Subscribe and Thrive: Key Metrics for Modeling SaaS Businesses

Software-as-a-service (SaaS) has exploded in popularity over the past decade. The model offers companies recurring subscription revenue and valuable customer insights. However, the metrics behind SaaS businesses require specialized expertise to analyze.

When evaluating or forecasting SaaS performance, core metrics like customer acquisition cost (CAC), lifetime value (LTV), and churn rate prove pivotal. Grasping how investors calculate and apply these KPIs unlocks sharper analysis.

This guide examines essential techniques for modeling SaaS metrics to boost comprehension.

You’ll learn:

• Definitions of key SaaS metrics and formulas
• Real-world examples calculating CAC, LTV, and churn
• Setting baseline assumptions and ranges
• Evaluating SaaS health using metric combinations
• Benchmarking metrics against peers
• Forecasting growth and profitability scenarios</li>

Let’s dive into the crucial KPIs at the heart of SaaS modeling.

The Evolution of SaaS Metrics

The metrics used to track SaaS company performance have evolved over time. In the early 2000s, top-line revenue growth dominated analysis. Meeting subscriber addition targets mattered most.

But as the SaaS market matured, investors dug deeper. Growth at any cost lost appeal. Profitable unit economics moved center stage.

This shift put metrics like CAC, LTV, and churn in the spotlight. With SaaS adoption accelerating across industries, competition meant scaling efficiently became crucial.

Quantifying customer economics highlighted improvement opportunities. Investors pushed SaaS leaders to optimize underperforming metrics through R&D, sales methodology enhancements, better onboarding, expanded features, and improved retention practices.

In essence, SaaS metrics underwent a revolution matching the software revolution, transforming the business itself. Understanding this history provides helpful context for modeling the sector.

Customer Acquisition Cost (CAC)

CAC represents the sales and marketing dollars spent to land each new customer. Lower CAC means more efficient customer growth. The formula is:

CAC = Total Sales & Marketing Costs / New Customers

For example, if a company spent $2 million last year on sales and marketing and added 10,000 new customers, their CAC would be:

$2,000,000 / 10,000 = $200

When modeling CAC, scrutinize trends over the past 2-3 years. Expanding markets may spur rising CAC if competition heats up. Segment CAC by channel also – direct sales bring pricier customers than inbound.

SaaS investors want to see CAC declining or stable over time. Spiking CAC that outpaces new customer signals growth challenges ahead.

Churn Rate

The churn rate shows the percentage of customers lost each year. It quantifies customer loyalty. For example, a 10% annual churn means 10% of customers cancel their subscriptions. The formula is simply:

Churn Rate = Customers Lost / Total Customers

A SaaS company with 100,000 customers that lost 12,000 over the past year would calculate their churn rate as follows:

12,000 / 100,000 = 12%

Forecasting churn requires studying retention drivers like customer satisfaction, product stickiness, and switching costs. Assess survey feedback, online reviews, support tickets, and exit interviews.

High churn above 15% signals customers are dissatisfied or leaving for alternatives. Targeting churn below 10% demonstrates retention success.

Why Investors Scrutinize Churn

Of all SaaS metrics, churn draws intense investor focus. Why does this loyalty indicator matter so much?

In a recurring revenue business, acquiring customers is only half the battle – retaining them is equally important. Churn quantifies retention risk.

High churn diminishes lifetime customer value. It pressures acquisition costs higher over time to replace lost accounts. Lower churn, in contrast, maximizes LTV.

SaaS switching costs also play a key role. Churn risk compounds if customers don’t face major hurdles leaving for competitors. Companies must perpetually win customers again and again through superior user experiences and value.

Investor fixation on churn makes sense, given these dynamics. Benchmarking churn versus peers quickly conveys retention capabilities and competitive positioning.

When forecasting or modeling a SaaS business, churn assumptions carry enormous weight. Evaluating churn trends offers clues to future success.

The SaaS Cohort Analysis

To deepen SaaS modeling skills, savvy analysts apply cohort analysis. This examines each new customer class over time.

For example, plot the 2020 subscriber cohort revenue contribution and churn rate each subsequent year. Then, do the same for the 2021, 2022, and 2023 cohorts.

This reveals how new cohorts mature. Are early retention rates consistent? Does churn stabilize or deteriorate as cohorts age?

Cohorts may also be segmented by customer size, vertical, geography, or other attributes. For instance, comparing enterprise versus SMB cohort retention often shows a stark contrast.

Incorporating cohort analysis into SaaS modeling provides richer insights. The granular view highlights where churn risk concentrates – whether in young cohorts or older ones.

Tracking cohort trajectories gives executives data to support targeted retention programs. It also helps analysts make accurate forecasts reflective of real churn dynamics.

Customer Lifetime Value (LTV)

LTV represents a customer’s revenue value over their lifespan with the company. This helps assess whether CAC pays off long-term. The formula is:

LTV = Gross Margin x Average Customer Lifespan / Churn Rate

Imagine a SaaS company with 80% gross margins, 5-year average customer lifespan, and 10% annual churn. Their LTV would be:

0.80 x 5 years / 10% = $32

Healthy SaaS businesses target LTV 3-5x higher than CAC – meaning customer value exceeds acquisition costs.

Modeling LTV requires estimating:

• Gross margin per customer
• Average customer lifespan
• Churn rate

Compare your LTV assumptions to historical ratios. For example, actual lifespan may exceed your estimate if attrition is lower than expected.

Setting Baseline SaaS Assumptions

Creating sensible baselines for metrics ensures realistic models. Consider these best practice assumption ranges:

• CAC: $150-$500+ depending on average deal size
• Gross Margin: 60-90%+ for pure SaaS
• Customer Lifespan: 3-7 years on average
• Churn Rate: 10-15% annually in healthy businesses
• LTV: 3-5x CAC as a savings target

Research peer benchmarks to inform baselines. For example, survey public SaaS leaders on their LTV/CAC ratios. Apply industry-specific assumptions when possible.

Of course, improving these metrics over time is key. For instance, extending average lifespan through retention efforts lifts LTV. Adjust inputs to model upside scenarios.

Evaluating Overall Health

Looking at metrics together gauges overall SaaS health. For instance, high LTV combined with low churn suggests loyal, valuable customers.

Common evaluations include:

• LTV/CAC Ratio – Shows return on acquisition spend
• Gross Margin – Measures profitability potential
• Net Margin – Factors in operating costs like R&D
• Customer Additions – Monitors growth

For example, expanding new customer adds at reasonable CAC and rising LTV signals an attractive growth profile. Review trends in combination.

Benchmarking Key Metrics

Comparing SaaS KPIs versus peers indicates competitive positioning. For instance, review public peers to identify leaders and laggards for:

• CAC efficiency – Lower is better
• LTV levels – Higher is better
• Gross margin – Leader/laggard spread
• Churn rate – Leaders retain customers better

This highlights areas where the company excels or trails competitors. Use benchmarking to set goals and model improvement scenarios.

Forecasting Growth

With key metrics in hand, model future performance. For example, how do changes in LTV or CAC flow through the projections?

• Higher LTV – Boosts lifetime customer value
• Lower CAC – Increases new customer adds
• Reduced Churn – Lengthens average lifespan

Model upside by optimizing metrics individually and then in combination. This forecasts how successfully improving SaaS KPIs may accelerate growth at target scale and margins.

Putting SaaS Metrics into Action

With a solid grasp of key SaaS metrics under your belt, how can finance professionals apply this knowledge day-to-day? Here are some impactful ways to put these KPIs into action:

• Build models incorporating baseline SaaS metrics – Create forecast models leveraging industry benchmark assumption ranges. Then, stress test upside and downside scenarios by fluctuating inputs. This illuminates how tweaks to metrics like lowered churn or improved LTV may accelerate growth.
• Calculate SaaS metrics for your company – Audit marketing expenses and new customer adds to determine current CAC. Estimate gross margin, churn, and lifetimes to quantify LTV. Compare to benchmarks and set improvement goals.
• Evaluate acquisition channels – Break down CAC by channel, like direct sales vs. website conversions. Identify the most efficient sources of new customers. Double down on those providing the best ratio of new customers to marketing spend.
• Present findings to leadership – Share recommendations on improving metrics that trail competitor averages. Outline specific actions tied to optimizations. Demonstrate how advancing KPIs translates to faster growth at scale.

Build Your Metric Fluency

Getting a handle on SaaS metrics elevates financial modeling capabilities. CAC, LTV, churn rate, gross margin, and customer adds offer immense insights into performance.

Mastering SaaS key performance indicators enables sharper forecasting. Set informed baselines leveraging benchmarks. Estimate improvement potential through scenario modeling.

Combined with executive team inputs on growth drivers, rigorous SaaS metrics analysis informs strategy and planning. The numbers tell the story – so make sure you speak their language.