Over the past few months, we have moved our operation into a virtual setting, reset budgets and revenue plans, and pivoted to target market and messaging aligned with the economic context. Now what? Are we being too aggressive? Or too conservative? What should we be tracking to understand these critical questions? Mark Roberge, managing director at Stage 2 Capital, professor at Harvard Business School, and former CRO at HubSpot, will share his frameworks to quantitatively answer when, where, and how to scale. He will present the scientific, data-driven approach he implements in companies to assess product-market-fit, go-to-market-fit, and the appropriate pace of scale. He will also illustrate front-line tactics used to accelerate companies toward re-establishing growth.

1. The Science of Re-establishing
Growth
Where, When, and How?
Mark Roberge @markroberge
Managing Director @Stage2Capital
Senior Lecturer, Harvard Business School
Former CRO @HubSpot

2. 2 @markroberge
When should we
begin scaling
again?

3. The Science of Re-establishing Growth
What is product-market-fit?
#1 Product-Market Fit

4. Goal of Phase
#1 Product-Market Fit
Customer Retention
Annual Revenue Retention > 100%
Annual Customer Retention > 90%
Annual Revenue Retention = [ARR (start of year) – Churn + Upgrades] / ARR (start of year)
Annual Customer Retention = [#Customers (start of year) – Customer Churn] / # Customers (start of year)
The Science of Re-establishing Growth
Customer retention is the best quantifiable measure of product-market-fit

5. Pothole Alert!
But customer retention is a lagging
indicator. We need to identify a
leading indicator to customer
retention to identify when
product-market-fit is achieved.

6. A Scientific, Data-Driven Approach to Product-Market-Fit
Define a leading indicator to customer retention
[Customer Retention Leading Indicator] is “True” if P% of customers achieve E event(s) within T
days

7. A Scientific, Data-Driven Approach to Product-Market-Fit
Industry examples of customer retention leading indicators
[Customer Retention Leading Indicator] is “True” if P% of customers achieve E event(s) within T
days
70% of customers send 2,000+ team messages in the first
30 days
85% of customers upload 1 file in 1 folder on 1 device within 1
hour
80% of customers use 5 features out of the 25 features
in the platform within 60 days

8. % of customers that achieve customer retention leading indicator by month of tenure
A Scientific, Data-Driven Approach to Product-Market-Fit
Instrument customer acquisition cohorts to identify when product-market-fit is achieved

9. A Scientific, Data-Driven Approach to Product-Market-Fit
Over time, verify the leading indicator correlates with customer retention
Strong correlation of leading indicator to customer retention
Weak correlation of leading indicator to customer retention
Evaluation of customers acquired between 12 and 18 months ago

10. Goal of Phase
#1 Product-Market Fit
Customer Retention
The Science of Re-establishing Growth
A scientific, data-driven approach to product-market-fit
[Product-Market-Fit] = “Yes”
if ([Customer Retention Leading Indicator] correlates with [Long Term Customer Retention]) AND
([Customer Retention Leading Indicator] is “True”)
Where [Customer Retention Leading Indicator] is “True” if P% of customers achieve E event(s) within T days

11. Goal of Phase
#1 Product-Market Fit
Customer Retention
The Science of Re-establishing Growth
What is “Go-to-Market-Fit”?
#2 Go-to-Market Fit

12. Goal of Phase
#1 Product-Market Fit
Customer Retention
The Science of Re-establishing Growth
“Go-to-Market-Fit” is acquiring and retaining customers consistently and scalably
#2 Go-to-Market Fit
Scalable Unit Economics
“Unit Economics” are also lagging indicators.
We need to understand the leading indicators to unit economics.

13. The Science of Re-establishing Growth
Defining the leading indicators to Unit Economics
LTV = Lifetime Value of a Customer
CAC = Customer Acquisition Cost
ACV = Annual Contract Value per customer
GM% = Gross Margin Percentage
SQL = Sales Qualified Lead
LTV/CAC > 3
LTV = ACV*GM% / [Annual Churn %]
CAC = [Demand Generation CAC] + [Salesperson CAC]
[Demand Generation CAC] = [Cost per SQL] / [SQL-to-Customer%]
[Salesperson CAC] = [Salesperson Monthly Cost] / [Customers Acquired per Month per Salesperson]
[Customers Acquired per Month per Salesperson] = [SQLs per Salesperson per Month] * [SQL-to-Customer%]

14. The Science of Re-establishing Growth
Defining the leading indicators to Unit Economics
(ACV*GM%/ [Annual Churn %]) / ([Cost per SQL] / [SQL-to-Customer%] + [Salesperson
Monthly Cost] / [SQLs per Salesperson per Month] * [SQL-to-Customer%]) > 3
Therefore, through relatively simple algebra, we have “go-to-market-fit” if the below equation is “True”
ACV = $20,000
GM% = 70%
Annual Churn % = 15%
Cost per SQL = $1,000
SQL-to-Customer % = 5%
Salesperson Monthly Cost = $15,000
SQLs per Salesperson per Month = 2
LTV = $93,333
Sales CAC = $7,500
Marketing CAC = $20,000
LTV/CAC = 3.4
For example…

15. The Science of Re-establishing Growth
Instrumenting the leading indicators to Unit Economics

16. Goal of Phase
#1 Product-Market Fit
Customer Retention
The Science of Re-establishing Growth
When should we scale?
When we have “product-market” and “go-to-market” fit.
#2 Go-to-Market Fit
Scalable Unit Economics

17. 17 @markroberge
How fast should
we scale?

18. Goal of Phase
The Science of Re-establishing Growth
Establish a pace. Watch the speedometer.
Growth Speedometer
#1 Product-Market Fit #2 Go-to-Market Fit #3 Growth and Moat
Experiment Scale
Customer Retention Scalable Unit Economics Revenue Growth Rate

19. The Science of Re-establishing Growth
Where should we scale?

20. The Science of Re-establishing Growth
Where should we scale?

21. The Science of Re-establishing Growth
Separate “Scale” teams from “Experiment” teams

22. Goal of Phase
#1 Product-Market Fit #2 Go-to-Market Fit #3 Growth and Moat
Learn Scale
Customer Retention Scalable Unit Economics Revenue Growth Rate
The Science of Re-establishing Growth
How to re-establish and execute growth?

23. Goal of Phase
Target Market
GTM Playbook
Compensation
Demand Gen.
Pricing
Sales Hire
Early Adopter
Personal Network + Referrals
#1 Product-Market Fit #2 Go-to-Market Fit #3 Growth and Moat
Learn Scale
Customer Retention
Win At All Cost
Solve for Customer Commitment
PM + AE
Based on Customer Retention
Scalable Unit Economics Revenue Growth Rate
The Science of Re-establishing Growth
Aligning GTM with the pursuit of Product-Market-Fit

24. Goal of Phase
Target Market
GTM Playbook
Compensation
Demand Gen.
Pricing
Sales Hire
Early Adopter
Personal Network + Referrals
Early Majority
1 Scalable, Measurable Medium
#1 Product-Market Fit #2 Go-to-Market Fit #3 Growth and Moat
Learn Scale
Customer Retention Scalable Unit Economics
Win At All Cost Codified, Scalable
Customer Retention + Unit
Economics
Solve for Customer Commitment Solve for Unit Economics
PM + AE Process Builder
Based on Customer Retention
The Science of Re-establishing Growth
Aligning GTM with the pursuit of Go-To-Market-Fit
Revenue Growth Rate

25. Goal of Phase
Target Market
GTM Playbook
Compensation
Demand Gen.
Pricing
Sales Hire
Early Adopter
Personal Network + Referrals
Early Majority
1 Scalable, Measurable Medium
#1 Product-Market Fit #2 Go-to-Market Fit #3 Growth and Moat
Learn Scale
Customer Retention Scalable Unit Economics Revenue Growth Rate
Win At All Cost Codified, Scalable
Solve for Customer Commitment Solve for Unit Economics
PM + AE Process Builder Process Executor
Add Promotion Path
Scale vs. Experiment vs. Ignore
Multiple Mediums.
Tightly Aligned with Sales.
Reinforced
Assess Disruption Risk
Customer Retention + Unit
Economics
Based on Customer Retention
The Science of Re-establishing Growth
Aligning GTM with Growth and Moat

26. Resources
https://blog.stage2.capital/science-of-scaling https://blog.stage2.capital/bottoms-up-sm-model
https://blog.stage2.capital/hiring-sales-leader https://blog.stage2.capital/

27. • Jay Simons – President, VP S&M @ Atlassian
• Lesley Young – Global Sales @ Facebook Workplace, Box
• Jed Nachman – COO, CRO @ Yelp
• Jon McNeill – Former President @Tesla, COO @Lyft
• Leela Srinivasan, CMO @ SurveyMonkey, LinkedIn, Upwork
• Emmanuelle Skala – SVP Customer Success @ Toast, Digital Ocean
• Oliver Jay – Head of Sales @ Asana, DropBox
• Sydney Sloan – CMO @ Salesloft, Jive, Adobe
• Brian Halligan – CEO @ HubSpot
• Hilary Headlee – Head of Sales Ops @ Zoom, MindBody
• Josh Allen – CRO @ Drift, CarGurus
• Carol Meyers – CMO @ Rapid7
• John Boucher – SVP @ Oracle, ServiceSource
• Kara Gilbert – Chief People Officer @ Turbonomic
• Andrew Rains – CRO @ Automotive Mastermind, VTS
• David Meerman Scott – Speaker & Best Selling Author
• Jeetu Mahtani – SVP International Sales @ HubSpot
• Lou Shipley – CEO @ Blackduck Software
• Tom Chavez – CEO @ Superset, Krux
• Bill Vellante – GM @Infor, Workday, Oracle
• Jim Norton – CRO@ Dosh, Conde Nast, AOL, Google
• Larry D’Angelo – Chief Sales Officer @ LogMeIn
• Mike Volpe – CEO @ Lola, CMO @ HubSpot
• Michael Manne – CRO @ Reonomy, Namely
• Mike McGuinness – Chief Customer Officer @ Veracode
• Mike Arntz – SVP Sales @ SmartSheet, NetSuite
VC Backed and Run by 120+ Sales, Marketing,
and Customer Success Executives

28. https://www.hbs.edu/faculty/Pages/profile.aspx?facId=869446&facInfo=pub

29. 29 @markroberge
All proceeds go to
Ayele Shakur
Build.org CEO

30. Thanks!!
Mark Roberge @markroberge
Managing Director @Stage2Capital
Senior Lecturer, Harvard Business School
Former CRO @HubSpot