Introducing the Lightspeed SaaS Operating Model

Unveiling the Lightspeed SaaS Operating Model, a revolutionary approach to software service delivery. This model promises to redefine the industry standards, offering a unique blend of speed, efficiency, and scalability. Prepare to explore a new frontier in SaaS operations.

Rolling up into the Summary P&L

Revenue is approximated as a % of Ending ARR

If a SaaS company is completing implementation to go live in a consistent window of time across customers and has contract terms for all their customers, Revenue should be approximately proportional to ARR.
For R&D, G&A and Other operating expenses, our model assumes a% of Revenue

A final note and extra credit (+1)

If you’re a founder of a SaaS startup in the phase of scaling your go-to-market strategy, you’ve probably thought of parts of this model before.

Hopefully, this template for SAAS operating model is a swiss army knife in your toolbox of financial planning for your startup.

Sales Expense by Segment

Row 133 – estimated cost to hire inside sales reps (Cell A133) = the average salary of an inside sales rep (Cells A134-A136) multiplied by ending reps in Row 9 (SMB), Row 21 (Mid-market), and Row 34 (Enterprise).

ARR by Segment

Input historicals in columns D-G

Forecasts of New, Expansion, and Churn ARR in columns H-W
New ARR from Trials: calculated by the number of expected trials divided by Average ACV per New Account from Trials (Row 80)
Expansion ARR based on Ending ARR (Rows 109-111) multiplied by the Upsell % in Cells A109-A111
Trial ARR: calculated based on the Churn % in cells A117-A119
Trials ARR 121-122 calculated from Churn% in Cell A121

Inside Sales Rep Productivity

This section only reflects the ending number of inside sales reps and does not include separate sections for new and churned reps.

To help plan for Inside Sales Reps who answer inbound requests from potential customers, see inside sales rep productivity.

Average ACV by Segment

Inputs drive trends of average Annual Contract Value (ACV) per Account over time.

For example, if a SaaS business is selling more into the large enterprise segment, it should see average ACV increase over time, while for a SMB business selling more to the SMB or focusing on self-service, then it should expect ACV to decrease.

Sales Expense by Segment

Model also estimates expenses to hire sales reps using average salaries.

Modeling ARR and sales rep productivity for a conventional SaaS startup

When there is no CFO in place, planning out a sales-driven ARR forecast that takes into account a mix of new and ramped sales reps with various quotas can get difficult.

To help founders think through hiring and setting quotas, a basic template for which I have shared the link below. It includes three sections
Bottoms Up Model: inputs such as sales reps, quotas, expected attainment, account growth, and other sales metrics
Top-Down Model: sales reps and quotas
Bottom-Up Model: saaS metrics and summary P&L

SaaS Metrics

Annual Net Retention Rate

Calculated by adding the last 4 quarters of Expansion ARR minus Churn ARR, then divide by Ending ARR from 4 quarters prior and add 1 to the end result.
A more accurate way to represent this metric is by averaging 12-month Net RETention by each monthly cohort. This is calculated as 1 – (Gross Logo Retention in the Quarter)⁴.

AE Rep Productivity by Segment

From the top row down, start with historical and projected headcount of Account Executives (AEs).

A segment refers to the target customer base of each type of rep, usually ranging from SMB to Mid Market to Enterprise.

AE Rep Productivity by Segment

SMB: input historical New and Churned AEs for SMB in rows 7-8, Mid Market in rows 19-10, and Enterprise in rows 32-33

Column A in Rows 11-39: inputs for expected ramp after 1, 2, 3, or 4 quarters after the AE was hired; Column D-G: historical Quota per AE and expected Productivity Factor for each segment; Column H-W: assumptions of New AEs to be hired, expected Churn of AEs, Quotas, and Productivity Factors

Inside Sales Rep Productivity

Enter the historical number of inside sales reps by Region in rows 45-47 and Trials generated per inside sales rep in rows 53-55.

In columns H-W, enter the expected number and trial expected by region and Quarterly Conversion Rate in cells H62-W62.

SaaS Metrics

A summary of how all the assumptions in the Bottoms Up Model affect your metrics

Calculated based on a proposed hiring plan and productivity goals
Helps you benchmark against companies in your industry
Inputs for future projections should follow the trend of the historical sales efficiency ratio

ARR by Segment

For a SaaS startup that sells to different segments, we include the split of ARR across segments to understand the effectiveness of the go-to-market (GTM) strategy among account types.

New, Expansion, Churn, and Total ARR per Segment and from Trials.

Average ACV by Segment

In rows 66-69, input the expected trends for each segment in columns H-W.

Rows 73-77 automatically calculate the average ACV across all accounts based on Ending ARR (Rows 125-130) divided by Ending Accounts (rows 94-98) in each segment.

Accounts

Sections for New Accounts, Cancelled Accounts, and Ending (Total) Accounts: New ARR (Section 1A) divided by Average ACV per New Account

Input the historical number of New Accounts for each segment.
Columns H-W: The expected number of new accounts is calculated as NewARR/Average ACV/New Account (section 1C).
In Rows 87-90, input the historical and project number of canceled accounts and in Rows 94-97 automatically calculating Ending Accounts by adding New and Cancelled accounts.