Six Metrics Every Business Should Track

At RJMetrics, I’m lucky to work with smart people at successful companies to help them analyze mountains of complex data.  What I find remarkable is how many of the same metrics are consistently relevant to companies across all sizes and industries.

Today, I will explore six such metrics that are related to customer retention and loyalty. If you’re not already tracking these metrics for your business, I suggest you start. Metrics like these should be closely watched and can help inform major decisions around marketing, customer retention, product development, and more.

Preparation: Define Users and Actions

Before we start, answer these two questions: who are your users and which of their actions matter?

The first part should be easy.  Your purchasers, members, subscribers, or visitors are your company’s lifeblood.  Depending on your industry, the second part could be a bit subtler.  In e-commerce, the obvious “action” is a purchase.  In social media, that action might be a login or user interaction.  For publishers, it may be a visit or page view.  As a rule of thumb, this action should be an undeniable indicator of value; users who do it more should be more valuable to your company.

With these two definitions in place, we are ready to do some analysis.

Metric One: Engaged Users

Usually, getting more users means increasing the value of your business.  However, simply looking at “total users since the dawn of time” is never enough.  Quality outweighs quantity when it comes to building long-term value.

As we saw in our analysis of Twitter’s Data, “total users” can be a tremendous overstatement of another metric that actually means much more: “engaged users.”  This metric examines the number of distinct users who have committed an action in any given time period.  This is your real customer base, and it’s directly tied to the value of your business.  Appropriately, it’s also the population that savvy investors and acquirers will consider when determining a valuation.

It is also worthwhile to examine is how this number has changed over time and in proportion to your total user base.  What percent of your total user base (any user who has ever committed an action) came back to act again last month?  Yesterday?  How has that proportion changed over time?  The direction of that chart can indicate how the quality of your average customer is evolving.

This chart shows active customers as a percent of the total customer base. The trend starts at 100% in the company's first month, then rapidly stabilizes (with some seasonality)

Metric Two: Repeat vs. First-Time Actions

To build on our study of engaged users, we need to distinguish between user acquisition and user retention.

Imagine a social network in which users sign up, commit one action, and then never return.  If this network was able to double the number of new signups it received each month, its “engaged users” chart would actually look quite impressive.  However, a quick look at a chart of “first time actions” vs. “repeat actions” would allow us to quickly see through the façade.

If each action generates value for your company, this metric allows us to view the relative value creation from new and existing users.  If this ratio is biased toward new users, you might soon hit a wall.  If it’s biased toward existing customers, you may have already hit one.

Metric Three: Time Between Actions

Once we’ve identified a universe of “repeat users,” we can gain more insights into their behavior by studying how much time passes between the average user’s actions.

An e-commerce site might see an average value of 75 days.  A gaming company might see an average value of 15 minutes.  It can be valuable to see how this value changes over the life of your business and over the life of a given customer.

For example, looking at the “average time between first and second action” for users registered in each month of your company’s life helps you determine if you’re getting better at retaining new users.  Similarly, comparing the “average time between first and second action” to the “average time between second and third action” (and so forth) can help you determine when and how to remarket to existing users.

Here, the "next purchase number" is shown on the x-axis. For many businesses, the average time between purchases drops with each subsequent purchase.

As with all of these metrics, examine how these numbers differ by customer segments (based on anything from demographic information to behavioral tendencies to acquisition channel).  The results might cause you to act differently when working to attract and retain customers.

Metric Four: Repeat Action Probability

This metric is a study of the “action funnel.”  For each user who acted once, how many acted a second time?  A third?

I like to look at this in two ways.  The first is as a count of actions by action number, illustrating the steepness of the funnel.  The second is as a “probability” based on historical data, illustrating how each action impacts the likelihood of the next.  (Of course, you should beware of interpreting this as an actual probability if you don’t have a lot of historical data.)

Here, "purchase number" is shown on the x-axis. For many businesses, each incremental action makes a subsequent action more likely.

Many of our customers are surprised when they see their data displayed this way.  A steep drop-off from action-to-action is quite common, as is a very large increase in the repeat probability from action to action.  The take-home message: loyalty snowballs quickly, but most users never start rolling at all.

Metric Five: Customer Lifetime Value

You’ve probably heard this term before, and rightfully so.  Customer lifetime value is specific to each customer and it allows you to identify just how valuable different customer segments really are.

If your “action” is something binary like a login, the value this metric tracks may be a count of those actions.  However, if it’s an action tied directly to a value like revenue or gross margin, such as a purchase, it is likely the sum of those action’s values.

To many, customer lifetime value is more than the amount of value generated by a customer so far.  It can be expanded to include a projection of subsequent value a customer is projected to generate.  Conducting this calculation can be complex, however—I’ll leave that for another post.  For now, you can use “value generated so far” as a good proxy when comparing users with similar first action dates.

To examine this metric, calculate it for every customer and then segment those customers as you see fit.  This can be a great jumping-off point for identifying customer segments who are performing well (so you can acquire more like them) and those who are underperforming (so you can find out why and reverse the trend).

Metric Six: Cohort Analysis

If I was stuck on a desert island and could only take one chart, it would be a cohort analysis.

The cohort analysis groups users into “cohorts” based on the time period in which they committed their first action (and/or other available attributes).  Then, it charts the value of each cohort’s actions in each subsequent month of their lifetime as users.

Several cohorts are typically shown on the same chart, allowing for a layered view of how these cohorts perform in general as well as relative to one another over the lifetime of your business.

Since most businesses see a dropoff in actions after the first period and there may be huge variation in the number or value of actions from cohort to cohort, the most consumable form of a cohort analysis chart shows each data point as a percent of the first period’s value.  These charts typically exclude the first month, since by definition that value is always 100% for each cohort.

A Cohort Analysis can incorporate elements of all the other metrics discussed in this post.

For a more detailed explanation, check out my previous post on cohort analysis.  I include it here today because it’s the one chart that incorporates the valuable information explored in each of the other five metrics.


These six metrics are at the core of some of the most powerful analyses conducted by the world’s largest and most successful businesses.  Advances in technology have made them accessible to companies of all sizes, and products like RJMetrics allow businesses to monitor them with minimal effort.

While business models differ, a core objective is often the same: creating value.  Tracking these metrics can empower any company to better understand their customers, generate greater value, and increase their chances of success.

RJMetrics Feature Spotlight: Historical Currency Converter

An increasing number of our clients maintain an international customer base, and many of them accept payments in multiple currencies.

However, storing multi-currency sales figures in a backend database doesn’t always involve making on-the-spot conversions to a single currency.  This can make it difficult to summarize or compare data because not all of the sales totals are in the same currency.

As a solution to this problem, we at RJMetrics are proud to announce our new Historical Currency Converter tool. We maintain a comprehensive database of 164 currencies and their historical daily spot rates over the last 20 years.  Using this information, we can standardize any data set into a single currency, allowing for advanced apples-to-apples analysis of all data points across all time.

Since all conversions are made using the appropriate foreign exchange rate at the time of the transaction (typically a spot rate from within 24 hours), you can be sure that the output is a strong proxy for the actual, standardized revenue calculated by your accounting department.

If you’re interested in learning more about RJMetrics, check out our website where you can learn more and try out a free demo.

RJMetrics Feature Spotlight: Analysis by Age

Today, we’ll be taking a look at another RJMetrics analytical tool: dynamic age calculations.

RJMetrics can calculate the “age” of any date stored in your system, providing helpful look at how much time has passed since a particular stored date.  This can be useful in a number of situations, including:

  • Studying the age of your user base (when their birth date or birth year is collected).
  • Studying the amount of time since a particular event, such as a user’s first purchase or most recent subscription payment.
  • Examining negative ages (the time until a future event), such as a graduation date or expiration date.

For this example, let’s take a look at the fictitious company Play Now (an online gaming site).  We will build a chart by selecting the trend ‘Average User Age’ in step 1 of the chart builder, and then we’ll group the data by “referrer” in step 3.  This results in the chart below:

Average User Age by Referral Source

As you can see, the average customer’s age varies significantly based on their referral source.  Sites like are referring the youngest users, while the site “” is referring the oldest.  This information could obviously be very helpful in combination with statistics like acquisition cost by channel and conversion rate by age.  It could also be used by a marketing department to make sure the right messaging is used in each channel.

Note that exporting the data behind this chart will result in a data set shown in seconds.  This allows for the greatest possible granularity and can always be converted to other time units with some simple equations.

Export Data - Excel

If you’re interested in learning more about RJMetrics, check out our website where you can learn more and try out a free demo.

RJMetrics Feature Spotlight: Time Between Events

Today, we are happy to highlight another great RJMetrics feature: the ability to study the “Time Between Events” for any timestamped records within your company’s data set.

This feature can be used to perform a number of valuable analyses, including:

  • Studying how engagement (spending, usage, etc) varies based on different customer attributes.
  • Improving forecasting by identifying an expected time between repeat purchases.
  • Studying how certain factors (number of purchases, behavioral tendencies, geography) may impact customer or user engagement.

In the following example, we profile the famous fictional company, Vandelay Industries. We chose the trend “Average Time Between Purchases” (in step 1) and grouped by customer purchase number (in step 3).  This resulted in the output below:

Average Time Between Purchases by Purchase Number

Here, we can see that the time between purchases decreases notably with each incremental purchase.  This means that, while it may take 10 months for the average customer to make her second purchase, it only takes 8 months for her to come back and make a third.  By the time the average customer has made her tenth purchase, that delay is down to just two months!  (Of course, as you can see in other charts, the number of customers that make that many purchases is quite small as a percentage of the entire base.)

As with all RJMetrics charts, you can scroll over each data point to see the underlying value, and you can always export the data to Excel or CSV (when exporting time data to Excel all values are shown in seconds to provide the greatest possible granularity).

If you’re interested in learning more about RJMetrics, check out our website where you can learn more and try out a free demo.

RJMetrics Feature Spotlight: Jump to Step Drop-Down Menu

Originally, navigation within the RJMetrics Chart Wizard was controlled by the “Back” and “Forward” buttons at the bottom of each step.  Our “Jump to Step” menu now allows users to jump forward or backward to any step of the wizard with a single click.

This added efficiency leads to faster, easier chart editing that makes the RJMetrics experience that much better.

'Jump to Step' Drop-Down Menu

If you’re interested in learning more about RJMetrics, check out our website where you can learn more and try out a free demo.

Foursquare Outpacing Gowalla as it Approaches 2 Million Users

[This post, written by our CEO Robert J. Moore, originally appeared on TechCrunch as a guest column. You can find that post here.]

Location-based social networks Foursquare and Gowalla are accumulating users (and headlines) with impressive momentum.  While both companies have been vocal about reaching major milestones, we wanted to take a closer look at the data behind these accomplishments.

For the past four weeks, we’ve been monitoring the Foursquare and Gowalla APIs to track growth rates and sample users and venues.  This data was loaded into an RJMetrics Dashboard, which provided the results found here with just a few clicks.  We will keep these estimates up-to-date with fresh data and you can view them any time at our Startup Data page.

Here are a few highlights from our findings:

  • As of today, Foursquare has just over 1.9 Million users.  Gowalla has around 340,000.
  • At its current pace, Foursquare will surpass 2 Million users within a week.
  • Foursquare is adding almost 10x as many new users per day as Gowalla and, despite a significantly larger base, has a daily percentage growth rate that is 75% higher than Gowalla’s.
  • Currently, Foursquare has about 5.6 Million venues and Gowalla has 1.4 Million venues.
  • 1 in 3 venues on Foursquare have been checked into only once or never. That number is 1 in 4 on Gowalla.
  • The most popular venue name is “Home,” followed by national fast food chains like “McDonald’s” and “Burger King”
  • On Foursquare, men outnumber women almost 2-to-1.  Exact gender breakouts are not available for Gowalla, but the most popular first names suggest a similar distribution.

User Growth

As of today, Foursquare has just over 1.9 Million users.  Gowalla has around 340,000.

Recent new user acquisition by day for each service is shown in the chart below.

Foursquare is clearly acquiring users at a much higher rate than Gowalla, and this ratio of new Foursquare users to new Gowalla users is shown below.  It averages almost 10-to-1.

The numbers become even more interesting when you consider each company’s daily growth rate.  This is the number of new users in a given day divided by the total user population from the previous day.

Since Foursquare is growing off of a much larger base, you might expect their percentage growth to be smaller than Gowalla’s.  However, as shown below, their daily growth rate averages about 75% higher than Gowalla’s.

Venue Growth

Similar trends when we look at daily venue growth.  Currently, Foursquare has about 5.6 Million venues (or about 3 per user) and Gowalla has 1.4 Million venues (or around 4 per user).  The rate at which new venues are being added is shown below:

User Characteristics

Foursquare and Gowalla share different information about their users via the public API, revealing different types of statistics about each population.

On Foursquare:

  • 64% of users are male, 33% are female, and 3% did not specify a gender
  • 55% of users have uploaded a photo
  • 28% of users have linked their Foursquare account to their Facebook account

On Gowalla:

  • 38% of users have linked their Gowalla account to their Facebook account and 53% have linked to their Twitter account
  • 57% of users have zero friends and another 13% have only one friend

Interestingly, across both services, the five most popular first names are identical:

  • Chris
  • Michael
  • David
  • John
  • Jason

Venue Characteristics

As with users, the available data differs between the two services.

On Foursquare:

  • 18% of venues have at least one “tip” associated with them
  • 3% of venues offer “specials”
  • 32% of venues have been checked into only once or never
  • The two most used venue categories are “Home” and “Corporate/Office”

On Gowalla:

  • 25% of venues have been checked into only once or never
  • 0.5% of venues have a Twitter username associated with them

Across both services, the most popular venue names are:

  • Home
  • Subway
  • Starbucks
  • McDonald’s’
  • Burger King
  • Walgreens

How We Did It

In most cases, this level of detail wouldn’t be accessible from the outside looking in. However, Foursquare and Gowalla have a few common characteristics that made it possible:

  • Both companies use auto-incrementing ID numbers (1,2,3,4…) for both users and venues.
  • Both companies have an API that allows us to access basic user and venue information by ID number.
  • The central limit theorem tells us, among other things, that a large enough random subset of a large data set will behave like its parent set with a high degree of statistical confidence.

Our scripts tracked the maximum registered user and venue IDs each hour, along with randomly sampling data points throughout the population.  This gave us a “density factor” that so that we could adjust the absolute numbers to reflect deactivated accounts, deleted venues, and other skipped IDs.

In the end, our sample size consisted of about 82,000 data points from Foursquare and 36,000 data points from Gowalla.  As with all such analyses, the results in this report are only estimates and could be skewed by flaws in our sampling methods or unconsidered outside factors.


Both services are showing impressive growth and are accumulating moutains of valuable, fascinating data.  However, Foursquare is clearly the dominant player and their lead is increasing every day.

Be sure to keep an eye on our Startup Data page to track how these numbers progress over time.  With Foursquare approaching the 2 Million member mark, it appears that this may only be the beginning.

RJMetrics is a hosted business intelligence tool that allows online businesses to quickly and easily capture the value within their data. To learn more about how we can help your business measure, manage, and monetize better, go to and follow us on Twitter.

RJMetrics Feature Spotlight: Read-Only Users

We recently added a new level of information security to the RJMetrics dashboard: read-only users. Read-only users are created and maintained just like regular users, but they are unable to edit charts, explore data, or otherwise alter the contents of their dashboards.

Some applications of the read-only user account are:

  • 3rd parties, such as prospective investors, who are only meant to access very specific data
  • Employees with roles unrelated to data analysis
  • Board members and other parties only interested in specific, explicit reports

To avoid confusion, read-only users see the message below on all of their dashboards:

Read Only User View

If you’re interested in learning more about RJMetrics, check out our website where you can learn more and try out a free demo.

New Look for

If you hop over to you may notice a few subtle changes in the way things look. In fact, the site has spent the last several months undergoing major reconstructive surgery, and today we’re taking off the bandages. Go check it out, and drop a comment below to let us know what you think.

RJMetrics Feature Spotlight: Custom Subdomains and Logos

As an added level of customization, we now offer company-specific subdomains. RJMetrics clients can request their own custom subdomain, such as “,” through which they can access their RJMetrics dashboards.  As always, their data is still available through our main portal at

This new feature is in addition to the custom logo placement that we provide in the top-left of each company’s dashboards.  The subdomain and custom logo combine to provide a highly white-labeled solution for our clients.

Below is a customized dashboard of an employee of our favorite fictional company, Vandelay Industries.  The user accesses his dashboard through and sees his company’s logo in the top-left corner of his dashboard.

Customized Subdomain and Logo in Action

If you’re interested in learning more about RJMetrics, check out our website where you can learn more and try out a free demo.

RJMetrics Feature Spotlight: Cohort Analysis Data Perspectives

Cohort analysis is a useful data analysis technique to help view loyalty trends, predict future revenue, and monitor churn. RJMetrics users have found this technique to be very effective in quantifying the value of a company’s current customer base. When we initially rolled out the cohort analysis feature, we offered the following special data perspectives in step 5 of the RJMetrics Chart Wizard:

  • Cumulative Cohort Totals
  • Percent of First Period (Show First Period)
  • Percent of First Period (Hide First Period)

We are pleased to announce two new valuable data perspectives:

  • Average Value Per Cohort Member
  • Cumulative Average Value Per Cohort Member

When creating a cohort chart using the RJMetrics Chart Wizard, you will find the two new data perspectives at the bottom of the ‘Data Perspective’ drop down menu. For this example we are viewing data belonging to our favorite fictitious company, Vandelay Industries.

Chart Wizard Step 5 - Data Perspective Drop Down Menu

Average Value Per Cohort Member

User-defined cohorts are now viewable by the average contribution per cohort member. This gives the user the ability to normalize against the size of the cohort.

In the graph below, we can now see the average first month’s spend for a number of Vandelay’s quarterly cohorts, along with their spend in subsequent months.

In many cases, this can be used to track how newer cohorts are behaving relative to their older counterparts.

Average Value Per Cohort Member Data Perspective

Cumulative Average Value Per Cohort Member

This is the cumulative version of the perspective described above.  If the user wishes to track their customers’ average lifetime spend across several cohorts over time, this perspective can deliver that view in just a click.

The chart below shows that Vandelay’s repeat purchase rate holds steady over time, translating to extremely high loyalty per average member, despite the amount of time since their first purchase.

Cumulative Average Value Per Cohort Member Data Perspective

These types of analysis can be used to help determine acceptable customer acquisition costs and predict the long-term purchasing behavior of new customers.

Additionally, you can restrict cohorts by customer attributes such as referral source or geography to provide more detailed views of how specific sub-groups behave over time.

Check out this earlier post to learn more about cohort analysis in RJMetrics.

If you’re interested in learning more about RJMetrics, check out our website where you can learn more and try out a free demo.