Looking to de-code churn drivers? Look for hot-spots, not averages

OK, I admit it. I’m recycling parts of a blog post from 2 years ago. 

The Normal Distribution, or “Bell Curve”

Our brains are wired somehow to think of everything in terms of a Normal Distribution, aka the Bell Curve. It’s a trap that can obscure important patterns in data such as churn drivers.

The shape of the curve means that we think of populations of data (such as customers) as being a somewhat homogeneous group if only we could compute the average. For example, how many minutes per day “on average” a user spends in your product. Or, the percentage of customers “on average” who renew their subscription.

The problem is that populations of people and customers almost never behave in a normal distribution. Instead, the more prevalent pattern of behavior is a Power Law, or Pareto Distribution.

The Pareto Distribution, or “80/20 rule”

The Pareto distribution is also known as the 80/20 rule. Except that in online worlds, the ratio can be even closer to “95/5″. So, it’s very likely that the 5, 10 or 15% of your customers that churn each year actually have a lot in common, and differ materially from the “average” customer.

Let’s de-construct this. Start by asking yourself a few questions.

Is churn the same across every acquisition channel?

Probably not. You’ll see variances between channels such as outside sales, inside sales, online channels, and even within the various online campaigns that can beget customers (email marketing, search engine marketing, display advertising, etc.).

Is churn the same across every customer segment you sell into?

Probably not. If you’re B2B and you sell into various industries, then you probably have some industries with better retention than others. You will also see differences between small, medium and large customers according to their company size (their annual revenue and/or employee count). If you’re B2C and you sell into various demographics, then you probably have some demographic segments with better retention than others (think age, household members, etc.)

Is churn the same across every region you sell into?

Probably not. Different countries have different retention characteristics. Sometimes, even across different states or provinces.

Is churn the same across product usage patterns?

Probably not. There are probably usage patterns such as feature use and frequency that correspond to higher and lower churning customer segments. Also, think about usage across a population of users inside an account. Some accounts will have a couple of very engaged users and the rest no so much. Others may have widespread, evenly distributed usage. You wouldn’t see these differences if you were working with “averages”. Yet, these different segments will probably have different retention rates.

De-coding churn is about the search for “hot-spots”

The game of de-coding churn is about finding variances from the average, instead of focusing on the average itself. There’s probably a high-risk segment in your customer base that can be described by a combination of multiple factors including acquisition channel, region, customer demographics and usage pattern. Think of this example: “churn is higher in the U.S. through the reseller channel by 25%, than the regional average” If you knew that, what would you do? You’d probably have a conversation with the person who recruits and trains re-sellers in the U.S. about doing a better job of it. You just found a “hot-spot”.

So why is the search for hot-spots so hard? It requires all the data about your customers to be in one place and fully dimensionalized (think “data cube”).

For most companies, getting data into this type of structure is done using Pivot Tables, which is time-consuming, error-prone and hard to maintain. This is why Big Data and analytics systems are becoming commonplace.

Happy hunting!

“Net Churn” is a bullshit metric

TLDR: Certain subscription-based vendors have conceived of a metric called “Net Churn”. The concept seems to have originated in publicly-traded SaaS companies. It’s probably in response to Wall Street analysts’ enduring attempts to decode the underlying measures of financial health in their renewal revenue streams.

Here’s how Net Churn is calculated:

  • You take the renewal revenue pool for a given month; let’s say it’s $1,000,000 of potential revenue
  • You take the actual renewals PLUS actual up-sells/cross-sells that you get from those customers at the same time, PLUS actual cancellations value PLUS down-sells; let’s say these sum to $950,000
  • Divide actual over potential and voila! Net churn rate

By using this math, “Net Churn” can be a number as high as 95% even over 100%.

This is financial chicanery.

An organization should measure each metric separately. Each is a unique and important financial lever. Combining measures into Net Churn blurs how well (or not) you’re doing at each.

For example, in a B2B business with annual subscriptions, consider these metrics:

  • Logo churn – this is the count or rate of customers in the renewal pool who actually renew. The calculated rate is always 100% or less
  • Revenue churn – this is the count or rate of renewal revenue in the pool that was actually renewed. The calculated rate is always 100% or less
  • Cancellations – this is the count or rate of cancellations in the period between renewals
  • Base renewals – this is the count or rate of revenue from customers who renew at the same financial commitment as the prior period
  • Up-sell/cross sell – this is the additional revenue captured beyond the renewal potential
  • Downsell – this is the renewal revenue from customers who lower their financial commitment for the same product for the renewal period versus their prior level (downsell is also referred to as partial churn in some organizations)

Ideally, you measure and optimize on all levers:

  • Logo churn – useful to see churn patterns across your whole customer base
  • Revenue churn – useful to see churn especially from high-value customers
  • Cancellations – a particularly concerning outcome worth understanding in depth
  • Base renewals – useful to see the customers who are steady-state. These customers may end up as future at-risk customers because they aren’t growing
  • Up-sell – useful to see the performance of premium versions of your product offerings (if you have them). Your marketers spend a lot of time tuning your offer hierarchy and this is a great way to validate their efforts
  • Cross-sell – useful to see the performance of complementary products in your portfolio (if you have them). Many vendors diversify their product portfolio, at great expense. The payback is often measured by how well your customer base consumes the new offerings
  • Downsell – useful to see customers who lessen their commitment, which may present clues to overselling license capacity in a prior period

Leaving aside the games that are played with Wall Street analysts, focusing on these metrics individually will quickly highlight several drivers of financial performance. Each metric reflects a unique opportunity to drive revenue growth.

The 4 dimensions of product usage

There are six ways to increase revenue with product usage data:

  • increase revenue by increasing trial conversions
  • increase revenue by spotting up-sell opportunities
  • reduce churn by spotting declining usage
  • increase revenue by finding and mobilizing evangelists
  • increase revenue by improving usability
  • increase revenue by increasing prices

Collecting and understanding each of the four dimensions of product usage enables us to maximize the revenue increases that result from this analysis.

Product usage dimension one: frequency

This is the easy one.  How often does a user engage your application?  Think “logins per week”.

Declining frequency, or no use at all, is a strong signal for churn risk.

And that’s about it.  Don’t be tempted to infer too much from this one metric, such as “active use = satisfied user”.  You’re covering the basics of measuring any use versus no use.

Dimension two: feature usage

Now we’re getting to the juicy stuff.  Imagine all of the features and functions in your application.

  • At a global level, which features are used the most?  Which are not used at all?
  • And for a specific user, which features do they use?
  • And for a group of users inside a business customer of yours, what’s their feature usage pattern as a group?  Is the product widely adopted, or only by one person?

To get started, consider the granularity of what you’re trying to measure.  Don’t go too deep at first by measuring every button action in your user interface.  Otherwise you get a list of dozens or even hundreds of unique actions.  Which is too much data and introduces “noise” into your analyses.  Even powerful statistical analysis will yield inconclusive findings if too many unique actions are thrown into the mix.

Instead, start with a list of 3-6 high-level features.  Where “feature” is a fairly high level concept like “uploaded a photo” or “shared with someone else” or “created a new project”.  You can always refine your approach to get to 10-20 features in your list.  You might even go beyond that once you master how to interpret and act on the data.

Remember: start with a few features to measure and grow only when it suits your needs.

Dimension three: configuration

Many products, especially business-to-business products, are highly configurable.  Even in a consumer app, users can configure many settings like notifications.

The act of configuring an app beyond its out-of-the-box settings is a form of user investment in your app.  And with that investment can come loyalty.

Just like feature usage, the goal is to get an understanding of the patterns of configuration settings that correspond to loyal users.

Looking at patterns on configuration settings also helps you spot new segments of users, and areas of the user experience you might want to optimize.

Dimension four: volumetrics

Volumetrics means the degree to which your app is used.  For example, you’re the vendor of an online project management app.  How many projects, over a period of time, does a user run in your app?  How many projects in total does a given customer run?

Another example: you’re the vendor of an online backup/sync/share app.  How many files does a user upload, and of what size?

Most apps have 1-3 primary data objects worth measuring.  The greater the volumetric usage, the greater the dependency of the user on your app.

Like measurement of features and configuration settings, volumetric measures can also lead to insights about user segmentation and user experience issues.


We’ve reviewed the four dimensions of product measurement: frequency, features, configurations and volumetrics.  We’ve talked about the revenue insights that can be gained, such as trial conversions, churn prevention and up-sells.

Customer analytics | 8 data sources you can use now

Customer analytics for SaaS companies is about reducing churn and increasing lifetime value of your existing customers.  While every company looks at acquisition metrics, SaaS vendors must do more because the customer lifecycle begins at the point of web conversion.

A customer that enrolls in a trial or subscription is beginning their relationship with your company. However, your SaaS offering will need to be constantly delivering the value your customer is seeking or you will fail at customer retention.

How do you know if you’re meeting customer expectations?

There is a recent body of research called behavioral economics.  In a nutshell, it states that what a customer does, versus what they say, is a truer measure of their satisfaction and intent. This means that a customer who says they are happy in a survey might not actually be happy.  Even worse is the customer who never tells you they’re unhappy, and they simply churn.

Customer analytics unlocks the power of this research by enabling SaaS vendors to understand their customers’ actions, focusing on product usage combined with everything else you can know.

How to get started?  Make an inventory of all of the data you have about your customers, and ways it can be used.

  • Traffic and volume metrics: Attributing customers to the channel that they came through will enable you to find patterns of valuable or less valuable customers.
  • Product usage: Logins, modules used, processes run, usage frequency.  This tells if your customers are using your product and what parts of it they are engaging with.
  • Sales CRM: (when you have a sales-assisted acquisition model)
  • Support CRM: Issues your customers needed help with. Use it to spot your customer’s frustration with your business.
  • Purchase & product plans: What your customer purchased and how much they paid.  Identifies customer tiers, potential up sells, upcoming renewals and payment status.
  • NPS ® (Net Promoter Score ℠): Surveys taken on a subset of your customers to gauge satisfaction.  Allows your customers to tell you qualitatively vs. quantitatively if they are happy with your service.
  • User comments: In emails, on support forums, or even Tweets. A second data source of customer sentiment that can be tied with NPS and Support CRM.
  • Customer intelligence: Key changes within your customer’s organization that could signal potential changes in the stability of your relationship.

Most SaaS companies review metrics from each of these sources independently.  Customer Analytics happens when we analyze our customers’ data across multiple touch points.   This reveals valuable insights you would have no way identifying if the data remained apart.

Look across all of your customer data, and consider what it means across the lifecycle.  The result? Better products and increased revenue.