Baseball and web marketing have something in common: statistics. There are a lot of people using those statistics to make decisions that impact results in a big way.
When Billy Beane of the Oakland A’s found better ways to read those statistics, he made better decisions. So much better that Oakland was competitive with the larger market teams that spent more than 3 times as much on player salaries. You may be familiar with the book Michael Lewis wrote about him, and Brad Pitt played him in a movie.
Billy Beane demonstrated that the commonly used statistics RBI’s (runs batted in) and batting average were inferior predictors of wins. Instead, he foc
used on on-base percentage and slugging percentage. Let’s take a hard look at three statistics in your analytics, and we’ll see why they’re misleading at worst and irrelevant at best.
Everyone watches it in hopes that it will go down. It’s the percentage of people who leave the site after seeing just one page. It’s right there on your dashboard when you log into Google Analytics. So it must be important, right? Not necessarily.
(Exception: Low bounce rates are very important to websites that are doing a lot of PPC marketing.)
Are bounce rates important?
Your bounce rate doesn’t matter, at least not for most sites. Why not? Because some of the most important activities in content marketing – blogging, social media, email marketing – result in a higher bounce rate.
If your bounce rate is below 65%, it’s too low. You’re not active enough in web marketing. Get blogging, get your newsletter out, be more active in social media, and your bounce rate will increase.
Additional read: Are bounce rates important? What is a good bounce rate?
This isn’t an irrelevant statistic, but it’s easy to misinterpret. Ideally, this number isn’t high or low. It’s a “Goldilocks” metric. For lead generation sites, the number should be between 2 and 5 (for ecommerce websites, it should be between 3 and 7). It shouldn’t take more than five pages to drop in, learn something, get to know you a bit, and decide if they’re interested.
Redesigning a website often reduces the number of pages per visit, since the improved design helps people find information more easily and efficiently. Ironically, as a website owner, you should seek to reduce the number of pages per visit through good design and clear navigation, thereby reducing the total number of page views.
(Exception: A high average pages per visit is important for sites that generate revenue through advertising.)
It’s that big blue and green pie chart you see as soon as you log into Google Analytics. Surely this is important, right? Nope!
It’s a nice looking chart, but I’ve never been able to make a meaningful decision from this statistic. It’s inherently misleading because it can always be read as both good or bad.
Percentage of new visitors is high:
Percentage of returning visitors is high:
Another problem is that it doesn’t really measure visitors, it measures devices. If I visit your site from work, home and my phone, I’m counted as three unique visitors… only I’m not.
So how do you make a good decision based on an ambiguous, inaccurate metric? You can’t. Ignore it.
If you ignore the distractions and focus on what’s really important, you’ll filter out the noise, make better choices and drive better results. Play Moneyball with your analytics and before long, Brad Pitt will be playing you on the big screen.
What stats do you find most important for your site?
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Thanks for the input @tacimala and @RicDragon. Your insights added to this piece. I agree completely. The stats can be meaningful if you drill down to the next level.
Maybe we should collaborate on a piece that talks about segmentation within Analytics. I’d learn something from that myself!
I would agree that these statistics are not valuable when looking at your overall data, but these are great things to look at when segmenting and breaking down the data. For example, traffic to the blog should have a higher bounce rate than the rest of the website. If you are working on lowering or increasing bounce rate, you should focus on segmenting out the areas that you are trying to lower to measure the output.
Another example would be a company focusing on generating phone leads – a user visits one page on the site and decides to make a phone call and purchase services. In this case, that will be a bounce visitor, but it will also be a conversion (for those doing phone call tracking). Another interesting piece is that it will register that visitor as 00:00:00 average visit since they have to visit two pages before that statistic can be calculated.
So I think the real questions to ask are what am I trying to improve, what skewed data should I segment out, and what stats should I look at to measure the changes?
Compelling as always, Andy. Great piece!
Hi Andy; I agree that these 3 stats can too easily be misunderstood. I’ve seen cases myself where there was a low bounce rate because users couldn’t find what they were looking for. Putting the info on every page increased the bounce rate, but increased calls.
I like to create segments of new visitors – or even visitors that typically return to the site a couple of times per month. Can we gently nudge our content to encourage more interaction with those users? That very well might be more pages per visit!