We are exhibiting at the Startup Expo at the Wharton Entrepreneurship Conference this Friday. Come by our booth to say hi and get a demo.

Jeff will also be representing us at eTail West next week.  To set up a meeting, contact him at jeff@rjmetrics.com.

<script type="text/javascript" src="http://tweetmeme.com/i/scripts/button.js"></script>

[RJMetrics makes hosted business intelligence software for online businesses.  Follow us @RJMetrics]

Let’s face it: the “Twitter is Awesome” storyline is so 2009.  The rise of the micro-blogging service is old news, and it’s clear that reporting on Twitter’s success is not driving pageviews like it used to.

As a result, reporters are treating Twitter like any other celebrity: when the good news stops selling, pray for a downfall.  If Britney Spears has taught us anything, it’s that celebrity downfalls are a media goldmine.  When you consider Twitter’s meteoric rise as one of the biggest stories of 2009, it’s no surprise that reporters would salivate over any contradictory news about the company in 2010.

How did I reach these cynical conclusions?  Well, last month I posted an updated set of Twitter data based on information from the company’s API.  This was in conjunction with a story over at MIT Technology Review.

MIT Technology Review Piece

The findings were extremely consistent with ones I published in TechCrunch last year (which came out while Twitter’s buzz was still peaking).  What was different this time was the number of news outlets who covered the report: around 200 globally by my count.  Thats about 10x more than last year’s nearly identical piece attracted.

While I considered my findings to be generally positive, what I found remarkable was how many blogs and media outlets chose to selectively reprint these findings, spin them negatively, and (in a few cases) present the data incorrectly or irresponsibly.  Almost every writer put the negative elements of the report front-and-center, making it clear to me that many media outlets are pushing a new storyline:  “Twitter isn’t so Awesome After All.”

Below are a few examples that really stood out.

Partial Quotes

Toward the end of my report, I provided five bullet points of summary findings.  They start out with what looks bad on the surface, but then dive in deeper to reach a more informed, balanced conclusion.  This piece and many others, however, manufactured a “quote” from me by taking the first three bullet points (which make things sound pretty dire for Twitter) and excluding the rest of them.  This put my words way out of context.

Where's the rest?

Fun With Ellipses

The line below is a great example of a blogger who cherry-picked the most negative sentence fragments from the report and strung them together to support his thesis.

Some bloggers boiled the report down to its most negative findings

Unfair Comparisons

This article said that “According to a study by RJMetrics Inc., only 17 percent of all Twitter users used the site in the last month, down [from] more than 70 percent in early 2007.”  This is technically true, but it omits the fact that Twitter only had 20,000 users at the start of 2007.  By the middle of 2007, that same stat was down around 20% where it has held relatively steady for over two years now.  That’s hardly a new development.

It is true that the “Tweeters by Month” number has been gradually slipping in the past few months.  Apparently, however, that alone wasn’t drastic enough a visual so this blogger added a massive downward arrow to drive the point home.  That image reappeared on about five times as many blogs as our original chart without the giant arrow.

Nice Arrow!

Sensationalizing

We were very careful to not use words like “inactive” or “abandoned” when describing Twitter accounts we profiled.  This is because our analysis didn’t factor in the many Twitter users who use the service to read Tweets exclusively, nor does it factor in dormant users who haven’t tweeted recently but may come back.  Many writers, however, weren’t so careful.  It was hard to find an article that didn’t call these accounts “abandoned” or “inactive.”

Inactive? Close enough.

Conclusion

There was a fair amount of discussion beneath most of the posts on this subject, with comments landing all over the map.  On social media blogs, readers passionately defended Twitter as “useful no matter how many people are active,” whereas general news sites attracted joyful readers saying things like “I knew it” and “Twitter is annoying!”

These strong reactions help make the picture clearer.  A negative story about Twitter is accessible to the vast majority of Americans who have heard of Twitter but aren’t Twitter users.  In a way, they validate a non-user’s decision to not bother with another fad.  This, in turn, puts Twitter users on the defensive, causing them broadcast their thoughts even more than usual.  This drives clicks.  This sells magazines.  Maybe these reporters know what they’re doing after all.

Regardless of why these stories came to be, Twitter shouldn’t worry.  They’re still adding new users like gangbusters and, if there’s one thing the American public loves more than a downfall, it’s a glorious comeback.  Look for that in 2011.

[RJMetrics makes hosted business intelligence software for online businesses.  Follow us @RJMetrics]

Coming off developing some personal projects in Ruby on Rails, the Capistrano deployment system was fresh in my mind, and had left a favorable impression on me for its versatility, ease of use, and lack of verbose XML configuration files. Although it’s written in Ruby, Capistrano is general enough to deploy just about anything – and in fact there are several guides that detail how to use it with PHP. So, I plodded ahead with it – and spent a few hours installing the Capistrano Multistage extension (to support both testing and production environments) and writing my own recipes for several of our PHP projects.

And, for all of my hard work, I continually ran into small gotchas and errors that would crop up when I switched between my OS X and Ubuntu development environments. After a day of tweaking, I couldn’t get Capistrano to act consistently, and, the more I stared at my recipes, the more I realized that I wasn’t really leveraging any of Capistrano’s power, but rather going out of my way to disable its most useful Rails-specific features. At the end of the day I was just using Capistrano and the Net::SSH Ruby library as a conduit for sending commands to remote machines – something that can be more easily accomplished via ssh.

The next morning I sat down and wrote the following bash scripts to replicate the Capistrano features that we need to deploy PHP projects from our git repositories here at RJMetrics, all in about 50 lines of code.

[deploy.sh]

#!/bin/bash
#################################################
# General deploy script.  Depends on
# environment-specific script in deploy/ dir
#################################################
set -e

APP_NAME=RJM_Web
GIT_ROOT=localhost:/repo
GIT_USER=chris
APP_REPO=$GIT_ROOT/$APP_NAME.git

# Detect exactly 1 argument
if (($# == 1)); then
    # Include .sh from the deploy folder
    DEPLOY_ENV=$1
    DEPLOY_FILE=deploy/$DEPLOY_ENV.sh
    if [ -f $DEPLOY_FILE ]; then
        source $DEPLOY_FILE
    else
        echo "Could not find deploy file for $DEPLOY_ENV environment,
                  it should be located in $DEPLOY_FILE"
        exit 1
    fi
    echo "Deploying $APP_NAME to $DEPLOY_ENV environment."
else
    echo "Usage: deploy.sh <environment-name>"
    exit 1
fi

CURRENT_DIR=$DEPLOY_PATH/$APP_NAME/current
RELEASE_NAME=`date +"%Y-%m-%d-%H%M%S"`
CURRENT_RELEASE=$DEPLOY_PATH/$APP_NAME/releases/$RELEASE_NAME

# From local machine, get hash of the head of the desired branch
# Required to checkout the branch - is there a better way to do this?
APP_HASH=`git ls-remote $APP_REPO $BRANCH | awk -F "\t" '{print $1}'`

for SERVER in ${DEPLOY_SERVER[@]}
do
    echo "Deploying on $SERVER"
    ssh -t $DEPLOY_USER@$SERVER "cd $DEPLOY_PATH/$APP_NAME/releases &&
                                 git clone -q $GIT_USER@$APP_REPO $RELEASE_NAME &&
                                 cd $RELEASE_NAME &&
                                 git checkout -q -b deploy $APP_HASH &&
                                 ln -nfs $CURRENT_RELEASE $CURRENT_DIR"
done
echo "Finished successfully"

[deploy/dev.sh]

#################################################
# Environment-specific script
# sourced by deploy.sh
#################################################
BRANCH=develop

DEPLOY_SERVER=(192.168.20.100 192.168.20.101 192.168.20.102)
DEPLOY_PATH=/home/deploy
DEPLOY_USER=deploy

The first snippet is the main deploy.sh script, which by default reads in a second, environment-specific script, like the one seen in the second snippet, from the deploy directory. So, for example, if you call

> ./deploy.sh dev

then the script will look for a second file named dev.sh in the deploy directory – analogous to the way the Capistrano Multistage extension works. Some of the conventions we use internally here at RJMetrics are implied in these scripts – e.g. we always name our git repository $APP_NAME.git, and we always deploy to a folder named $APP_NAME – these are, of course, customizable.

So there you have it – a simple, effective way to deploy PHP projects, without all of the overhead and Rails-specific functionality of Capistrano. Some features that I plan to add are roll-back functionality, and the ability to define a remote cache for each deployment server to speed up the git clone. Stay tuned.

Since my guest post on TechCrunch last fall, I’ve received frequent requests to update and expand upon my Twitter data analysis. As the Twitter API continues to improve, our ability to profile the company from the outside-looking-in becomes even stronger. I recently conducted an updated round of analysis and will be posting my findings in a series of posts here at The Metric System in the coming weeks.

Updating this analysis in RJMetrics was just a matter of a few clicks, and we could easily highlight the most interesting tidbits of new information. If you’d like that level of control of your own business’s data, you should try out our demo to learn more.

Today, I’m starting with an update of the basics: users and engagement.

Results Summary

We analyzed data through the end of 2009. Where the data overlapped with our previous analysis (which ran through August 2009), the findings were highly consistent. However, the updated data revealed the following noteworthy trends:

• Twitter ended 2009 with just over 75 million user accounts.
• The monthly rate of new user accounts peaked in July 2009 and is currently around 6.2 million new accounts per month (or 2-3 per second). This is about 20% below July’s peak rate.
• A large percentage of Twitter accounts are inactive, with about 25% of accounts having no followers and about 40% of accounts having never sent a single Tweet.
• About 80% of all Twitter users have tweeted fewer than ten times.
• Only about 17% of registered Twitter accounts sent a Tweet in December 2009, an all-time-low.
• Despite these facts, Twitter users are becoming more engaged over time when we control for sample age.

How We Did It (Again)

We are not associated with Twitter in any way and have no direct access to their backend data. However, we do have access to their API. This allowed us to take advantage of a few unique characteristics of the Twitter data set:

• A Twitter user’s activity data (tweets, followers, etc) is all public by default
• Twitter’s API allowed us to automatically download up to 20,000 data points per hour
• Twitter uses auto-incrementing ID numbers (1,2,3,4…) for both users and tweets, and the percentage of unused numbers can easily be detected via the API
• 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

We downloaded just under 2 million tweets from about 50,000 users to conduct this analysis. As before, one restriction we should note is that we were only able to download the most recent 3,200 tweets for each user. This means we may be missing part of the data for “power users” who have sent over 3,200 tweets (they represent less than a tenth of a percent of the Twitter population).

Number of Twitter Users

By leveraging the auto-incrementing ID numbers used by Twitter and our ability to predict the ratio of used to unused IDs, we identified the following data on new Twitter users by month:

As you can see, the number of new users per month is currently at about 6.2 million. This is an enormous number, but it is about 20% lower than the July 2009 peak of 7.8 million. In fact, the past 6 months have shown steady decline in the number of new account registrations.

The chart below is is a cumulative version of the chart above. It shows us the remarkable growth experienced by Twitter in 2009:

Twitter’s hockey stick continues to blaze a trail upward and to the right. As we identified above, the recent data points are showing a linear, rather than exponential, growth trajectory. Twitter ended 2009 with just over 75 million active user accounts.

Average Number of Followers

This is the area where we saw the most change from the August data. According to the data, the average Twitter user has 27 followers, down from 42 followers in August 2009. The distribution of users by follower count is shown below:

Back in August, the “0 Followers” piece of the pie was about 20%. Now it’s closer to 25%. A likely reason for the drop in average number of followers is Twitter’s rapid growth rate. A third of Twitter’s user base has joined up in the past 4 months, and we know (as shown in the chart below) that users acquire more followers the longer they are on the system.

As long as Twitter is acquiring new users at such a rapid rate compared to its average historical month, the average number of followers will be pushed downward by the “youngness” of the average user and the average number of followers will continue to drop. However, it is impossible to tell from this chart if the small stature of the 2009 bar is only due to user age or if additional variables (such as a change in user engagement levels) are at play.

To find out the truth, we will incorporate user engagement data into our analysis. A reliable way of quantifying user engagement is by studying Twitter’s most measurable user activity: tweeting.

Number of Tweets

Below we show a distribution of Twitter users by the number of status updates (“tweets”) they have ever sent.

As before, the “youngness” of the Twitter user population is surely skewing this data toward lower numbers, but even still the numbers we see are quite astounding: about 80% of all Twitter users have tweeted fewer than ten times. This is up from the 75% we detected when analyzing data through August 2009.

As you might expect, the average number of Tweets increases over a user’s lifetime. This is demonstrated below, where we show the average number of Tweets sent by the user’s join year.

Active Tweeters

Twitter has a massive number of user accounts, but we now know that a large percentage of them aren’t active. The chart below, which shows the percentage of registered accounts that sent at least one tweet each month, helps us reconcile these facts and quantify how engagement may be changing over time.

The percentage averages around 20% of accounts, although it was above 50% in Twitter’s earliest days. One concerning trend, however, is that this metric has consistently declined over the past six months, and is currently at an all-time low of 17%.

Note that, due to Twitter’s rapid user growth, the absolute number of users who tweeted that same month is probably one of the company’s highest ever, despite this all-time low percentage.

Cohort Analysis

We can use a cohort analysis to look at user behavior and loyalty over time. Below, we show five “cohorts,” each representing a specific week in Twitter’s history. A cohort consists of every user who made their first tweet in the specified week. The lines in this chart show what percentage of those users come back and tweet again in each subsequent week.

While there is a sizable usage dropoff in week 2, repeat tweeting then holds predictably steady at around 20% of the cohort. This is consistent with the findings in our previous analysis.

To take things a step further, we can define the y-axis of the chart to show the number of tweets sent across everyone in the cohort (rather than just the number of people in the cohort who sent anything). This will help show the total content contribution of the cohort in subsequent time periods. We group cohorts by month in this case:

This image alone shows us why Twitter remains a powerhouse despite the high percentage of inactive users.

First, despite the fact that only 20% of tweeters come back to tweet in their second month, those who do come back tweet so much that it makes up for all the people who left.

Second, users who joined more recently (the 2009 cohorts) tweet with far greater frequency in their first few months than their 2008 counterparts. This means that active users are actually becoming more engaged over time.

Conclusion

Analyzing Twitter is like a roller coaster ride:

• When you look at new account registrations, no one can deny that Twitter is still growing like a rocketship (that’s good).
• However, upon closer inspection, the rate of new user signups has dropped meaningfully from its peak and many new users never do anything with their accounts (that’s bad).
• Furthermore, the percentage of accounts sending out tweets has steadily declined over the past six months (that’s worse).
• However, our cohort analysis reveals tremendous loyalty and engagement from those Twitter users who stay on the system after their first week as members (that’s good).
• In fact, those users who stay become more active over time, so much so that they make up for the missing activity from those users who leave (that’s incredibly good).

And, with 75 million total accounts, an active userbase of around 20% still leaves around 15 million highly active tweeters.

The data we pulled from Twitter’s API found a good home inside the RJMetrics dashboard, and your company’s data can too. We would love to share how to capture real value from the data in your backend database using RJMetrics. Try out our online demo to learn more.

Choosing a Goal

We used Google Website Optimizer (GWO) to run the tests, and our first step was deciding on a goal.  We decided that a good goal needs to be achievable, measurable, and a meaningful step of the funnel that ends in RJMetrics making money.  We also wanted the goal to be as close to the end of the funnel where we make money as possible.

For most businesses, a sale or other revenue-generating event is an obvious goal to use.  In our case, however, prospective customers need to talk to a member of our team before making a purchase.  This means our website doesn’t generate revenue directly, leaving us no revenue-based actions to use as goal completions.  There are several reasons for this set-up, but the important thing is that it is not going to change for this A/B test.

Another goal we considered was the successful education of our visitors about our product.  Educating potential customers about RJMetrics and the problems we solve is certainly an achievable goal that drives new customer acquisition.   However, it is difficult to reliably measure. We could track how many people watch our promotional video to completion or how many pages they visit.  But these specific events are not always meaningful, and frankly I don’t really care about them.  Additionally, they are way over on the wrong side of the funnel.

A third option for our goal was the completion of our “get a free demo” form.  It’s trivially easy to measure and provides prospects with a feel for what our software can do (by analyzing Vandelay Industries’ data).  It also provides us with contact information for these now somewhat-qualified sales leads.  This places it relatively far down the funnel.  While it would be nicer to further qualify the leads before considering one a “goal completion,” that would be much more difficult to reliably monitor and would reduce our already-small sample size.

We decided that the demo form completion was the right goal for us to be optimizing for right now.

Conducting the Test

The old version of our landing page is still available at our How It Works page.   It looks like this:
The page has a Flash movie that gives an overview of our target customers, the problems they face, and how RJMetrics can help.  Below the flash movie, we have some marketing copy that gives additional color for people who would rather read than watch. We did not explicitly talk about a goal when creating the page, but if I had to describe what it does, I would say that this page gives information on RJMetrics and gives prospects a way to try out our free demo.

The winning version of the page is our new landing page.  It looks like this:

We removed the movie and marketing copy and increased the size of the form fields. We also added a description of what will happen after filling out form. The goal was to minimize the commitment required and make the reciprocity explicit. This page is unambiguous about its goal: driving demo form completion.

The Results

We did a total of 3,519 trials.  The original version yielded a conversion rate of 2.1%, and the winning yielded a conversion rate of 6.1%, providing an improvement of over 190%.

We learned a few valuable lessons from this experiment:
1) We need to set an explicit goal for our website, or at the very least for individual pages.
2) Remove anything that does not explicitly drive our goal forward. In this case, more content meant fewer conversions.
3) We need to be testing a lot more.

2009 Highlights

2009 was about building a fantastic product that could provide our customers with actionable insights and real value.  We accomplished this with new customer implementations, data collection, and user-driven product enhancements.

In Janurary, we integrated the first outside testers of our hosted business intelligence software. This was also the first month we generated revenue.

In March, we announced compatibility with Postgres and Microsoft SQL database platforms (and integrated our first clients on those platforms).  Combined with our MySQL compatibility, this made RJMetrics accessible to the vast majority of database-driven websites.

By May, we had brought on over a half-dozen enterprise clients and moved out of Bob’s attic into the Waterfront Technology Center in Camden, NJ.  (In line with the move to Camden, we also dropped our first rap video, Business Intelligence.)

In July, we announced significant feature enhancements that have since become staples of the RJMetrics experience.  These included composite charts, growth perspectives, and ranking.  Automated cohort analysis would follow in September.  For each of these major milestones, there were hundreds of small improvements and efficiencies that improved the user experience and overall value of our product.

In November, we were delighted to hire Jeff Shumer, Account Executive Chef extraordinaire.  We will also announce a new engineer joining our team soon.  Both employees were hired using existing cash flow, and we are proud to have reached the hiring stage without raising any outside capital.

Overall, in 2009, we have grown our customer base and revenue substantially. In fact, not only is our company growing, but the second derivative of our revenue is positive, too.  After generating our first sales in Q1, sales grew 107%, 108%, and 109% in Q2-Q4, respectively. The chart below should help put this in perspective.

Goals for 2010

Simplify, streamline, and automate the  client onboarding process

We work very hard to make implementation effortless for our clients, but it’s still a lot of work for us.  In 2010, we aim to cut the man-hours required to implement a new client by half while improving the consistency of the results we deliver.

Empower our users to be more autonomous

We focus on delivering prompt and helpful support, and our customers make it clear that it’s appreciated.  However, some of our power users know exactly what they want, and they could probably get it faster if we took human interaction out of the equation.  This jibes with my lifelong goal of replacing myself with a computer. So, we are going to expose more administrative functionality to our clients.

Add data sources

We currently use data from our clients’ backend databases, Google Analytics, and Twitter.  We’re going to add to this list based on where our customers will be able to realize the most benefits.

Optimize the way our users get information that makes them money and saves them time

I’m very purposefully not saying that I want to add features.  There will be features added, but there will also be features removed.  I don’t care which there are more of.  What I do care is that each incremental change makes our software more awesome.  If that criterion is met, feature counts don’t matter.  If that criterion is not met, feature counts still don’t matter.

Write more

I want to be a better writer, and posting to our blog has unquestionably been the highest ROI marketing that we have done. Furthmore, stating goals publicly makes us more likely to achieve them (hence, the second half of this post).  I am going to post once per week.

Systematize our sales and marketing activities

We’re doing several experiments to determine our cost of lead generation and customer acquisition through different channels. We are going to create a process where our investments in new customer acquisition can scale in a capital efficient way.

Continue Revenue and Customer Growth

There will (hopefully) come a time when our size becomes an anchor that slows our growth rate.  I don’t feel like we are there yet.  If we succeed in most or all of the things I mentioned above, the financial metrics will follow.

While he’s never done a song about business intelligence or cohort analysis, Jay-Z’s discography is a rich library of business advice that has been topping the charts over a decade.   Don’t believe me?  Here are the top five lyrical excerpts that have helped shape our business philosophy.

5. Adapt to Serve Your Market

“I dumbed down for my audience to double my dollars
They criticized me for it yet they all yell ‘HOLLA!’”

Song: Moment of Clarity
Album: The Black Album
Year: 2003

Here, Jay-Z admits that he has compromised his artistic vision to make his music more commercially accessible.  Mainstream artists are often accused of “selling out” for doing exactly this, but here Jay-Z openly admits to the practice and justifies it with a simple fact: it made him rich.

As a business owner, your vision may not always appeal to the largest possible market.  The key is to keep an open mind about new opportunities and be ready to adjust your plans to seize them.  Like Jay-Z, many entrepreneurs have made their millions by constantly refining their vision and adjusting their strategy to reach the largest possible market.

4. Be a Renegade

“No lie, just know I chose my own fate
I drove by the fork in the road and went straight”

Song: Renegade
Album: The Blueprint
Year: 2001

In 2001, Jay-Z and Eminem were young stars, each several albums away from the iconic statuses they hold today.  Renegade, their rare collaboration from that year, casts the two as ”Renegades” who risk becoming outcasts as they attempt to change the face of hip hop.  Today, we know that these renegades succeeded in their mission.

This track is a lesson in innovation and the rewards that are possible if you take the right risks.  While it’s often safer and easier to use your talents to feed the existing machine, there is far more opportunity in disrupting it.

3. Stay in Food and Beverage

“Shoulda stayed in food and beverage
Too much flossing
Too much Sam Rothstein”

Song: Lost One
Album: Kingdom Come
Year: 2006

Here, Jay draws a powerful business lesson from the Scorsese classic Casino. In the film, Robert De Niro plays Sam “Ace” Rothstein, a handicapper who is chosen by the mob to run a new casino in 1970s Las Vegas. Due to his criminal record, Rothstein is forced to runs things under lowly title of “Food and Beverage Director.” When he begins to pursue a more public image in the interest of personal fame, we see his downfall as it parallels the downfall of mob rule in Vegas.

As Jay’s lyrics suggest, if Ace had stayed heads-down and focused on his original goal of running a profitable enterprise, he might have never fallen. The business lesson is clear: don’t start a company to become famous; start a company to build a company. Keep your eye on the best interests of your business, and don’t let the tempting distraction of personal fame compromise your original goals.

2. Be a Business, Man

“I’m not a businessman
I’m a business, man
Let me handle my business, damn”

Song: Diamonds From Sierra Leone (Remix)
Album: Kanye West’s Late Registration
Year: 2005

With this classic lyric, Jay-Z delivers a valuable lesson for business operators by drawing a distinction between the entrepreneur and the working stiff.  His business’s success has made him something much more than just an average man, and that has enabled him to do far greater things with his life and the lives of those around him.

In the lines that follow, Jay-Z calls out to all of the family members and employees whose livelihoods depend on his continued success and whose lives are better because of what he has become.  Jay-Z reminds us that running your own business will both consume and enrich your life– and that the tradeoff is definitely worth it.

1. Data is King

“Men Lie.
Women Lie.
Numbers Don’t.”

Song: Reminder
Album: The Blueprint 3
Year: 2009

As one the best-selling recording artists of all time, Jay-Z has a pretty good response to anyone who challenges his dominance: check the numbers. This same comeback is also a foolproof way for him to dismiss unaccomplished rivals.

Similarly, in business, there is nothing more important than the numbers behind a company. When the hype dies down, the companies with the strongest fundamentals are the fiercest competitors. And the companies with the strongest understanding of their data are the best-equipped to steer those numbers in the right direction.

If you think there is value in your data that might be going uncaptured, it’s probably time to learn more about RJMetrics.

We are looking for an exceptional User Experience and Interface Engineer.

RJMetrics is a fast-growing, innovative database analytics and business intelligence software company.  We sell our hosted software to startups and growth-stage ecommerce, social media, and software as a service businesses.  We blow our customers away with powerful analytics, ease of use, and exceptional customer service.   RJMetrics was launched in early 2009, reached profitability in just six months, and has been growing revenue at over 100% per quarter.

We are located 15 minutes outside of Center City Philadelphia in the Rutgers Camden Business Incubator (3 blocks from the PATCO and River Line trains).  We also occasionally make rap videos, guest post on TechCrunch, get on the front page of digg, and win awards.

Responsibilities

• Improve the usability and design of our AJAX-driven BI dashboards
• Extend the functionality of our Flash-based data visualizations
• Work closely with the founders (you will be 1^st non-founder technical hire)
• Visual design oversight of all public-facing company collateral (website, advertisements, white papers, etc)
• Contribute to the development of internal tools, processes, and policies
• Occasionally pitch in with whatever needs to get done (blog posts, customer integrations, strategic planning, cabinetmaking, etc)

Requirements

• Significant experience working with Flash (ActionScript 3), Photoshop/Illustrator, HTML, CSS, and JavaScript (JQuery).  Experience with PHP and MySQL a plus
• Must have built or contributed to something awesome that we can try out
• Experience with user-centric design
• Interest in ecommerce, social networks, and software as a service businesses (our clients)

Nice to haves

• Startup Experience
• Data visualization experience
• Experience working with APIs
• Understanding of business metrics/business intelligence
• Understanding of statistics and  database theory  (we are total nerds)

What you’ll get

• Be THE expert on interface and design for our company
• Contribute to important software that end-users love
• Make a major impact on an early stage company
• Competitive salary and stock options

This is a full time position located on site.

Please apply here.

We’re proud to have been named to the OnDC 100, put together by AlwaysOn Magazine. The list highlights “100 private companies contributing to the renewed and continued prosperity of our country”. See the full list at AlwaysOn’s website.