on Manager Mary they were able to drive a huge increase in their numbers that enabled cost-effectively scaling the business and propelling them onto a successful IPO.

hubspot

What kind of CMO does any CEO want on their team?  That kind.  The kind worried about the whole business and looking at it holistically and analytically.

In-Memory Analytics: The Other Kind – A Key Success Factor for Your Career

I’m not going to talk about columnar databases, compression, horizontal partitioning, SAP Hana, or real-time vs. pre-aggregated summarization in this post on in-memory analytics.  I’m going to talk about the other kind of in-memory analytics.  The kind that can make or break your career.

What do you mean, the other kind of in-memory analytics?  Quite simply, the kind you keep in your head (i.e., in human memory).  Or, better put, the kind you should be expected to keep in your head and be able to recite on demand in any business meeting.

I remember when I worked at Salesforce, I covered for my boss a few times at the executive staff meeting when he was traveling or such.  He told me:  “Marc expects everyone to know the numbers, so before you go in there, make sure you know them.”  And I did.  On the few times I attended in his place, I made a cheat sheet and studied it for an hour to ensure that I knew every possible number that could reasonably be asked.  I’d sit in the meeting, saying little, and listening to discussion not directly related to our area.  Then, boom, out of left field, Marc asked:  “what is the Service Cloud pipeline coverage ratio for this quarter in Europe?”

“3.4,” I replied succinctly.  If I hadn’t have known the number I’m sure it would been an exercise in plucking the wings off a butterfly.  But I did, so the conversation quickly shifted to another topic, and I lived to fight another day.

Frankly, I was happy to work in an organization where executives were expected to know — in their heads, in an instant — the values of the key metrics that drive their business.  I weak organizations you constantly hear “can I get back to you on that” or “I’m going to need to look that one up.”

If you want to run a business, or a piece of one,  and you want to be a credible leader — especially in a metrics-driven organization — you need to have “in-memory” the key metrics that your higher-ups and peers would expect you to know.

This is as true of CEO pitching a venture capitalist and being asked about CAC ratios and churn rates as it is of a marketing VP being asked about keywords, costs, and conversions in an online advertising program.  Or a sales manager being asked about their forecast.

In fact, as I’ve told my sales directors a time or two:  “I should be able to wake you up at 3:00 AM and ask your forecast, upside, and pipeline and you should be able to answer, right then, instantly.”

That’s an in-memory metric.  No “let me check on that.”  No “I’ll get back to you.”  No “I don’t know, let me ask my ops guy,” which always makes me think: who runs the department, you or the ops guy — and if you need to ask the ops guy all the numbers maybe he/she should be running the department and not you?

I have bolded the word “expect” four times above because this issue is indeed about expectations and expectations are not a precise science.  So, how can you figure out the expectations for which analytics you should hold in-memory?

  • Look at your department’s strategic goals and determine which metrics best measure progress on them.
  • Ask peers inside the company what key metrics they keep in-memory and design your set by analogy.
  • Ask peers who perform the same job at different companies what key metrics they track.
  • When in doubt, ask the boss or the higher-ups what metrics they expect you to know.

Finally, I should note that I’m not a big believer in the whole “cheat sheet” approach I described above.  Because that was a special situation (covering for the boss), I think the cheat sheet was smart, but the real way to burn these metrics into your memory is to track them every week at your staff meeting, watching how they change week by week and constantly comparing them to prior periods and to a plan/model if you have one.

The point here is not “fake it until you make it” by running your business in a non-metrics-focused way and memorizing figures before a big meeting, but instead to burn the metrics review into your own weekly team meeting and then, naturally, over time you will know these metrics so instinctively that someone can wake you up at 3:00 AM and you can recite them.

That’s the other kind of in-memory analytics.  And, much as I love technology, the more important kind for your career.

Kellblog’s 2017 Predictions  

New Year’s means three things in my world:  (1) time to thank our customers and team at Host Analytics for another great year, (2) time to finish up all the 2017 planning items and approvals that we need to get done before the sales kickoff (including the one most important thing to do before kickoff), and time to make some predictions for the coming year.

Before looking at 2017, let’s see how I did with my 2016 predictions.

2016 Predictions Review

  1. The great reckoning begins. Correct/nailed.  As predicted, since most of the bubble was tied up in private companies owned by private funds, the unwind would happen in slow motion.  But it’s happening.
  2. Silicon Valley cools off a bit. Partial.  While IPOs were down, you couldn’t see the cooling in anecdotal data, like my favorite metric, traffic on highway101.
  3. Porter’s five forces analysis makes a comeback. Partial.  So-called “momentum investing” did cool off, implying more rational situation analysis, but you didn’t hear people talking about Porter per se.
  4. Cyber-cash makes a rise. CorrectBitcoin more doubled on the year (and Ethereum was up 8x) which perversely reinforced my view that these crypto-currencies are too volatile — people want the anonymity of cash without a highly variable exchange rate.  The underlying technology for Bitcoin, blockchain, took off big time.
  5. Internet of Things goes into trough of disillusionment. Partial.  I think I may have been a little early on this one.  Seems like it’s still hovering at the peak of inflated expectations.
  6. Data science rises as profession. Correct/easy.  This continues inexorably.
  7. SAP realizes they are a complex enterprise application company. Incorrect.  They’re still “running simple” and talking too much about enabling technology.  The stock was up 9% on the year in line with revenues up around 8% thus far.
  8. Oracle’s cloud strategy gets revealed – “we’ll sell you any deployment model you want as long as your annual bill goes up.”  Partial.  I should have said “we’ll sell you any deployment model you want as long as we can call it cloud to Wall St.”
  9. Accounting irregularities discovered at one or more unicorns. Correct/nailed.  During these bubbles the pattern always repeats itself – some people always start breaking the rules in order to stand out, get famous, or get rich.  Fortune just ran an amazing story that talks about the “fake it till you make it” culture of some diseased startups.
  10. Startup workers get disappointed on exits. Partial.  I’m not aware of any lawsuits here but workers at many high flyers have been disappointed and there is a new awareness that the “unicorn party” may be a good thing for founders and VCs, but maybe not such a good thing for rank-and-file employees (and executive management).
  11. The first cloud EPM S-1 gets filed. Incorrect.  Not yet, at least.  While it’s always possible someone did the private filing process with the SEC, I’m guessing that didn’t happen either.
  12. 2016 will be a great year for Host Analytics. Correct.  We had a strong finish to the year and emerged stronger than we started with over 600 great customers, great partners, and a great team.

Now, let’s move on to my predictions for 2017 which – as a sign of the times – will include more macro and political content than usual.

  1. The United States will see a level of divisiveness and social discord not seen since the 1960s. Social media echo chambers will reinforce divisions.  To combat this, I encourage everyone to sign up for two publications/blogs they agree with and two they don’t lest they never again hear both sides of an issue. (See map below, coutesy of Ninja Economics, for help in choosing.)  On an optimistic note, per UCSD professor Lane Kenworthy people aren’t getting more polarized, political parties are.

news

  1. Social media companies finally step up and do something about fake news. While per a former Facebook designer, “it turns out that bullshit is highly engaging,” these sites will need to do something to filter, rate, or classify fake news (let alone stopping to recommend it).  Otherwise they will both lose credibility and readership – as well as fail to act in a responsible way commensurate with their information dissemination power.
  1. Gut feel makes a comeback. After a decade of Google-inspired heavily data-driven and A/B-tested management, the new US administration will increasingly be less data-driven and more gut-feel-driven in making decisions.  Riding against both common sense and the big data / analytics / data science trends, people will be increasingly skeptical of purely data-driven decisions and anti-data people will publicize data-driven failures to popularize their arguments.  This “war on data” will build during the year, fueled by Trump, and some of it will spill over into business.  Morale in the Intelligence Community will plummet.
  1. Under a volatile leader, who seems to exhibit all nine of the symptoms of narcissistic personality disorder, we can expect sharp reactions and knee-jerk decisions that rattle markets, drive a high rate of staff turnover in the Executive branch, and fuel an ongoing war with the media.  Whether you like his policies or not, Trump will bring a high level of volatility the country, to business, and to the markets.
  1. With the new administration’s promises of $1T in infrastructure spending, you can expect interest rates to raise and inflation to accelerate. Providing such a stimulus to already strong economy might well overheat it.  One smart move could be buying a house to lock in historic low interest rates for the next 30 years.  (See my FAQ for disclaimers, including that I am not a financial advisor.)
  1. Huge emphasis on security and privacy. Election-related hacking, including the spearfishing attack on John Podesta’s email, will serve as a major wake-up call to both government and the private sector to get their security act together.  Leaks will fuel major concerns about privacy.  Two-factor authentication using verification codes (e.g., Google Authenticator) will continue to take off as will encrypted communications.  Fear of leaks will also change how people use email and other written electronic communications; more people will follow the sage advice in this quip:

Dance like no one’s watching; E-mail like it will be read in a deposition

  1. In 2015, if you were flirting on Ashley Madison you were more likely talking to a fembot than a person.  In 2016, the same could be said of troll bots.  Bots are now capable of passing the Turing Test.  In 2017, we will see more bots for both good uses (e.g., customer service) and bad (e.g., trolling social media).  Left unchecked by the social media powerhouses, bots could damage social media usage.
  1. Artificial intelligence hits the peak of inflated expectations. If you view Salesforce as the bellwether for hyped enterprise technology (e.g., cloud, social), then the next few years are going to be dominated by artificial intelligence.  I’ve always believed that advanced analytics is not a standalone category, but instead fodder that vendors will build into smart applications.  They key is typically not the technology, but the problem to which to apply it.  As Infer founder Vik Singh said of Jim Gray, “he was really good at finding great problems,” the key is figuring out the best problems to solve with a given technology or modeling engine.  Application by application we will see people searching for the best problems to solve using AI technology.
  1. The IPO market comes back. After a year in which we saw only 13 VC-backed technology IPOs, I believe the window will open and 2017 will be a strong year for technology IPOs.  The usual big-name suspects include firms like Snap, Uber, AirBnB, and SpotifyCB Insights has identified 369 companies as strong 2017 IPO prospects.
  1. Megavendors mix up EPM and ERP or BI. Workday, which has had a confused history when it comes to planning, acquired struggling big data analytics vendor Platfora in July 2016, and seems to have combined analytics and EPM/planning into a single unit.  This is a mistake for several reasons:  (1) EPM and BI are sold to different buyers with different value propositions, (2) EPM is an applications sale, BI is a platform sale, and (3) Platfora’s technology stack, while appropriate for big data applications is not ideal for EPM/planning (ask Tidemark).  Combining the two together puts planning at risk.  Oracle combined their EPM and ERP go-to-market organizations and lost focus on EPM as a result.  While they will argue that they now have more EPM feet on the street, those feet know much less about EPM, leaving them exposed to specialist vendors who maintain a focus on EPM.  ERP is sold to the backward-looking part of finance; EPM is sold to the forward-looking part.  EPM is about 1/10th the market size of ERP.  ERP and EPM have different buyers and use different technologies.  In combining them, expect EPM to lose out.

And, as usual, I must add the bonus prediction that 2017 proves to be a strong year for Host Analytics.  We are entering the year with positive momentum, the category is strong, cloud adoption in finance continues to increase, and the megavendors generally lack sufficient focus on the category.  We continue to be the most customer-focused vendor in EPM, our new Modeling product gained strong momentum in 2016, and our strategy has worked very well for both our company and the customers who have chosen to put their faith in us.

I thank our customers, our partners, and our team and wish everyone a great 2017.

# # #

 

A Fresh Look at How to Measure SaaS Churn Rates

[Editor’s note:  revised 3/27/17 with changes to some definitions.]

It’s been nearly three years since my original post on calculating SaaS renewal rates and I’ve learned a lot and seen a lot of new situations since then.  In this post, I’ll provide a from-scratch overhaul on how to calculate churn in an enterprise SaaS company [1].

While we are going to need to “get dirty” in the detail here, I continue to believe that too many people are too macro and too sloppy in calculating these metrics.  The details matter because these rates compound over time, so the difference between a 10% and 20% churn rate turns into a 100% difference in cohort value after 7 years [2].  Don’t be too busy to figure out how to calculate them properly.

The Leaky Bucket Full of ARR

I conceptualize SaaS companies as leaky buckets full of annual recurring revenue (ARR).  Every time period, the sales organization pours more ARR into the bucket and the customer success (CS) organization tries to prevent water from leaking out [3].

This drives the leaky bucket equation, which I believe should always be the first four lines of any SaaS company’s financial statements:

Starting ARR + new ARR – churn ARR = ending ARR

Here’s an example, where I start with those four lines, and added two extra (one to show a year over year growth rate and another to show “net new ARR” which offsets new vs. churn ARR):

leaky

For more on how to present summary SaaS startup financials, go here.

Half-Full or Half-Empty:  Renewals or Churn?

Since the renewal rate is simply one minus the churn rate, the question is which we should calculate?  In the past, I favored splitting the difference [4], whereas I now believe it’s simpler just to talk about churn.  While this may be the half-empty perspective, it’s more consistent with what most people talk about and is more directly applicable, because a common use of a churn rate is as a discount rate in a net present value (NPV) formula.

Thus, I now define the world in terms of churn and churn rates, as opposed to renewals and renewal rates.

Terminology: Shrinkage and Expansion

For simplicity, I define the following two terms:

  • Shrinkage = anything that makes ARR decrease. For example, if the customer dropped seats or was given a discount in return for signing a multi-year renewal [5].
  • Expansion = anything that makes ARR increase, such as price increases, seat additions, upselling from a bronze to a gold edition, or cross-selling new products.

Key Questions to Consider

The good news is that any churn rate calculation is going to be some numerator over some denominator.  We can then start thinking about each in more detail.

Here are the key questions to consider for the numerator:

  • What should we count? Number of accounts, annual recurring revenue (ARR), or something else like renewal bookings?
  • If we’re counting ARR should we think at the product-level or account-level?
  • To what extent should we offset shrinkage with expansion in calculating churn ARR? [6]
  • When should we count what? What about early and late renewals?  What about along-the-way expansion?  What about churn notices or non-payment?

Here are the key questions to consider for the denominator:

  • Should we use the entire ARR pool, that portion of the ARR pool that is available to renew (ATR) in any given time period, or something else?
  • If using the ATR pool, for any given renewing contract, should we use its original value or its current value (e.g., if there has been upsell along the way)?

What Should We Count?  Logos and ARR

I believe the two metrics we should count in churn rates are

  • Logos (i.e., number of customers). This provides a gross indication of customer satisfaction [7] unweighted by ARR, so you can answer the question:  what percent of our customer base is turning over?
  • This provides a very important indication on the value of our SaaS annuity.  What is happening to our ARR pool?

I would stay completely away from any SaaS metrics based on bookings (e.g., a bookings CAC, TCV, or bookings-based renewals rate).  These run counter to the point of SaaS unit economics.

Gross and Net Shrinkage; Account-Level Churn

Let’s look at a quick example to demonstrate how I now define gross and net shrinkage as well as account-level churn [8].

gross and net shrinkage

Gross shrinkage is the sum of all the shrinkage. In the example, 80 units.

Net shrinkage is the sum of the shrinkage minus the sum of the expansion. In the example, 80-70 = 10 units.

To calculate account-level churn, we proceed, account by account, and look at the change in contract value, separating upsell from the churn.  The idea is that while it’s OK to offset shrinkage with expansion within an account that we should not do so across accounts when working at the account level [9].  This has the effect of splitting expansion into offset (used to offset shrinkage within an account) and upsell (leftover expansion after all account-level shrinkage has been offset).  In the example, account-level churn is 30 units.

Make the important note here that how we calculate you churn – and specifically how we use expansion ARR to offset shrinkage—not only affects our churn rates, but our reported upsell rates as well.  Should we proudly claim 70 units of upsell (and less proudly 80 units of churn), 30 units of churn and 20 of upsell, or simply 10 units of churn?  I vote for the second.

While working at the account-level may seem odd, it is how most SaaS companies work operationally.  First, because they charter customer success managers (CSMs) to think at the account level, working account by account doing everything they can to preserve and/or increase the value of the account.  Second, because most systems work at and finance people think at the account level – e.g., “we had a customer worth 100 units last year, and they are worth 110 units this year so that means upsell of 10 units.  I don’t care how much is price increase vs. swapping some of product A for product B.” [11]

So, when a SaaS company reports “churn ARR,” in its leaky bucket analysis, I believe they should report neither gross churn nor net churn, but account-level churn ARR.

Timing Issues and the Available to Renew (ATR) Concept

Churn calculations bring some interesting challenges such as early/late renewals, churn notices, non-payment, and along-the-way expansion.

A renewals booking should always be taken in the period in which it is received.  If a contract expires on 6/30 and the renewal is received in on 6/15 it should show up in 2Q and if received on 7/15 it should up in 3Q.

For churn rate calculations, however, the customer success team needs to forecast what is going to happen for a late renewal.  For example, if we have a board meeting on 7/12 and a $150K ARR renewal due 6/30 has not yet been happened, we need to proceed based on what the customer has said.  If the customer is actively using the software, the CFO has promised a renewal but is tied up on a European vacation, I would mark the numbers “preliminary” and count the contract as renewed.  If, however, the customer has not used the software in months and will not return our phone calls, I would count the contract as churned.

Suppose we receive a churn notice on 5/1 for a contract that renews on 6/30.  When should we count the churn?  A Bessemer SaaS fanatic would point to their definition of committed monthly recurring revenue (CMRR) [12] and say we should remove the contact from the MRR base on 5/1.  While I agree with Bessemer’s views in general — and specifically on things like on preferring ARR/MRR to ACV and TCV — I get off the bus on the whole notion of “committed” ARR/MRR and the ensuing need to remove the contract on 5/1.  Why?

  • In point of fact the customer has licensed and paid for the service through 6/30.
  • The company will recognize revenue through 6/30 and it’s much easier to do so correctly when the ARR is still in the ARR base.
  • Operationally, it’s defeatist. I don’t want our company to give up and say “it’s over, take them out of the ARR base.” I want our reaction to be, “so they think they don’t want to renew – we’ve got 60 days to change their mind and keep them in.” [13]

We should use the churn notice (and, for that matter, every other communication with the customer) as a way of improving our quarterly churn forecast, but we should not count churn until the contract period has ended, the customer has not renewed, and the customer has maintained their intent not to renew in coming weeks.

Non-payment, while hopefully infrequent, is another tricky issue.  What do we do if a customer gives us a renewal order on 6/30, payable in 30 days, but hasn’t paid after 120?  While the idealist in me wants to match the churn ARR to the period in which the contract was available to renew, I would probably just show it as churn in the period in which we gave up hope on the receivable.

Expansion Along the Way (ATW)

Non-payment starts to introduce the idea of timing mismatches between ARR-changing events and renewals cohorts.  Let’s consider a hopefully more frequent case:  ARR expansion along the way (ATW).  Consider this example.

ATW expansion

To decide how to handle this, let’s think operationally, both about how our finance team works and, more importantly, about how we want our customer success managers (CSMs) to think.  Remember we want CSMs to each own a set of customers, we want them to not only protect the ARR of each customer but to expand it over time.  If we credit along-the-way upsell in our rate calculations at renewal time, we shooting ourselves in the foot.  Look at customer Charlie.  He started out with 100 units and bought 20 more in 4Q15, so as we approach renewal time, Charlie actually has 120 units available to renew (ATR), not 100 [14].  We want our CSMs basing their success on the 120, not the 100.  So the simple rule is to base everything not on the original cohort but on the available to renew (ATR) entering the period.

This begs two questions:

  • When do we count the along-the-way upsell bookings?
  • How can we reflect those 40 units in some sort of rate?

The answer to the first question is, as your finance team will invariably conclude, to count them as they happen (e.g., in 4Q15 in the above example).

The answer to the second question is to use a retention rate, not a churn rate.  Retention rates are cohort-based, so to calculate the net retention rate for the 2Q15 cohort, we divide its present value of 535 by its original value of 500 and get 107%.

Never, ever calculate a retention rate in reverse – i.e., starting a group of current customers and looking backwards at their ARR one year ago.  You will produce a survivor biased answer which, stunningly, I have seen some public companies publish.  Always run cohort analyses forwards to eliminate survivor bias.

Off-Cycle Activity

Finally, we need to consider how to address off-cycle (or extra-cohort) activity in calculating churn and related rates.  Let’s do this by using a big picture example that includes everything we’ve discussed thus far, plus off-cycle activity from two customers who are not in the 2Q16 ATR cohort:  (1) Foxtrot, who purchased in 3Q14, renewed in 3Q15, and who has not paid, and (2) George, who purchased in 3Q15, who is not yet up for renewal, but who purchased 50 units of upsell in 2Q16.

big picture

Foxtrot should count as churn in 2Q16, the period in which we either lost hope of collection (or our collections policy dictated that collection we needed to de-book the deal). [15]

George should count as expansion in 2Q16, the period in which the expansion booking was taken.

The trick is that neither Foxtrot nor George is on a 2Q renewal cycle, so neither is included in the 2Q16 ATR cohort.  I believe the correct way to handle this is:

  • Both should be factored into gross, net, account-level churn, and upsell.
  • For rates where we include them in the numerator, for consistency’s sake we must also include them in the denominator. That means putting the shrinkage in the numerator and adding the ATR of a shrinking (or lost) account in denominator of a rate calculation.  I’ll call this the “+” concept, and define ATR+ as inclusive of such additional logos or ARR resulting from off-cycle accounts [16].

Rate Calculations

We are now in the position to define and calculate the churn rates that I use and track:

  • Simple churn rate = net shrinkage / starting period ARR * 4.  Or, in English, the net change in ARR from existing customers divided by starting period ARR (multiplied by 4 to annualize the rate which is measured against the entire ARR base). As the name implies, this is the simplest churn rate to calculate. This rate will be negative whenever expansion is greater than shrinkage. Starting period ARR includes both ATR and non-ATR contracts (including potentially multi-year contracts) so this rate takes into account the positive effects of the non-cancellability of multi-year deals.  Because it takes literally everything into account, I think this is the best rate for valuing the annuity of your ARR base.
  • Logo churn rate = number of discontinuing logos / number of ATR+ logos. This rate tells us the percent of customers who, given the chance, chose to discontinue doing business with us.  As such, it provides an ARR-unweighted churn rate, providing the best sense of “how happy” our customers are, knowing that there is a somewhat loose correlation between happiness and renewal [16].  Remember that ATR+ means to include any discontinuing off-cycle logos, so the calculation is 1/16 = 6.3% in our example.
  • Retention rate = current ARR [time cohort] / time-ago ARR [time cohort]. In English, the current ARR from some time-based cohort (e.g., 2Q15) divided by the year-ago ARR from that same cohort.  Typically we do this for the one-year-ago or two-years-ago cohorts, but many companies track each quarter’s new customers as a cohort which they measure over time.  Like simple churn, this is a great macro metric that values the ARR annuity, all in.
  • Gross churn rate = gross shrinkage / ATR+. This churn rate is important because it reveals the difference between companies that have high shrinkage offset by high expansion and companies which simply have low shrinkage.  Gross churn is a great metric because it simply shows the glass half-empty view:  at what rate is ARR leaking out of your bucket before offset it with refills in the form of expansion ARR.
  • Account-level churn rate = account-level churn / ATR+. This churn rate foots to the reported churn ARR in our leaky bucket analysis (which uses account-level churn), partially offsets shrinkage with expansion at an account-level, and is how most SaaS companies actually calculate churn.  While perhaps counter-intuitive, it reflects a philosophy of examining, at an account basis, what happens to value of our each of our customers when we allow shrinkage to be offset by expansion (which is what we want our CSM reps doing) leaving any excess as upsell.  This should be our primary churn metric.
  • Net churn rate = net shrinkage / ATR+.  This churn rate offsets shrinkage with expansion not at the account level, but overall.  This is similar to the simple churn rate but with the disadvantage of looking only at ATR and not factoring in the positive effects of non-cancellability of multi-year deals.    Ergo, I prefer using the simple churn rate to the net churn rate in valuing the SaaS annuity.

# # #

Notes

[1] Replacing these posts in the process.

[2] The 10% churn group decays from 100 units to 53 in value after 7 years, while the 20% group decays to 26.

[3] We’ll sidestep the question of who is responsible for installed-based expansion in this post because companies answer it differently (e.g., sales, customer success, account management) and the good news is we don’t need to know who gets credited for expansion to calculate churn rates.

[4] Discussing churn in dollars and renewals in rates.

[5] For example, if a customer signed a one-year contract for 100 units and then was offered a 5% discount to sign a three-year renewal, you would generate 5 units of ARR churn.

[6] Or, as I said in a prior post, should I net first or sum first?

[7] And yes, sometimes unhappy customers do renew (e.g., if they’ve been too busy to replace you) and happy customers don’t (e.g., if they get a new key executive with different preferences) but counting logos still gives you a nice overall indication.

[8] Note that I have capitulated to the norm of saying “gross” churn means before offset and thus “net” churn means after netting out shrinkage and expansion.  (Beware confusion as this is the opposite of my prior position where I defined “net” to mean “net of expansion,” i.e., what I’d now call “gross.”)

[9] Otherwise, you can just look at net shrinkage which offsets all shrinkage by all expansion.  The idea of account-level churn is to restrict the ability to offset shrinkage with expansion across accounts, in effect, telling your customer success reps that their job is to, contract by contract, minimize shrinkage and ensure expansion.

[10] “Offset” meaning ARR used to offset shrinkage that ends up neither churn nor upsell.

[11] While this approach works fine for most (inherently single-product) SaaS startups it does not work as well for large multi-product SaaS vendors where the failure of product A might be totally or partially masked by the success of product B.  (In our example, I deliberately had all the shrinkage coming from downsell of product A to make that point.  The product or general manager for product A should own the churn number that product and be trying to find out why it churned 80 units.)

[12] MRR = monthly recurring revenue = 1/12th of ARR.  Because enterprise SaaS companies typically run on an annual business rhythm, I prefer ARR to MRR.

[13] Worse yet, if I churn them out on 5/1 and do succeed in changing their mind, I might need to recognize it as “new ARR” on 6/30, which would also be wrong.

[14] The more popular way of handling this would have been to try and extend the original contract and co-terminate with the upsell in 4Q16, but that doesn’t affect the underlying logic, so let’s just pretend we tried that and it didn’t work for the customer.

[15] Whether you call it a de-booking or bad receivable, Foxtrot was in the ARR base and needs to come out.  Unlike the case where the customer has paid for the period but is not using the software (where we should churn it at the end of the contract), in this case the 3Q15 renewal was effectively invalid and we need to remove Foxtrot from the ARR base at some defined number of days past due (e.g., 90) or when we lose hope of collection (e.g., bankruptcy).

[16] I think the smaller you are the more important this correction is to ensure the quality of your numbers.  As a company gets bigger, I’d just drop the “+” concept whenever it’s only changing things by a rounding error.

[17] Use NPS surveys for another, more precise, way of measuring happiness.  See [7] as well.

SaaS Startup One-Slide Financials Dashboard

In the course of my board and advisory work, I get to look at a lot of software as a service (SaaS) startups financials and I’m often surprised how people choose to present their companies.

Because people — e.g., venture capital (VC) investors — judge you by the metrics you track, the order in which you track them, and how clearly you present them, I think it’s very important to put real thought into how you want to present your company’s one-slide financial and key operating metrics.

As both an author and analytics enthusiast, I also believe in minimalism and reader empathy.  We should neither bury the reader in facts nor force them to perform basic calculations that answer easily anticipated questions.

I always try to remember this Blaise Pascal quote (which is often misattributed to Mark Twain):

I would have written you a shorter letter, but I did not have time to do so.

So, in this spirit, let me offer my one-slide SaaS startup financials and key operating metrics dashboard, which captures all the key high-level questions I’d have about any enterprise SaaS company.

saas-one-slide-financial-dashboard

While this is certainly not a complete set of SaaS metrics, it provides a great summary of the state of your annual recurring revenue (ARR), your trajectory, your forecast, and your performance against plan.  Most important, perhaps, it shows that you are focused on the right thing by starting with 5 lines dedicated not to TCV, bookings, or GAAP revenue, but the key value driver for any SaaS business:  ARR.

If you like it, you can download the spreadsheet here.