Mining for gold: 3 sources of product usage data

Update: the vendor landscape described here has evolved considerably since writing this. But the basic concepts still apply.

In a prior blog, I talked about ways to drive revenue growth with product usage data. Including reducing churn, spotting up-sell opportunities, increasing trial conversions etc.

In a follow-up blog, I discussed the four dimensions of product usage that you need to analyze:

  • Frequency: how often a user interacts with your product
  • Features: which features are used
  • Volumetrics: how often a feature is used, or a data record is created
  • Configuration settings: ways a user configures the app to suit their needs

By now you’re probably interested to unlock the value of product usage data for your company. Which begs the question: where do you get this data in the first place?

There are three sources of usage data. Generally speaking, usage data comes from:

  1. Clickstream data
  2. Log events
  3. Database queries

We’ll review each in succession.

Clickstream data

Clickstream data is generated by an end-user’s interaction with your product interface. For example, logging in to a web browser. Or performing an action in your mobile app.

This type of usage data can help you understand frequency of use (usually but not always) and coarse-grained feature usage, depending on how you “tag” usage events from the browser.

Several products are good sources of clickstream data for your browser-based app:

  • KISSmetrics
  • Mixpanel
  •, a popular Javascript plug-in that feeds lots of other clickstream tools

An aside: why not Google Analytics for product usage? Unlike these other tools, Google’s terms of service prohibit you from collecting user-specific data of any sort. Thus, it becomes very difficult to understand which user or even company is accessing your application and how. Stick to the products above.

Getting usage data for Mobile apps can come from several of the packages above, plus some that are purpose-built for mobile:

  • Flurry. Note that Flurry monetizes by driving ad placements, so it’s not for everyone
  • Tapstream

Pros of clickstream data

  • Easy to deploy
  • Little to no engineering team involvement
  • Good for basic engagement metrics

Cons of clickstream data

  • Not detailed enough to reveal important features and user segments
  • Brittle to maintain

Log events

Depending on how well your engineers have instrumented your server-side code, they may be generating usage data as log events. For example, your Web or App Servers might be generating Apache logs that contain details about the user’s actions, especially feature usage.

Pros of log data

  • Log events can be very specific and accurate in depicting feature usage, compared to page-level clickstream data from a browser

Cons of log data

  • Log events need to be parsed, which can be challenging if you’re doing it for yourself in a database or Excel file
  • Log events can contain types of events that aren’t meaningful to you, because they describe system behaviors not user behaviors (think error logs)
  • Your engineers need to be involved to do a good job of log instrumentation

Database queries

On the server side of your application is some sort of database (such as MySQL, Hadoop, MongoDB, etc.). Each user action may have a corresponding “transaction” or record in the database that forms a picture of usage. For example, if a user started a new “Project” in your Project Management app, then that record was created in the database at a specific time by a specific user.

These records can be queried from your database to produce events or counts of events (such as daily summaries).

Other functions of your application may behave as “set it and forget it” where your app is automating processes without requiring a user action each time. In this case, usage events are generated even though the user hasn’t logged in lately. Database queries may be the only means to collect this type of usage event.

Pros of database queries

  • The most comprehensive picture of usage

Cons of database queries

  • Your Engineering or Operations team has to retrieve the data for you


There’s no “silver bullet” to getting usage data.

In many companies, you have easy access to one type of usage data and not the others. And, no single source of usage data depicts your application’s usage in a comprehensive way.

Think of it as a journey. It’s best to get started with the data on hand. As you learn to make sense of it, and drive business results, you’re armed with the justification to get other types of usage data. Sometimes this means further instrumenting your product. In other cases, it’s simply about getting another team to help you access their data.

But the journey is well worth the effort, because usage data is the foundation of understanding the health of your customer.

6 ways to increase revenue with product usage data

TLDR: Analyzing product usage data can lead to several revenue generating opportunities. While most SaaS companies have some form of usage measurement, few have achieved a complete view of usage.

Let’s review all of the revenue levers at stake.

One: increase revenue by increasing trial conversions

Most trials convert because the user achieves rapid time-to-value, such as minutes or hours from the time you provision their trial account.

The conversion rate drops off significantly when provisioned users don’t actually engage with your product until days into the trial. In fact, most provisioned users who don’t access your application in the first few days probably don’t convert ever. So, usage data gives you an “early warning” about provisioned users who aren’t active in the first hours; you can react by stimulating their access with emails, phone calls and such.

As you master usage analytics, you’ll also discover the features that are most correlated to conversion. You can then engage trial users with a purpose: nurture them to experience the “best” features for conversion.

To summarize, the blueprint for successful conversion is about getting users to achieve rapid time-to-value, and to specifically experience the features that lead to conversion.

Two: increase revenue by spotting up-sell opportunities

Highly engaged users tend to adopt your application faster than others. In fact, the velocity of adoption is a critical measure for your business. These users are open to expansion revenue propositions and should receive attention when exhibiting “high velocity” behavior.

Even in the “average” user segment, progress toward some license limit should be the trigger to capture expansion revenue. Some examples:

  • users who have used 80% of their licensed seats
  • users who have consumed 80% of their capacity (as expressed in data volume or some other metering method)

The other important consideration is to present the up-sell opportunity at the earliest moment of need. Too often, vendors use an artificial event such as a renewal date to propose a higher-priced offering. But what if the customer had a need months ago? Selling at the moment of need is the best way to maximize this revenue stream. Pay attention to velocity and license limits as a result.

Three: reduce churn by spotting declining usage

Just as increasing adoption is a very strong positive signal, decreasing adoption is a strong negative signal. One could even argue that if usage is not growing, trouble lies ahead.

When a customer is at-risk as evidenced in usage data, the time to intervene is now. Whatever is causing customer dissatisfaction needs immediate attention. This “moment of opportunity” shouldn’t be confused with the renewals cycle. Customers can and do “churn” before their contract expires, by ceasing their usage. You’re highly unlikely to receive a renewal if you address their dissatisfaction at renewal time instead of the time they began to disengage.

Four: increase revenue by finding and mobilizing evangelists

Evangelists are a far more effective means to acquire new customers than any other means thanks to the power of recommendation and social proof. The challenge is that evangelists are often hard to spot. You have to find, enroll and activate them instead of waiting for them to naturally behave this way.

Usage data is a goldmine here. The heaviest adopters, and users of your most differentiated features, are hidden in your usage data. The programs you have to activate evangelists are super-charged when you’re identifying all of the potential recruits in usage data.

Five: increase revenue by improving usability

If we believe that adoption begets customer retention, then barriers to adoption must be systematically found and fixed. Usage data enables you to spot areas in your application that have usability issues.

Some examples of how to find usability issues in usage data:

  • No actions beyond login. Is the user having trouble getting started? Does the product have a good “first-use” mode?
  • Patterns of navigation that suggest users are getting “stuck”: a user is on the cusp of performing a task, then goes to a product help page, then goes back to the task and never successfully completes it
  • Users abandoning a guided process partway through, such a wizard. Is the process designed in a way that they can’t, or don’t want, to complete it?

Six: increase revenue by increasing prices

When you understand the correlation between usage and retention, you understand the sources of value creation in your product. Did you price your product on this basis? In other words, are you leaving money on the table by not linking your price to the features that generate value? Most organizations set prices for many other reasons than their unique selling points.

Where to go from here: getting and using product usage data

Now we have a framework for the value drivers of usage data. And they can add up to major revenue lift.

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.