Is Australia the land of opportunity for your retail brand?

Australia has a resident population of more than 24 million and, according to eMarketer, the country’s ecommerce sales are predicted to reach A$32.56 billion by 2017. The country’s remote location in the APAC region means that unlike European countries or the USA, traditionally there have been a lack of global brands sold locally.

Of course, we also know that many expatriates, particularly from inside the Commonwealth, have made Australia their home and are keen to buy products they know and love from their country of origin.

All of these factors present a huge and potentially lucrative opportunity for non-Australian brands wanting to open up their new and innovative products to a fresh market, or compete for market share.

But it’s not just non-Australian retailers who are at an advantage here: Australia was late to the ecommerce party because native, established brands were trading well without it. Subsequently, Australian retailers’ ecommerce technology stacks are much more recent and not burdened by legacy systems. This makes it much easier to extend, or get started with, best-of-breed technologies and cash in on a market that’s booming. To put some of this into perspective, Magento’s innovative ecommerce platform currently takes 42% of Australia’s market share and the world’s first adopter of Magento 2.0 was an Australian brand.

The GST loophole

At the moment, local retailers are campaigning against a rule that exempts foreign websites from being charged a 10% general sales tax (GST) on purchases under A$1,000. And in 2013, Australian consumers made $3.11 billion worth of purchases under A$1,000.[1]

While the current GST break appears to put non-Australian retailers at an advantage, Australian-based brands such as Harvey Norman are using it to their advantage by setting up ecommerce operations in Asia to enjoy the GST benefit.

Australian consumers have also countered the argument by saying that price isn’t always the motivator when it comes to making purchasing decisions.

It’s not a place where no man has gone before

Often, concerns around meeting local compliance and lack of overseas business knowledge prevent outsiders from taking the leap into cross-border trade. However, this ecommerce passport, created by Ecommerce Worldwide and NORA, is designed to support those considering selling in Australia. The guide provides a comprehensive look into everything from the country’s economy and trade status, to logistics and dealing with international payments.

Global expansion success stories are also invaluable sources of information. For instance, it’s not just lower-end retailers that are fitting the bill, with brands like online luxury fashion retailer Net-a-Porter naming Australia as one of its biggest markets.

How tech-savvy are the Aussies?

One of the concerns you might have as a new entrant into the market is how you’ll reach and sell to your new audience, particularly without having a physical presence. The good news is that more than 80% of the country is digitally enabled and 60% of mobile phone users own a smartphone – so online is deeply rooted into the majority of Australians’ lives. [2]

Marketing your brand

Heard the saying “Fire bullets then fire cannonballs”? In any case, you’ll want to test the waters and gauge people’s reactions to your product or service.

It all starts with the website because, without it, you’re not discoverable or searchable, and you’ve nowhere to drive people to when running campaigns. SEO and SEM should definitely be a priority, and an online store that can handle multiple regions and storefronts, like Magento, will make your life easier. A mobile-first mentality and well thought-out UX will also place you in a good position.

Once your new web store is set up, you should be making every effort to collect visitors’ email addresses, perhaps via a popover. Why? Firstly, email is one of the top three priority areas for Australian retailers, because it’s a cost-effective, scalable marketing channel that enables true personalization.

Secondly, email marketing automation empowers you to deliver the customer experience today’s consumer expects, as well as enabling you to communicate with them throughout the lifecycle. Check out our ‘Do customer experience masters really exist?’ whitepaper for some real-life success stories.

Like the Magento platform, dotmailer is set up to handle multiple languages, regions and accounts, and is designed to grow with you.

In summary, there’s great scope for ecommerce success in Australia, whether you’re a native bricks-and-mortar retailer, a start-up or a non-Australian merchant. The barriers to cross-border trade are falling and Australia is one of APAC’s most developed regions in terms of purchasing power and tech savviness.

We recently worked with ecommerce expert Chloe Thomas to produce a whitepaper on cross-border trade, which goes into much more detail on how to market and sell successfully in new territories. You can download a free copy here.

[1] Australian Passport 2015: Cross-Border Trading Report

[2] Australian Passport 2015: Cross-Border Trading Report

Reblogged 3 years ago from blog.dotmailer.com

78% Of SEOs Believe That 2016 Is Going To Be A “Great” Year For Business

What does this year have in store for local search engine optimization professionals? Columnist Myles Anderson shares results from BrightLocal’s Local SEO Industry Survey, which may provide some insights.

The post 78% Of SEOs Believe That 2016 Is Going To Be A “Great” Year For Business…

Please visit Search Engine Land for the full article.

Reblogged 3 years ago from feeds.searchengineland.com

​The 2015 Online Marketing Industry Survey

Posted by Dr-Pete

It’s been another wild year in search marketing. Mobilegeddon crushed our Twitter streams, but not our dreams, and Matt Cutts stepped out of the spotlight to make way for an uncertain Google future. Pandas and Penguins continue to torment us, but most days, like anyone else, we were just trying to get the job done and earn a living.

This year, over 3,600 brave souls, each one more intelligent and good-looking than the last, completed our survey. While the last survey was technically “2014”, we collected data for it in late 2013, so the 2015 survey reflects about 18 months of industry changes.

A few highlights

Let’s dig in. Almost half (49%) of our 2015 respondents involved in search marketing were in-house marketers. In-house teams still tend to be small – 71% of our in-house marketers reported only 1-3 people in their company being involved in search marketing at least quarter-time. These teams do have substantial influence, though, with 86% reporting that they were involved in purchasing decisions.

Agency search marketers reported larger teams and more diverse responsibilities. More than one-third (36%) of agency marketers in our survey reported working with more than 20 clients in the previous year. Agencies covered a wide range of services, with the top 5 being:

More than four-fifths (81%) of agency respondents reported providing both SEO and SEM services for clients. Please note that respondents could select more than one service/tool/etc., so the charts in this post will not add up to 100%.

The vast majority of respondents (85%) reported being directly involved with content marketing, which was on par with 2014. Nearly two-thirds (66%) of agency content marketers reported “Content for SEO purposes” as their top activity, although “Building Content Strategy” came in a solid second at 44% of respondents.

Top tools

Where do we get such wonderful toys? We marketers love our tools, so let’s take a look at the Top 10 tools across a range of categories. Please note that this survey was conducted here on Moz, and our audience certainly has a pro-Moz slant.

Up first, here are the Top 10 SEO tools in our survey:

Just like last time, Google Webmaster Tools (now “Search Console”) leads the way. Moz Pro and Majestic slipped a little bit, and Firebug fell out of the Top 10. The core players remained fairly stable.

Here are the Top 10 Content tools in our survey:

Even with its uncertain future, Google Alerts continues to be widely used. There are a lot of newcomers to the content tools world, so year-over-year comparisons are tricky. Expect even more players in this market in the coming year.

Following are our respondents’ Top 10 analytics tools:

For an industry that complains about Google so much, we sure do seem to love their stuff. Google Analytics dominates, crushing the enterprise players, at least in the mid-market. KISSmetrics gained solid ground (from the #10 spot last time), while home-brewed tools slipped a bit. CrazyEgg and WordPress Stats remain very popular since our last survey.

Finally, here are the Top 10 social tools used by our respondents:

Facebook Insights and Hootsuite retained the top spots from last year, but newcomer Twitter Analytics rocketed into the #3 position. LinkedIn Insights emerged as a strong contender, too. Overall usage of all social tools increased. Tweetdeck held the #6 spot in 2014, with 19% usage, but dropped to #10 this year, even bumping up slightly to 20%.

Of course, digging into social tools naturally begs the question of which social networks are at the top of our lists.

The Top 6 are unchanged since our last survey, and it’s clear that the barriers to entry to compete with the big social networks are only getting higher. Instagram doubled its usage (from 11% of respondents last time), but this still wasn’t enough to overtake Pinterest. Reddit and Quora saw steady growth, and StumbleUpon slipped out of the Top 10.

Top activities

So, what exactly do we do with these tools and all of our time? Across all online marketers in our survey, the Top 5 activities were:

For in-house marketers, “Site Audits” dropped to the #6 position and “Brand Strategy” jumped up to the #3 spot. Naturally, in-house marketers have more resources to focus on strategy.

For agencies and consultants, “Site Audits” bumped up to #2, and “Managing People” pushed down social media to take the #5 position. Larger agency teams require more traditional people wrangling.

Here’s a much more detailed breakdown of how we spend our time in 2015:

In terms of overall demand for services, the Top 5 winners (calculated by % reporting increase – % reporting decrease were):

Demand for CRO is growing at a steady clip, but analytics still leads the way. Both “Content Creation” (#2) and “Content Curation” (#6) showed solid demand increases.

Some categories reported both gains and losses – 30% of respondents reported increased demand for “Link Building”, while 20% reported decreased demand. Similarly, 20% reported increased demand for “Link Removal”, while almost as many (17%) reported decreased demand. This may be a result of overall demand shifts, or it may represent more specialization by agencies and consultants.

What’s in store for 2016?

It’s clear that our job as online marketers is becoming more diverse, more challenging, and more strategic. We have to have a command of a wide array of tools and tactics, and that’s not going to slow down any time soon. On the bright side, companies are more aware of what we do, and they’re more willing to spend the money to have it done. Our evolution has barely begun as an industry, and you can expect more changes and growth in the coming year.

Raw data download

If you’d like to take a look through the raw results from this year’s survey (we’ve removed identifying information like email addresses from all responses), we’ve got that for you here:

Download the raw results

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 4 years ago from tracking.feedpress.it

An Open-Source Tool for Checking rel-alternate-hreflang Annotations

Posted by Tom-Anthony

In the Distilled R&D department we have been ramping up the amount of automated monitoring and analysis we do, with an internal system monitoring our client’s sites both directly and via various data sources to ensure they remain healthy and we are alerted to any problems that may arise.

Recently we started work to add in functionality for including the rel-alternate-hreflang annotations in this system. In this blog post I’m going to share an open-source Python library we’ve just started work on for the purpose, which makes it easy to read the hreflang entries from a page and identify errors with them.

If you’re not a Python aficionado then don’t despair, as I have also built a ready-to-go tool for you to use, which will quickly do some checks on the hreflang entries for any URL you specify. 🙂

Google’s Search Console (formerly Webmaster Tools) does have some basic rel-alternate-hreflang checking built in, but it is limited in how you can use it and you are restricted to using it for verified sites.

rel-alternate-hreflang checklist

Before we introduce the code, I wanted to quickly review a list of five easy and common mistakes that we will want to check for when looking at rel-alternate-hreflang annotations:

  • return tag errors – Every alternate language/locale URL of a page should, itself, include a link back to the first page. This makes sense but I’ve seen people make mistakes with it fairly often.
  • indirect / broken links – Links to alternate language/region versions of the page should no go via redirects, and should not link to missing or broken pages.
  • multiple entries – There should never be multiple entries for a single language/region combo.
  • multiple defaults – You should never have more than one x-default entry.
  • conflicting modes – rel-alternate-hreflang entries can be implemented via inline HTML, XML sitemaps, or HTTP headers. For any one set of pages only one implementation mode should be used.

So now imagine that we want to simply automate these checks quickly and simply…

Introducing: polly – the hreflang checker library

polly is the name for the library we have developed to help us solve this problem, and we are releasing it as open source so the SEO community can use it freely to build upon. We only started work on it last week, but we plan to continue developing it, and will also accept contributions to the code from the community, so we expect its feature set to grow rapidly.

If you are not comfortable tinkering with Python, then feel free to skip down to the next section of the post, where there is a tool that is built with polly which you can use right away.

Still here? Ok, great. You can install polly easily via pip:

pip install polly

You can then create a PollyPage() object which will do all our work and store the data simply by instantiating the class with the desired URL:

my_page = PollyPage("http://www.facebook.com/")

You can quickly see the hreflang entries on the page by running:

print my_page.alternate_urls_map

You can list all the hreflang values encountered on a page, and which countries and languages they cover:

print my_page.hreflang_values
print my_page.languages
print my_page.regions

You can also check various aspects of a page, see whether the pages it includes in its rel-alternate-hreflang entries point back, or whether there are entries that do not see retrievable (due to 404 or 500 etc. errors):

print my_page.is_default
print my_page.no_return_tag_pages()
print my_page.non_retrievable_pages()

Get more instructions and grab the code at the polly github page. Hit me up in the comments with any questions.

Free tool: hreflang.ninja

I have put together a very simple tool that uses polly to run some of the checks we highlighted above as being common mistakes with rel-alternate-hreflang, which you can visit right now and start using:

http://hreflang.ninja

Simply enter a URL and hit enter, and you should see something like:

Example output from the ninja!

The tool shows you the rel-alternate-hreflang entries found on the page, the language and region of those entries, the alternate URLs, and any errors identified with the entry. It is perfect for doing quick’n’dirty checks of a URL to identify any errors.

As we add additional functionality to polly we will be updating hreflang.ninja as well, so please tweet me with feature ideas or suggestions.

To-do list!

This is the first release of polly and currently we only handle annotations that are in the HTML of the page, not those in the XML sitemap or HTTP headers. However, we are going to be updating polly (and hreflang.ninja) over the coming weeks, so watch this space! 🙂

Resources

Here are a few links you may find helpful for hreflang:

Got suggestions?

With the increasing number of SEO directives and annotations available, and the ever-changing guidelines around how to deploy them, it is important to automate whatever areas possible. Hopefully polly is helpful to the community in this regard, and we want to here what ideas you have for making these tools more useful – here in the comments or via Twitter.

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 4 years ago from tracking.feedpress.it

How to Use Server Log Analysis for Technical SEO

Posted by SamuelScott

It’s ten o’clock. Do you know where your logs are?

I’m introducing this guide with a pun on a common public-service announcement that has run on late-night TV news broadcasts in the United States because log analysis is something that is extremely newsworthy and important.

If your technical and on-page SEO is poor, then nothing else that you do will matter. Technical SEO is the key to helping search engines to crawl, parse, and index websites, and thereby rank them appropriately long before any marketing work begins.

The important thing to remember: Your log files contain the only data that is 100% accurate in terms of how search engines are crawling your website. By helping Google to do its job, you will set the stage for your future SEO work and make your job easier. Log analysis is one facet of technical SEO, and correcting the problems found in your logs will help to lead to higher rankings, more traffic, and more conversions and sales.

Here are just a few reasons why:

  • Too many response code errors may cause Google to reduce its crawling of your website and perhaps even your rankings.
  • You want to make sure that search engines are crawling everything, new and old, that you want to appear and rank in the SERPs (and nothing else).
  • It’s crucial to ensure that all URL redirections will pass along any incoming “link juice.”

However, log analysis is something that is unfortunately discussed all too rarely in SEO circles. So, here, I wanted to give the Moz community an introductory guide to log analytics that I hope will help. If you have any questions, feel free to ask in the comments!

What is a log file?

Computer servers, operating systems, network devices, and computer applications automatically generate something called a log entry whenever they perform an action. In a SEO and digital marketing context, one type of action is whenever a page is requested by a visiting bot or human.

Server log entries are specifically programmed to be output in the Common Log Format of the W3C consortium. Here is one example from Wikipedia with my accompanying explanations:

127.0.0.1 user-identifier frank [10/Oct/2000:13:55:36 -0700] "GET /apache_pb.gif HTTP/1.0" 200 2326
  • 127.0.0.1 — The remote hostname. An IP address is shown, like in this example, whenever the DNS hostname is not available or DNSLookup is turned off.
  • user-identifier — The remote logname / RFC 1413 identity of the user. (It’s not that important.)
  • frank — The user ID of the person requesting the page. Based on what I see in my Moz profile, Moz’s log entries would probably show either “SamuelScott” or “392388” whenever I visit a page after having logged in.
  • [10/Oct/2000:13:55:36 -0700] — The date, time, and timezone of the action in question in strftime format.
  • GET /apache_pb.gif HTTP/1.0 — “GET” is one of the two commands (the other is “POST”) that can be performed. “GET” fetches a URL while “POST” is submitting something (such as a forum comment). The second part is the URL that is being accessed, and the last part is the version of HTTP that is being accessed.
  • 200 — The status code of the document that was returned.
  • 2326 — The size, in bytes, of the document that was returned.

Note: A hyphen is shown in a field when that information is unavailable.

Every single time that you — or the Googlebot — visit a page on a website, a line with this information is output, recorded, and stored by the server.

Log entries are generated continuously and anywhere from several to thousands can be created every second — depending on the level of a given server, network, or application’s activity. A collection of log entries is called a log file (or often in slang, “the log” or “the logs”), and it is displayed with the most-recent log entry at the bottom. Individual log files often contain a calendar day’s worth of log entries.

Accessing your log files

Different types of servers store and manage their log files differently. Here are the general guides to finding and managing log data on three of the most-popular types of servers:

What is log analysis?

Log analysis (or log analytics) is the process of going through log files to learn something from the data. Some common reasons include:

  • Development and quality assurance (QA) — Creating a program or application and checking for problematic bugs to make sure that it functions properly
  • Network troubleshooting — Responding to and fixing system errors in a network
  • Customer service — Determining what happened when a customer had a problem with a technical product
  • Security issues — Investigating incidents of hacking and other intrusions
  • Compliance matters — Gathering information in response to corporate or government policies
  • Technical SEO — This is my favorite! More on that in a bit.

Log analysis is rarely performed regularly. Usually, people go into log files only in response to something — a bug, a hack, a subpoena, an error, or a malfunction. It’s not something that anyone wants to do on an ongoing basis.

Why? This is a screenshot of ours of just a very small part of an original (unstructured) log file:

Ouch. If a website gets 10,000 visitors who each go to ten pages per day, then the server will create a log file every day that will consist of 100,000 log entries. No one has the time to go through all of that manually.

How to do log analysis

There are three general ways to make log analysis easier in SEO or any other context:

  • Do-it-yourself in Excel
  • Proprietary software such as Splunk or Sumo-logic
  • The ELK Stack open-source software

Tim Resnik’s Moz essay from a few years ago walks you through the process of exporting a batch of log files into Excel. This is a (relatively) quick and easy way to do simple log analysis, but the downside is that one will see only a snapshot in time and not any overall trends. To obtain the best data, it’s crucial to use either proprietary tools or the ELK Stack.

Splunk and Sumo-Logic are proprietary log analysis tools that are primarily used by enterprise companies. The ELK Stack is a free and open-source batch of three platforms (Elasticsearch, Logstash, and Kibana) that is owned by Elastic and used more often by smaller businesses. (Disclosure: We at Logz.io use the ELK Stack to monitor our own internal systems as well as for the basis of our own log management software.)

For those who are interested in using this process to do technical SEO analysis, monitor system or application performance, or for any other reason, our CEO, Tomer Levy, has written a guide to deploying the ELK Stack.

Technical SEO insights in log data

However you choose to access and understand your log data, there are many important technical SEO issues to address as needed. I’ve included screenshots of our technical SEO dashboard with our own website’s data to demonstrate what to examine in your logs.

Bot crawl volume

It’s important to know the number of requests made by Baidu, BingBot, GoogleBot, Yahoo, Yandex, and others over a given period time. If, for example, you want to get found in search in Russia but Yandex is not crawling your website, that is a problem. (You’d want to consult Yandex Webmaster and see this article on Search Engine Land.)

Response code errors

Moz has a great primer on the meanings of the different status codes. I have an alert system setup that tells me about 4XX and 5XX errors immediately because those are very significant.

Temporary redirects

Temporary 302 redirects do not pass along the “link juice” of external links from the old URL to the new one. Almost all of the time, they should be changed to permanent 301 redirects.

Crawl budget waste

Google assigns a crawl budget to each website based on numerous factors. If your crawl budget is, say, 100 pages per day (or the equivalent amount of data), then you want to be sure that all 100 are things that you want to appear in the SERPs. No matter what you write in your robots.txt file and meta-robots tags, you might still be wasting your crawl budget on advertising landing pages, internal scripts, and more. The logs will tell you — I’ve outlined two script-based examples in red above.

If you hit your crawl limit but still have new content that should be indexed to appear in search results, Google may abandon your site before finding it.

Duplicate URL crawling

The addition of URL parameters — typically used in tracking for marketing purposes — often results in search engines wasting crawl budgets by crawling different URLs with the same content. To learn how to address this issue, I recommend reading the resources on Google and Search Engine Land here, here, here, and here.

Crawl priority

Google might be ignoring (and not crawling or indexing) a crucial page or section of your website. The logs will reveal what URLs and/or directories are getting the most and least attention. If, for example, you have published an e-book that attempts to rank for targeted search queries but it sits in a directory that Google only visits once every six months, then you won’t get any organic search traffic from the e-book for up to six months.

If a part of your website is not being crawled very often — and it is updated often enough that it should be — then you might need to check your internal-linking structure and the crawl-priority settings in your XML sitemap.

Last crawl date

Have you uploaded something that you hope will be indexed quickly? The log files will tell you when Google has crawled it.

Crawl budget

One thing I personally like to check and see is Googlebot’s real-time activity on our site because the crawl budget that the search engine assigns to a website is a rough indicator — a very rough one — of how much it “likes” your site. Google ideally does not want to waste valuable crawling time on a bad website. Here, I had seen that Googlebot had made 154 requests of our new startup’s website over the prior twenty-four hours. Hopefully, that number will go up!

As I hope you can see, log analysis is critically important in technical SEO. It’s eleven o’clock — do you know where your logs are now?

Additional resources

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 4 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 4 years ago from tracking.feedpress.it