4 Underused Data Points For Conversion Optimization
Columnist Jeremy Smith discusses four underused data points that, if you pay attention and act on them, can improve your conversion rates.
Most of the time when we conversion optimizers talk shop, we look at one all-important number: the conversion rate.
This makes sense. After all, the goal of a conversion optimizers is to…say it with me…optimize for conversions!
But in reality, conversion optimizers look at lots of data. Our world is full of numbers, percentages, decimal points, pie charts, line graphs, and prickly problems with zeros attached to them.
Sadly, I see some of these rich data points being totally neglected by conversion optimizers. Site Speed, Bounce Rate, Statistical Significance and Year-Over-Year Comparisons are four such underused data points that, if you pay attention and act on them, can improve your conversion rates.
Data Point #1: Site Speed
What It Is: Site speed, as defined by Google, is “the average amount of time (in seconds) it takes that page to load, from initiation of the pageview (e.g., click on a page link) to load completion in the browser.” In other words, site speed is how fast or slow your site appears in a user’s browser.
Why It Matters: Here’s the big idea on site speed: When your site is slow, it ruins your conversion rates.
On the flip side, when your site is fast, it boosts your conversion rates.
For example, when LeadPages reduced page load time for one of their customers, they got a boost of nearly 9% in conversions. Similarly, when Shopzilla made their site load faster, they saw a whopping 12% boost in revenue. That’s right — their bottom line revenue increased because their site got faster.
The takeaway is obvious: If you’re a conversion rate optimizer, then your main goal is not just to boost conversion rates, but to boost revenue. One simple way to boost revenue is to increase site speed.
Pingdom will give you all the detailed geek-out data you could ever want, and Google PageSpeed insights will tell you how to fix it.
How To Interpret Site Speed Data: To make this really simple, I’m going to cut away the jargon crap and just tell you if your site speed sucks or not.
- If your site loads in more than 5 seconds, you’re screwed.
- If your site loads in 4-5 seconds, you’re struggling.
- If your site loads in 2-4 seconds, you’re doing okay.
- If your site loads in 1-2 seconds, you’re good.
- If your site loads in <1 second, you’re awesome.
If you’re a developer or a genius with an advanced knowledge of load times, you know that my four little bullet points up there are way too simplistic. There are a legion of factors that affect page load time, plus the issues of graceful degradation, URL resolution, layout load time, and other response complexities that make it all a pretty complicated issue.
Be that as it may, I wanted to make it simple for you by giving you some ballpark ranges.
How To Improve Your Site Speed: Thankfully, Google — may the Internet gods bless them — has given every one of us clear instructions on how to make our site faster. To get these instructions:
- Plug your site into Google PageSpeed Insights
- Do what they tell you to do
Every site you analyze will have something to improve on. Google.com itself, the paragon of site excellence, has a few things that they should consider fixing.
Obviously, you’ll need to get your developer on board to help fix any issues.
That’s all there is to it. If you pay attention to that little data point — site load time — and make improvements, your conversion rate will go up.
Note: I recently published a magnum opus on site speed and how it dramatically affects a site’s conversion optimization. I recommend that you read that article to get the full-bore version of how a slow site can destroy your conversion rates.
Data Point #2: Bounce Rate
What It Is: The bounce rate is “the percentage of visitors to a particular website who navigate away from the site after viewing only one page.” In other words, if 10 people land on your website and 4 of them leave without visiting another page, that would give you a bounce rate of 40%.
The bounce rate is one of the saddest, most dejected, and most misunderstood little statistics in the wide world of digital marketing. Even SEOs get confused about it.
What do people misunderstand? A bouncer (not the guy who stands in front of a bar) could be a person who visited your site, read your content, loved your stuff, and then left.
Thus, a high bounce rate doesn’t mean that your website is trash or that you’re dealing with a lunatic of a user. It simply means that your page didn’t have anything compelling enough for a visitor to click on.
Why It Matters: Bounce rate matters for conversion rates. Why? Because if a visitor is bouncing, then they are obviously not buying. If you can decrease your bounce rate, then you will invariably increase your conversion rates. The two work in an inverse relationship.
- The more visitors stay and click on your other pages, the more visitors are likely to buy your product and/or convert.
- If visitors do not stay and click on other pages, they are gone with the wind. A lost conversion. A lowered conversion rate.
Where To Find Your Bounce Rate: The bounce rate is all over Google Analytics. Go to Audience → Overview, and you’ll see the site-wide bounce rate.
You can also drill down to find the bounce rate across user types, individual pages, etc.
How To Interpret Bounce Rate Data: Like I did for site speed, let me give you a cut-the-crap definition of what’s “good” and “bad” in the bounce rate world.
- 0-15% – Either you have a Unicorn of website, or something’s screwed up; check your analytics code or tracking implementation
- 15-40% – Very good
- 41-54% – Average
- 55-70% – Higher than average
- 71-90% – Call your doctor
- 91%+ – What on earth is going on here?!
Now, take your proverbial salt shaker and shake generously on the bullet points above.
Why? Because a bounce rate is kind of a private and personal thing, sort of like your preferred brand of deodorant. Every industry, niche, and website type has their own averages and variables. You need to know what the averages are for your page types, your site, and your industry and then act accordingly.
Generally speaking of course, the lower the bounce rate the better.
How To Improve Your Bounce Rate: What should you do to lower your bounce rate? The inverse of this question is, How do I increase my conversion rate?
I established earlier that lower bounce rates are directly tied to higher conversion rates.
Here are three things you can do to decrease bounce rates and improve conversion rates simultaneously.
- Make it extremely obvious what the user is supposed to do next. Every page should have a goal, an obvious action that the user should take. Don’t assume that a visitor is smart enough to intuit the next action he or she should take. Instead, tell them what to do.
- Create large, loud, powerful, and compelling calls-to-action (CTAs). The goal here is a click — an action that will take the user farther down the conversion funnel. To this end, make your calls-to-action very big and powerful. Whether it’s a button, a text, or flaming nunchucks, you need to make the CTA compelling.
- Use a pop-up capture form. A pop-up is one of the greatest ways to deal a death blow to high bounce rates. Depending on how you set it up, the pop-up presents the user with an overt invitation to do something.
My blog gets a decent amount of organic traffic to individual article pages. There is the possibility that the user will read and leave, creating a bounce (and a non-conversion). To forestall this, I use this slide-in capture form. It kills bounce rates and scores conversions.
Data Point #3: Statistical Significance
Dataheads, rejoice. We’re going to talk about statistical significance.
What It Is: Statistical significance is how sure you are that a test is accurate and trustworthy.
In conversion optimization, we do a lot of testing: split testing, multivariate testing, A/B testing, multifactorial testing, etc.
All too often, we look at those test results and think, WAHOO! The test is done! The answer is clear!
But what we don’t realize is that the test we just ran is not statistically significant. If you want to get geeky about it, check out the image below.
Why It Matters: Statistical significance determines whether or not your test is valid. To state the matter simply, if your test lacks statistical significance, then the results cannot be trusted. Without statistical significance, you do not have the confidence with which to make site changes for conversion improvement.
Where To Find Statistical Significance Data: Some testing platforms give you the p-value automatically. Most don’t.
If your testing platform does not give you the p-value or statistical significance, then you can use this handy tool from GetDataDriven.com.
How To Interpret Statistical Significance Data: The calculator above interprets the data for you. All you have to do is plug in four numbers: the number of visitors on the page for test A, the number of overall conversions for test A, and the same two metrics for test B. The calculator tells you whether or not your test has statistical significance.
It’s that easy.
How To Improve Statistical Significance: So, let’s say your tests aren’t passing the statistical significance test. What do you do?
Usually, you want to test larger and longer.
- Test Larger. The larger the number of visitors and conversions, the better you’ll be able to draw legitimate inferences from the test.
- Test Longer. A longer-running test not only gives you bigger numbers to work with, but it also provides longer-running averages.
It’s also important to figure out your baseline before you test, also known as A/A testing.
Data Point #4: The Year-Over-Year Comparison
What It Is: This final data point consideration, the year-over-year comparison, isn’t so much a specific point as it is an amalgam of several other data points.
(If you’re a startup, and don’t have any year-over-year data, then save this article in Evernote or something so you can look at it a year from now.)
Year-over-year (YOY), is defined by Investopedia as “a method of evaluating two or more measured events to compare the results at one time period with those from another time period (or series of time periods), on an annualized basis.”
As defined by Jeremypedia, it is “comparing data from last year to data from this year, and seeing if you’re getting better or getting worse.” (And I didn’t even have to use the word “annualized.” Sheesh!)
If you want something even better than year-over-year comparisons, you can do Year-over-year-over-year-over-year-over… you get the idea.
Why It Matters: Year-over-year comparisons are one of the most helpful long-term ways of viewing data. With YOY data, you can identify trends, prepare for slow seasons, analyze growth rates, and find out how and why your site is gaining more conversions.
Where To Find YOY Data: To get year-over-year comparisons, just compare last year’s data with this year’s data.
But what data?!
Here is my list of favorite YOY comparisons:
- Conversion rates
- Bounce rates
Since most of the world’s data-driven population uses Google Analytics, there’s an easy way to get YoY comparisons. When generating reports, simply set the data parameters as usual, then check the “compare to” box and select “previous year.”
How To Interpret YOY Data: The takeaways from YOY comparisons are usually pretty obvious. If numbers are going up year-over-year, good. If numbers are doing down year-over-year, bad.
But you need to look at this data strategically. Keep in mind that every industry experiences seasonality.
Seasonality has to do with recurring trends that track year after year. For example, the flower industry has seasonal spikes around Valentine’s Day and Mother’s Day. The Christmas tree industry has spikes around, um, Christmas. Fireworks around the 4th of July.
How To Improve Year Over Year: YOY data allows you to be strategic in the way that you approach seasonality. Seasonal fluctuations of your niche may be more subtle and, at first, unpredictable. YOY comparisons help you to identify and respond to those differences.
Year-over-year data not only shows you what happened in the past, but it also predicts what will happen in the future. This prophetic glance ahead is a huge benefit for marketing minds, who want to determine how and why to get ahead.
Data, whatever its form or function, is useful stuff. What I’ve seen in the conversion optimization community, however, is a blindness to some simple yet critical data points.
There are more than four, of course, but I think that these are some of the most important data points to look at:
- Site Speed
- Bounce rate
- Statistical significance
- Year-over-year comparisons
The great thing about these data points is that they’re easy to get, easy to understand, and easy to take action on.
Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.