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.

Baseball: A Case Study

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.

Web Analysis

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.