6. Analyzing SaaS Performance

Introduction: 

Analyzing performance in a SaaS company requires a structured approach to track customer behavior, measure retention, and identify opportunities for growth. This analysis is critical for driving key metrics such as ARR, NRR, and churn while providing actionable insights for Customer Success (CS), Product, and Revenue teams.

1. Growth Accounting Formula: Understanding Inflows and Outflows

Growth Accounting is a widely used framework for forecasting and analyzing trends. It operates on the principles of inflows and outflows, making it applicable across various base metrics. By mapping these metrics across different stages, it becomes easier to visualise the net change by stacking all inflows and outflows over two time periods.

Once this net change is understood, the next step is to identify the underlying drivers of these changes. This analysis typically begins with two key approaches:

1. Dimensions: Examining specific attributes such as geography, customer segments, or product lines.
2. Cohorts: Analyzing groups of customers based on shared characteristics, like their onboarding date or initial usage patterns.

This structured approach provides a clear and actionable understanding of performance trends, helping to guide strategic decisions effectively.

1. Customer Segmentation: Tailoring Engagement Strategies

What Is Customer Segmentation?

Customer segmentation groups customers based on attributes like industry, geography, ARR, or usage patterns to deliver tailored strategies for retention and growth. It is very likely each CSM book of business is segmented based on multiple attributes for example customer with MRR > 1500 and MRR < 10000 for companies based in the Benelux. 

Why It Matters:

• Ensures high-touch engagement for enterprise accounts.
• Scales low-touch engagement for SMBs effectively.

Example:

• Enterprise customers receive QBRs and dedicated CSMs.
• SMB customers receive automated check-ins and self-service resources.

2. Cohort Analysis: Understanding Customer Behavior Over Time

What Is Cohort Analysis?

Cohort analysis groups customers based on shared attributes (e.g., signup date, product usage patterns) and tracks their performance over time. This approach highlights trends and patterns within specific customer segments, providing deeper insights into retention and churn dynamics.

Why It Matters:

• Identifies specific patterns within customer groups.
• Evaluates the success of new features, campaigns, or onboarding processes.
• Enables tailored engagement strategies for each cohort.

Example:

• Cohort 1: Customers sign ups in July 2022 have a 7% conversion rate after 3 months.
• Cohort 2: Customers sign ups in October 2022 have a 5% conversion rate after 3 months.
• Cohort 3: Customers sign ups in January 2023 have a 3.8 % conversion rate after 3 months

Example causes:

• It could be signalling an issue with the onboarding process,
• Bad quality of leads
• Adding more features to the product with big UI changes adding complexity to the product

2. Churn Analysis in SaaS: Understanding, Tracking, and Minimizing Churn

Churn analysis is a critical process for SaaS businesses to understand why customers leave, how much revenue is lost, and what can be done to improve retention. In SaaS, churn is typically defined as the percentage of customers or revenue lost over a specific period due to cancellations or downgrades. However, it’s more than just a metric—it’s a diagnostic tool that offers actionable insights to refine products, enhance engagement, and optimize customer acquisition strategies.

Why Churn Analysis Is Essential?

1. Revenue Protection:
   Churn directly impacts your bottom line, representing not just lost revenue but also the cost of replacing churned customers. It’s more cost-effective to retain an existing customer than to acquire a new one.
2. Customer Insights:
   A robust churn analysis can uncover opportunities to address customer pain points, improve service engagement, and tailor offerings to meet customer needs better.
3. Strategic Decision-Making:
   By segmenting churn data, businesses can refine acquisition strategies, improve onboarding, and make targeted investments in customer success efforts.

Types of Churn in SaaS:

To analyze churn effectively, it’s important to distinguish between its different forms:

1. Customer Churn:
   The percentage of customers lost during a specific period. This metric provides a high-level view of overall customer retention.
2. Revenue Churn:
   The percentage of revenue lost during a specific period due to cancellations or downgrades. It highlights the financial impact of churn.
3. Involuntary Churn:
   Customers lost due to failed payment methods, such as expired credit cards. While seemingly minor, this type of churn can significantly add up if not addressed.
4. Voluntary Churn:
   Customers who choose to cancel their subscriptions due to dissatisfaction, lack of perceived value, or better alternatives. Voluntary churn often offers the richest insights for improvement.
5. Downgrade Churn:
   The percentage of customers who reduce their spending by moving to lower-priced tiers.
6. Good Churn:
   Customers who were not a good fit for the product and leave voluntarily. While not desirable, it helps streamline the customer base toward those with a stronger product-market fit.

How Churn Analysis Helps SaaS Businesses:

Churn analysis empowers businesses to:

• Support Customers at Key Moments:
  If customers frequently churn after a specific time (e.g., three months), the business can proactively engage them during this critical period.
• Refine Products and Services:
  By analyzing churn patterns, businesses can identify areas for improvement, such as feature gaps or usability issues.
• Optimize Customer Acquisition Strategies:
  Segmenting churn by acquisition source can reveal which channels bring in the stickiest customers, helping refine marketing efforts.

3. Actionable Churn Analysis Techniques:

1. Behavioral Cohort Analysis

Groups customers based on specific behaviors, such as feature usage or engagement levels. This approach helps identify patterns among the most and least engaged customers.

Why Use It?

• Understand how behavior correlates with retention.
• Highlight areas to focus on for improving engagement.

Example:

You can see the engagement rate of various time-based cohorts (February 2018) over a period of time (7 days). Essentially, the engagement rate dwindles as the days pass, but the February 22 cohort gets to 0% at the Day 7 mark, while all of the February 28 cohort leaves after the first day. You can triangulate these findings with other data to uncover insights behind this difference in engagement rate progression.

Once the main factors contributing to the churn have been understood and isolated, corrective actions to follow for example faster better onboarding process to help reduce customer time to value.

2. Acquisition Cohort Analysis

Segments customers by when they signed up, canceled, or renewed.

Why Use It?

• Pinpoint churn trends over time.
• Assess onboarding effectiveness for specific acquisition cohorts.

3. Subscription Plan Analysis

Analyzes churn rates by subscription tier.

Why Use It?

• Determine whether certain tiers are underperforming in retention.
• Optimize pricing or feature offerings for low-retention plans.

4. Billing Interval Analysis

Compares churn between customers on monthly vs. annual billing cycles.

Why Use It?

• Identify whether customers on shorter billing cycles are more likely to churn.

5. Key Metrics to Track in Churn Analysis

1. Service Engagement:
   Declining engagement is a strong indicator of future churn. Tracking feature usage and frequency of interactions can help identify at-risk customers early.
2. Customer Service Interactions:
   Monitor changes in support engagement. A lack of engagement could mean customers are either fully satisfied or entirely disengaged.
3. Competitor Pricing Trends:
   Pricing changes from competitors can trigger churn, making it essential to track market dynamics.

6. Practical Application for CSMs:

CSMs can leverage cohort analysis to refine their engagement strategies and address specific challenges faced by different customer segments. Here’s how:

1. Identify High-Risk Cohorts:

• Use cohort analysis to pinpoint groups with lower retention rates or higher churn.
• Tailor engagement efforts for these customers, such as offering additional training or proactive check-ins.

2. Measure the Impact of Engagement Strategies:

• Track cohorts before and after implementing new engagement strategies (e.g., personalized onboarding or automated health check-ins).
• Analyze whether retention improves in subsequent cohorts as a result.

3. Refine Onboarding and Adoption Processes:

• Evaluate how quickly customers in different cohorts achieve key milestones, such as completing onboarding or adopting core features.
• Focus on reducing time-to-value for cohorts with slower adoption rates.

4. The Land-and-Expand Model: A SaaS Growth Strategy

The land-and-expand model is a popular growth strategy in SaaS businesses. It focuses on acquiring customers ('land') and increasing their value over time through upselling and cross-selling ('expand'). This approach maximizes the value of existing accounts and drives predictable, sustainable growth.

1. How the Land-and-Expand Model Works:

1. The Land Phase: Acquiring Customers

The goal of the land phase is to secure initial customer adoption and establish a foothold in the organization. This involves:

• Efficient Onboarding: Ensuring customers achieve time-to-value quickly.
• Building Trust: Demonstrating the product’s value and reliability early.
• Targeting Entry Points: Often starting with smaller teams or limited product licenses.

Example:

A SaaS company sells its project management software to a 50-person marketing team in a larger enterprise. The initial contract is for 10 users, generating $12,000 ARR at $100/user/month.

2. The Expand Phase: Growing Customer Value

In the expand phase, the focus shifts to increasing the account’s revenue potential through:

• Upselling: Encouraging customers to purchase higher-tier plans, add-on features, or premium services.
• Cross-Selling: Promoting complementary products that enhance the value of the primary solution.
• Wider Adoption: Expanding usage across other teams, departments, or regions within the organization.

Example:

After a successful onboarding experience, the marketing team adds 20 more users, increasing the ARR to $36,000. Six months later, the sales and HR teams adopt the platform, further increasing ARR to $60,000.

2. Benefits of the Land-and-Expand Model

• Revenue Efficiency: Expansion revenue (upselling and cross-selling) is typically more cost-effective than acquiring new customers.
• Customer Stickiness: Expanding within an account increases product usage and dependency, reducing churn risk.
• Predictable Growth: A strong land-and-expand strategy drives Net Revenue Retention (NRR) above 100%, a critical metric for SaaS success.

3. Key Metrics in the Land-and-Expand Model

1. Expansion ARR: Tracks additional revenue generated from existing customers through upsells and cross-sells.
- Example: A customer initially worth $10,000 ARR grows to $25,000 ARR after adding features and licenses.

2. Net Revenue Retention (NRR): Measures the overall revenue retained and expanded from existing customers.
- Formula: NRR = (Starting ARR + Expansion ARR - Churned ARR) / Starting ARR × 100

3. Customer Lifetime Value (CLV): Reflects the total revenue a customer generates over their lifetime. A successful expand strategy significantly boosts CLV.

3. Using Cohort Analysis with ARR in the SaaS Model:

Cohort analysis is a powerful tool for visualizing and understanding how Annual Recurring Revenue (ARR) evolves within a SaaS business. By combining ARR metrics with cohort segmentation, companies can track the performance of customer groups over time, highlighting the success of the Land-and-Expand motion. This model represents the “landing” of new customers and their subsequent “expanding” as they adopt more features, increase usage, or add licenses.

When older and newer cohorts consistently show growth in ARR, it’s a strong indicator of accelerating revenue and healthy customer retention. This trend is often depicted in a layered growth chart, commonly featured in SaaS companies’ S-1 filings when preparing to go public. Below is slack’s ARR cohort visual demonstrating not just steady growth but the compounding value of successful expansion strategies across cohorts, driving long-term scalability and profitability.

4. Challenges in the Land-and-Expand Model

While effective, the land-and-expand model has its challenges:

1.  Customer Engagement: Requires consistent and meaningful engagement from the CS team to maintain satisfaction and identify opportunities.
2.  Sales and CS Alignment: Clear communication and collaboration are essential to ensure seamless transitions from land to expand.
3.  Measuring Success: Tracking expansion revenue and ensuring it aligns with broader company goals requires robust systems and processes.

5. The Role of Customer Success in Land-and-Expand

CSMs are pivotal to the success of the expand phase:

• Identifying Expansion Opportunities: Use data-driven insights to pinpoint customers ready for upgrades or cross-sells.
• Driving Adoption: Ensure customers fully adopt and realize the value of the product, making expansion a natural next step.
• Collaborating with Sales: Work closely with Account Executives to strategize expansion plays and align on targets.

Example:

A CSM notices a customer with high engagement but limited feature adoption. By proposing a training session on advanced features, the CSM helps the customer upgrade to a higher-tier plan, contributing to an additional $5,000 ARR.

Key Takeaways for SaaS Companies

1. Focus on Early Wins: Deliver exceptional value in the land phase to set the stage for expansion.
2. Invest in Tools: Use analytics and customer health scoring tools to identify expansion opportunities.
3. Align Teams: Foster strong collaboration between Customer Success, Sales, and Product teams.