4 ways to achieve customer engagement on a mobile device

Marketers who want true customer engagement, take heed!

The opportunity to engage on mobile is now! Brands (like yours) need to adopt a mobile-centric strategy if they wish to extend their reach, acquire and retain customers, and increase their marketing ROI.

Why’s that? Ultimately, it’s because consumers’ shopping habits rely heavily on the smartphone and its capabilities. Today we’re inseparable from our mobiles.

The device equips customers with:

  • quick access to information
  • social proof
  • convenience of purchase
  • easy selection process and checkout
  • extensive product and service choice

Brands are under pressure to deliver a seamless ‘at-home’ experience now that the shopfront sits on the consumer’s coffee table. Since mobile is inherently personal to the individual, marketers need to be prioritizing personalization at every stage of the customer journey.

 

Here are 4 ways to deliver the best experience on mobile:

1. Implement a welcome program that’s fit for mobile

First impressions are what build the initial foundations of a long-lasting customer relationship. Brands aiming to nurture a loyal customer base should take an active interest in new subscribers. Winning them over on mobile can make all the difference.

  • Confirm subscription via SMS
  • Send a mobile optimized welcome email (promote your app if you have one)
  • Invite subscribers to fill in a fully responsive preference center
  • Segment contacts based on the information you capture

2. Deliver an on-the-go aftersales experience

The post-purchase journey is a honeymoon period (your customers are really into you, so it’s important to be really into them, too). This is where you can drive valuable mobile moments that build that all-important brand love.

Customers expect:

  • timely transactional notifications
  • informative delivery updates in real time
  • value-add aftersales content (‘how-tos’, reviews, promotions related to past behavior)

Whether these messages are delivered via email, SMS or push, they need to be contextual and relevant. Every mobile moment should mean something to the customer.

3. Engage customers at meaningful moments

Loyalty doesn’t come from one single purchase. Brands have got to invest in their customers – that means providing rich content and tailored product recommendations. It costs five times more to acquire a customer than to retain one, so nurturing tactics should be the cornerstone of your mobile marketing strategy.

Top tips:

  • Trigger a product review via email/SMS and offer an incentive to boost responses
  • Combine preference data with behavioral insight to power relevant communications
  • Send broadcast promotions/event-based notifications via SMS and push (flash sales, content drops, new arrivals, appointment/renewal/replenishment reminders)
  • Anniversaries are a great conversation starter – think birthdays, throwbacks, one-year-since-first-purchase etc.

4. Keep customers hooked wherever they are

Customers inevitably fall off the radar, and it’s a challenge for every business. Since acquisition is pricier than retention, marketers need to refine their re-engagement tactics and prevent customers from lapsing. But fear not: if you’re going to win them back, it’s going to be on mobile.

  • Agree on your lapse criteria (i.e. customer hasn’t opened an email in three months or purchased in six)
  • Build a winback program that incorporates SMS, push and email (using whichever channel subscribers are likelier to engage on)
  • Consider retargeting ads on Facebook and Google

 

Audience segmentation is the most important tactic for marketers to practice. The experience on mobile must be as personalized as possible; consumers won’t engage with messages that lack context or relevancy.

So, when planning out your mobile strategy, think about the reasoning behind every communication in the customer lifecycle. The devil is always in the data.

For deeper insights on how to engage customers on a mobile device, download our best practice guide here.

The post 4 ways to achieve customer engagement on a mobile device appeared first on The Marketing Automation Blog.

Reblogged 1 week ago from blog.dotmailer.com

The Linkbait Bump: How Viral Content Creates Long-Term Lift in Organic Traffic – Whiteboard Friday

Posted by randfish

A single fantastic (or “10x”) piece of content can lift a site’s traffic curves long beyond the popularity of that one piece. In today’s Whiteboard Friday, Rand talks about why those curves settle into a “new normal,” and how you can go about creating the content that drives that change.

For reference, here’s a still of this week’s whiteboard. 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 chatting about the linkbait bump, classic phrase in the SEO world and almost a little dated. I think today we’re talking a little bit more about viral content and how high-quality content, content that really is the cornerstone of a brand or a website’s content can be an incredible and powerful driver of traffic, not just when it initially launches but over time.

So let’s take a look.

This is a classic linkbait bump, viral content bump analytics chart. I’m seeing over here my traffic and over here the different months of the year. You know, January, February, March, like I’m under a thousand. Maybe I’m at 500 visits or something, and then I have this big piece of viral content. It performs outstandingly well from a relative standpoint for my site. It gets 10,000 or more visits, drives a ton more people to my site, and then what happens is that that traffic falls back down. But the new normal down here, new normal is higher than the old normal was. So the new normal might be at 1,000, 1,500 or 2,000 visits whereas before I was at 500.

Why does this happen?

A lot of folks see an analytics chart like this, see examples of content that’s done this for websites, and they want to know: Why does this happen and how can I replicate that effect? The reasons why are it sort of feeds back into that viral loop or the flywheel, which we’ve talked about in previous Whiteboard Fridays, where essentially you start with a piece of content. That content does well, and then you have things like more social followers on your brand’s accounts. So now next time you go to amplify content or share content socially, you’re reaching more potential people. You have a bigger audience. You have more people who share your content because they’ve seen that that content performs well for them in social. So they want to find other content from you that might help their social accounts perform well.

You see more RSS and email subscribers because people see your interesting content and go, “Hey, I want to see when these guys produce something else.” You see more branded search traffic because people are looking specifically for content from you, not necessarily just around this viral piece, although that’s often a big part of it, but around other pieces as well, especially if you do a good job of exposing them to that additional content. You get more bookmark and type in traffic, more searchers biased by personalization because they’ve already visited your site. So now when they search and they’re logged into their accounts, they’re going to see your site ranking higher than they normally would otherwise, and you get an organic SEO lift from all the links and shares and engagement.

So there’s a ton of different factors that feed into this, and you kind of want to hit all of these things. If you have a piece of content that gets a lot of shares, a lot of links, but then doesn’t promote engagement, doesn’t get more people signing up, doesn’t get more people searching for your brand or searching for that content specifically, then it’s not going to have the same impact. Your traffic might fall further and more quickly.

How do you achieve this?

How do we get content that’s going to do this? Well, we’re going to talk through a number of things that we’ve talked about previously on Whiteboard Friday. But there are some additional ones as well. This isn’t just creating good content or creating high quality content, it’s creating a particular kind of content. So for this what you want is a deep understanding, not necessarily of what your standard users or standard customers are interested in, but a deep understanding of what influencers in your niche will share and promote and why they do that.

This often means that you follow a lot of sharers and influencers in your field, and you understand, hey, they’re all sharing X piece of content. Why? Oh, because it does this, because it makes them look good, because it helps their authority in the field, because it provides a lot of value to their followers, because they know it’s going to get a lot of retweets and shares and traffic. Whatever that because is, you have to have a deep understanding of it in order to have success with viral kinds of content.

Next, you want to have empathy for users and what will give them the best possible experience. So if you know, for example, that a lot of people are coming on mobile and are going to be sharing on mobile, which is true of almost all viral content today, FYI, you need to be providing a great mobile and desktop experience. Oftentimes that mobile experience has to be different, not just responsive design, but actually a different format, a different way of being able to scroll through or watch or see or experience that content.

There are some good examples out there of content that does that. It makes a very different user experience based on the browser or the device you’re using.

You also need to be aware of what will turn them off. So promotional messages, pop-ups, trying to sell to them, oftentimes that diminishes user experience. It means that content that could have been more viral, that could have gotten more shares won’t.

Unique value and attributes that separate your content from everything else in the field. So if there’s like ABCD and whoa, what’s that? That’s very unique. That stands out from the crowd. That provides a different form of value in a different way than what everyone else is doing. That uniqueness is often a big reason why content spreads virally, why it gets more shared than just the normal stuff.

I’ve talk about this a number of times, but content that’s 10X better than what the competition provides. So unique value from the competition, but also quality that is not just a step up, but 10X better, massively, massively better than what else you can get out there. That makes it unique enough. That makes it stand out from the crowd, and that’s a very hard thing to do, but that’s why this is so rare and so valuable.

This is a critical one, and I think one that, I’ll just say, many organizations fail at. That is the freedom and support to fail many times, to try to create these types of effects, to have this impact many times before you hit on a success. A lot of managers and clients and teams and execs just don’t give marketing teams and content teams the freedom to say, “Yeah, you know what? You spent a month and developer resources and designer resources and spent some money to go do some research and contracted with this third party, and it wasn’t a hit. It didn’t work. We didn’t get the viral content bump. It just kind of did okay. You know what? We believe in you. You’ve got a lot of chances. You should try this another 9 or 10 times before we throw it out. We really want to have a success here.”

That is something that very few teams invest in. The powerful thing is because so few people are willing to invest that way, the ones that do, the ones that believe in this, the ones that invest long term, the ones that are willing to take those failures are going to have a much better shot at success, and they can stand out from the crowd. They can get these bumps. It’s powerful.

Not a requirement, but it really, really helps to have a strong engaged community, either on your site and around your brand, or at least in your niche and your topic area that will help, that wants to see you, your brand, your content succeed. If you’re in a space that has no community, I would work on building one, even if it’s very small. We’re not talking about building a community of thousands or tens of thousands. A community of 100 people, a community of 50 people even can be powerful enough to help content get that catalyst, that first bump that’ll boost it into viral potential.

Then finally, for this type of content, you need to have a logical and not overly promotional match between your brand and the content itself. You can see many sites in what I call sketchy niches. So like a criminal law site or a casino site or a pharmaceutical site that’s offering like an interactive musical experience widget, and you’re like, “Why in the world is this brand promoting this content? Why did they even make it? How does that match up with what they do? Oh, it’s clearly just intentionally promotional.”

Look, many of these brands go out there and they say, “Hey, the average web user doesn’t know and doesn’t care.” I agree. But the average web user is not an influencer. Influencers know. Well, they’re very, very suspicious of why content is being produced and promoted, and they’re very skeptical of promoting content that they don’t think is altruistic. So this kills a lot of content for brands that try and invest in it when there’s no match. So I think you really need that.

Now, when you do these linkbait bump kinds of things, I would strongly recommend that you follow up, that you consider the quality of the content that you’re producing. Thereafter, that you invest in reproducing these resources, keeping those resources updated, and that you don’t simply give up on content production after this. However, if you’re a small business site, a small or medium business, you might think about only doing one or two of these a year. If you are a heavy content player, you’re doing a lot of content marketing, content marketing is how you’re investing in web traffic, I’d probably be considering these weekly or monthly at the least.

All right, everyone. Look forward to your experiences with the linkbait bump, and I will see you again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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Reblogged 3 years ago from tracking.feedpress.it

How to Rid Your Website of Six Common Google Analytics Headaches

Posted by amandaecking

I’ve been in and out of Google Analytics (GA) for the past five or so years agency-side. I’ve seen three different code libraries, dozens of new different features and reports roll out, IP addresses stop being reported, and keywords not-so-subtly phased out of the free platform.

Analytics has been a focus of mine for the past year or so—mainly, making sure clients get their data right. Right now, our new focus is closed loop tracking, but that’s a topic for another day. If you’re using Google Analytics, and only Google Analytics for the majority of your website stats, or it’s your primary vehicle for analysis, you need to make sure it’s accurate.

Not having data pulling in or reporting properly is like building a house on a shaky foundation: It doesn’t end well. Usually there are tears.

For some reason, a lot of people, including many of my clients, assume everything is tracking properly in Google Analytics… because Google. But it’s not Google who sets up your analytics. People do that. And people are prone to make mistakes.

I’m going to go through six scenarios where issues are commonly encountered with Google Analytics.

I’ll outline the remedy for each issue, and in the process, show you how to move forward with a diagnosis or resolution.

1. Self-referrals

This is probably one of the areas we’re all familiar with. If you’re seeing a lot of traffic from your own domain, there’s likely a problem somewhere—or you need to extend the default session length in Google Analytics. (For example, if you have a lot of long videos or music clips and don’t use event tracking; a website like TEDx or SoundCloud would be a good equivalent.)

Typically one of the first things I’ll do to help diagnose the problem is include an advanced filter to show the full referrer string. You do this by creating a filter, as shown below:

Filter Type: Custom filter > Advanced
Field A: Hostname
Extract A: (.*)
Field B: Request URI
Extract B: (.*)
Output To: Request URI
Constructor: $A1$B1

You’ll then start seeing the subdomains pulling in. Experience has shown me that if you have a separate subdomain hosted in another location (say, if you work with a separate company and they host and run your mobile site or your shopping cart), it gets treated by Google Analytics as a separate domain. Thus, you ‘ll need to implement cross domain tracking. This way, you can narrow down whether or not it’s one particular subdomain that’s creating the self-referrals.

In this example below, we can see all the revenue is being reported to the booking engine (which ended up being cross domain issues) and their own site is the fourth largest traffic source:

I’ll also a good idea to check the browser and device reports to start narrowing down whether the issue is specific to a particular element. If it’s not, keep digging. Look at pages pulling the self-referrals and go through the code with a fine-tooth comb, drilling down as much as you can.

2. Unusually low bounce rate

If you have a crazy-low bounce rate, it could be too good to be true. Unfortunately. An unusually low bounce rate could (and probably does) mean that at least on some pages of your website have the same Google Analytics tracking code installed twice.

Take a look at your source code, or use Google Tag Assistant (though it does have known bugs) to see if you’ve got GA tracking code installed twice.

While I tell clients having Google Analytics installed on the same page can lead to double the pageviews, I’ve not actually encountered that—I usually just say it to scare them into removing the duplicate implementation more quickly. Don’t tell on me.

3. Iframes anywhere

I’ve heard directly from Google engineers and Google Analytics evangelists that Google Analytics does not play well with iframes, and that it will never will play nice with this dinosaur technology.

If you track the iframe, you inflate your pageviews, plus you still aren’t tracking everything with 100% clarity.

If you don’t track across iframes, you lose the source/medium attribution and everything becomes a self-referral.

Damned if you do; damned if you don’t.

My advice: Stop using iframes. They’re Netscape-era technology anyway, with rainbow marquees and Comic Sans on top. Interestingly, and unfortunately, a number of booking engines (for hotels) and third-party carts (for ecommerce) still use iframes.

If you have any clients in those verticals, or if you’re in the vertical yourself, check with your provider to see if they use iframes. Or you can check for yourself, by right-clicking as close as you can to the actual booking element:

iframe-booking.png

There is no neat and tidy way to address iframes with Google Analytics, and usually iframes are not the only complicated element of setup you’ll encounter. I spent eight months dealing with a website on a subfolder, which used iframes and had a cross domain booking system, and the best visibility I was able to get was about 80% on a good day.

Typically, I’d approach diagnosing iframes (if, for some reason, I had absolutely no access to viewing a website or talking to the techs) similarly to diagnosing self-referrals, as self-referrals are one of the biggest symptoms of iframe use.

4. Massive traffic jumps

Massive jumps in traffic don’t typically just happen. (Unless, maybe, you’re Geraldine.) There’s always an explanation—a new campaign launched, you just turned on paid ads for the first time, you’re using content amplification platforms, you’re getting a ton of referrals from that recent press in The New York Times. And if you think it just happened, it’s probably a technical glitch.

I’ve seen everything from inflated pageviews result from including tracking on iframes and unnecessary implementation of virtual pageviews, to not realizing the tracking code was installed on other microsites for the same property. Oops.

Usually I’ve seen this happen when the tracking code was somewhere it shouldn’t be, so if you’re investigating a situation of this nature, first confirm the Google Analytics code is only in the places it needs to be.Tools like Google Tag Assistant and Screaming Frog can be your BFFs in helping you figure this out.

Also, I suggest bribing the IT department with sugar (or booze) to see if they’ve changed anything lately.

5. Cross-domain tracking

I wish cross-domain tracking with Google Analytics out of the box didn’t require any additional setup. But it does.

If you don’t have it set up properly, things break down quickly, and can be quite difficult to untangle.

The older the GA library you’re using, the harder it is. The easiest setup, by far, is Google Tag Manager with Universal Analytics. Hard-coded universal analytics is a bit more difficult because you have to implement autoLink manually and decorate forms, if you’re using them (and you probably are). Beyond that, rather than try and deal with it, I say update your Google Analytics code. Then we can talk.

Where I’ve seen the most murkiness with tracking is when parts of cross domain tracking are implemented, but not all. For some reason, if allowLinker isn’t included, or you forget to decorate all the forms, the cookies aren’t passed between domains.

The absolute first place I would start with this would be confirming the cookies are all passing properly at all the right points, forms, links, and smoke signals. I’ll usually use a combination of the Real Time report in Google Analytics, Google Tag Assistant, and GA debug to start testing this. Any debug tool you use will mean you’re playing in the console, so get friendly with it.

6. Internal use of UTM strings

I’ve saved the best for last. Internal use of campaign tagging. We may think, oh, I use Google to tag my campaigns externally, and we’ve got this new promotion on site which we’re using a banner ad for. That’s a campaign. Why don’t I tag it with a UTM string?

Step away from the keyboard now. Please.

When you tag internal links with UTM strings, you override the original source/medium. So that visitor who came in through your paid ad and then who clicks on the campaign banner has now been manually tagged. You lose the ability to track that they came through on the ad the moment they click on the tagged internal link. Their source and medium is now your internal campaign, not that paid ad you’re spending gobs of money on and have to justify to your manager. See the problem?

I’ve seen at least three pretty spectacular instances of this in the past year, and a number of smaller instances of it. Annie Cushing also talks about the evils of internal UTM tags and the odd prevalence of it. (Oh, and if you haven’t explored her blog, and the amazing spreadsheets she shares, please do.)

One clothing company I worked with tagged all of their homepage offers with UTM strings, which resulted in the loss of visibility for one-third of their audience: One million visits over the course of a year, and $2.1 million in lost revenue.

Let me say that again. One million visits, and $2.1 million. That couldn’t be attributed to an external source/campaign/spend.

Another client I audited included campaign tagging on nearly every navigational element on their website. It still gives me nightmares.

If you want to see if you have any internal UTM strings, head straight to the Campaigns report in Acquisition in Google Analytics, and look for anything like “home” or “navigation” or any language you may use internally to refer to your website structure.

And if you want to see how users are moving through your website, go to the Flow reports. Or if you really, really, really want to know how many people click on that sidebar link, use event tracking. But please, for the love of all things holy (and to keep us analytics lovers from throwing our computers across the room), stop using UTM tagging on your internal links.

Now breathe and smile

Odds are, your Google Analytics setup is fine. If you are seeing any of these issues, though, you have somewhere to start in diagnosing and addressing the data.

We’ve looked at six of the most common points of friction I’ve encountered with Google Analytics and how to start investigating them: self-referrals, bounce rate, iframes, traffic jumps, cross domain tracking and internal campaign tagging.

What common data integrity issues have you encountered with Google Analytics? What are your favorite tools to investigate?

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Eliminate Duplicate Content in Faceted Navigation with Ajax/JSON/JQuery

Posted by EricEnge

One of the classic problems in SEO is that while complex navigation schemes may be useful to users, they create problems for search engines. Many publishers rely on tags such as rel=canonical, or the parameters settings in Webmaster Tools to try and solve these types of issues. However, each of the potential solutions has limitations. In today’s post, I am going to outline how you can use JavaScript solutions to more completely eliminate the problem altogether.

Note that I am not going to provide code examples in this post, but I am going to outline how it works on a conceptual level. If you are interested in learning more about Ajax/JSON/jQuery here are some resources you can check out:

  1. Ajax Tutorial
  2. Learning Ajax/jQuery

Defining the problem with faceted navigation

Having a page of products and then allowing users to sort those products the way they want (sorted from highest to lowest price), or to use a filter to pick a subset of the products (only those over $60) makes good sense for users. We typically refer to these types of navigation options as “faceted navigation.”

However, faceted navigation can cause problems for search engines because they don’t want to crawl and index all of your different sort orders or all your different filtered versions of your pages. They would end up with many different variants of your pages that are not significantly different from a search engine user experience perspective.

Solutions such as rel=canonical tags and parameters settings in Webmaster Tools have some limitations. For example, rel=canonical tags are considered “hints” by the search engines, and they may not choose to accept them, and even if they are accepted, they do not necessarily keep the search engines from continuing to crawl those pages.

A better solution might be to use JSON and jQuery to implement your faceted navigation so that a new page is not created when a user picks a filter or a sort order. Let’s take a look at how it works.

Using JSON and jQuery to filter on the client side

The main benefit of the implementation discussed below is that a new URL is not created when a user is on a page of yours and applies a filter or sort order. When you use JSON and jQuery, the entire process happens on the client device without involving your web server at all.

When a user initially requests one of the product pages on your web site, the interaction looks like this:

using json on faceted navigation

This transfers the page to the browser the user used to request the page. Now when a user picks a sort order (or filter) on that page, here is what happens:

jquery and faceted navigation diagram

When the user picks one of those options, a jQuery request is made to the JSON data object. Translation: the entire interaction happens within the client’s browser and the sort or filter is applied there. Simply put, the smarts to handle that sort or filter resides entirely within the code on the client device that was transferred with the initial request for the page.

As a result, there is no new page created and no new URL for Google or Bing to crawl. Any concerns about crawl budget or inefficient use of PageRank are completely eliminated. This is great stuff! However, there remain limitations in this implementation.

Specifically, if your list of products spans multiple pages on your site, the sorting and filtering will only be applied to the data set already transferred to the user’s browser with the initial request. In short, you may only be sorting the first page of products, and not across the entire set of products. It’s possible to have the initial JSON data object contain the full set of pages, but this may not be a good idea if the page size ends up being large. In that event, we will need to do a bit more.

What Ajax does for you

Now we are going to dig in slightly deeper and outline how Ajax will allow us to handle sorting, filtering, AND pagination. Warning: There is some tech talk in this section, but I will try to follow each technical explanation with a layman’s explanation about what’s happening.

The conceptual Ajax implementation looks like this:

ajax and faceted navigation diagram

In this structure, we are using an Ajax layer to manage the communications with the web server. Imagine that we have a set of 10 pages, the user has gotten the first page of those 10 on their device and then requests a change to the sort order. The Ajax requests a fresh set of data from the web server for your site, similar to a normal HTML transaction, except that it runs asynchronously in a separate thread.

If you don’t know what that means, the benefit is that the rest of the page can load completely while the process to capture the data that the Ajax will display is running in parallel. This will be things like your main menu, your footer links to related products, and other page elements. This can improve the perceived performance of the page.

When a user selects a different sort order, the code registers an event handler for a given object (e.g. HTML Element or other DOM objects) and then executes an action. The browser will perform the action in a different thread to trigger the event in the main thread when appropriate. This happens without needing to execute a full page refresh, only the content controlled by the Ajax refreshes.

To translate this for the non-technical reader, it just means that we can update the sort order of the page, without needing to redraw the entire page, or change the URL, even in the case of a paginated sequence of pages. This is a benefit because it can be faster than reloading the entire page, and it should make it clear to search engines that you are not trying to get some new page into their index.

Effectively, it does this within the existing Document Object Model (DOM), which you can think of as the basic structure of the documents and a spec for the way the document is accessed and manipulated.

How will Google handle this type of implementation?

For those of you who read Adam Audette’s excellent recent post on the tests his team performed on how Google reads Javascript, you may be wondering if Google will still load all these page variants on the same URL anyway, and if they will not like it.

I had the same question, so I reached out to Google’s Gary Illyes to get an answer. Here is the dialog that transpired:

Eric Enge: I’d like to ask you about using JSON and jQuery to render different sort orders and filters within the same URL. I.e. the user selects a sort order or a filter, and the content is reordered and redrawn on the page on the client site. Hence no new URL would be created. It’s effectively a way of canonicalizing the content, since each variant is a strict subset.

Then there is a second level consideration with this approach, which involves doing the same thing with pagination. I.e. you have 10 pages of products, and users still have sorting and filtering options. In order to support sorting and filtering across the entire 10 page set, you use an Ajax solution, so all of that still renders on one URL.

So, if you are on page 1, and a user executes a sort, they get that all back in that one page. However, to do this right, going to page 2 would also render on the same URL. Effectively, you are taking the 10 page set and rendering it all within one URL. This allows sorting, filtering, and pagination without needing to use canonical, noindex, prev/next, or robots.txt.

If this was not problematic for Google, the only downside is that it makes the pagination not visible to Google. Does that make sense, or is it a bad idea?

Gary Illyes
: If you have one URL only, and people have to click on stuff to see different sort orders or filters for the exact same content under that URL, then typically we would only see the default content.

If you don’t have pagination information, that’s not a problem, except we might not see the content on the other pages that are not contained in the HTML within the initial page load. The meaning of rel-prev/next is to funnel the signals from child pages (page 2, 3, 4, etc.) to the group of pages as a collection, or to the view-all page if you have one. If you simply choose to render those paginated versions on a single URL, that will have the same impact from a signals point of view, meaning that all signals will go to a single entity, rather than distributed to several URLs.

Summary

Keep in mind, the reason why Google implemented tags like rel=canonical, NoIndex, rel=prev/next, and others is to reduce their crawling burden and overall page bloat and to help focus signals to incoming pages in the best way possible. The use of Ajax/JSON/jQuery as outlined above does this simply and elegantly.

On most e-commerce sites, there are many different “facets” of how a user might want to sort and filter a list of products. With the Ajax-style implementation, this can be done without creating new pages. The end users get the control they are looking for, the search engines don’t have to deal with excess pages they don’t want to see, and signals in to the site (such as links) are focused on the main pages where they should be.

The one downside is that Google may not see all the content when it is paginated. A site that has lots of very similar products in a paginated list does not have to worry too much about Google seeing all the additional content, so this isn’t much of a concern if your incremental pages contain more of what’s on the first page. Sites that have content that is materially different on the additional pages, however, might not want to use this approach.

These solutions do require Javascript coding expertise but are not really that complex. If you have the ability to consider a path like this, you can free yourself from trying to understand the various tags, their limitations, and whether or not they truly accomplish what you are looking for.

Credit: Thanks for Clark Lefavour for providing a review of the above for technical correctness.

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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

Exposing The Generational Content Gap: Three Ways to Reach Multiple Generations

Posted by AndreaLehr

With more people of all ages online than ever before, marketers must create content that resonates with multiple generations. Successful marketers realize that each generation has unique expectations, values and experiences that influence consumer behaviors, and that offering your audience content that reflects their shared interests is a powerful way to connect with them and inspire them to take action.

We’re in the midst of a generational shift, with
Millennials expected to surpass Baby Boomers in 2015 as the largest living generation. In order to be competitive, marketers need to realize where key distinctions and similarities lie in terms of how these different generations consume content and share it with with others.

To better understand the habits of each generation,
BuzzStream and Fractl surveyed over 1,200 individuals and segmented their responses into three groups: Millennials (born between 1977–1995), Generation X (born between 1965–1976), and Baby Boomers (born between 1946–1964). [Eds note: The official breakdown for each group is as follows: Millennials (1981-1997), Generation X (1965-1980), and Boomers (1946-1964)]

Our survey asked them to identify their preferences for over 15 different content types while also noting their opinions on long-form versus short-form content and different genres (e.g., politics, technology, and entertainment).

We compared their responses and found similar habits and unique trends among all three generations.

Here’s our breakdown of the three key takeaways you can use to elevate your future campaigns:

1. Baby Boomers are consuming the most content

However, they have a tendency to enjoy it earlier in the day than Gen Xers and Millennials.

Although we found striking similarities between the younger generations, the oldest generation distinguished itself by consuming the most content. Over 25 percent of Baby Boomers consume 20 or more hours of content each week. Additional findings:

  • Baby Boomers also hold a strong lead in the 15–20 hours bracket at 17 percent, edging out Gen Xers and Millennials at 12 and 11 percent, respectively
  • A majority of Gen Xers and Millennials—just over 22 percent each—consume between 5 and 10 hours per week
  • Less than 10 percent of Gen Xers consume less than five hours of content a week—the lowest of all three groups

We also compared the times of day that each generation enjoys consuming content. The results show that most of our respondents—over 30 percent— consume content between 8 p.m. and midnight. However, there are similar trends that distinguish the oldest generation from the younger ones:

  • Baby Boomers consume a majority of their content in the morning. Nearly 40 percent of respondents are online between 5 a.m. and noon.
  • The least popular time for most respondents to engage with content online is late at night, between midnight and 5 a.m., earning less than 10 percent from each generation
  • Gen X is the only generation to dip below 10 percent in the three U.S. time zones: 5 a.m. to 9 a.m., 6 to 8 p.m., and midnight to 5 a.m.

When Do We Consume Content

When it comes to which device each generation uses to consume content, laptops are the most common, followed by desktops. The biggest distinction is in mobile usage: Over 50 percent of respondents who use their mobile as their primary device for content consumption are Millennials. Other results reveal:

  • Not only do Baby Boomers use laptops the most (43 percent), but they also use their tablets the most. (40 percent of all primary tablet users are Baby Boomers).
  • Over 25 percent of Millennials use a mobile device as their primary source for content
  • Gen Xers are the least active tablet users, with less than 8 percent of respondents using it as their primary device

Device To Consume Content2. Preferred content types and lengths span all three generations

One thing every generation agrees on is the type of content they enjoy seeing online. Our results reveal that the top four content types— blog articles, images, comments, and eBooks—are exactly the same for Baby Boomers, Gen Xers, and Millennials. Additional comparisons indicate:

  • The least preferred content types—flipbooks, SlideShares, webinars, and white papers—are the same across generations, too (although not in the exact same order)
  • Surprisingly, Gen Xers and Millennials list quizzes as one of their five least favorite content types

Most Consumed Content Type

All three generations also agree on ideal content length, around 300 words. Further analysis reveals:

  • Baby Boomers have the highest preference for articles under 200 words, at 18 percent
  • Gen Xers have a strong preference for articles over 500 words compared to other generations. Over 20 percent of respondents favor long-form articles, while only 15 percent of Baby Boomers and Millennials share the same sentiment.
  • Gen Xers also prefer short articles the least, with less than 10 percent preferring articles under 200 words

Content Length PreferencesHowever, in regards to verticals or genres, where they consume their content, each generation has their own unique preference:

  • Baby Boomers have a comfortable lead in world news and politics, at 18 percent and 12 percent, respectively
  • Millennials hold a strong lead in technology, at 18 percent, while Baby Boomers come in at 10 percent in the same category
  • Gen Xers fall between Millennials and Baby Boomers in most verticals, although they have slight leads in personal finance, parenting, and healthy living
  • Although entertainment is the top genre for each generation, Millennials and Baby Boomers prefer it slightly more than than Gen Xers do

Favorite Content Genres

3. Facebook is the preferred content sharing platform across all three generations

Facebook remains king in terms of content sharing, and is used by about 60 percent of respondents in each generation studied. Surprisingly, YouTube came in second, followed by Twitter, Google+, and LinkedIn, respectively. Additional findings:

  • Baby Boomers share on Facebook the most, edging out Millennials by only a fraction of a percent
  • Although Gen Xers use Facebook slightly less than other generations, they lead in both YouTube and Twitter, at 15 percent and 10 percent, respectively
  • Google+ is most popular with Baby Boomers, at 8 percent, nearly double that of both Gen Xers and Millennials

Preferred Social PlatformAlthough a majority of each generation is sharing content on Facebook, the type of content they are sharing, especially visuals, varies by each age group. The oldest generation prefers more traditional content, such as images and videos. Millennials prefer newer content types, such as memes and GIFs, while Gen X predictably falls in between the two generations in all categories except SlideShares. Other findings:

  • The most popular content type for Baby Boomers is video, at 27 percent
  • Parallax is the least popular type for every generation, earning 1 percent or less in each age group
  • Millennials share memes the most, while less than 10 percent of Baby Boomers share similar content

Most Shared Visual ContentMarketing to several generations can be challenging, given the different values and ideas that resonate with each group. With the number of online content consumers growing daily, it’s essential for marketers to understand the specific types of content that each of their audiences connect with, and align it with their content marketing strategy accordingly.

Although there is no one-size-fits-all campaign, successful marketers can create content that multiple generations will want to share. If you feel you need more information getting started, you can review this deck of additional insights, which includes the preferred video length and weekend consuming habits of each generation discussed in this post.

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