Presentation I gave to a Foundry CFO summit on SaaS indicators and which ones are leading vs. lagging

1. Thoughts on Lagging, Leading, and
Predictive Indicators
Dave Kellogg
Foundry CFO Summit
10/27/22
Revision 1.5
www.Kellblog.com
https://twitter.com/Kellblog

2. Who is This Guy?
• Independent consultant, EIR at Balderton
Capital, and blogger.
• Former operator
• CEO: MarkLogic, Host Analytics (Planful)
• CMO: Versant, BusinessObjects, Alation (gig)
• GM: Salesforce.com
• Independent board director*
• Alation, Aster Data, CyberGuru, Granular, Nuxeo,
Profisee, Scoro, SMA
• Advisor*
• Examples: Bluecore, GainSight, Tableau, MongoDB,
Pigment, Recorded Future
• Angel / investor
• Examples: Alation, Cube, Cuein, DataGrail, FloQast,
Growblocks, Hex, Saurus, Skyflow
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* List includes both current and former roles

3. While the CEO is on the bridge looking forward,
many finance teams are on the stern, offering in-depth analyses of the ship’s wake
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4. I ❤ SaaS Metrics
• ARR growth
• Net dollar retention (NDR, aka NRR)
• CAC ratio
• Magic number
• CAC payback period
• Churn rate
• LTV/CAC
• Rule of 40 score
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5. I ❤ SaaS Metrics, But Which Are They?
• ARR growth
• Net dollar retention (NDR, aka NRR)
• CAC ratio
• Magic number
• CAC payback period
• Churn rate
• LTV/CAC
• Rule of 40 score
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Forward-looking o
backward-looking?

6. Forward-Looking Metrics Have Never Been More Important
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7. Forward-Looking Metrics Have Never Been More Important
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2001 Dotcom Bubble Burst 2008 Financial Crisis
(Lehman was one of my investors, AMA.)
2022 To-Be-Named Crisis
(Except for the other two times)

8. Why Are Leading Metrics So Important Right Now?
Potential Inflection Point
• Cannot extrapolate recent past
• Need to look up and out
• Up the funnel and outside the company
Fear-Greed Meter Recalibration
• Some VCs flip too fast to red
• Some founders perma-stuck on green
• This is good! We’re counter-cyclical!!
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Only the Paranoid Survive, Grove

9. The real question isn’t, “what’s happening?”
it’s, “what’s happening to us?”
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10. What’s a Leading Indicator?
• Let’s take an example
• Annual recurring revenue (ARR)
• Leading?
• Lagging?
• IMHO, it’s both
• ARR leads subscription revenue
• ARR lags new ARR bookings (and churn ARR)
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11. Let’s Try Again
• Stage 1 oppties
• Leading?
• Lagging?
• Again, IMHO it’s both
• S1 oppties lead S2 oppties and … closed deals
• Insert s2-to-close rate and sales cycle length
• S1 oppties lag MQLs and leads
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12. The Marketing Funnel View
• Each layer leads the one below
• And lags the one above
• Please note the potentially
considerable irony in telling
marketing to focus up-funnel
• For 30 years we’ve been moving
them down: stop celebrating
leads, celebrate MQLs; no, s1
oppties; no, s2 oppties; no, closed
deals; no, closed deals that don’t
churn, …
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Visitors
Names
Responses
Leads
MQL
SAL

13. And The Two Funnels Tie Together
SAL (S1)
SQL (S2)
Solution Fit
Demo
Vendor of Choice
Legal
Won
Visitors
Names
Responses
Leads
MQL
SAL
Marketing Sales
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14. The Worst Day of My Marketing Life
• In the early CRM days, we watched
pipeline coverage
• Current-quarter pipeline / current-
quarter target
• We knew about the 3x rule
• Pipeline coverage was a pretty good
predictor of bookings
• Maybe better than the CRO forecast
• We could use pipeline coverage to
manage the business
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15. And Then This Guy Came Along
● We have determined that reps with 3x pipeline
coverage generally hit their number
● Ergo, I will pummel anyone unless they have 3x
pipeline coverage
● At all levels in the organization
● And then what happened?
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16. Everyone Had 3x Coverage! • And it was mostly ruined
as a predictive metric
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17. “What gets measured, gets managed.” -- Peter Drucker
“What gets measured, gets managed.” – VF Ridgway
The Dysfunctional Consequences of Performance Measurements (1956)
“And managing a metric -- e.g., setting OKRs on it, putting into it broadly-viewed standard reports --ruins it
as predictor / free indicator of business performance.” – DA Kellogg (2022)
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See https://medium.com/centre-for-public-impact/what-gets-measured-gets-managed-its-wrong-and-drucker-never-said-it-fe95886d3df6

18. So When We Say We Want Leading Indicators
● Is that to predict business performance or to manage it?
● Because you don’t really get to choose both
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19. Demos, Ice Cream, and Drownings
● Deals that get to demo have a 31% chance
of closing
○ Therefore, we need more demos
● Wait, do they close at 31% because we did
the demo or because we filtered out so
many tire kickers along the way?
○ If we reduce the filtering to do more demos
won’t that reduce the close rate?
● Drowning deaths and ice cream
production are strongly correlated
○ Does that mean that ice cream production
causes drownings?
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20. So What Do We Actually Want?
Operational Metrics To Manage and Predict
• Manage -- metrics on which we can assign
OKRs to managers
• Predict – what can best tell us where
we’re going to land?
• Example: it’s a bad idea to tell marketing
to go worry about names because they’re
leading indicator
Analytics To Determine Where To Go
• Focus – on what should we focus time
and money?
• Example: it’s a good idea to ask the data
science team which customers close
bigger/faster/higher
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21. Operational Metrics, The Classics
• Funnel volume, conversion rates
• Traffic
• MQLs
• MQL > S1
• S1 > S2
• S2 > S4
• S4 > close
• Pipeline progression
• Opportunity histogram
• Triangulation forecasting
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Now is the time when you benefit from having done it right all the way along

22. Note That It’s Actually A Rotten Time To
Change How We Do Everything
• Compounds extrapolation risk with
• Invalidation of historical comparison data
• That is, let’s not go change pipeline stage definitions, MQL definition,
pipeline scrubbing process, …
• (Let’s do that in fair weather, not foul.)
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23. Operational Metrics, A Little More Creative
• Activity as predictor of closing
• The non-obvious Gong use-case
• Renewal intent as predictor of churn
• Better than NPS (loosely coupled)
• Post-deployment CSAT
• Product usage
• Win/close rates segmented by industry
and use case
• Who buys your product and why is
quite possibly changing
• Example: selling CI based on
onboarding vs. productivity
• Relationship score as predictor of close
• Build some score that indicates if you’re
selling higher and to the business
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24. Analytics to Guide Us
• Ceteris paribus, we want deals that close
• Bigger – more ARR per unit work
• Faster – more velocity is “like adding a month to your year”
• Higher – win rate, i.e., probability of close
• Ceteris paribus, we want customers who
• Renew – need to at least pass CAC payback period
• Expand – everybody loves NRR
• How do we find them?
• This is an analytics / data science problem
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25. Build Data Models (ICP Re-evaluation)
• Use an internal team if you have one, find an MSDS intern if you don’t
• Techniques: logistic regression, random forests, …
• Goal: model that predicts outputs given inputs
• Inputs are attributes of a customer (as many as you can get)
• Outputs are what “success” means to you – surprisingly elusive
• What is success?
• Renewed? Expanded? Deployed? Landed big? Landed fast? Is a reference?
• Beware two things
• Interpretability problem: these can score customers, but typically can’t say “go look for blue eyes”
• Extrapolation problem: we are at inflection point -- this might produce Web3 as a great target
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26. Conclusions
• This is your company
• The CFO is on the right
• Now is the time you get rewarded for having done metrics right
• (If you haven’t, the best time to start is today.)
• We want leading indicators to help us with our instrument landing
• A funnel view is inherently leading when you “look up”
• Beware using managed metrics for prediction
• But we also want to figure out which airport to fly to
• That’s where model-building (ICP re-evaluation) can help
https://www.getsurrey.co.uk/news/surrey-news/cockpit-view-shows-pilot-land-10373724