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 aol.com are referring the youngest users, while the site “gamesite.com” 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.

Conclusion

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 RJMetrics.com 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 RJMetrics.com

If you hop over to RJMetrics.com 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 “company.rjmetrics.com,” through which they can access their RJMetrics dashboards.  As always, their data is still available through our main portal at https://dashboard.rjmetrics.com/.

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 https://vandelay.rjmetrics.com/ 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.

The Improbability of Garrett Wittles’ Streak

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Garrett Wittels – Improbable Streak?

Since the beginning of the 2010 NCAA Baseball season, Garrett Wittels (3B – FIU Panthers – Div. I) has had at least one hit every game. He is threatening Robin Ventura for the NCAA Div. I record 58-game streak (1987) with his current 56-game streak. If Garrett Wittels opts to return next year for his Junior season at FIU, he will have an opportunity to set the new NCAA Div. I hitting streak record.

After reading about this streak in an ESPN article, I started to wonder how improbable such a streak really was even given Wittels’ high batting average. I also was curious to see how likely he is to break Ventura’s mark of 58 straight games.  Thankfully, I work at RJMetrics where fun analyses like these are part of my job description.  I soon had some answers.

Assuming Wittels returns for the 2011 NCAA season and given the batting average derived from the start of the streak to now (.413), I calculated the following probabilities:

  • Wittels falls short of the record: 34.3%
  • Wittels ties the record: 6.6%
  • Wittels breaks the record streak: 59.1%

Below is a chart displaying the probability that the Garrett Wittels’ streak would last as many games as it has (by game number).  I used his current-season batting average and his number of at-bats in each game as the input to each statistic.  As you can see, these probabilities compound quickly, making this many consecutive games with at least one hit extremely unlikely.

Wittels' Streak Probability - Pre-Streak Perspective

Some highlights of Wittels’ hitting streak probabilities:

  • Probability of 10+ game hitting streak: 78.18%
  • Probability of 20+ game hitting streak: 34.09%
  • Probability of 30+ game hitting streak: 11.08%
  • Probability of 40+ game hitting streak: 2.45%
  • Probability of 50+ game hitting streak: .73%
  • Probability of 56+ game hitting streak: .45%

Please note that these probabilities are from the perspective of Wittels and his unique hitting profile:

  • Very high variable batting average (ranging from .401 to .600 – 34th in nation)
  • High at bats per game (ranging from 3 to 6)
  • Were calculated given Wittels’ batting average entering each game and at bat opportunities during each game (both of which are not known before a streak begins)

Forecasted Probabilities: (assuming constant hitting average of .413 and constant at bats average of 4.321)

  • Probability of 56+ game hitting streak: 100% (Joe DiMaggio – MLB)
  • Probability of 57+ game hitting streak: 90.01%
  • Probability of 58+ game hitting streak: 81.02%(Robin Ventura – Div. I)
  • Probability of 59+ game hitting streak: 72.93% (Damian Constantino – Div. III)
  • Probability of 60+ game hitting streak: 65.65%
  • Probability of 61+ game hitting streak: 59.09% (Joe DiMaggio – AAA)
  • Probability of 70+ game hitting streak: 22.92%
  • Probability of 80+ game hitting streak: 8.00%
  • Probability of 90+ game hitting streak: 2.79%
  • Probability of 100+ game hitting streak: .98%

Please note that the forecasted probabilities are calculated given that the streak has already gone through 56 games. In all likelihood, Wittels will break Robin Ventura’s 58 game streak (72.93%). In fact, there’s a 50/50 shot he stretches the streak beyond 61 games, successfully setting the hitting streak record across all levels of baseball.

Assuming we’re lucky enough for Wittels to stay for his Junior year, keep your eyes peeled for the 2011 FIU season opening series against the University of Massachusetts on February 18th-20th.