Results

More than 30 million people use LinkedIn, widely-regarded as one of the more well-know social networking site for white-collar professionals, but until now, most marketers didn’t know exactly who those users were.  They do now.  According to Ad Age, in the first public study of LinkedIn’s demographics and pyschographics, completed by Anderson Analytics, Linked In, and SPSS,  LinkedIn has become a tool for information workers to find jobs, network with other business people, and keep in touch with old collegaues.

According to the survey, 30% (9 million) of LinkedIn users are savvy networkers who earn nearly $93,500 per year and have a work puchase-power of $88,000.  This group earns a “great” in social networking influence; 69% read blogs, and 9% maintain their own blogs.  They are most likely to use Gmail and visit technews sites like Slashdot.

The next largest group, clocking in at 28% (8.4 million), are senior executives earning a mean of just over $104,000 per year with a purchasing power at work of $99,000.  Most of this group is happily employed and uses LinkedIn for business contact networking.  This group skews male, and more likely to visit news sites like Fox News.

The third largest group, accounting for 22% (6.2 million), use LinkedIn because friends convinced them to join.  This group is careful about who they connect to, and only connect to people they know in person.  They earn nearly $88,000 per year and have a purchasing power of work of $76,000.

The smallest group that uses LinkedIn, accounting for 21% (6.1 million), are the hard-core job searchers.  Seventy percent of this group is employed full-time but actively looking for another position.  Of all four groups, this one skews the youngest and most female (52%).  They earn just over $87,500 per year and have a work purchasing power of $84,000.

To gather this data, researchers sampled 70,000 members and surveyed a sample of 800 with a 10-minute online questionairre.

How does 7 million views in less than 48 hours grab you?  By any measure, that’s a lot of eyeballs in a short amount of time, but according to Visible Measures, that’s exactly how many people viewed President-Elect Barack Obama’s victory speech via nearly 500 placements, which represent every video related to his speech.   McCain didn’t fair too poorly, either:  his gracious concession speech was seen by 1 million people via over 180 placements.

Of those placement sources, YouTube accounted for the lion’s share.  Though Visible Measures tracks approximately 150 video sharing sites, YouTube accounted for 98% of those who viewed Obama’s victory speech.

Given its digital nature, and the technology used to broadcast and receive data, the web is inherently measurable.  On the web there is no passive audience; everyone leaves some sort of footprint here or there.  And savvy business professionals know how to read the tea leaves—the web analytics—in order to craft successful business strategies.

But web analytics aren’t just for business.  According to the Wall Street Journal, political advisors are using web analytics to gauge, measure, and predict political performance and even influence elections.
The new stats for the new digital millennium?  Facebook supporters.  Blog mentions.  View rates for videos on YouTube.  Search types and search rates on search engines.  Comments—both tenor and amount—on sites that allow users to comment on content.

But as with all statistics, one must remember that correlation does not imply causation.  Does a video view rate really imply direct support for a particular candidate?  Probably not.  But according to some professors, the number of Facebook supporters may imply the sort of more involved support that helps win caucuses.  Professor Christine Williams of Bentley University, as quoted by the Wall Street Journal, discovered that Facebook support may correspond to caucus performance, because becoming a Facebook supporter involves the same sort of motivation or commitment one might make when supporting a candidate in a caucus, which requires one to make a physical commitment.  During the Iowa Democrat caucuses of 2008, Senator Barack Obama (D-IL) bested all other candidates in Facebook support and blog mentions, and he handily beat Senator Hillary Clinton (D-NY).

Instead of hard conclusions, perhaps, web analytics are best used to discover and identify trends.  Number of Google searchers, for example, may be a good indicator of relative strength; the more buzz a candidate generates, the more people may be inspired to learn more about the candidate via a search engine like Google.  According to Nate Silver, founder of fivethirtyeight.com, as quoted in the Wall Street Journal, a paucity of Google searches for Rudy Giuliani suggested he wasn’t creating much of an online buzz, and this was a leading indicator of his inability to mount a successful campaign for the GOP nomination.

Back in the dark ages of personal computing, we walked five miles uphill to school, ate shards of broken glass with our thin, watery gruel, and bought our computer software in big, chunky boxes that contained anywhere from one to five hundred and forty three installation disks.  And those were floppy disks, too—not your fancy CDs.  And that’s the way we liked it.

So don’t complain to me about the frustrations of installing software via the internet.  Slow download times?  Hell—we alter kakers had to drive to the mall before we could even think of installing anything.  Too many steps in the online installation process?  Have you ever seen one of those 150-page installation manuals from 1990s?  Even the worst installation experience today has to be better than the average installation process of yesteryear, yes?

You’d think so—however, if you do, you’d be wrong.  Software client application installations still have high abandonment rates, but until now, it was very difficult for software developers to determine where, exactly, potential users were abandoning the installation process.  Here now to untangle this mess comes Kampyle, an Israeli analytics company that now offers software developers the ability to discover where and why users abandon the installation process.

The concept behind Kampyle’s service offering is pretty simple.  They put an application on top of your existing installation or uninstallation process.  If a user abandons an installation, or uninstalls your product, Kampyle’s application fires off a browser window, asking the user to provide feedback.  If the user agrees to provide feedback, the app opens a form, giving the user a simple mechanism to sound off.

Kampyle then aggregates the feedback it receives and reports to you in customizable business dashboards so you can use the feedback to improve your processes. According to TechCrunch, the service has amassed 3,000 customers since its debut five months ago.

Not bad, eh?  You have to be able to measure and quantify before you can improve, and Kampyle is giving you a way to do precisely that.

No matter what the Tampa Bay Rays do in the 2008 playoffs—and as of now, they’re set to take on the Boston Red Sox later this week for the American League Pennant, without question, 2008 was perhaps the most pivotal year in the franchise’s brief 10-year history.  Why?  Because from 1998 to 2007, the Rays finished last in the American League East every single year except for once, in 2004, when they finished fourth (out of five teams).  This year?  The Rays finished first, beating the second place Red Sox by two full games.

Could it have been their marketing makeover that put them over?

In 2006, former Goldman Sachs managing partner Stuart Sternberg purchased the struggling franchise—then known as the Devil Rays—and installed former Procter & Gamble brand manager Darcy Raymond as the Ray’s Vice President of Branding and Fan Experience.  Under Raymond’s watch, the team changed its name from the Devil Rays to the Rays (the former a nickname for the manta ray, the latter, according to Sternberg, “a beacon that radiates throughout Tampa Bay and across the entire state of Florida”), changed their color scheme from green to blue, and instituted a list of “customer touch points” to make sure the game experience is fun for the fans.

These touch points are no laughing matter.  Rays executives constantly monitor and measure them to gauge customer satisfaction.  According to Raymond, as quoted in Ad Age, “It’s a lot like what P&G does with brand-equity models.  We know when our cleaning scores dip or when our security wasn’t helpful enough.”

Of course, all these initiatives would amount to little if the Rays didn’t play well on the field.  This year, with the addition of former Mets pitcher Scott Kazmir and the emergence of third baseman Evan Longoria, the Rays made—in one year, no less—a last-to-first transformation worthy of legend.  Once the perennial doormats of the American League East, this year Tampa Bay wrested the division title from both the Boston Red Sox and the New York Yankees—teams with a combined payroll of nearly a third of a billion dollars.

Tampa Bay’s payroll?  A modest $48 million, or, on average, less than the cost of two years of Alex Rodriguez’s service as the Yankee third baseman.

On the commercial side of things, however, the Rays still have a long way to go.  Despite their rags to riches story, their home attendance ranked 26th out of MLB’s 30 teams.  Rays executives point to a one-year lag for “worst to first” teams to explain this phenomenon; it takes time for the locals to hop on the bandwagon.  Also, the Rays’ home field—Tropicana Field—deserves part of the blame.  In addition to being renowned throughout Major League Baseball as one of the worst places to play the game—its awful artificial playing surface and in-play ceiling catwalks, for example—its location isn’t convenient to Tampa Bay or Orlando.

Here, again, the Rays’ executives’ passion for metrics will serve them well.  By tracking enough negative metrics for the Tropicana Field experience, they’ll have enough hard evidence to support, perhaps, the construction of a ballpark worthy of an AL East contender.

As Robert Burns wrote, “the best-laid plans of mice and men often go awry,” and in the world of online business, this old axiom rings especially true.  To ensure your best-laid online business plans do not go awry, you need a proper web analytics program to help you ride herd on your business metrics and beat your competition.

Luckily for you, the Internet is awash in measurable data; much of it real time data.  For web-based businesses, collecting data is not difficult—but reams of raw data does not a proper web analytics program make.  Generally speaking, a good web analytics program—one that helps you succeed online—consists of five elements.  You must collect the right business data; you must present it in a meaningful way; you must analyze it properly; you must use the data to optimize your business strategies; and you must use the data to innovate—continually.

Only Measure What You Can Use
The Internet is a sea of information; with that information comes enough raw data to fill the sea several times over.  Because so much data is available—at your fingertips, no less—it’s difficult to make sense of it all.  Moreover, often it’s difficult to determine if you collected the right data in the first place.

To avoid data overload, begin by focusing on Key Performance Indicators (KPI)—those metrics that measure impact on your key business objectives.  Then, narrow the scope even further:  focus only on those KPIs that give you actionable information.  Information you cannot act upon will not help you grow your business.

Once you determine the KPIs that give you the best actionable information, establish measurable business goals.  With good, measurable metrics and clearly articulated business goals, you should be able to see how you’re doing by measuring your performance against those goals—and current performance versus past performance—at any point in time.

Report Your Findings—Clearly and Succinctly
Business does not occur in a vacuum; you will not achieve your business goals without help from others.  So don’t keep the valuable business intelligence you gather to yourself—report it to others, often, so that everyone on your team stays on the same page.

When you report your findings, don’t be ambiguous—leave nothing open to interpretation.  The best tool for reporting business intelligence is the business dashboard—a simple, easy to read, easy to understand visual data display, often in real time.  The best business dashboards present data comparatively, as standalone data is rarely useful.

Your business dashboards should make extensive use of filters and alerts.  Filters help ensure your reporting contains relevant, actionable data in the proper context.  Alerts—email alerts, text message alerts, and others—help ensure information is pushed out to those who need it automatically, whenever they need it, so immediate action can be taken.

Analyze Your Findings
You went through the time and trouble to collect this valuable information, now make it work for you by analyzing it.  More specifically, analyze it in the proper context—as compared to your Return on Investment (ROI).

Successful online businesses are able to gauge the ROI of each and every business initiative.  This allows them to reallocate investment dollars and realign business tactics quickly and accurately.  With the proper metrics, and the best reporting tools—business dashboards—you should be able to tell, at a glance, how a particular investment, like an advertising campaign or a special promotion, is affecting your ROI.  Moreover, you should be able to determine the value of specific online content in meeting your ROI.

Make Use of Your Analysis
One of the best attributes of doing business online is the ability to make on-the-fly adjustments to your business tactics for little cost.  Use your analysis to make cut-and-dried business decisions, but don’t be afraid to test in order to find out what works and what does not.  To be successful, you must use your analysis to test, and then optimize, every element of your business program.

Multivariate testing, in which one or more components of your site are tested in a live environment, can help you attribute gains or losses in your key metrics to the proper variables.  Generally speaking, you should test a small portion of your incoming traffic by directing them to the changes you wish to test, while minimizing the risk to the remaining incoming traffic by directing them to the “control” portion of your site.  Of those being tested, most of your efforts should be geared towards trying to “beat” the control group by improving existing variables.

Don’t Rest on Your Laurels
While a vigorous testing program can help you determine what works and doesn’t work now, it can also direct you towards what may work—and what may not work—in the future.  Remember, the online world is evolving rapidly; what resonates with your users now might not resonate with them tomorrow.

To ensure your business remains viable for the long term, you must be willing to embrace risk.  But don’t just take shots in the dark; use the data you gathered and the analysis you performed to inform the risk you are willing to take.  Think about the things that work well for you now, and use the business intelligence you gathered to come up with ways to make them work even better.  Can a process or transaction be streamlined?  Can a labor-intensive procedure be automated?  Will static content areas be more useful if you can make them dynamic instead?  Use what you know to make informed choices; keep the pipeline full of fresh, innovative ideas—and don’t be afraid to test them.  After all, those who risk the most usually stand to gain the most.

Finally
A good web analytics program is crucial for online success, but a good web analytics program consists of more than just the ability to collect reams and reams of data.  In order to help you be successful online, your web analytics program should collect clear, actionable data; report that data in meaningful, unambiguous ways; allow for vigorous comparative analysis to your ROI for specific business initiatives; provide you with the ability to test results against isolated variables; and help you innovate for the future.  With a good web analytics program in place, you will be well-positioned to beat your competitors and make your business the best-in-class on the web.

According to AdAge, in a talk at the Lehman Brothers Back to School Consumer Conference, Kellogg Co. Chief Marketing Officer Mark Baynes told the group that for its Special K brand, its Return on Investment (ROI) for online marketing efforts had surpassed its ROI for broadcast television marketing efforts for the past eighteen months.

While Baynes would not say how Kellogg measured ROI, according to AdAge, Kellogg has made frequent mention of the success of its “Special K Challenge,” in which dieters are encouraged to eat two bowls of Special K cereal per day for two weeks.  Its site that supports this initiative offers customizable dieting plans for consumers, tips from fitness, fashion, and lifestyle and nutrition “coaches,” a Yahoo email support group, and a link to purchase Special K via Amazon.com.

Judging from the content of the Special K website, it’s reasonable to conclude that a certain amount of engagement and community-building has paid dividends for Kellogg in promoting the Special K brand as not just a cereal, but a lifestyle choice.  Evidently that sentiment is  conveyed through a three-dimensional engagment experience far better than a one-dimensional experience such as television, radio, or print.

According to the Wall Street Journal this weekend, Russia’s attack on Georgia earlier this month could have a significant impact on an unexpected third party—American defense contractors. And a significant impact at that. Quoted in the Journal, an analyst from JSA Research in Newport, RI, called the Russian invasion “a bell-ringer for defense stocks.”

To understand how this could be, one must first consider how the Defense Department plans and prepares for war. Generally speaking, Pentagon officials plan and prepare to fight the “next war” by predicting who our next opponent may be, anticipating what types of weapons technology might be best to defeat them, and arming ourselves appropriately. Prior to the terrorist attacks of September 11, 2001, Pentagon officials largely assumed our future enemies would be traditional nation-states, complete with traditional militaries comprised of armies, navies, and air forces. Accordingly, since technology is almost always the deciding factor in traditional warfare, we armed ourselves with the latest and greatest weapons. The cost, of course, was staggering—and continues to be.

But does an armory well-stocked with the latest, most technologically advanced weapons, always make for the best prepared fighting force? The answer might seem obvious, but after the terrorist attacks of September 11, the waters become a bit murky. After all, what good is a hanger stocked with F-22s and a port filled with Zumwalt class destroyers when a squad-sized force of men armed with box cutters can bring an entire nation to its knees? Indeed, say those in defense circles who feel that too much emphasis is placed on mammoth, multi-decade, extraordinary defense programs like the Air Force’s F-22 Raptor, the Army’s Future Combat Systems, and the Navy’s DDG-1000 Zumwalt-class destroyer.

According to this camp, which includes Defense Secretary Robert Gates, more emphasis should be placed on the sort of things that help us counter the current threat—lightly armed, highly mobile irregular forces like the ones opposing the United States in Iraq and Afghanistan. Secretary Gates and his ideological allies in defense circles seemed to have had the stronger hand, until tension between Russia and Georgia erupted into a shooting war—a traditional one at that—earlier this month. Suddenly, Pentagon officials were reminded that traditional militaries and traditional methods of war fighting were not extinct.

Quoted in the Journal, Chairman of the House Appropriations Committee Representative John Murtha (D-PA) said “We’ve spent so many resources and so much attention on Iraq that we’ve lost sight of future threats down the road. The current conflict between Russia and Georgia is a perfect example.” In other words, potential threats from traditional nation-states are still with us; to counter those threats, we must have the technology the traditional military demands to meet—and defeat—the threat. Hence, the recent boom in defense-related stocks, as investors gamble that the Pentagon will be more likely to procure more costly weaponry to counter the resurrected potential threat from traditional nation-states—Russia or China, for example.

Thus, we see the opposite ends of the spectrum. On one side is a lean-and-mean military poised to counter the current threat; on the other is a juggernaut capable of crushing even the largest and most technically advanced militaries. According to the Journal, Secretary Gates seems to be charting a course that combines elements of both: “developing capabilities to carry out unconventional warfare missions while fielding forces capable of handily defeating adversaries like Russia’s or China’s militaries.” But how can Secretary Gates run such a fine line? Moreover, how could those in the defense industry work together to arm the United States in the most appropriate way?

One thing that would certainly help in both instances is the ability to measure, pro-actively and up front, the effects each potential procurement would have on the health of American defense. Certainly the data is out there for the Pentagon to do this, as is the technology for meshing together seemingly disparate bits of data in order to form a more accurate picture of a program’s impact—or potential impact—on defense as a whole. Defense officials and defense contractors could work together to establish a set of metrics they could use when designing and implementing defense procurement programs. Once those procurements have been approved, they could use similar measurements to refine and perfect those programs based on real-time performance data. By collaborating together in this manner, defense contractors could learn from each other and produce better, more effective weapons systems. After all, a rising tide raises all ships.

A better, faster, more versatile military is possible through constant measurement and refinement. And a more cost effective one at that—which is music to the ears of the American taxpayer.

Apple has released a software patch which may help iPhone 3G users who experience an abnormally high amount of dropped calls.  According to cNet, “The release notes accompanying the new firmware refer rather obliquely to ‘bug fixes,’ the same term Apple used when it released the 2.0.1 firmware update a few weeks ago.”  Speculation is running rampant as Apple tries to deliver on its promises of faster internet connections with its iPhone 3G.  Comment boards are awash in debate over what could have caused the issue and whether the patch is truly a fix or a workaround.  For bleeding edge consumers, having the coolest new gadget is coming with some unforeseen drag factor.

The glory of the 3G iPhone is its promise of incredibly fast internet connection speed.  For a device that is geared toward the more multi-media focused, the ability to deliver on such promises is paramount.  Anything less than spectacular and the iPhone gets slammed as an overpriced gadget.  When connectivity is maintained, the iPhone 3G definitely can provide streaming video on a large glass screen that leaves other smartphones in the dust.  But since its release on July 11th, which Talkibie covered for New Hampshire, customers have noticed more dropped calls than with their previous iPhones.  That’s when the speculation began.

Tech savvy users took their concerns to the internet.  Tech websites like cNet and TechCrunch, news sites like The Wall Street Journal and BusinessWeek, and forum boards lit up with articles and comments regarding iPhone 3G performance.  “Owners also lament frequent shifting between high-speed and slower-speed networks during calls and Web sessions,” found BusinessWeek reports.  The Wall Street Journal reported a mobile analyst with Gartner, Inc. “has experienced spotty network reception with his own iPhone 3G, most recently inside San Francisco’s baseball stadium — AT&T Park.”  Apple quickly acted to resolve the issue with update 2.0.2, in the hopes of allaying the general outcry.

All indications point to a possible chipset issue.  The iPhone 3G uses an Infineon chipset to manage which AT&T network is needed to stream video and other large data files.  Unfortunately, the interaction of Apple’s management software with Infineon’s chipset and AT&T service creates a volatile mix that ends up dropping connectivity more often than normal.  As BusinessWeek explains, “One source says Apple programmed the Infineon chip to demand a more powerful 3G signal than the iPhone really requires. So if too many people try to make a call or go on the Internet in a given area, some of the devices will decide there’s insufficient power and switch to the slower network—even if there is enough 3G bandwidth available.”  So while the chipset is getting most of the bad press, it is more likely a combination of factors bringing connectivity issues to the surface.

Apple has taken the first step in mitigating customer ire but is there more to be done?  As Dulaney explained to the Wall Street Journal, “It’s not about whether you have problems or don’t have them.  It’s how quickly you address them that matters.”  The turnaround of a couple weeks since initial reports points to Apple’s commitment to doing right by the customer after the fact.  But could Apple have addressed the issue before it released the iPhone 3G?

As covered in previous Talkibie articles, performance monitoring should be a baseline for businesses intent on staying ahead of potential system crippling events.  iPhone stories abound about the “call home” functionality built into handsets that will rat out whether a customer is using hacked systems and shut down the phone.  Would it be that much of a stretch to collect the data for amount of calls dropped, or perhaps number of times 3G bandwidth was downgraded?  By building monitoring and measurement features into reporting software, (with an opt-out feature, of course), Apple can better monitor their product performance.  Instead, Apple must take a reactive stance instead of a proactive one.

Arguably, testing a product before mass release should catch any glaring issues.  Apple faces a unique challenge in that it has a vast following that tries to find out when the “next, big thing” will be released.  Testing without product information leaks to the press or blog sites is almost impossible.  Secretive, limited testing is Apple’s modus operandi.  BusinessWeek states “The problem is affecting 2% to 3% of iPhone traffic, the people say. That compares with a dropped-call rate of around 1% for all traffic for AT&T (T).”  Limited testing didn’t catch the discrepancy.  Full system metrics might have helped Apple predict the chipset management issue long before their hand was forced.

My old college business professor would love Netflix.  He could build a whole semester’s worth of subject matter studying Netflix, alone.  From innovative service model to adaptive positioning techniques, Netflix offers a wealth of case studies for students.  Today’s lesson:  the importance of learning from past mistakes.

Netflix’s original business model was based on delivery of DVD movies via the postal service to a subscriber’s home.  By using a software model that is able to manage a customer’s movie queue and harnessing the United States Postal Service’s existing distribution channel, Netflix could deliver a movie to your door in a day or two.  Back in March of 2008, Netflix experienced a “glitch” in its delivery service software and movie shipment was delayed for 24 hours.  Netflix credited customer accounts for 5% of their monthly viewing bill in reparation.  When a business suffers an interruption in service that is company wide, there are usually studies and action plans that result in the implementation of corrective measures to assure that the same problem doesn’t resurface.  Netflix is, apparently, an unusual company.

For a period of three days, Netflix, again, experienced a significant failure of its shipping system.  This impacted a sizeable chunk of the 8.4 million members who subscribe to Netflix for a steady flow of DVDs via mail.  From Tuesday through Thursday, only partial shipments were able to be made due to a “pretty severe technical” issue, as Steve Swasey, representative for Netflix, told the Wall Street Journal.  Netflix web delivery of movies was unaffected.  And while the crisis appears to be over, managers and business students everywhere should be asking “How did this happen again?”

If a company is to maintain reliable service, it must have a means of measurements in place to allow for failure prediction.  Very rarely will a computer, or even a mechanical process, suffer catastrophic failure.  Over time, links in the process deteriorate and slow down to substandard levels.  For mechanical products, regular maintenance and cleaning will keep a machine in top form.  For computer systems, monitoring software is available with threshold alarm functions.  This allows for predictive monitoring to be put in place and used effectively.  Predictive monitoring gives businesses an opportunity to catch and address an issue as a slowdown, well before it becomes a failure.  While the initial cost for such a package may seem high, the costs in lost production, loss of reputation, and in customer refunds are much higher.

There are quite a few things Netflix has done right.  As covered in previous Talkibie articles, Netflix has done a fabulous job integrating its service using new technological delivery methods.  And it’s done a great job in customer relations, owning up to failures and taking proportionate steps to win back loyalty.  March’s day of lost service garnered a 5% refund to customers.  This most recent outage of three days will net existing customers a 15% refund and new customers a full week of free service.

But in the area of learning from past mistakes, Netflix seems to have dropped the ball.  Investors and shareholders will want to know what proactive steps Netflix is taking to ensure that its service doesn’t fall into the same trap a third time.  The answer is to use technology to keep the company online.  Class dismissed.