Ever wanted to see how many times your link has been shared on Facebook? The folks at HackerNews found out – just use the link below (with your own URL) to get a count. Unfortunately, some URLs (such as YouTube) may be normalized.
http://graph.facebook.com/http://seattle.twestival.com
will return:
LinkedIn counts can be found as well:
http://www.linkedin.com/cws/share-count?url=http://seattle.twestival.com
Enjoy!
I’m an analyst. I love data, and I love the idea of being able to track every last thing done to understand people better. I think numbers make everything better. It’s why I love baseball and twitter and political polls and fantasy football and it’s why I majored in economics as an undergrad and it’s why I love the web. I also work at Clear as an analyst digging into prospect and existing customer behavior on Clear.com. I thought I should get that out of the way first, before I weigh in on the subject of the FTC recommending a “do not track” mechanism for the web.
I think a “do not track” plan is insanely shortsighted, and I think the fact that many are likening it to the “do not call” registry shows just how out of touch they truly are. I understand the need for privacy online (in fact, I advocate for it), but I don’t think that a need for privacy is mutually exclusive of a need for data and information – both can coexist quite well together.
On most every site you visit, you are served with advertisements. Whether it’s an ad for Viagra, the shocking benefits of the açaí berry, or the new Windows Phone 7, you’re peppered with ads from the moment you enter a site until the moment you leave. They come in all shapes and sizes too: interstitial ads, banners, leaderboards and text ads. Regardless of whether you’re tracked or not, you’ll be served these ads (unless you have an ad blocking program). If you’re being served these ads no matter what, why the concern about tracking?
Well, the FTC is concerned that many online companies have been negligent in their duties to protect the privacy of internet users. One of the main points of contention is that ad agencies (and other companies) track users around the web, then turn around and sell the data – without telling the user about it.
But here’s my problem: the FTC is focused on the wrong thing. They’re focused on creating ways to turn off tracking entirely, rather than regulating the industry and making sure privacy controls are in place. If all tracking is anonymous, what’s the harm in collecting (and even selling) information about users? It’s just information – just like polling data or the census.
A Do Not Call registry has the obvious advantage of stopping telemarketers from calling you unsolicited. A Do Not Track registry just stops the tracking. You’ll still see every ad, except as opposed to those ads being targeted toward you, the thirty-something social media user, or the forty-year old female who reads cooking blogs, you’ll just get the random generic ads targeted to everyone else. That sounds like a disincentive rather than an advantage to me.
Imposing rules on the internet just because information is more readily available is a copout. This law would unfairly affect internet information gatherers, where brick-and-mortar establishments remain untouched. For example: What’s to stop Starbucks from taking information about every cup of coffee you drink? Or going over to Tully’s and Seattle’s Best Coffee and sitting and watching what they order too? Nothing. In fact, it would be considered good business, as Starbucks continues to craft which drinks are the most popular to customers. The FTC would have no recourse to tell Starbucks not to engage in such practices, and would similarly have no jurisdiction to impose sanctions on an agency gathering the research themselves, deciding to sell the information they gained to Peet’s Coffee and Caffe Vita.
How about politics? We have all kinds of anonymous polling, canvases and censuses, and all of our elected officials are voted on by the public. We do this for the sake of information, and for the sake of learning more about our population. The idea is that increased information helps create educated decisions. Yet rather than give companies access to data, a Do Not Track registry takes that away.
In my mind, the FTC is unfairly punishing businesses who have done a good job of collecting data by lumping them into a group with those that use data nefariously. Imposing greater standards on privacy protection, while creating rules governing how businesses may use this data is laudable – creating a national Do Not Track registry, however, is the wrong way to go about it. If you want more restrictions on privacy, focus on privacy – don’t focus on creating an opt-out registry. That’s like trying to cure AIDS by creating a topical cream to deal with lesions. You’re missing the point.
Perspective is a funny thing. It’s so important to our everyday decisions in life, yet something that is commonly overlooked in analysis. Every decision we make is based on perspective. As data becomes more and more available, a large proponent of people who do not know how to take that data into perspective grows. They’ll overvalue certain sets of data without taking into account small sample size or confounding variables that lead to faulty conclusions.
The problem with the increased availability of data is that we start to focus SOLELY on the numbers, without thinking about the vast importance of perspective and relationships. Ultimately a number is just a number. Whether we’re looking at web traffic, batting averages in a baseball game or a company’s stock. Without perspective, we have no idea whether 1,000,000 pageviews is a good thing, a .250 batting average is a bad thing, or if a $20 stock valuation is the right price. It’s only once we take all factors into account (competition, peripheral factors, causal relationships), that we can truly begin to see the big picture.
If you’re heavily interested or involved in analysis, then baseball is probably the sport you want to get into. There is no sport that has a longer, more rich history of statistical measurement and analysis than baseball.*
*For the record, I am extremely biased on this subject. Not only did I play baseball since I was 5 years old, but I also credit baseball for my knowledge of simple math skills and ability to do fractions (having to calculate batting averages and ERAs does wonders!)
So, since every statistic can be tracked, it allows people to easily (and often) draw unfounded conclusions based on certain data points. Someone might look at a batting average of .350 and think “Joe Smith is a fantastic hitter!” without taking into account certain other facts like: the pitchers he’s faced (maybe he only played in games where lower-tier pitchers were throwing), the number of at-bats (small sample size is often a huge factor), an unrealistic batting average on balls in play (if the league averages .300 when they put a ball into play, and Joe Smith averages .450, maybe he’s getting unreasonably lucky), etcetera.
Even if his average is legitimate, we must then decipher the reasons for his success. Maybe he’s found a new hitting coach, changed his batting stance, or changed his workout program over the summer. Maybe Joe Smith moved to a new baseball park that suits his style of hitting. Perspective when measuring and analyzing any statistical set of data is extremely important, and baseball statistics illustrate this perfectly.
So, this brings us to web (and social media) analysis. Just because we have tools to measure all of our web traffic doesn’t mean that we are truly understanding what it is that we are measuring. Still, we often run into issues of targeting the wrong metrics and singularly focusing on data rather than contributing factors.
Many people typically focus on certain mainstream metrics (unique users, pageviews, time on site), without ever analyzing or paying attention to the peripherals (entrance page, referring sites, bounce rate). Focusing on a website’s peripherals allows us to realize not just that people are coming to a website, but more importantly, why.
The other big issue arises when choosing to form conclusions based on initial hypotheses. For example, thinking: “if there’s an increase in traffic, it’s because of our marketing efforts; if there’s a decrease in traffic, it’s because of a problem with our content.” As any 10th grade science teacher can tell you: it’s wrong to base your conclusion upon the hypothesis that you started with. Yet when doing analysis, we often tend to see what we want to see. Yet, just as in baseball, there are hundreds of factors involved. Seasonality (what time of year is it?), decreased demand for a product, or many other confounding factors (maybe someone else stumbled upon an article of yours) sometimes act as much stronger factors than your own efforts. You should always start with a hypothesis, but being unwilling to bend if that initial theory comes into question (or being uninterested in digging deeper) can often lead to incorrect conclusions.
Let’s take an example of a company that has been trying to ramp up social media efforts. They’ve started a Twitter and a Facebook account, and they suddenly see a great spike in their traffic. Quickly breaking out the Microsoft Excel spreadsheet, the social media specialist (who is tracking their web stats with Google Analytics) sends his CEO the chart below and says “hey, look! The main traffic dashboard shows a spike right when we launched a new social media campaign!” The company rejoices and finally sees the value of social media.
…Only, that’s not actually what happened. Yes, the social media specialist launched a campaign. Yes the traffic boosted. But if he’d taken 30 seconds longer to actually investigate, he’d have realized where the traffic was actually coming from. It wasn’t a bump in traffic from the Twitter or Facebook domains, or even in direct traffic, which he might have been able to attribute to one of the two. Nope, it was a bump in traffic from stumbleupon.com to a blog post he’d written 3 months ago. But he was so excited to affirm his belief that it was the Twitter and Facebook launch that week, he didn’t even feel the need to do research.
Don’t be that guy. If you are actually doing analysis on everything your company does online, you’ve already taken one major step. Don’t negate that by being careless. Take into account all of the variables before you rush to a conclusion. We’ve all been excited about the campaigns we run, and the efforts we’re taking to increase sales, traffic, conversions, whatever. But then take the next step in making sure you’re using the numbers right to attribute your successes to the right place.
The increased availability of data is a gift, but one that must always be wielded with caveats and perspective. Data is not inherently good or bad, but it is imperative that we view and analyze it with the proper perspective before moving forth with conclusions. However, if you don’t care about factual accuracy or moral integrity, you would be far from the first to lie with statistics.
All I’m saying is that we are currently in an era where we are inundated with statistics. Numbers, data points and rudimentary analysis are constantly thrown at us. If we just take it at face value, or decide to solely use it to fit our hypotheses, we’re only hurting ourselves.
To whom it may concern,
I have noticed that for a long time (but especially recently), you’ve been picking up dirty habits. Considering there is no good system for vetting you, I would like to address these two issues here. Additionally, I’ll give a couple prime examples of your large blunders as well as a current example.
(Source: xkcd)
Extrapolation is Dangerous
Ex. 1: The value of radio stations continued to rise in the 1990′s. Corporate behemoths like Clear Channel saw the growth and thought they could buy up all the stations and have control over the airwaves. Due to the rise of the internet and the changes in the market, radio is dying and Clear Channel’s creditors are trying to force the company into bankruptcy.
Ex. 2: In the late 1990′s, AOL was a media super giant. Its valuation was so high that it allowed it to purchase Time Warner in January of 2000, when the sky was the limit for AOL with subscriber growth through the roof (deal finally closed in January 2001). Since 2002, AOL’s subscriber base has not seen one quarter of growth. Time Warner has announced that AOL will now be split into a separate company by the end of 2009, fully breaking ties with AOL.
Ex. 3: Facebook and Twitter have each seen exponential growth. Facebook recently turned down a valuation of $8 billion, and Twitter was valued at $250 million in January (Facebook tried to purchase it for $500 million).
(Source: Stephen R. Johnson)
Correlation does not imply Causation
Ex. 1: People who drink coffee have health problems. [No, people who drink coffee also often smoke and are workaholics. Those people do have health problems.]
Ex. 2: Eating breakfast is highly correlated with success for students. [No, people who miss breakfast are often absent or tardy students, or parents who make their child's breakfast are more involved in their lives (read: schoolwork, success)]
Ex. 3: As ad spending on a product increases, so do its sales.
Analysis doesn’t just take into account numbers and graphs with lines pointing in one direction or another. A good analyst will look at the other (sometimes hidden) variables and shed some light on whether the data shows mere correlation or actual causation. To that end, an analyst has to keep in mind that there are thousands of variables that will change over a one, two, five year period, which is what makes extrapolation so dangerous. Predicting Facebook’s pageviews in 2013 is no more certain than predicting AOL’s subscriber base or the radio industry’s growth. Just because a pattern exists doesn’t mean that it will last. Or that it isn’t just coincidence. Keep that in mind the next time you write a report.
Please. Friends don’t let friends extrapolate or imply causation. It’s just poor form.
Sincerely,
Jaremy
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