Revenue analysis: Methods, metrics, and how to act on them

What is revenue analysis?

Revenue analysis means studying revenue growth analytics, which is the practice of examining a business's income streams to gain insights for increasing profitability. 

While revenue analysis dives into the why and how behind revenue figures to inform strategic decisions, revenue reporting primarily focuses on presenting a summary of past revenue data. Revenue forecasting uses historical data and trends to predict future revenue.  

Here are some use cases of revenue analysis:

• For SaaS businesses, it’s analyzing subscription data to understand churn and optimize pricing tiers.  
• For e-commerce businesses, it means identifying best-selling products and customer segments to improve marketing campaigns.  
• For subscription businesses, it’s evaluating the impact of coupons and upgrades on revenue.  

Why does revenue analysis matter?

Revenue analysis aligns revenue generation with pricing strategies and product development by revealing which products or services drive the most income and how customer behavior responds to different pricing models. 

It helps spot revenue leaks by pinpointing areas like high churn rates or underpriced offerings. Furthermore, revenue analysis enables the identification of power users — those highly engaged customers with significant lifetime value — and uncovers options for revenue expansion through upsells, cross-sells, or new market segments. 

Note: For SaaS firms, revenue analysis is crucial for understanding the performance of different AI pricing models and using strategies like competitive pricing. Analyzing which SaaS pricing models to use also helps give you a potential competitive edge.

Revenue analysis methods

To effectively understand and improve financial performance, firms use several revenue analysis methods. The revenue analytics definition encompasses these various approaches, each offering unique insights into revenue generation and customer behavior.

Cohort-based revenue analysis

Cohort-based revenue analysis studies the revenue generated by groups of users acquired within a specific time frame (a cohort) over their customer lifecycle. This method helps understand how customer value evolves and identify trends in acquisition quality and long-term retention.

Example: A SaaS company analyzes its January cohort of new subscribers. By tracking their spending each month, the company can see how average revenue per user changes over time for this specific group. 

This reveals if initial promotions are effective in attracting valuable long-term customers or if churn within the cohort is higher than expected.

Product-line or plan-based analysis

This type of revenue analysis focuses on the revenue contribution of different product lines or subscription plans. It helps spot top-performing offerings, understand customer preferences, and inform decisions about product development, pricing, and marketing focus.

Example: An e-commerce business analyzes revenue generated by its clothing, electronics, and home goods categories. The analysis might reveal that while electronics have a higher average order value, the clothing line has a higher volume of sales and a more consistent revenue stream. 

For a subscription business, a plan-based analysis would compare the revenue generated by basic, pro, and enterprise tiers, highlighting which plans are most popular and profitable.

Customer segment analysis

Customer segment analysis means dividing customers into distinct groups based on shared characteristics (e.g., demographics, behavior, industry) and then analyzing the revenue generated by each segment. 

This approach allows for targeted marketing, customized product offerings, and a better understanding of which customer groups are most valuable.

Example: A B2B software company segments its customers by company size (SMB, mid-market, enterprise). Revenue analysis by segment might show that while enterprise clients contribute the highest overall revenue, the SMB segment has the fastest growth rate, indicating a significant future opportunity.

Revenue per product or feature analysis 

Analyzing how individual products or features contribute to revenue helps identify which parts of your offering drive sales and which may need revision.

Example: Analyzing uptake and revenue from a newly released AI tool vs. a core platform feature.

Expansion vs. contraction analysis

Analyzing changes in revenue from existing customers highlights growth and potential losses:

• Expansion revenue: It’s the extra revenue generated from existing customers. Examples include upselling them to higher-tier plans or cross-selling additional products or features.  
• Contraction revenue: This is the revenue lost from existing customers. Examples include downgrades to lower-tier plans or a reduction in the usage of a usage-based service.  

One-time vs. Recurring vs. Usage-based revenue analysis

Understanding the nature of your revenue streams provides insights into stability and growth potential:

• One-time revenue: This is revenue generated from a single, non-repeating transaction.  An example is the purchase of a perpetual software license.
• Recurring revenue: This is predictable revenue generated at regular intervals.  Examples include monthly or annual subscription fees.  
• Usage-based revenue: This is revenue directly tied to the consumption of a service. An example is billing based on the number of API calls made or data consumed.

Usage-based vs. Flat-rate revenue tracking

Revenue analysis metrics

Revenue analysis relies on various key performance indicators (KPIs) to measure financial performance and the effectiveness of revenue-generating activities. Let’s zoom in on a few:

• Monthly recurring revenue (MRR): The predictable revenue a subscription-based business expects to receive every month.
• Annual recurring revenue (ARR): The predictable revenue a subscription-based business expects to receive every year (MRR multiplied by 12).
• Customer lifetime value (LTV): The total revenue a business can expect from a single customer account throughout the entire business relationship.
• Churn-adjusted growth: A measure of revenue growth that takes into account revenue lost from customer churn, providing a more accurate picture of net growth.

How to analyze revenue growth (with examples)

Analyzing revenue growth involves looking at trends over time and understanding the contributions of both new and existing customers. Key aspects to monitor include the overall growth rate, the consistency of growth, and the relative impact of acquiring new customers versus expanding revenue from the existing base. 

Net new revenue reflects sales to first-time customers, while expansion revenue comes from upselling, cross-selling, or increased usage by current customers.  

Let’s look at some examples.

Revenue spike from product bundling

A company might implement a product bundling strategy, offering a discount for purchasing multiple products together.  

Example: An e-commerce retailer introduces a "Summer Fun Bundle" including a popular swimsuit, sunscreen, and a beach towel at a discounted price compared to buying each item separately. Analyzing revenue data after the bundle launch could reveal a spike in overall revenue during that period. 

Further analysis might show which individual products within the bundle experienced increased sales and the average order value for customers purchasing the bundle versus individual items. This helps assess the efficacy of the bundling strategy in driving short-term revenue growth.  

Usage-based growth from premium-tier customers

For businesses with usage-based pricing, analyzing the behavior of premium-tier customers can reveal significant growth drivers.

Example: A cloud storage provider offers different pricing tiers based on storage limits and features. Revenue analysis might show that customers on the "Enterprise" tier, who pay based on the amount of data stored and the number of users, have significantly raised their usage over the past quarter. 

This increased usage directly translates to higher revenue. Analyzing the specific activities driving this growth (e.g., large project uploads) can inform strategies for nurturing these high-value customers and potentially upselling other customers to similar usage levels.  

SaaS revenue growth analysis

Analyzing revenue growth often involves looking at the net change in MRR or ARR, considering both new subscriptions and changes in existing subscriptions (upgrades, downgrades, churn).  

Example: A SaaS company analyzes its quarterly revenue growth. The report shows a 15% increase in MRR. Further investigation reveals that this growth is composed of 10% from new customer acquisition, 8% from existing customers upgrading to higher-priced plans, and a 3% loss due to customer churn. 

This detailed revenue analysis provides a clear picture of the drivers behind the growth and highlights the impact of churn, informing strategies for customer acquisition and retention.

Common revenue analysis mistakes

Effective revenue analysis demands a strategic and comprehensive approach. Overlooking key aspects can lead to flawed interpretations and misguided actions. Here are common pitfalls in revenue analysis and practical solutions to address them.

Looking only at top-line revenue

While total revenue indicates the scale of sales, it offers a limited perspective on the financial health and sustainability of a business. A high top-line figure can mask issues like low profitability, unsustainable user acquisition costs, or reliance on low-margin products.

Revenue growth without considering profitability can be misleading. For instance, aggressive discounting might inflate revenue but erode margins. High churn rates can necessitate constant new customer acquisition just to keep the top line, which is not a sustainable growth strategy.

Solution: Implement multi-dimensional revenue analysis. Go beyond the top line by consistently tracking and analyzing:

• Gross profit margin: Understand the profitability of your core offerings by factoring in the direct costs of goods or services.
• Net profit margin: Gain a holistic view of profitability after all operating expenses, interest, and taxes.
• Customer acquisition cost (CAC): Determine the cost-effectiveness of your customer acquisition efforts by comparing it to customer lifetime value (LTV).
• Revenue by customer segment: Understand the profitability and growth potential of different customer groups.

Ignoring pricing model influence

Different pricing models inherently generate revenue in distinct ways and influence key metrics. Analyzing revenue without considering the specific dynamics of your pricing model can lead to inaccurate benchmarks and ineffective strategies.

A SaaS business with a freemium model will have a different revenue growth trajectory and key metrics to monitor compared to one with a purely subscription-based model. 

Usage-based models require close monitoring of consumption patterns, while tiered models necessitate understanding the movement of customers between tiers.

Solution: Segment your revenue analysis based on your distinct pricing models. For each model, focus on relevant metrics:

• Subscription: Track MRR, ARR, churn rate, customer retention rate, and upgrade/downgrade patterns.
• Usage-based: Monitor consumption trends, identify high-usage customers, and analyze the correlation between usage and revenue.
• Tiered: Analyze customer distribution across tiers, upgrade/downgrade rates between tiers, and the revenue contribution of each tier.
• Freemium: Track conversion rates from free to paid tiers, the lifetime value of paying customers acquired through the free tier, and the cost of supporting free users.

Not tracking usage trends over time

For firms with usage-based or tiered pricing, customer usage patterns are leading indicators of both revenue growth potential and churn. Ignoring these trends deprives you of valuable insights into user engagement and the long-term health of your revenue streams.

A sudden drop in usage for a previously high-usage customer in a usage-based model could signal dissatisfaction or an intent to churn. Conversely, consistent growth in usage might indicate an opportunity for upselling to a higher tier.

Solution: Implement robust usage tracking and analysis tools. Visualize usage trends over different timeframes (weekly, monthly, quarterly) for various customer segments and pricing tiers. Identify:

• Growing usage: Proactively engage these customers to understand their needs and explore upselling opportunities.
• Declining usage: Investigate the reasons for the decrease and implement strategies to re-engage or address potential issues.
• Feature adoption: Analyze which features are most and least used by different customer segments to inform product development and customer onboarding.

Manual data exports instead of real-time metrics

Relying on manual data collection and spreadsheet-based analysis is inefficient and time-consuming — it also introduces the risk of human error and delays the availability of critical revenue insights. This lag in information can hinder your ability to react quickly to market changes or emerging trends.

By the time manually compiled reports are generated, the data might already be outdated, leading to decisions based on a historical rather than a current understanding of the business. Plus, manual processes often lack the granularity needed for in-depth analysis.

Solution: Invest in and integrate revenue analytics software and tools that provide real-time dashboards and automated reporting. These solutions should:

• Centralize data: Connect to various data sources (CRM, billing, marketing automation) to provide a unified view of revenue data.
• Automate reporting: Generate key revenue metrics and reports automatically, eliminating manual effort and reducing errors.
• Offer granular analysis: Enable slicing and dicing of revenue data by various dimensions (customer segment, product, time period, pricing model) for deeper insights.
• Provide actionable insights: Highlight trends, anomalies, and opportunities for revenue growth and optimization.

Using Orb for real-time revenue visibility

Orb helps SaaS and GenAI companies unlock their usage data, enabling flexible pricing, seamless billing, and faster growth, without the limitations of rigid billing systems. Gaining real-time visibility into your revenue streams is crucial for making timely and impactful decisions. 

Here’s how Orb provides that clarity:

• Unified data source: Orb RevGraph acts as a single source of truth by ingesting all your raw event data. It also combines usage data with pricing logic to eliminate data silos, ensure all revenue-related insights are based on a consistent and accurate foundation, and provide a holistic view of your revenue in real time.
• Real-time usage monitoring: With Orb, you can monitor customer usage in real time. This immediate feedback loop allows you to see the direct impact of usage on potential revenue as it happens, allowing engagement with high-value or at-risk customers.
• Agile pricing experimentation: Use Orb Simulations to test and refine pricing strategies using historical usage data, without impacting your live environment. Accurately forecast the revenue impact of pricing models before implementation, allowing you to choose the most effective approach for revenue growth.
• Instantaneous revenue calculations: Orb's platform provides precise and auditable calculations in real time. This means you have an up-to-the-minute account of earned revenue, eliminating delays of batch processing or manual calculations.
• Built-in revenue analytics: Orb provides built-in revenue analytics and reporting capabilities. These tools allow you to visualize key revenue metrics, spot trends, and gain quick insights into your revenue performance across various customer segments, pricing tiers, and periods.
• Customer usage dashboards: Orb offers customer usage dashboards that match invoice data. Transparency allows both you and your customers to see exactly how usage translates into billing in real time, fostering trust and providing clear visibility into revenue drivers.
• Tech stack integration: Orb fits into your existing financial stack by easily integrating with other tools like payment gateways and accounting software. This ensures a smooth flow of real-time revenue data across your critical systems, providing a complete and up-to-date financial overview.

Move beyond static reporting and gain real-time revenue visibility. Explore our platform and flexible pricing plans ideal for your business.