Subscription Analytics

What is Subscription Analytics?

Subscription analytics is the process of collecting, analyzing, and interpreting data related to a company’s subscriber base. It’s an essential piece of the puzzle for any subscription-based business, helping them track revenue, subscriber churn, retention, and business growth.

Subscription analytics covers a range of data points, from basic customer information like age, gender, and location to subscription-specific metrics such as monthly revenue or average order size. Companies use this information to improve their customer experience, understand their revenue and financial health, and make data-driven decisions.

For companies to succeed in the modern subscription economy, they need up-to-the-minute data that steers them in the right direction. Most of the time, subscription analytics is an automated, software-enabled process that helps companies accomplish that goal. It allows companies to look at their data in real-time — something manual processes have a hard time doing.

Synonyms

• Real-time subscription analytics
• Subscriber data analytics
• Subscription metrics

Importance of Subscription Analytics for SaaS Businesses

Like all subscription businesses, SaaS companies monetize almost entirely through recurring revenue. In other words, practically every facet of the SaaS business model (and how its success is communicated) revolves around its subscribers.

Measure Business Performance

Whether it’s tracking monthly active users (MAU) or calculating customer lifetime value (CLV), subscription analytics helps SaaS companies measure their performance over time.

Subscription analytics get everyone on the same page when it comes to busienss performance:

• Sales, marketing, and customer success teams depend on subscription analytics to assess their campaigns and refine their strategies.
• Product teams rely on accurate representations of the subscriber base to measure product performance, identify user trends, and create new features.
• CFOs and accountants prepare financial reports, monitor cash flow, and forecast future revenue using current and historical subscription revenue data.
• Executives need accurate metrics to make informed decisions about the direction of the company.
• Prospective investors use data insights when evaluating potential investments in SaaS businesses.
• Current investors look to subscription metrics to understand how the company is using their money (and whether or not it’s working).

Without analytics, there would quite literally be nothing to measure the success of a SaaS business. It’d be a complete guessing game.

Enhance User Experience

Since SaaS products are often deployed across entire divisions or companies, user experience is an essential factor for any SaaS customer. And they’re more likely to share a bad experience than a good one — nearly 50% of customers would tell others about a product with poor UX, compared to fewer (44%) who would mention a great one.

Subscription analytics helps SaaS companies understand exactly how users interact with their products, identify areas of improvement, and customize the user experience to fit each customer’s needs.

For instance, by analyzing subscriber usage and engagement, product teams identify which features customers love the most (or not so much). Over time, this valuable data empowers product teams to make updates and product changes that closely align with customer needs.

Understand Customer Needs

Talking to customers is the best way to understand what they’re looking for. The next best way is to look at their behavior.

By analyzing customer data, SaaS companies can uncover customer needs they might have initially glossed over. This allows them to target their marketing and sales efforts more effectively, create better content, and develop features that customers actually want.

Subscription analytics also provides a wealth of insight into customer segmentation and product differentiation. This makes it easier to create targeted campaigns that cater to specific customer segments (like millennial customers or enterprise clients).

Monitor Financial Health

Subscription analytics is the life jacket for SaaS companies navigating through a sea of competition. By diving deep into metrics like average revenue per user (ARPU) and churn rate, teams can quickly identify any financial concerns and steer their strategies toward lasting profitability.

On a macro level, software vendors also rely on subscriber data to understand their net revenue retention (NRR). When revenue is stable or growing within a company’s existing subscriber base, it shows they’re creating valuable, customer-centric products.

Identify Upsell and Cross-Sell Opportunities

Upselling and cross-selling are some of the best (and most sustainable) ways to grow revenue.

• They don’t require any customer acquisition effort, so they’re automatically more profitable than acquiring new customers.
• Existing customers already understand the value of the product — there’s less convincing involved on the sales side of things.
• When buyers are already familiarized with a SaaS product, a company has a 60% to 70% success rate when selling them (selling to a new customer yields between 5% and 20%).
• Value-added products and services improve the customer experience and help SaaS customers further optimize their workflows.

Thus, effective upsells/cross-sells are two of the only ways to achieve NRR greater than 100%.

This is huge for SaaS companies. When they can achieve healthy internal revenue growth, they have an easier time fundraising, expanding their product offerings, solidifying their product-market fit, and expanding into new territories.

Optimize Pricing

Price optimization isn’t an exact science. Most SaaS companies find it puzzling. And only 6% of SaaS companies have carried out sophisticated pricing research for their products.

Most SaaS companies use flat-rate pricing but also offer usage-based pricing of some kind (e.g., seat-based, pay-as-you-go).

Subscription analytics account for all the intricacies in SaaS pricing models and help revenue leaders understand the full financial picture. By crunching data from hundreds or thousands of customers, pricing teams can accurately identify optimal price points for different price tiers to maximize their revenue potential.

Understand Customer Churn

Subscription churn tells SaaS companies a lot. Most importantly, it helps them understand why some customers leave.

Involuntary churn accounts for between 20% and 40% of all SaaS churn. And most of it is preventable — though to do so, they rely on analytics telling them how many customers have failed payments or missed subscription renewals.

Analytics can also help SaaS teams identify problems customers face that cause them to cancel their subscriptions (things like lack of product understanding or difficulty using the interface). With the right data, customer success teams can intervene before a customer leaves and save potentially lucrative relationships.

Score Leads and Deals

Lead scoring (and, in a broader sense, deal scoring) rely heavily on data. Machine learning models, in particular, need a reliable source of information to accurately score leads/deals and make optimal recommendations for sales and marketing teams.

Subscription analytics are the main source of insights for lead/deal scoring models. Segment-specific understanding of CLV, ARPU, and several other metrics make it easy for sellers to understand who to prioritize and when to abandon an unresponsive prospect.

Inform High-Level Decision Making

Organizational leaders and company execs need actionable insights to make high-level decisions. But if the people at the top don’t get accurate data from their analytics tools, those decisions are misinformed at best. The first thing to suffer is top-line revenue.

With subscription analytics dashboards, every data point tells a story. It’s easily communicable and helps everyone in the boardroom stay on the same page.

Metrics Tracked by Subscription Analytics

Subscription metrics are the main source of data for a subscription analytics platform. Let’s take a look at the most important ones.

Monthly Recurring Revenue (MRR)

Monthly recurring revenue (MRR) is the most simple, straightforward, and necessary subscription metric. It’s calculated by adding all the revenue from current subscriptions (excluding one-time fees) for a given month.

MRR gives companies a snapshot of their current revenue and gives them a reliable way to track their growth. It’s also the basis of trends analyses, YoY comparisons, and benchmarking.

Annual Recurring Revenue (ARR)

Annual recurring revenue (ARR) is like MRR, but for the whole year. It’s for bigger-picture decision-making and forecasting — things like understanding customer lifetime value (CLV), creating product roadmaps, and evaluating the effectiveness of marketing and sales tactics.

At any given time, ARR is a more accurate representation of a company’s revenue stability since it factors in seasonality and demand fluctuations. MRR reflects short-term movements, progress toward a larger revenue goal.

Average Revenue Per User (ARPU)

Average revenue per user (ARPU) is one of the most crucial metrics for any business selling subscription services. It tells them how much money the typical customer spends (ideally per segment).

This helps them:

• Set sales quotas
• Understand what it takes to grow to $X MRR
• Score deals
• Identify the most valuable customers

Customer Lifetime Value (CLV)

Like ARPU, customer lifetime value (CLV) is a per-customer metric. It’s the estimated revenue each customer will generate over their lifetime.

Since customers who stay longer are more valuable (on average) than those who leave early, CLV helps companies understand how to optimize their customer experience and better retain clients. Also, understanding which types of customers have higher CLV lets them prioritize those prospects for sales and marketing teams.

To improve CLV, subscription companies can focus on retention efforts, expanding offerings through upsells/cross-sells, and creating usage and adoption strategies when onboarding new customers.

Customer Acquisition Cost (CAC)

Customer acquisition cost (CAC) accounts for all the costs associated with obtaining a single customer — marketing, sales, time and resources spent on onboarding.

To achieve a positive ROI, a business needs to retain its customers long enough to pay back the CAC (and then some). That’s why CAC is such a powerful metric, especially when it’s paired with CLV. Together, the two give subscription businesses a complete context of their customer lifecycle.

Churn Rate/Retention Rate

Churn rate (and its inverse, customer retention rate) tells companies how often their customers are leaving. It’s a key metric that helps them understand customer behavior, optimize product-market fit, and craft better user experiences to reduce churn.

By understanding customer churn by segment or usage, companies can identify which customer groups are having the most success with their product and how to better retain the rest.

Growth Efficiency/Magic Number

The Growth Efficiency Index (GEI) — also known as the Magic Number — measures the cost of earning $1 in net ARR. A GEI of 1 means that for every dollar gained, a company spends another. So in this case, their CAC and CLV are the same.

A lower Magic Number is better, since it means the company spends less money to acquire and retain customers. In those instances, the company would have achieved revenue optimization.

Lead Velocity Rate (LVR)

Lead velocity is a simple measure of sales efficiency. It describes the speed at which leads move through the sales funnel. Businesses calculate it by measuring how many new leads enter the funnel every month versus how many reach the end.

Lead velocity rate (LVR) is useful for understanding a company’s sales growth, tracking lead pipeline health, and optimizing outreach efforts. It also helps companies compare their performance to industry benchmarks and set appropriate goals.

Subscriber Return on Investment (sROI)

When examining every metric, subscription analytics should always come back to profitability. Subscriber return on investment (sROI) is the metric for that. It measures how much money a company earned versus how much they spent per subscriber, quantifying an individual customer’s value.

Unlike other metrics, sROI looks at all stages of the customer journey, including onboarding and customer success efforts. If a company is losing money on its customers, it has a negative sROI (and vice versa).

Methods to Improve Subscription Analytics

Customer Segmentation

Customer segmentation is a key piece of subscription analytics. It helps companies break down data by customer, usage, product type, and other identifiers to identify patterns and draw insights that can be used to drive decision-making.

Having the right segmentation criteria will make it easier to spot trends in churn rate, CLV, MRR/ARR, and other metrics. With well-defined segments, companies can target their efforts more responsibly and measure the effectiveness of their efforts.

Cohort Analysis

Cohort analysis — i.e., analyzing groups of customers based on if/when they joined a service — is helpful for understanding subscriber retention. It’s also useful when evaluating certain aspects of the customer lifecycle, such as onboarding and feature adoption.

By looking at cohorts over time, companies can measure customer loyalty and satisfaction rather than individual user preferences or behaviors. This gives them a broader view of the customer journey that can be used to inform product and marketing decisions.

A/B Testing

A/B testing is a great way to measure the effectiveness of changes made in response to data-driven insights. It helps companies quantify outcomes from various efforts and adjust them accordingly.

For example, if they want to optimize onboarding flows, they can test two different digital onboarding processes. When enough new customers adopt the product faster using one or the other, the organization has its answer.

Dashboards & Reports

Using dashboards and reports is essential for tracking subscription analytics in real-time (and getting everyone in the company to understand them). Tracking all relevant KPIs as well as key trends and patterns helps companies spot opportunities or issues as they arise.

Dashboards and reports should be tailored to the company’s needs and provide access to both raw data and actionable insights. That way, teams can track progress over time and make data-driven decisions quickly and confidently.

Trends in Subscription Analytics Software

Automation

Subscription analytics is a lot easier with automation. With it, businesses can run reports without manual intervention or delay, giving them an up-to-date view of their data — which is invaluable for long-term decision-making.

With automated reporting, businesses can also get early warnings on trends and avoid revenue leakage. The data can be used to trigger customer messages or create tailored campaigns — features that are hard to do manually.

These days, just about every subscription analytics platform has automation built into it.

Integration

Integrations with other software are also essential for subscription analytics. It’s what makes data gathering easier and more accurate. Going from manual to automated reports is a huge step forward, but it’s only as good as the underlying data.

Nearly every modern analytics solution can pull in data from multiple sources, like billing systems, customer communication tools, and help desk apps. This gives businesses a more holistic view of their operations and customers — essential for better decision-making.

Analytics-as-a-Service

Analytics-as-a-Service (AaaS) is a cloud system that provides subscription analytics on-demand. AaaS gives businesses access to their data, no matter where it’s stored — whether it’s in databases or spreadsheets, or hosted by other third-party services.

AaaS is fast and offers more flexibility than traditional software solutions. Plus, since the service is cloud-based, businesses don’t have

Advanced Data Visualization and Predictive Analytics

BI tools have come a long way in recent years, and subscription analytics are past the point of relying on simple bar charts and pie charts. Advanced data visualization tools offer much more granular insights — such as customer segmentation, churn analysis, and usage trends over time.

Thanks to AI/machine learning, subscription analytics are also getting better at predicting subscriber behavior. Spotting at-risk customers, scoring deals, and running personalization campaigns are all becoming considerably easier with modern subscription analytics platforms.

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