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When most people think of web analytics, they think about page view tracking; basically, measuring which pages on a website are being viewed.

Page view tracking is a well-established technology, but it’s no longer meeting the needs of many of the most well-known companies in social media. Companies like Facebook (), Zynga, Slide, and RockYou are spending tons of resources building their own internal analytics tools.

There’s a reason for this: Social media is highly competitive, and the biggest advantage you can have is data. To improve and grow, these companies need to gather as much information as they can, and they need more than simple page view tracking.

In the following sections I will cover three of the most important things to measure for social applications.

1. Funnel Analysis: Measuring Conversion Rates

One critical kind of analysis that social apps require is called Funnel Analysis. This is a way of measuring conversion rates, which is the lifeblood of all applications. The term “conversion rate” refers to the total number of visitors who came to a site, compared to the number of visitors who did a desired action (such as creating an account or purchasing an item).

What Funnel Analysis gives you is a more granular way of analyzing conversion rates. Instead of simply looking at signups divided by total visitors, you figure out the steps that have to be taken to get a user to sign up and measure the individual conversion rates between steps. As you can see from the image above, there’s often a pretty steep dropoff between each step, giving you the namesake funnel shape. (Note: the image uses made up stats and is for illustration purposes only.)

This more granular look at conversion rates can have surprising results. Let’s take a look at Twitter’s () signup funnel:

1. Hit homepage
2. Go to signup page, fill out registration form
3. Browse suggested topics
4. Add e-mail friends
5. Search for someone

As you can see, the signup process is pretty complicated, and will benefit from detailed analysis. We might find, for example, that there’s a huge dropoff rate (a “dropoff” occurs when many of the people who made it to one step don’t make it to the next) at the “Add e-mail friends” step. Once we’ve discovered a dropoff rate like this, we have to figure out the root cause. The dropoff rate at the “Add e-mail friends” step could mean that users are unsure how to continue, causing them to leave, or they might not want to add their e-mail information. We would have to test to make sure.

Ultimately, Funnel Analysis is about finding and improving trouble spots in a website. With continual analysis, changes can be measured and ideas can be tested over time.

2. Engagement Tracking: Measuring What People Do

As I mentioned earlier, pageview tracking is becoming less and less relevant for many web companies. Instead of the basic unit of measurement being the pageview, they are starting to track more directly relevant things, like the actions people are taking. Twitter, for example, may want to know how many tweets the average person sends and what they are searching for, not how many pages they viewed. Pageviews are just a way of approximating the information we really want, and as the web grows more interactive, they become less and less relevant.

Think about this: Sites exist today on which you never actually change the page. These are highly interactive sites, but they are impossible to track with pageviews, so traditional analytics tools are useless.

This will only become more common as time goes on and more companies develop highly interactive applications and adopt AJAX loading techniques.

3. Visitor Retention: How Many People Come Back?

This next technique measures a fairly complex but extremely valuable metric for successful web applications.

You can think of Visitor Retention as a measure of how “sticky” your site is. What we’re really measuring is the percentage of people who come back again and again. The most common way of approaching this is to look at a group of users from a single time period (a week, for example) and track their behavior over time.

Here’s an example of a retention table that should help clarify things:

Each row shows the weekly retention rates for a single group of users (sometimes known as a “cohort”). The first row, for example, is the cohort seen between December 7 and December 13, 2009. We can see that 15.15% of the users in that group came back after 1 week, 13.4% after 2 weeks, and so on.

This is crucial information, particularly for social applications, because most of the value lies in the size of the community. An application with low retention is like an empty shell — many installs but few active users — and you don’t want to build an empty shell. You want a thriving, vibrant community.

Retention is a huge factor in building a strong community for a few reasons: You don’t have much of a community if everyone is a newcomer (so more old users is a good thing), and the nature of retention is such that you get disproportionate returns on any increases you make. Without going into too much detail, an example would be that increasing retention by 33% might give you 50% more users in the long run.

Twitter is again a good example for us, as the network has been plagued by low retention rates. Twitter may seem successful now, but their low retention rate is troubling. In the past, companies that seemed to be extremely successful (think early Facebook apps) ultimately lost their edge because they couldn’t retain their users.

It’s entirely possible that Twitter itself could be a fad. With such low retention, I wouldn’t necessarily be surprised — but it is still too early to tell.

Conclusion.

There’s a lot to learn about analytics from the frontrunners in social media. The intense competition has resulted in many new and innovative ways to track and analyze visitor data.

We covered three such concepts in detail today: Funnel analysis, which lets you track conversion rates across whole parts of your site, engagement tracking, which is becoming more relevant than pageviews, and visitor retention analysis, which helps you understand and optimize the number of repeat visitors you get.

Written by Tim Trefren. One of the founders of Mixpanel, Inc. a real-time analytics service that helps companies understand how users behave with web applications.

Presented by christopher copywriter.