​​Measure Your Mobile Rankings and Search Visibility in Moz Analytics

Posted by jon.white

We have launched a couple of new things in Moz Pro that we are excited to share with you all: Mobile Rankings and a Search Visibility score. If you want, you can jump right in by heading to a campaign and adding a mobile engine, or keep reading for more details!

Track your mobile vs. desktop rankings in Moz Analytics

Mobilegeddon came and went with slightly less fanfare than expected, somewhat due to the vast ‘Mobile Friendly’ updates we all did at super short notice (nice work everyone!). Nevertheless, mobile rankings visibility is now firmly on everyone’s radar, and will only become more important over time.

Now you can track your campaigns’ mobile rankings for all of the same keywords and locations you are tracking on desktop.

For this campaign my mobile visibility is almost 20% lower than my desktop visibility and falling;
I can drill down to find out why

Clicking on this will take you into a new Engines tab within your Keyword Rankings page where you can find a more detailed version of this chart as well as a tabular view by keyword for both desktop and mobile. Here you can also filter by label and location.

Here I can see Search Visibility across engines including mobile;
in this case, for my branded keywords.

We have given an extra engine to all campaigns

We’ve given customers an extra engine for each campaign, increasing the number from 3 to 4. Use the extra slot to add the mobile engine and unlock your mobile data!

We will begin to track mobile rankings within 24 hours of adding to a campaign. Once you are set up, you will notice a new chart on your dashboard showing visibility for Desktop vs. Mobile Search Visibility.

Measure your Search Visibility score vs. competitors

The overall Search Visibility for my campaign

Along with this change we have also added a Search Visibility score to your rankings data. Use your visibility score to track and report on your overall campaign ranking performance, compare to your competitors, and look for any large shifts that might indicate penalties or algorithm changes. For a deeper drill-down into your data you can also segment your visibility score by keyword labels or locations. Visit the rankings summary page on any campaign to get started.

How is Search Visibility calculated?

Good question!

The Search Visibility score is the percentage of clicks we estimate you receive based on your rankings positions, across all of your keywords.

We take each ranking position for each keyword, multiply by an estimated click-thru-rate, and then take the average of all of your keywords. You can think of it as the percentage of your SERPs that you own. The score is expressed as a percentage, though scores of 100% would be almost impossible unless you are tracking keywords using the “site:” modifier. It is probably more useful to measure yourself vs. your competitors rather than focus on the actual score, but, as a rule of thumb, mid-40s is probably the realistic maximum for non-branded keywords.

Jeremy, our Moz Analytics TPM, came up with this metaphor:

Think of the SERPs for your keywords as villages. Each position on the SERP is a plot of land in SERP-village. The Search Visibility score is the average amount of plots you own in each SERP-village. Prime real estate plots (i.e., better ranking positions, like #1) are worth more. A complete monopoly of real estate in SERP-village would equate to a score of 100%. The Search Visibility score equates to how much total land you own in all SERP-villages.

Some neat ways to use this feature

  • Label and group your keywords, particularly when you add them – As visibility score is an average of all of your keywords, when you add or remove keywords from your campaign you will likely see fluctuations in the score that are unrelated to performance. Solve this by getting in the habit of labeling keywords when you add them. Then segment your data by these labels to track performance of specific keyword groups over time.
  • See how location affects your mobile rankings – Using the Engines tab in Keyword Rankings, use the filters to select just local keywords. Look for big differences between Mobile and Desktop where Google might be assuming local intent for mobile searches but not for desktop. Check out how your competitors perform for these keywords. Can you use this data?

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!

Reblogged 3 years ago from tracking.feedpress.it

Pinpoint vs. Floodlight Content and Keyword Research Strategies – Whiteboard Friday

Posted by randfish

When we’re doing keyword research and targeting, we have a choice to make: Are we targeting broader keywords with multiple potential searcher intents, or are we targeting very narrow keywords where it’s pretty clear what the searchers were looking for? Those different approaches, it turns out, apply to content creation and site architecture, as well. In today’s Whiteboard Friday, Rand illustrates that connection.

Pinpoint vs Floodlight Content and Keyword Research Strategy Whiteboard

For reference, here are stills of this week’s whiteboards. Click on it to open a high resolution image in a new tab!

Video Transcription

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week we’re going to chat about pinpoint versus floodlight tactics for content targeting, content strategy, and keyword research, keyword targeting strategy. This is also called the shotgun versus sniper approach, but I’m not a big gun fan. So I’m going to stick with my floodlight versus pinpoint, plus, you know, for the opening shot we don’t have a whole lot of weaponry here at Moz, but we do have lighting.

So let’s talk through this at first. You’re going through and doing some keyword research. You’re trying to figure out which terms and phrases to target. You might look down a list like this.

Well, maybe, I’m using an example here around antique science equipment. So you see these various terms and phrases. You’ve got your volume numbers. You probably have lots of other columns. Hopefully, you’ve watched the Whiteboard Friday on how to do keyword research like it’s 2015 and not 2010.

So you know you have all these other columns to choose from, but I’m simplifying here for the purpose of this experiment. So you might choose some of these different terms. Now, they’re going to have different kinds of tactics and a different strategic approach, depending on the breadth and depth of the topic that you’re targeting. That’s going to determine what types of content you want to create and where you place it in your information architecture. So I’ll show you what I mean.

The floodlight approach

For antique science equipment, this is a relatively broad phrase. I’m going to do my floodlight analysis on this, and floodlight analysis is basically saying like, “Okay, are there multiple potential searcher intents?” Yeah, absolutely. That’s a fairly broad phase. People could be looking to transact around it. They might be looking for research information, historical information, different types of scientific equipment that they’re looking for.

<img src="http://d1avok0lzls2w.cloudfront.net/uploads/blog/55b15fc96679b8.73854740.jpg" rel="box-shadow: 0 0 10px 0 #999; border-radius: 20px;"

Are there four or more approximately unique keyword terms and phrases to target? Well, absolutely, in fact, there’s probably more than that. So antique science equipment, antique scientific equipment, 18th century scientific equipment, all these different terms and phrases that you might explore there.

Is this a broad content topic with many potential subtopics? Again, yes is the answer to this. Are we talking about generally larger search volume? Again, yes, this is going to have a much larger search volume than some of the narrower terms and phrases. That’s not always the case, but it is here.

The pinpoint approach

For pinpoint analysis, we kind of go the opposite direction. So we might look at a term like antique test tubes, which is a very specific kind of search, and that has a clear single searcher intent or maybe two. Someone might be looking for actually purchasing one of those, or they might be looking to research them and see what kinds there are. Not a ton of additional intents behind that. One to three unique keywords, yeah, probably. It’s pretty specific. Antique test tubes, maybe 19th century test tubes, maybe old science test tubes, but you’re talking about a limited set of keywords that you’re targeting. It’s a narrow content topic, typically smaller search volume.

<img src="http://d1avok0lzls2w.cloudfront.net/uploads/blog/55b160069eb6b1.12473448.jpg" rel="box-shadow: 0 0 10px 0 #999; border-radius: 20px;"

Now, these are going to feed into your IA, your information architecture, and your site structure in this way. So floodlight content generally sits higher up. It’s the category or the subcategory, those broad topic terms and phrases. Those are going to turn into those broad topic category pages. Then you might have multiple, narrower subtopics. So we could go into lab equipment versus astronomical equipment versus chemistry equipment, and then we’d get into those individual pinpoints from the pinpoint analysis.

How do I decide which approach is best for my keywords?

Why are we doing this? Well, generally speaking, if you can take your terms and phrases and categorize them like this and then target them differently, you’re going to provide a better, more logical user experience. Someone who searches for antique scientific equipment, they’re going to really expect to see that category and then to be able to drill down into things. So you’re providing them the experience they predict, the one that they want, the one that they expect.

It’s better for topic modeling analysis and for all of the algorithms around things like Hummingbird, where Google looks at: Are you using the types of terms and phrases, do you have the type of architecture that we expect to find for this keyword?

It’s better for search intent targeting, because the searcher intent is going to be fulfilled if you provide the multiple paths versus the narrow focus. It’s easier keyword targeting for you. You’re going to be able to know, “Hey, I need to target a lot of different terms and phrases and variations in floodlight and one very specific one in pinpoint.”

There’s usually higher searcher satisfaction, which means you get lower bounce rate. You get more engagement. You usually get a higher conversion rate. So it’s good for all those things.

For example…

I’ll actually create pages for each of antique scientific equipment and antique test tubes to illustrate this. So I’ve got two different types of pages here. One is my antique scientific equipment page.

<img src="http://d1avok0lzls2w.cloudfront.net/uploads/blog/55b161fa871e32.54731215.jpg" rel="box-shadow: 0 0 10px 0 #999; border-radius: 20px;"

This is that floodlight, shotgun approach, and what we’re doing here is going to be very different from a pinpoint approach. It’s looking at like, okay, you’ve landed on antique scientific equipment. Now, where do you want to go? What do you want to specifically explore? So we’re going to have a little bit of content specifically about this topic, and how robust that is depends on the type of topic and the type of site you are.

If this is an e-commerce site or a site that’s showing information about various antiques, well maybe we don’t need very much content here. You can see the filtration that we’ve got is going to be pretty broad. So I can go into different centuries. I can go into chemistry, astronomy, physics. Maybe I have a safe for kids type of stuff if you want to buy your kids antique lab equipment, which you might be. Who knows? Maybe you’re awesome and your kids are too. Then different types of stuff at a very broad level. So I can go to microscopes or test tubes, lab searches.

This is great because it’s got broad intent foci, serving many different kinds of searchers with the same page because we don’t know exactly what they want. It’s got multiple keyword targets so that we can go after broad phrases like antique or old or historical or 13th, 14th, whatever century, science and scientific equipment ,materials, labs, etc., etc., etc. This is a broad page that could reach any and all of those. Then there’s lots of navigational and refinement options once you get there.

Total opposite of pinpoint content.

<img src="http://d1avok0lzls2w.cloudfront.net/uploads/blog/55b1622740f0b5.73477500.jpg" rel="box-shadow: 0 0 10px 0 #999; border-radius: 20px;"

Pinpoint content, like this antique test tubes page, we’re still going to have some filtration options, but one of the important things to note is note how these are links that take you deeper. Depending on how deep the search volume goes in terms of the types of queries that people are performing, you might want to make a specific page for 17th century antique test tubes. You might not, and if you don’t want to do that, you can have these be filters that are simply clickable and change the content of the page here, narrowing the options rather than creating completely separate pages.

So if there’s no search volume for these different things and you don’t think you need to separately target them, go ahead and just make them filters on the data that already appears on this page or the results that are already in here as opposed to links that are going to take you deeper into specific content and create a new page, a new experience.

You can also see I’ve got my individual content here. I probably would go ahead and add some content specifically to this page that is just unique here and that describes antique test tubes and the things that your searchers need. They might want to know things about price. They might want to know things about make and model. They might want to know things about what they were used for. Great. You can have that information broadly, and then individual pieces of content that someone might dig into.

This is narrower intent foci obviously, serving maybe one or two searcher intents. This is really talking about targeting maybe one to two separate keywords. So antique test tubes, maybe lab tubes or test tube sets, but not much beyond that.

Ten we’re going to have fewer navigational paths, fewer distractions. We want to keep the searcher. Because we know their intent, we want to guide them along the path that we know they probably want to take and that we want them to take.

So when you’re considering your content, choose wisely between shotgun/floodlight approach or sniper/pinpoint approach. Your searchers will be better served. You’ll probably rank better. You’ll be more likely to earn links and amplification. You’re going to be more successful.

Looking forward to the comments, and we’ll see you again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!

Reblogged 3 years ago from tracking.feedpress.it

Why We Can’t Do Keyword Research Like It’s 2010 – Whiteboard Friday

Posted by randfish

Keyword Research is a very different field than it was just five years ago, and if we don’t keep up with the times we might end up doing more harm than good. From the research itself to the selection and targeting process, in today’s Whiteboard Friday Rand explains what has changed and what we all need to do to conduct effective keyword research today.

For reference, here’s a still of this week’s whiteboard. Click on it to open a high resolution image in a new tab!

What do we need to change to keep up with the changing world of keyword research?

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week we’re going to chat a little bit about keyword research, why it’s changed from the last five, six years and what we need to do differently now that things have changed. So I want to talk about changing up not just the research but also the selection and targeting process.

There are three big areas that I’ll cover here. There’s lots more in-depth stuff, but I think we should start with these three.

1) The Adwords keyword tool hides data!

This is where almost all of us in the SEO world start and oftentimes end with our keyword research. We go to AdWords Keyword Tool, what used to be the external keyword tool and now is inside AdWords Ad Planner. We go inside that tool, and we look at the volume that’s reported and we sort of record that as, well, it’s not good, but it’s the best we’re going to do.

However, I think there are a few things to consider here. First off, that tool is hiding data. What I mean by that is not that they’re not telling the truth, but they’re not telling the whole truth. They’re not telling nothing but the truth, because those rounded off numbers that you always see, you know that those are inaccurate. Anytime you’ve bought keywords, you’ve seen that the impression count never matches the count that you see in the AdWords tool. It’s not usually massively off, but it’s often off by a good degree, and the only thing it’s great for is telling relative volume from one from another.

But because AdWords hides data essentially by saying like, “Hey, you’re going to type in . . .” Let’s say I’m going to type in “college tuition,” and Google knows that a lot of people search for how to reduce college tuition, but that doesn’t come up in the suggestions because it’s not a commercial term, or they don’t think that an advertiser who bids on that is going to do particularly well and so they don’t show it in there. I’m giving an example. They might indeed show that one.

But because that data is hidden, we need to go deeper. We need to go beyond and look at things like Google Suggest and related searches, which are down at the bottom. We need to start conducting customer interviews and staff interviews, which hopefully has always been part of your brainstorming process but really needs to be now. Then you can apply that to AdWords. You can apply that to suggest and related.

The beautiful thing is once you get these tools from places like visiting forums or communities, discussion boards and seeing what terms and phrases people are using, you can collect all this stuff up, plug it back into AdWords, and now they will tell you how much volume they’ve got. So you take that how to lower college tuition term, you plug it into AdWords, they will show you a number, a non-zero number. They were just hiding it in the suggestions because they thought, “Hey, you probably don’t want to bid on that. That won’t bring you a good ROI.” So you’ve got to be careful with that, especially when it comes to SEO kinds of keyword research.

2) Building separate pages for each term or phrase doesn’t make sense

It used to be the case that we built separate pages for every single term and phrase that was in there, because we wanted to have the maximum keyword targeting that we could. So it didn’t matter to us that college scholarship and university scholarships were essentially people looking for exactly the same thing, just using different terminology. We would make one page for one and one page for the other. That’s not the case anymore.

Today, we need to group by the same searcher intent. If two searchers are searching for two different terms or phrases but both of them have exactly the same intent, they want the same information, they’re looking for the same answers, their query is going to be resolved by the same content, we want one page to serve those, and that’s changed up a little bit of how we’ve done keyword research and how we do selection and targeting as well.

3) Build your keyword consideration and prioritization spreadsheet with the right metrics

Everybody’s got an Excel version of this, because I think there’s just no awesome tool out there that everyone loves yet that kind of solves this problem for us, and Excel is very, very flexible. So we go into Excel, we put in our keyword, the volume, and then a lot of times we almost stop there. We did keyword volume and then like value to the business and then we prioritize.

What are all these new columns you’re showing me, Rand? Well, here I think is how sophisticated, modern SEOs that I’m seeing in the more advanced agencies, the more advanced in-house practitioners, this is what I’m seeing them add to the keyword process.

Difficulty

A lot of folks have done this, but difficulty helps us say, “Hey, this has a lot of volume, but it’s going to be tremendously hard to rank.”

The difficulty score that Moz uses and attempts to calculate is a weighted average of the top 10 domain authorities. It also uses page authority, so it’s kind of a weighted stack out of the two. If you’re seeing very, very challenging pages, very challenging domains to get in there, it’s going to be super hard to rank against them. The difficulty is high. For all of these ones it’s going to be high because college and university terms are just incredibly lucrative.

That difficulty can help bias you against chasing after terms and phrases for which you are very unlikely to rank for at least early on. If you feel like, “Hey, I already have a powerful domain. I can rank for everything I want. I am the thousand pound gorilla in my space,” great. Go after the difficulty of your choice, but this helps prioritize.

Opportunity

This is actually very rarely used, but I think sophisticated marketers are using it extremely intelligently. Essentially what they’re saying is, “Hey, if you look at a set of search results, sometimes there are two or three ads at the top instead of just the ones on the sidebar, and that’s biasing some of the click-through rate curve.” Sometimes there’s an instant answer or a Knowledge Graph or a news box or images or video, or all these kinds of things that search results can be marked up with, that are not just the classic 10 web results. Unfortunately, if you’re building a spreadsheet like this and treating every single search result like it’s just 10 blue links, well you’re going to lose out. You’re missing the potential opportunity and the opportunity cost that comes with ads at the top or all of these kinds of features that will bias the click-through rate curve.

So what I’ve seen some really smart marketers do is essentially build some kind of a framework to say, “Hey, you know what? When we see that there’s a top ad and an instant answer, we’re saying the opportunity if I was ranking number 1 is not 10 out of 10. I don’t expect to get whatever the average traffic for the number 1 position is. I expect to get something considerably less than that. Maybe something around 60% of that, because of this instant answer and these top ads.” So I’m going to mark this opportunity as a 6 out of 10.

There are 2 top ads here, so I’m giving this a 7 out of 10. This has two top ads and then it has a news block below the first position. So again, I’m going to reduce that click-through rate. I think that’s going down to a 6 out of 10.

You can get more and less scientific and specific with this. Click-through rate curves are imperfect by nature because we truly can’t measure exactly how those things change. However, I think smart marketers can make some good assumptions from general click-through rate data, which there are several resources out there on that to build a model like this and then include it in their keyword research.

This does mean that you have to run a query for every keyword you’re thinking about, but you should be doing that anyway. You want to get a good look at who’s ranking in those search results and what kind of content they’re building . If you’re running a keyword difficulty tool, you are already getting something like that.

Business value

This is a classic one. Business value is essentially saying, “What’s it worth to us if visitors come through with this search term?” You can get that from bidding through AdWords. That’s the most sort of scientific, mathematically sound way to get it. Then, of course, you can also get it through your own intuition. It’s better to start with your intuition than nothing if you don’t already have AdWords data or you haven’t started bidding, and then you can refine your sort of estimate over time as you see search visitors visit the pages that are ranking, as you potentially buy those ads, and those kinds of things.

You can get more sophisticated around this. I think a 10 point scale is just fine. You could also use a one, two, or three there, that’s also fine.

Requirements or Options

Then I don’t exactly know what to call this column. I can’t remember the person who’ve showed me theirs that had it in there. I think they called it Optional Data or Additional SERPs Data, but I’m going to call it Requirements or Options. Requirements because this is essentially saying, “Hey, if I want to rank in these search results, am I seeing that the top two or three are all video? Oh, they’re all video. They’re all coming from YouTube. If I want to be in there, I’ve got to be video.”

Or something like, “Hey, I’m seeing that most of the top results have been produced or updated in the last six months. Google appears to be biasing to very fresh information here.” So, for example, if I were searching for “university scholarships Cambridge 2015,” well, guess what? Google probably wants to bias to show results that have been either from the official page on Cambridge’s website or articles from this year about getting into that university and the scholarships that are available or offered. I saw those in two of these search results, both the college and university scholarships had a significant number of the SERPs where a fresh bump appeared to be required. You can see that a lot because the date will be shown ahead of the description, and the date will be very fresh, sometime in the last six months or a year.

Prioritization

Then finally I can build my prioritization. So based on all the data I had here, I essentially said, “Hey, you know what? These are not 1 and 2. This is actually 1A and 1B, because these are the same concepts. I’m going to build a single page to target both of those keyword phrases.” I think that makes good sense. Someone who is looking for college scholarships, university scholarships, same intent.

I am giving it a slight prioritization, 1A versus 1B, and the reason I do this is because I always have one keyword phrase that I’m leaning on a little more heavily. Because Google isn’t perfect around this, the search results will be a little different. I want to bias to one versus the other. In this case, my title tag, since I more targeting university over college, I might say something like college and university scholarships so that university and scholarships are nicely together, near the front of the title, that kind of thing. Then 1B, 2, 3.

This is kind of the way that modern SEOs are building a more sophisticated process with better data, more inclusive data that helps them select the right kinds of keywords and prioritize to the right ones. I’m sure you guys have built some awesome stuff. The Moz community is filled with very advanced marketers, probably plenty of you who’ve done even more than this.

I look forward to hearing from you in the comments. I would love to chat more about this topic, and we’ll see you again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!

Reblogged 3 years ago from tracking.feedpress.it

Misuses of 4 Google Analytics Metrics Debunked

Posted by Tom.Capper

In this post I’ll pull apart four of the most commonly used metrics in Google Analytics, how they are collected, and why they are so easily misinterpreted.

Average Time on Page

Average time on page should be a really useful metric, particularly if you’re interested in engagement with content that’s all on a single page. Unfortunately, this is actually its worst use case. To understand why, you need to understand how time on page is calculated in Google Analytics:

Time on Page: Total across all pageviews of time from pageview to last engagement hit on that page (where an engagement hit is any of: next pageview, interactive event, e-commerce transaction, e-commerce item hit, or social plugin). (Source)

If there is no subsequent engagement hit, or if there is a gap between the last engagement hit on a site and leaving the site, the assumption is that no further time was spent on the site. Below are some scenarios with an intuitive time on page of 20 seconds, and their Google Analytics time on page:

Scenario

Intuitive time on page

GA time on page

0s: Pageview
10s: Social plugin
20s: Click through to next page

20s

20s

0s: Pageview
10s: Social plugin
20s: Leave site

20s

10s

0s: Pageview
20s: Leave site

20s

0s

Google doesn’t want exits to influence the average time on page, because of scenarios like the third example above, where they have a time on page of 0 seconds (source). To avoid this, they use the following formula (remember that Time on Page is a total):

Average Time on Page: (Time on Page) / (Pageviews – Exits)

However, as the second example above shows, this assumption doesn’t always hold. The second example feeds into the top half of the average time on page faction, but not the bottom half:

Example 2 Average Time on Page: (20s+10s+0s) / (3-2) = 30s

There are two issues here:

  1. Overestimation
    Excluding exits from the second half of the average time on page equation doesn’t have the desired effect when their time on page wasn’t 0 seconds—note that 30s is longer than any of the individual visits. This is why average time on page can often be longer than average visit duration. Nonetheless, 30 seconds doesn’t seem too far out in the above scenario (the intuitive average is 20s), but in the real world many pages have much higher exit rates than the 67% in this example, and/or much less engagement with events on page.
  2. Ignored visits
    Considering only visitors who exit without an engagement hit, whether these visitors stayed for 2 seconds, 10 minutes or anything inbetween, it doesn’t influence average time on page in the slightest. On many sites, a 10 minute view of a single page without interaction (e.g. a blog post) would be considered a success, but it wouldn’t influence this metric.

Solution: Unfortunately, there isn’t an easy solution to this issue. If you want to use average time on page, you just need to keep in mind how it’s calculated. You could also consider setting up more engagement events on page (like a scroll event without the “nonInteraction” parameter)—this solves issue #2 above, but potentially worsens issue #1.

Site Speed

If you’ve used the Site Speed reports in Google Analytics in the past, you’ve probably noticed that the numbers can sometimes be pretty difficult to believe. This is because the way that Site Speed is tracked is extremely vulnerable to outliers—it starts with a 1% sample of your users and then takes a simple average for each metric. This means that a few extreme values (for example, the occasional user with a malware-infested computer or a questionable wifi connection) can create a very large swing in your data.

The use of an average as a metric is not in itself bad, but in an area so prone to outliers and working with such a small sample, it can lead to questionable results.

Fortunately, you can increase the sampling rate right up to 100% (or the cap of 10,000 hits per day). Depending on the size of your site, this may still only be useful for top-level data. For example, if your site gets 1,000,000 hits per day and you’re interested in the performance of a new page that’s receiving 100 hits per day, Google Analytics will throttle your sampling back to the 10,000 hits per day cap—1%. As such, you’ll only be looking at a sample of 1 hit per day for that page.

Solution: Turn up the sampling rate. If you receive more than 10,000 hits per day, keep the sampling rate in mind when digging into less visited pages. You could also consider external tools and testing, such as Pingdom or WebPagetest.

Conversion Rate (by channel)

Obviously, conversion rate is not in itself a bad metric, but it can be rather misleading in certain reports if you don’t realise that, by default, conversions are attributed using a last non-direct click attribution model.

From Google Analytics Help:

“…if a person clicks over your site from google.com, then returns as “direct” traffic to convert, Google Analytics will report 1 conversion for “google.com / organic” in All Traffic.”

This means that when you’re looking at conversion numbers in your acquisition reports, it’s quite possible that every single number is different to what you’d expect under last click—every channel other than direct has a total that includes some conversions that occurred during direct sessions, and direct itself has conversion numbers that don’t include some conversions that occurred during direct sessions.

Solution: This is just something to be aware of. If you do want to know your last-click numbers, there’s always the Multi-Channel Funnels and Attribution reports to help you out.

Exit Rate

Unlike some of the other metrics I’ve discussed here, the calculation behind exit rate is very intuitive—”for all pageviews to the page, Exit Rate is the percentage that were the last in the session.” The problem with exit rate is that it’s so often used as a negative metric: “Which pages had the highest exit rate? They’re the problem with our site!” Sometimes this might be true: Perhaps, for example, if those pages are in the middle of a checkout funnel.

Often, however, a user will exit a site when they’ve found what they want. This doesn’t just mean that a high exit rate is ok on informational pages like blog posts or about pages—it could also be true of product pages and other pages with a highly conversion-focused intent. Even on ecommerce sites, not every visitor has the intention of converting. They might be researching towards a later online purchase, or even planning to visit your physical store. This is particularly true if your site ranks well for long tail queries or is referenced elsewhere. In this case, an exit could be a sign that they found the information they wanted and are ready to purchase once they have the money, the need, the right device at hand or next time they’re passing by your shop.

Solution: When judging a page by its exit rate, think about the various possible user intents. It could be useful to take a segment of visitors who exited on a certain page (in the Advanced tab of the new segment menu), and investigate their journey in User Flow reports, or their landing page and acquisition data.

Discussion

If you know of any other similarly misunderstood metrics, you have any questions or you have something to add to my analysis, tweet me at @THCapper or leave a comment below.

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Reblogged 3 years ago from tracking.feedpress.it