What is subscription analytics?
Subscription analytics is the process of gathering, studying, and making sense of information about a business’s subscribers. It’s an essential part of any subscription-based business because it helps them keep track of sales, lost subscribers, new subscribers, and business growth.
Many types of data can be used in subscription analytics, from general customer information like age, gender, and location to metrics that are specific to subscriptions like monthly revenue or average order size. Businesses use this data to make choices based on facts, improve the customer experience, and learn more about their sales and finances.
Modern subscription economies need up-to-date data pointing them in the right direction for businesses to succeed. A lot of the time, subscription analytics is a software-based method that helps businesses reach their goals. It lets businesses see their data right away, which is hard to do with manual methods.
Synonyms
- Real-time subscription analytics
- Subscriber data analytics
- Subscription metrics
Why subscription analytics are essential for SaaS companies
Like all subscription businesses, SaaS companies make most of their money from repeated sales. Almost every part of the SaaS business model (and how its success is talked about) is based on its users.
Check how well your business is doing.
With subscription data, SaaS companies can track monthly active users (MAU) and determine customer lifetime value (CLV). This helps them see how well they’re doing over time.
When it comes to business success, subscription analytics make sure that everyone is on the same page.
- Subscription analytics help the sales, marketing, and customer success teams evaluate their efforts and improve their plans.
- Product teams need accurate representations of the subscriber base to measure product success, find user trends, and make new features.
- CFOs and accountants use current and historical subscription income data to make financial reports, keep an eye on cash flow, and guess how much money the business will make.
- Executives need accurate metrics to see where the company is going and make intelligent choices.
- Potential funders look at data insights when deciding whether to invest in SaaS businesses.
- Right now, investors look at subscription data to see how the business spends their money and if it’s working.
Without data, a SaaS business would have no way to know how successful it is. It would be impossible to know.
Improve the experience of users.
The user experience is significant for all SaaS customers because whole departments or companies often use these products. And they’re more likely to talk about a bad experience than a good one—nearly half of customers would tell others about a product with bad UX, but only 44% would talk about a great one.
SaaS businesses can use subscription analytics to find exactly how users interact with their products, find ways to improve them and tailor the user experience to each customer’s needs.
For example, product teams find out which benefits customers love (or don’t love) most by looking at how subscribers use and interact with the service. Over time, this helpful information gives product teams the power to make changes and updates that align with customers’ wants.
Know what your customers want.
For the most accurate picture of what people want, talk to them. Seeing how they act is the next best thing.
SaaS companies can find out about customer wants they might have missed by looking at customer data. This helps them focus their sales and marketing efforts more precisely, make better content, and add features that customers want.
Subscription data also gives you a lot of information about dividing customers into groups and making products stand out. This makes it easier to make ads specifically aimed at groups of customers, like millennials or business clients.
Keep an eye on your finances.
When there is a lot of competition in the SaaS market, subscription data can help companies stay afloat. By looking closely at measures like churn rate and average revenue per user (ARPU), teams can quickly spot any money problems and make changes to their plans that will help them make money in the long term.
For the big picture, software companies also use user data to figure out their net revenue retention (NRR). When a company’s current subscribers bring in the same amount of money or more, they make good products people want.
Find chances to cross-sell and up-sell
Some of the best and most long-lasting ways to make more money are to upsell and cross-sell.
- They don’t need any work to get new customers, so they’re instantly more profitable than getting new ones.
- Customers who already have the product know how valuable it is, so pushing them to repurchase it doesn’t take as much work.
- A company has a 60% to 70% success rate when selling a SaaS product to someone who already knows about it. When selling to a new customer, the success rate is only 5% to 20%.
- Products and services that add value improve the customer experience and help SaaS users improve their workflows.
So, upsells and cross-sells that work are two of the only ways to get an NRR above 100%.
SaaS businesses need to know this a lot. When they have healthy internal revenue growth, it’s easier for them to raise money, give more products, ensure they are a good fit for the market, and move into new areas.
Improve pricing
Getting the best price isn’t a precise science. It makes sense to most SaaS businesses. And only 6% of SaaS companies have thoroughly studied how much to charge for their products.
Most SaaS companies give flat rates and pricing based on usage, such as pay-as-you-go or seat-based pricing.
Subscription analytics considers all the different ways that SaaS prices things and helps people in charge of income see the big picture of the business’s finances. By analyzing data from hundreds or thousands of customers, pricing teams can correctly find the best price points for each price tier to make the most money.
Learn About Customer Loss
A lot can be learned from subscription churn. Most importantly, it helps them figure out why some people leave.
About 20% to 40% of all SaaS users leave without choosing to. Most of it can be avoided, but they need data about how many customers have failed to pay or missed contract renewals to do that.
Also, analytics can help SaaS teams figure out what issues customers have with their subscriptions that make them want to quit, like not understanding the product or having trouble with the interface. With the correct information, customer success teams can step in before a customer leaves to keep a connection that could turn into a profitable one.
Get deals and leads.
Data is a big part of both lead scoring and deal scoring in the broader sense. For machine learning models to correctly score leads and deals and make the best suggestions for sales and marketing teams, they need a reliable source of information.
Models that score leads and deals get most of their information from subscription data. Knowing CLV, ARPU, and other data for each segment makes it easy for sellers to decide who to focus on and when to give up on a prospect who isn’t responding.
Help people make big decisions.
To make big choices, business executives and leaders of organizations need insights they can implement. But if the people at the top don’t get correct information from their analytics tools, then, at best, the decisions they make are wrong. Top-line sales are the first thing that will go down.
Every piece of data in subscription analytics platforms has a story to tell. It helps everyone in the meeting stay on the same page and is easy to discuss.
Subscription Analytics Keeps Track of These Metrics
One of the main ways that a subscription analytics tool gets data is through subscription metrics. Let us look at the most important ones.
Recurring monthly income (MRR)
The most basic, straightforward, and essential subscription metric is monthly recurring income (MRR). It’s found by adding all the money made from payments (not one-time fees) in a given month.
MRR is a good way for businesses to keep track of their growth and get a quick picture of their present revenue. It’s also used to look at trends, compare years, and set benchmarks.
Annual Recurring Income (ARR)
This is like MRR but for the whole year. It’s called annual recurring revenue (ARR). It’s used to make decisions and predictions about the bigger picture, like figuring out customer lifetime value (CLV), making product roadmaps, and checking how well marketing and sales strategies work.
At any given time, ARR is a more accurate measure of how stable a company’s income is because it considers seasonality and changes in demand. MRR shows short-term changes and progress toward a bigger income goal.
The average amount of money made per user
One of the most important numbers for any business that sells subscription services is the average income per user (ARPU). It tells them how much money an average customer spends, ideally for each group.
It helps them:
- Set goals for sales
- Know what you need to do to reach $X MRR
- Get deals
- Figure out which customers are the most important.
Value of a customer over their lifetime
The customer lifetime value (CLV) is also a measure based on a single customer. It’s an estimate of how much each customer will bring in over their lives.
CLV helps businesses determine how to improve the customer experience and keep customers longer because customers who stay longer are usually more helpful than customers who leave early. If they know what kinds of customers have a higher CLV, they can also tell the sales and marketing teams to focus on those possibilities.
To increase CLV, subscription businesses can focus on keeping customers, adding more products through upsells and cross-sells, and developing ways for new customers to use and accept their services.
Cost to Get a New Customer (CAC)
Customer acquisition cost, or CAC, is the total amount spent on marketing, sales, and the time and resources needed to bring a new customer on board.
To get a good ROI, a company needs to keep people long enough to cover its acquisition cost (and then some). That’s why CAC is so helpful, especially when combined with CLV. These two combine to give subscription businesses a complete picture of their customer journey.
Rates of Churn and Retention
The customer retention rate, the opposite of the churn rate, shows how often people leave a business. It’s a critical measure that helps them determine how customers act, ensure the product fits the market, and improve user experiences to keep customers from leaving.
By looking at customer churn by section or usage, businesses can determine which groups of people are doing best with their product and how to keep the rest.
Magic Number or Growth Efficiency
The Magic Number, or Growth Efficiency Index (GEI), shows how much it costs to make $1 in net ARR. A GEI of 1 means a business spends as much as it earns. This means that their CLV and CAC are the same.
The business pays less to get and keep customers if the Magic Number is low. That’s when the business would have reached revenue efficiency.
Speed of the lead (LVR)
Lead velocity is an easy way to determine how well sales are going. It talks about how quickly leads move through the sales process. To figure it out, businesses compare the number of new leads each month to the number of leads that make it to the end of the funnel.
Lead velocity rate (LVR) helps you figure out how fast a company is growing its sales, keep an eye on the health of its lead flow, and make the most of your outreach efforts. It also helps businesses set goals and compare their success to standards in their field.
Return on investment for subscribers (ROI)
Membership analytics should always come back to making money when they look at all the metrics. The way to measure this is by subscriber return on investment (ROI). It compares how much money a business makes to how much it spends on each user, which figures out how valuable each customer is.
Unlike other metrics, sROI looks at the whole customer path, from getting them to buying something to ensuring they are happy. You can see that a business has a negative SRROI if it is losing money on its users.
Ways to Make Subscription Analytics Better
Divide customers into groups.
A big part of subscription data is dividing customers into groups. Companies can divide data into groups based on customer, usage, product type, and other factors to find trends and learn things to help them make decisions.
It is essential to have the proper segmentation criteria to see changes in churn rate, CLV, MRR/ARR, and other metrics. Companies can more accurately focus their efforts and see how well they work if they have clear segments.
Analysis of Cohorts
Cohort analysis, which looks at groups of customers based on when and if they started a service, can help you figure out how to keep subscribers. It can also be used to look at specific parts of the customer journey, like onboarding and feature adoption.
By tracking groups of customers over time, businesses can find out how loyal and happy their customers are without looking at each user’s tastes or actions. This gives them a bigger picture of the customer’s journey, which they can use to make decisions about products and marketing.
Tests A and B
An excellent way to find out how well changes made based on data-driven ideas work is to use A/B testing. It helps businesses determine how many attempts are working and make changes as needed.
For instance, if they want to improve how onboarding works, they can try two different digital onboarding methods. When enough new customers use either one to get the goods faster, the company knows what to do.
Reports and dashboards
You need to use dashboards and reports to keep track of subscription data in real-time and ensure everyone in the company understands them. Companies can see problems or chances as they arise by monitoring all the critical KPIs and trends and patterns.
Dashboards and reports should be made to fit the needs of the business and give users access to raw data and information that can be used immediately. Teams can then see how things are going over time and make quick, sure choices based on data.
Changes in software for subscription analytics
Automating Things
Automation makes it a lot easier to do subscription statistics. Businesses can run reports without waiting for someone to do it or do it themselves. This gives them an up-to-date view of their data, which is very helpful for making long-term decisions.
Businesses can also get early warnings about trends and stop losing money with automated reports. The information can be used to send personalized messages to customers or make campaigns just right for them, which is hard to do by hand.
Almost all subscription data platforms today have automation built right in.
Fitting in
It’s also essential for subscription analytics that it works with other tools. It’s what makes getting information faster and more accurate. Moving from paper reports to automatic ones is a big step forward, but the data the reports are based on is still very important.
Almost all current analytics tools can get data from multiple sources, such as help desk apps, billing systems, and tools for communicating with customers. This helps companies make better decisions by giving them a bigger picture of their customers and processes.
As-a-Service Analytics
Analytics-as-a-Service (AaaS) is a cloud-based service that lets you subscribe to analytics and use them whenever needed. AaaS lets businesses get to their data no matter where it’s kept, like in files or databases or on other companies’ servers.
AaaS is faster than traditional software options and gives you more freedom. The service is also cloud-based, so businesses don’t have to
More advanced data visualization and analytics for making predictions
Business intelligence (BI) tools have come a long way in the past few years. Simple bar charts and pie charts are not enough for subscription analytics. More advanced tools for visualizing data give you much more detailed information, like dividing customers into groups, figuring out why they leave, and seeing how their usage changes over time.
AI and machine learning are also helping subscription data get better at guessing how subscribers will act. With today’s subscription analytics tools, finding customers who might be at risk, close deals, and run personalization campaigns is much easier.
