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How sticky is your traffic?
It’s easy to get hung up focusing on monthly uniques, or registrations, or pageviews. But those can be shallow metrics. If your website were a meal, how many people are just taking a taste and how many people are coming back for seconds?
Sure, it’s great to have lots of new people checking out your site, but what’s the point if they’re not getting hooked? Loyalty can reveal how much value they’re getting from your site. Selling a product is always a fantastic way to measure this, but if you’re not currently selling something, user loyalty is a great metric to check up on.
At Fluther, we’ve always had the intuitive sense that our users were loyal. They bake us cookies and drop them off at the office. They makes us schwag with our logo and send us postcards. It’s a relationship filled with lots of “lurve”—and it’s one of the most direct ways I’ve felt like our startup is bettering the world, however humbly.
But does all that fuzzy stuff translate into numbers? Luckily, Google Analytics makes answering this question a snap.
Export that data and graph it in your favorite spreadsheet. And then, we present, the shape of loyalty: the horseshoe.
The Horseshoe of Loyalty basically states you have two classes of returning users: the people on the left, who check it out a few times and taper off, and the people on the right, the heavy users who can’t get enough. A horseshoe is healthy because it shows a balance between the old timers and the new blood. If it were all new blood, it would show you’re losing lots of people, and yet if it were all old-timers, it would show you’re having trouble getting new exposure. The horseshoe represents a healthy balance of new and old.
Obviously, focusing on your returning users is only one window into understanding your traffic, but it’s an easy one to overlook, and can shed some valuable light on how deep your users are getting into your service.
A small note, clearly the shape of the horseshoe has to do with the conflating of columns that Google Analytics does for you, and it would look much closer to a normal logarithm without that. But don’t let that throw you off—we care about the rate that the log decreases, which is actually well-captured in the columns. Everyone should see some bump in their data, the question is how big of a bump. From other data I could find, this can vary greatly (remember to ignore the 1 time visits to get returning users).
Ok, got it? Now don’t let this become what Eric Ries would call a Vanity Metric. If you want to improve it, make a change, run a test, verify your change had a positive impact, and repeat.