Category Archives: Sales

B2B SaaS Metrics 2020 Benchmark Report: A Discussion with Ray Rike and The SaaS CFO

The purpose of this post is to embed the video recording of my recent appearance on Monday Night Metrics with Ray Rike of RevOps^2 and Ben Murray, also known by the sobriquet, The SaaS CFO.

In this fast-paced episode we move through topical discussions of the major SaaS metrics followed by investors and operators alike, and look at the size-segmented benchmarks presented in Ray’s 2020 B2B SaaS Metrics report.

I think the episode is suitable both for the SaaS metrics beginner because we review the basics for most metrics as well as for the grizzled professional because we dive into topical (and sometimes fairly non-obvious) discussions for many of them.

Here’s the video:

Thanks to Ray and Ben for having me!

Structuring the Organization and Duties of Product Marketing and Competitive Analysis

I sometimes get asked about how to structure an enterprise software marketing organization and the relative roles of product marketing vs. competitive analysis.  In this post, I’ll share my (somewhat contrarian) thoughts on this topic.

My first job in marketing, which served as my bridge from a technical to a sales-and-marketing career, was as a competitive analyst.  Specifically, I was the dedicated Sybase competitive analyst at Ingres in the late 1980s, in a corporate job, but working out of the New York City sales office.  Because, at the time, Sybase was a strong new entrant with a beachhead strategy in financial services, this was rough equivalent of working for the Wehrmacht on Omaha Beach on D-Day.  I learned not only by watching Sybase’s market invasion, but more importantly by watching how the local reps [1] and corporate [2] responded to it.

I’m a huge believer in competitive analysis, which probably started when I first heard this quote watching Patton as an adolescent:

“Rommel, you magnificent bastard, I read your book!” [3]

My other formative experience came from watching yet another movie, Wall Street, where antagonist Gordon Gekko refers to Sun Tzu’s The Art of War.

While Gekko doesn’t use my favorite quote for these purposes [4], his reference to the book was very much in vogue at the time, and probably why I first read it.  My favorite quote from The Art of War is this one:

“If you know the enemy and know yourself, you need not fear the result of a hundred battles. If you know yourself but not the enemy, for every victory gained you will also suffer a defeat. If you know neither the enemy nor yourself, you will succumb in every battle.”

Regular readers know I believe the mission of marketing is to make sales easier.  So the question becomes:  in enterprise software, how do we structure product marketing and competitive in the best way to do just that?

First, let’s review some common mistakes:

  • Not specializing competitive, instead declaring that each product marketing manager (PMM) will cover their respective competitors.  Too much scope, too little focus.
  • Understaffing competitive.  Even in organizations where competitive exists as its own team, it’s not uncommon to see a ratio of 5-10 PMMs per competitive analyst in terms of staffing.  This is too unbalanced.
  • Chartering competitive as strategic.  While I often euphemize the competitive team as “strategic marketing” or “market intelligence,” that’s not supposed to actually change their mission into some think tank.  They exist to help sales win deals.  Don’t let your competitive team get so lofty that they view deal support as pedestrian.
  • Putting competitive under product marketing.  This both blurs the focus and, more importantly, eliminates a healthy tension [5].  If your messaging doesn’t work in the field, the CMO should want to hear about it early (e.g., in their own staff meeting) and have a chance to fix it before it escalates to the corporate QBR and a potential sales attack on marketing in front of the CEO.
  • Putting competitive in the field.  This happens when marketing abdicates responsibility for producing sales-ready competitive materials and someone else picks up the ball, usually the sales productivity team, but sometimes field marketing [6].   This disconnects corporate product marketing from the realities of the field, which is not healthy.

Now, let’s tell you how I think structuring these departments.

  • Product marketing exists to build messaging and content [7] that describe the features and benefits of the product [8].  The job is to articulate.  They are experts in products.
  • Competitive analysis exists to research competitors, devise plays, and build tools to help sales win deals.  The job is to win.  They are experts in the competitors.

As long as we’re in movie quote mode, here’s one of my favorite quotes from James Mason’s character in The Verdict [9]:

I’d prepared a case and old man White said to me, “How did you do?” And, uh, I said, “Did my best.” And he said, “You’re not paid to do your best. You’re paid to win.”

While he was speaking to about lawyers, he might as well have been speaking to competitive:  you’re paid to win.

That’s why I believe competitive needs to be holistic and play-oriented.  Simply put, take everything you know about a competitor  — e.g., products, leadership, history, tactics — and devise plays that will help you win against them.  Then train sales on how to run those plays and supp0rt them in so doing.

If you adopt this mindset you end up with an organization where:

  • Product marketing and competitive are separate functions, both reporting directly to the CMO
  • Product marketing is product-oriented, focused on articulation of features and benefits
  • Competitive is competitor-oriented, focused on using all available information to create plays that win deals and support sales in executing them
  • Product marketing staffing is driven by the number of products you’re covering
  • Competitive staffing is driven by the number of competitors you’re covering (and at what depth level or tier).
  • You end up with a ratio of more like 3:1 than 10:1 when it comes to the relative staffing of product marketing and competitive

You think of these organizations as a matrix:

# # #

Notes

[1]  In the case of the reps, their response was to walk away from financial services deals because they knew they were likely to lose.  This, of course, had the effect of making it easier for Sybase to enter the market.  The smart reps went to Westchester and Long Island and sold in other verticals.  The dumb ones battled Sybase on Wall Street, lost deals, missed mortgage payments, broke marriages, and got fired — all for doing what the c0mpany strategically should have wanted them to do:  to slow down the invasion.   A classic case of micro and macro non-alignment of interests.

[2] The corporate response was to blame sales management.  Rather than seeing the situation as a strategic problem where an enemy was breaking through lines with an integrated strategy (e.g., partners), they chose to see it as an operational or execution problem.  Think:  we’re hiring bad reps in NYC and losing a lot deals — fire the sales manager and get some new talent in there.

[3]  Good Strategy, Bad Strategy tells the presumably more common inverse tale, where during the Gulf War in 1991 General Schwarzkopf was widely credited with a left-hook strategy described as “surprise,” “secret,” and “brilliant,” that was clearly published in the US Army Field Manual 100-5 saying the following, complete with an illustration of a left hook.

Envelopment avoids the enemy’s front, where its forces are most protected and his fires most easily concentrated. Instead, while fixing the defender’s attention forward by supporting or diversionary attacks, the attacker maneuvers his main effort around or over the enemy’s defenses to strike at his flanks and rear.

[4] Gekko refers to:  “Every battle is won before it’s ever fought.”

[5] Organization design is all about creating and managing healthy tensions.  Such tensions are a key reason why I like marketing reporting to the CEO (and not sales), customer success reporting to the CEO (and not the CRO/sales), and engineering reporting to the CEO (and not product), for a few examples.

[6] At one point, way back, Oracle had a huge market intelligence organization, but housed within Americas Marketing, a field marketing organization.

[7] Content being collateral (e.g., web content, white papers, e-books), presentations (internal and external), and demonstrations — all built around communicating the key messages in their messaging blueprint.

[8] Often, but not always, with a primary emphasis on differentiation.

[9] It’s not lost on me that the character was morally bankrupt and was implicitly saying to win at any and all costs.  But I nevertheless still love the quote.  (And yes, win within normal legal and societal constraints!  But win.)

Fortella Webinar: Crisis Mode — I Need More Pipeline Now!

Please join me and Fortella founder Rahul Sachdev for a webinar this Thursday (6/24/21) at 10am Pacific entitled Crisis Mode — I Need More Pipeline Now!

Fortella, which I’ve served as an advisor over the past year or so, makes a revenue intelligence platform.  The company recently published an interesting survey report entitled The State of B2B Marketing:  What Sets the Best Marketers Apart?  Rahul is super passionate about marketing accountability for revenue and the use of AI and advanced analytics in so doing, which is what drew me to want to work with him the first place.  He’s also an avid Kellblog reader, to the point where he often reminds me of things I’ve said but forgotten!

In this webinar we’ll drive a discussion primarily related to two Kellblog posts:

Among other things, I expect we’ll discuss:

  • That pipeline isn’t a monolith and that we need to look inside the pipeline to see things by opportunity type (e.g., new vs. expansion), customer type (e.g., size segment, industry segment) and by source (e.g., inbound vs. partners).  We also need to remember that certain figures we burn into our heads (e.g., sales cycle length) are merely the averages of a distribution and not impenetrable hard walls.
  • By decomposing pipeline we can identity that some types close faster (and/or at a higher conversion rate) than others, and ergo focus on those types when we are in a pinch.
  • How to think about pipeline coverage ratios, including to-go coverage, the target coverage ratio, and remembering to look not just at ARR dollar coverage but opportunities/rep.
  • The types of campaigns one can and should run when you are in a pipeline pinch
  • How we can avoid getting into pipeline pinches through planning (e.g., an inverted funnel model) and forecasting (e.g., next quarter pipeline).

I hope to see you there.  Register here.

Appearance on the Metrics That Measure Up Podcast

“Measure or measure not.  There is no try.”

— My response to being called the Yoda of SaaS metrics.

Just a quick post to highlight my recent appearance on the Metrics That Measure Up podcast, hosted by Ray Rike, founder and CEO of RevOps^2, a firm focused on SaaS metrics and benchmarking.

Ray’s a great guy, passionate about metrics, unafraid of diving into the details, and the producer of a great metrics-focused podcast that has featured many quality guests including Bryon Deeter, Tom Reilly, David Appel, Elay Cohen, Mark Petruzzi / Paul Melchiorre, Sally Duby, Amy Volas, and M.R. Rangaswami.

In the episode, Ray and I discuss:

  • Top SaaS metrics — e.g., annual recurring revenue (ARR), ARR growth, net dollar retention (NDR), net promoter score (NPS), employee NPS, and customer acquisition cost (CAC) ratio
  • How metrics vary with scale
  • Avoiding survivor bias, both in calculating churn rates and in comparisons to public comparison benchmarks (comps) [1]
  • How different metrics impact the enterprise value to revenue (EV/R) multiple — and a quick place to examine those correlations (i.e., the Meritech comps microsite).
  • Win rates and milestone vs. cohort analysis
  • Segmenting metrics, such as CAC and LTV/CAC, and looking at sales CAC vs. marketing CAC.
  • Blind adherence to metrics and benchmarks
  • Consumption-based pricing (aka, usage-based pricing)
  • Career advice for would-be founders

If you enjoy this episode I’m sure you’ll enjoy Ray’s whole podcast, which you can find here.

# # #

Notes

[1] Perhaps more availability bias (or, as Ray calls it, selection bias) than survivor bias, but either way, a bias to understand.

Navel Gazing, Market Research, and the Hypothesis File

Ask most startups about their go-to-market (GTM) these days and they’ll give you lots of numbers.  Funnel metrics.  MQLs, SQLs, demos, and associated funnel conversion rates.  Seen over time, cut by segment.  Win/loss rates and close rates as well, similarly sliced.  Maybe an ABM scorecard, if applicable.

Or maybe more financial metrics like customer acquisition cost (CAC) ratio, lifetime value (LTV) or net dollar retention (NDR) rate.  Maybe a Rule of 40 score to show how they’re balancing growth and profitability.

And then you’ll have a growth strategy conversation and you’ll hear things like:

  • People don’t know who we are
  • But the people who know us love us
  • We’re just not seeing enough deals
  • Actually, we are seeing enough deals, but we’re not making the short list enough
  • Or, we’re making the short list enough, but not winning enough.

And there are always reasons offered:

  • We’re not showing enough value
  • We’re not speaking to the economic buyer
  • We’re a vitamin, not a pain killer
  • We’re not aligned with their business priorities
  • People don’t know you can solve problem X with our solution
  • Prospects can’t see any differentiation among the offerings; we all sound the same [3]
  • They don’t see us as a leader
  • They don’t know they need one
  • They know they need one but need to finish higher priorities first

It’s an odd situation.  We are literally drowning in funnel data, but when it comes to actually understanding what’s happening, we know almost nothing.  Every one of the above explanatory assertions are assumptions.   They’re aggregated anecdotes [4].  The CRM system can tell us a lot about what happens to prospects once they’re in our funnel, but

  1. We’re navel gazing.  We’re only looking at that portion of the market we engaged with.  It’s humbling to take those assertions and mentally preface them with:  “In that slice of the market who found us and engaged with us, we see XYZ.”  We’re assuming our slice is representative.  If you’re a early-stage or mid-stage startup, there’s no reason to assume that.  It’s probably not.
  2. Quantitative funnel analysis is far better at telling you what happened than why it happened.  If only 8% of our stage 2 opportunities close within 6 quarters, well, that’s a fact [5].  But companies don’t even attempt to address most of the above explanatory assertions in their CRM, and even those times when they do (e.g., reason codes for lost deals), the data is, in my experience, usually junk [6].  And even on the rare occasion when it’s not junk, it’s still the salesrep’s opinion as to what happened and the salesrep is not exactly an unbiased observer [7].

What’s the fix here?  We need to go old school.  Let’s complement that wonderful data we have from the CRM with custom market research, that costs maybe $30K to $50K, and that we run maybe 1-2x/year and ideally right before our strategic planning process starts [8].  Better yet, as we go about our business, every time someone says something that sounds like a fact but is really an assumption, let’s put it into a “hypothesis file” that becomes a list of a questions that we want answered headed into our strategic and growth planning.

After all, market research can tell us:

  • If people are aware of us, but perhaps don’t pick us for the long list because they have a negative opinion of us
  • How many deals are happening per quarter and what percent of those deals we are in
  • Who the economic buyer is and ergo if we are speaking to them
  • What the economic buyer’s priorities are and if we are aligning to them
  • When features are most important to customers shopping in the category
  • What problems-to-be-solved (or use-cases) they associate with the category
  • Perceived differences among offerings in the category
  • Satisfaction with various offerings with the category
  • If and when they intend to purchase in the category
  • And much more

Net — I think companies should:

  • Keep instilling rigor and discipline around their pipeline and funnel
  • Complement that information with custom market research, run maybe 1-2x/year
  • Drive that research from a list of questions, captured as they appear in real time and prompted by observing that many of these assertions are hypotheses, not facts — and that we can and should test them with market research.

 

# # #

Notes

[1] As many people use “demo” as a sales process stage.  Not one I’m particularly fond of [2], I might add, but I do see a lot of companies using demo as an intermediate checkpoint between sales-accepted opportunity and closed deal — e.g., “our demo-to-close rate is X%”

[2] I’m not fond of using demo as a stage for two reasons:  it’s vendor-out, not customer-in and it assumes demo (or worse yet, a labor-intensive custom demo) is what’s required as proof for the customer when many alternatives may be what they want — e.g., a deep dive, customer references, etc.  The stage, looking outside-in, is typically where the customer is trying to answer either (a) can this solve my problem or (b) of those that can solve my problem is this the one I want to use?

[3] This is likely true, by the way.  In most markets, the products effectively all look the same to the buyer!  Marketing tries to accentuate differentiation and sales tries to make that accentuated differentiation relevant to the problem at hand, but my guess is more often than not product differentiation is the explanation for the selection, but not the actual driver — which might rather be things like safety / mistake aversion, desire to work with a particular vendor / relationship, word of mouth recommendations, belief that success is more likely with vendor X than vendor Y even if vendor X may (perhaps, for now) have an inferior product)

[4] As the saying goes, the plural of anecdote is not data.

[5] And a potentially meaningless one if you don’t have good discipline around stages and pipeline.

[6] I don’t want to be defeatist here, but most startups barely have their act together on defining and enforcing / scrubbing basics like stages and close dates.  Few have well thought-out reason codes.

[7] If one is the loneliest number, salespersonship is the loneliest loss reason code.

[8] The biggest overlooked secret in making market research relevant to your organization — by acting on it — is strategically timing its arrival.  For example, win/loss reports that arrive just in time for a QBR are way more relevant than those that arrive off-operational-cycle.

A Ten-Point Sales Management Framework for Enterprise SaaS Startups

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Examples:

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

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

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

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

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

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

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

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

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

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

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

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

# # #

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