Who is Winning at the Game of Marketing? Email vs Social Media Stats

People have been arguing/debating over the value of these 2 for some time now. Because of this constant debate, we decided to put some perspective into the argument. Let the numbers decide. If you are a smart marketer, you will … Continue reading

People have been arguing/debating over the value of these 2 for some time now.

Because of this constant debate, we decided to put some perspective into the argument. Let the numbers decide.

If you are a smart marketer, you will follow the data.

You can still try out new stuff before you have the data, it’s OK to experiment, but it’s even better to make sure you have overview over some of the most popular marketing channels out there.

Instead of choosing sides, we are going to provide you with some interesting statistics and let you decide which one is winning at the game of marketing: email or social media.

email_social_media

Email – old, but effective; Social Media – new, hip, ROI not so great

Depending on your industry/niche you might be inclined to like one over the other.

Email                                     VS             Social Media Stats

  1. As of 2013, there are 3.6 billion email accounts (Source)
  2. 91% of consumers check their email daily (Source)
  3. People spend 13 hours of their workweek in their email inbox. (Source)
  4. 74% of consumers prefer to receive commercial communications via email (Source)
  5. 54% of emails sent by businesses are marketing messages (Source).
  6. 60% of marketers believe email marketing produces positive ROI (Source)
  7. 66% of consumers have made a purchase online as a result of an email marketing message (Source).
  8. Email marketing has an ROI of 4,300% (Source).
  9. 17% of marketers don’t track or analyze email metrics for their organization (Source)
  10. 76% of email opens occur in the first two days after an email is sent (Source).
  1. 4.2 billion people access social media sites via mobile devices (Source)
  2. 95% Of Facebook users log into their accounts daily (Source)
  3. 27% of total U.S. internet time is spent on social networking sites. (Source)
  4. Social media produces almost double the marketing leads of trade shows, telemarketing, direct mail, or PPC. (Source: Source)
  5. 52% of marketers cite difficulties in accurately measuring ROI as their biggest source of frustration in social marketing. (Source)
  6. 52% of all marketers have found a customer via Facebook in 2013. (Source)
  7. More than 23% of marketers are investing in blogging and social media (Source)
  8. 30% of traffic from social media is from SlideShare.net (Source)
  9. 53% of social media marketers don’t measure their success. (Source)
  10. 53% say a Youtube Video influenced their purchase at least once (Source)

***

Gathering the stats for this article was very revealing.

It was funny to see to see how some people bash email for being an “old technology”, but ignore to see the ROI of it. Old technology doesn’t mean obsolete technology, and new technology doesn’t mean it’s going to be a cash-cow you can milk at your will.

We are big fans of email marketing, that’s why we’ve built PadiAct, but that doesn’t mean we don’t find social media valuable.

A smart marketer should know how to use different marketing channels to drive more conversions. Conversion isn’t as easy as 1-2-3. You need to influence decisions over multiple marketing channels.

We don’t have favorite channels, we only care about what brings in revenue.

That’s why we are going to use whatever works for us and brings in more revenue.

For us, at this moment email is way more valuable than social, but we don’t neglect the fact that social media is a new opportunity for businesses to make themselves more visible, that’s why we keep a social appearance.

Which one do you prefer? Social media or Email? Which one brings in for you more business? Which one has better ROI?

Let us know through a comment.

Advanced Content Tracking with Universal Analytics

A while ago I wrote Advanced Content Tracking – a post about how to measure if users are actually reading your content. I’ve been getting a lot of requests to update this code for Universal Analytics. So here it is – an updated script specifically for use with Universal Analytics. This Google Analytics customization collects […]

Advanced Content Tracking with Universal Analytics is a post from: Analytics Talk by Justin Cutroni

The post Advanced Content Tracking with Universal Analytics appeared first on Analytics Talk.

A while ago I wrote Advanced Content Tracking – a post about how to measure if users are actually reading your content. I’ve been getting a lot of requests to update this code for Universal Analytics.

So here it is – an updated script specifically for use with Universal Analytics.

This Google Analytics customization collects data as users scroll down a page. It uses events to track when a post loads, when the user scrolls more than 150 pixels, when the user reaches the bottom of the content and when the user reaches the bottom of the page.

This technique uses Google Analytics events to track a user as they scroll down a page of content.

This technique uses Google Analytics events to track a user as they scroll down a page of content.

The end result is some cool data about how many users actually read content. Here’s a sample of what the data looks like. This is just an basic event report with the Event Action and Event Label.

You can access the Reading data in your Event reports. Here we see a single article and how often users scrolled, read the whole article and got to the bottom of the page.

You can access the Reading data in your Event reports. Here we see a single article and how often users scrolled, read the whole article and got to the bottom of the page.

The Scroll Tracking Code

Here is the JavaScript code that measures user scrolling.


TIP – You can use the tabs at the top of the code window to try the script. Just click on Result.

What’s changed in this version?

First, the blog post title is now collected as part of the event. Specifically I’m pulling the page title from the HTML and putting it into the event label. This makes it easier to drill down and see which pages people are reading. This was possible before using the Page Title dimension, but using the event label makes it just a bit easier. See the image above.

Another thing I change is I now use a Custom Dimension rather than a Custom Variable, to collect the ‘reader type’. Custom variables do not exist in Universal Analytics.

This change will impact your data! You will no longer see data in the Custom Variables report – because you’re not using Custom Variables. Custom Dimensions are only available in Custom Reports and Custom Dashboards.

I also changed how the Custom Dimensions are set. This script will set a Custom Dimension when the user reaches the bottom of the post content – not the bottom of the page. When they reach the bottom of the content they are categorized as a scanner or a reader.

  • A scanner is someone that simple scrolls to the bottom of the content in less than 60 seconds.
  • A reader is someone that take more than 60 seconds to reach the bottom of the content.

This is hardly a scientific way to categorize users, but it works for me :)

Finally, I added three custom metrics to store the time metrics: time to scroll, time to content bottom and time to page bottom.

Remember, in order to configure Custom Dimensions and Custom Metric you must first add them via your Google Analytics admin settings.

Other than the above changes the functionality is still the same.

Implementing the code

Step 1: There are a few code changes that you must make in order for this code to work on YOUR site.

1. Turn off debugging: This flag will display alert messages, rather than send GA data, when the user scrolls, reaches the bottom of the content and reaches the bottom of the page. If you do not set this to FALSE your users will get all sorts of alert messages :)

2. Decide how far you want for scroll depth: I send an event after the user scrolls 150 px. You can change this value, but I believe it works fine and does capture user engagement.

3. Specify where the bottom of your content is: This is the most important setting. This script sends an event when the user gets to the bottom of a post. That’s determined by the HTML. For me, the HTML is identified as .entry-content, as shown in this code.

if (bottom >= $('.entry-content').scrollTop() + $('.entry-content').innerHeight() && !endContent) {

You must change this line of code to identify a piece of HTML on your site that signifies the end of the content. This is the hardest part of the implementation.

Step 2: Add the code before the closing on your site. Make sure it appears AFTER the Universal Analytics page tag. It should look something like this when complete:

<head>

... all sorts of tags ...

<script>
  //
  // Universal Analytics page tag
  //
  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');

  ga('create', 'UA-XXXXXX-YY');
  ga('send', 'pageview');

  //
  // Scroll tracking script
  //
  jQuery(function($) {
    // Debug flag
    var debugMode = true;

    // Default time delay before checking location
    var callBackTime = 100;

    // # px before tracking a reader
    var readerLocation = 150;

    // Set some flags for tracking & execution
    var timer = 0;
    var scroller = false;
    var endContent = false;
    var didComplete = false;

... More code here ...

</script>

That should be it. You should see data instantly in the Real Time Event reports.

I encourage you to read the instructions in my original post.

Finally, a lot of people have asked me about implementing this script with Google Tag Manager. This really warms my heart :) I love tag management!

You can use this script with Google Tag Manager – but it takes a bit of work. I’ll write a separate post on that topic.

That’s it. I hope you find this script useful. Feel free to modify it to fit your needs. I’ve really enjoyed the data that it generates – it’s helped me better understand my readers and content.

Advanced Content Tracking with Universal Analytics is a post from: Analytics Talk by Justin Cutroni

The post Advanced Content Tracking with Universal Analytics appeared first on Analytics Talk.

10 Valentine’s Day Marketing Ideas for Ecommerce Websites

Oh, Valentine’s Day! One of the finest days of the year, a day when ecommerce businesses can drive some serious revenue. If you are on the hunt for some fresh ecommerce marketing ideas or you just need some inspiration for … Continue reading

Oh, Valentine’s Day!

One of the finest days of the year, a day when ecommerce businesses can drive some serious revenue.

If you are on the hunt for some fresh ecommerce marketing ideas or you just need some inspiration for your Valentine’s Day Marketing campaign, you came to the right place.

Before we go into the list, you should also check out our 2 other articles about Valentine’s Day:

Now, let’s get back to our list.

10 Valentine’s Day Ecommerce Marketing Ideas

1. Increase your email list on the short term

Even though you shouldn’t be lazy and you should be focused on collecting email leads at a good rate throughout the whole year, we forgive you if you didn’t, and we offer you a solution to get a huge amounts of subscribers on the short term.

What you should do is use PadiAct and target visitors with a pop-up and a clever copy.

Here’s an example of what targeting rules you can use:

returning_visitors

You can remove the targeting rule about timing if you want to target people as soon as they enter the website, but depending on the website, sometimes is better to leave people some time before you show them the pop-up.

Here’s a pop-up subscription box example and a copy related to your targeting rules:

pop-up-overlay

The Pop-up looks pretty simple, I know, but I’m sure it can drive great results, as eConsultancy reports that a pop-up overlay will increase opt-ins by up to 400%.

To maximize your results from this, consider A/B testing certain scenarios against each other, so that you use the best subscription form for your website.

2. Create a last-minute package

Design a last-minute package for the late birds who didn’t realize that Valentine’s Day is knocking at their door.

You could send out emails a few days before Valentine’s Day to be sure you can also deliver in time any orders.

Extra tip: think about a similar package for the early birds. You need to make everyone happy.

3. Create an ebook called “How to save Valentine’s Day”

How to Save Valentine's Day

“How to Save Valentine’s Day – an Amazon Bestseller”

Offer it as an incentive to people who didn’t order to get their products delivered on time, but will get their products 1 or 2 days after the V Day. Maybe you’ll save a few relationships.

4. Run an online Kissing Booth Content

People can upload pictures of them and their loved ones kissing and they can win products, coupons or they can get different offers at a great price.

To make everyone a winner what you could do is to offer a small discount to all participants.

5. Run A Black-Friday Type of V Day Flash Sale

This year’s Valentine’s Day falls on Friday, so you can use that” excuse” to organize a Flash Sale at midnight with huge discounts or incredible promo offers.

Use this opportunity to get rid of unnecessary stock and or sell bundles.

Maybe you can use this opportunity to drive more revenue from your email list, by making this Flash Sale only available to your email subscribers.

6. Create an Valentine’s Day web app

The app should allow people to send love themed e-cards to their loved ones.

Also, together with the e-cards people can also send a voucher or can subscribe their loved ones to your newsletter.

With this one, you are hitting 2 birds with one stone, making people happy and also getting closer to a sale.

7. Offer free luxurious wrapping to available on Valentine’s Day

Insert a tick-box in your checkout process that allows people to check it if they want their order to be gift wrapped in a luxurious paper.

People don’t want to loose too much time with the gift wrapping, so you are saving them time and money and also closing a sale for yourself. Sweet, right?

8. Create gender specific showcases

Help your customers by telling them what gifts are right for their loved ones. We all know, searching for a gift can be a very complicated tasks, nobody wants to screw it up, so this would definitely make your customers’ life easier.

Also considering that the average spent in the US is $130/gift, this will be a great chance to sell more expensive items.

Here’s how Ralph Lauren did it.

Valentines-Day-Website-Example

Example via PrestaShop Blog.

9. Create specific showcase based on FB relationship status

Thanks to Facebook, people now probably care more about their relationship status than ever. So, you can leverage this to create some kind of specific lists based on FB relationship status.

I think this might appeal to a younger audience, so if you catering to teens this might be an exciting opportunity for you.

10. Rebrand your live chat into “Cupid’s Hotline”

For until you consider Valentine’s Day done as a holiday rebrand your live chat into Cupid’s Hotline.

Instruct your customer representatives on how to suggest gift ideas to your customers. Teach them how to up-sell, cross-sell and how to provide added-value.

This way you make the idea of live chat way more appealing to your customers, and also, you are capitalizing on your customer representatives communication skills to drive more sales.

Final thoughts

As you can see, there are plenty of things you can try to drive more sales on Valentine’s Day, to get your business noticed or to make your customers happy.

You just need to pick a few ideas, adapt them to fit your needs, and push them hard so that you maximize your efforts.

Please let us know if you enjoyed any of our ideas, or if you have an idea of yours, please share it with us.

 

How to Increase Your Open Rates on Valentine’s Day

Everyone wants a piece of the pie that it’s called Valentine’s Day. With men expected to spend more than $130 on average and women to spend on average at least $50, online marketers are dying to get a share of … Continue reading

Everyone wants a piece of the pie that it’s called Valentine’s Day.

With men expected to spend more than $130 on average and women to spend on average at least $50, online marketers are dying to get a share of a period that topped 18.6 billion dollars in 2013.

But wait, there’s more…

In 2011 the online sales reached 2.65 billion dollars. So yeah, we have the premises to achieve incredible results, and because email it’s still the best channel to drive sales, we need to take email marketing seriously.

But for that we need to come up with some smart email subject lines to get our emails opened.

7 Email Subject Lines Ideas

Sean Platt in an article for Copyblogger says this subject line is the most effective he ever met. He saw open rates of over 90%, and in some tests of over 100%, that means some users opened the email more than once.

He didn’t use it for Valentine’s Day, but I’m sure if you can target a specific segment, e.g. the single segment of your list, you can achieve the same results as Sean Platt did.

You will never guess what subject line he is talking about: “You are not alone”.

Retail Email Blog does a very good job of documenting the most interesting subject lines that are used on Valentine’s Day email marketing campaigns. Our favorite selection from their Season Finale Post Series are:

What’s better than flowers or candy for Valentine’s Day?
Used by Fredericks of Hollywood in their 2011 Valentine’s Day campaign.

Cupid made us do it – 14% OFF EVERYTHING! Just today
Used by Norm Thompson also in 2011.

Love at First Sight, Plus Complimentary Shipping‏
Used by Tiffani in 2010.

20% Off + Tips to Create a Mood in the Bedroom
Used by Art.com in 2007

Pizza Restaurant, via Inbox Vision, teaches us a great lesson on copywriting with 2 excellent subject lines:

You had me at hello.
Clever use of the famous catchphrase from Jerry Maguire.

The greatest love lines of all times and pizza to share this weekend.
Nothing more cheesy, yet effective, than great love lines from the movies and a pizza :).

Achieve High Open Rates: Mailchimp Style

The guys from Mailchimp have a great habit on creating really inspiring posts.

In a particular one, they’ve talked about the best and the worst email subject lines that were delivered through their platform.

The secret to getting stellar open rates is very simple, some people may consider it almost stupid simple: “Describe the subject of your email”.

The best email subject lines that described the subject of the email achieved open rates of over 60%, up to over 87%.

The worst email subject lines, achieved between 1 and 14%.

If you are a Mailchimp user you can capitalize on their Subject Line Researcher that helps you find great ideas for your headline by referencing to how the words you chose performed in other campaigns. Cool, right?

Creating your own formula

If you are trying to create a stellar subject line based on what you read in this article, then you should check out this small guideline I’ve assembled for your:

1. Make me curious

If you want me to open your email, appeal to my curiosity, nothing makes me more curious than a good question, an inciting copy or a great offer that waits to be unveiled.

2. Incentivize me

One great method to get me to click on your email is to incentivize me by offering me a discount, tips & tricks, or something that I can talk about with my friends.

3. Talk about love

It’s Valentine’s Day so you got to talk to me about love, why it matters and yes, you can also say that you love me (even though you probably love me more for my money :P ).

4. Don’t get me confused

You already read Mailchimp’s formula for achieving stellar open rates, so it’s imperative that I understand that your email is about Valentine’s Day. I understand that you probably are a fancy copywriter, but nothing makes you happier than me reading your email and eventually buying something from you. That’s what I call copywriting and a love connection.

Final thoughts

There’s not really much to do with this email subject line thing. Create something relevant and incredibly attractive for your prospects. You already know this, but you probably only needed a few ideas to get ideas flowing through your head.

I hope you got some insights from this article and I’m sure if you planned your campaign correctly and tailored a great email subject line, you can get the most from this Valentine’s Day.

Your email content must also be  great in order to get conversions, but your first obstacle it will always be, to get the email opened and viewed by your list.

Hits, Sessions & Users: Understanding Digital Analytics Data

We talk about data every day – sessions, visits, conversions, pages, hits, etc. etc. etc. But sometimes we fail to understand how all of these metrics fit together and where they come from. Let’s take a look at how digital analytics tools organize data. All digital analytics data is organized into a general hierarchy of […]

Hits, Sessions & Users: Understanding Digital Analytics Data is a post from: Analytics Talk by Justin Cutroni

The post Hits, Sessions & Users: Understanding Digital Analytics Data appeared first on Analytics Talk.

We talk about data every day – sessions, visits, conversions, pages, hits, etc. etc. etc. But sometimes we fail to understand how all of these metrics fit together and where they come from. Let’s take a look at how digital analytics tools organize data.

All digital analytics data is organized into a general hierarchy of users, sessions and hits. It doesn’t matter where the data comes from, it could be a website or a mobile app or a kiosk. This model works for web, apps or anything else.

Digital analytics data is organized into a hierarchy of hits, sessions and users.

Digital analytics data is organized into a hierarchy of hits, sessions and users.

Sometimes we use the terms visitors instead of users and visits instead of sessions – they’re analogous. The onset of mobile devices (and other devices, like set top boxes) have prompted us to introduce new terms into our vocabulary.

It’s important to understand each piece of the hierarchy and how it builds on the other to create a view of our customers and potential customers. Because, at the end of the day, we need to use this data to evaluate our decisions and look for new business opportunities.

Let’s start at the bottom, with hits, and work our way up to users.

Hits

A hit is the most granular piece of data in an analytics tool. It’s how most analytics tools send data to a collection server. In reality, a hit is a request for a small image file. This image request is how the data is transmitted from a website or app to the data collection server.

All data is sent using a hit. Most hits are actually the request for an invisible image file.

All data is sent using a hit. Most hits are actually the request for an invisible image file.

There are many different kinds of hits depending on your analytics tool. Here are some of the most common hits in Google Analytics:

Pageviews/Screenviews: A pageview (for web, or screenview for mobile) is usually automatically generated and measures a user viewing a piece of content. A pageview is one of the fundamental metrics in digital analytics. It is used to calculate many other metrics, like Pageviews per Visit and Avg. Time on Page.

Events: An event is like a counter. It’s used to measure how often a user takes action on a piece of content. Unlike a pageview which is automatically generated, an event must be manually implemented. You usually trigger an event when the user takes some kind of action. The action may be clicking on a button, clicking on a link, swiping a screen, etc. The key is that the user is interacting with content that is on a page or a screen.

Transactions: A transaction is sent when a user completes an ecommerce transaction. You must manually implement ecommerce tracking to collect transactions. You can send all sorts of data related to the transaction including product information (ID, color, sku, etc.) and transactional information (shipping, tax, payment type, etc.)

Social interaction hit: A social interaction is whenever a user clicks on a ReTweet button, +1 button, or Like button. If you want to know if people are clicking on social buttons then use this feature! Social interaction tracking must be manually implemented.

Customized user timings:User timings provide a simple way to measure the actual time between two activities. For example, you can measure the time between when a page loads and when the user clicks a button. Custom timings must be implemented with additional code.

That’s a lot of hit types!

All hit types are sent to Google Analytics via a tracking code. The tracking code variation depends on what you are tracking. If you are tracing a website then JavaScript code, named analytics.js, generates the hits. If you are tracking a mobile app then an SDK (either Android or iOS) generates the hits. If you are tracking a kiosk, then YOU generate the hits with the measurement protocol.

Regardless of the hit type, the hits are all formatted in a similar manner. They are a request for an invisible image and contain data in query string parameters.

http://www.google-analytics.com/collect?v=1&_v=j16&a=164718749&t=pageview&_s=1&dl=http%3A%2F%2Fcutroni.com%2F&ul=en-us&de=UTF-8&dt=Analytics%20Talk%20-%20Digital%20Analytics%20for%20Business&sd=24-bit&sr=1920x1080&vp=1308x417&je=1&fl=12.0%20r0&
_utma=32856364.1751219558.1391525474.1391525475.1391525475.1&
_utmz=32856364.1391525475.1.1.utmcsr%3D(direct)
%7Cutmccn%3D(direct)%7Cutmcmd%3D(none)&_utmht=1391525534970&
_u=cACC~&cid=1751219558.1391525474&tid=UA-91817-11&z=378275262

For all the nerds out there, the data hits can be sent via a GET request or a POST request. This is really important to know, because the amount of data can change. With a GET request you can only send 2048 characters of data. Technically a post can be any length (it’s a setting on most servers), but it’s around 8000 characters when sending data to Google Analytics.

The information in a hit is transformed into dimensions during processing. Every report is just a single dimension, and the corresponding metrics for each value. that you see in your reports.

Each report in Google Analytics shows all of the values for a single dimension, and the corresponding metrics for each value.

Each report in Google Analytics shows all of the values for a single dimension, and the corresponding metrics for each value.

A quick note on mobile…

The mobile SDKs do not send data in real time. They actually store the hits locally and them send them in bursts. This is called dispatching and it’s used for a couple of reasons. First, mobile devices are not always connected to a network. So analytics must store the hits until it detects a connection and then it sends the hits. Second, sending hits in a bunches can help conserve battery life. Don’t worry, dispatching does not impact session calculations – which we’ll talk about right now :)

Session

A session is simply a collection of hits, from the same user, grouped together. By default, most analytics tools, including Google Analytics, will group hits together based on activity. When the analytics tool detects that the user is no longer active it will terminate the session and start a new one when the user becomes active.

Most analytics tools use 30 minutes of inactivity to separate sessions. This 30-minute period is called the timeout.

A session is a collection of hits. It ends when there has been 30 minutes of inactivity.

A session is a collection of hits. It ends when there has been 30 minutes of inactivity.

Google Analytics, and most tools, use the time between the first hit and the last hit to calculate the time on site. The time between hits is also used to calculate other metrics, like time on page. You can read more in my overview of how Google Analytics performs time calculations.

Most tools let you change the default timeout to better suit your needs. For example, if you have a lot of video on your site you might want to change the timeout – especially if your video last more than 30 minutes.

Why?

If a user is watching a 60 minute video (and by watching I mean that no other hits are sent to analytics) their session will end 30 minutes after the first hit. To insure that the session lasts until the end of the video you could change the timeout to match the longest video length.

OR, a better way to extend the session, would be to send additional hits while the user is watching the video. Think about it – more hits create more data points that can be used to calculate time. Trust me, take 12 minutes to read more about how Google Analytics performs time calculations.

Now that we know that hits are grouped together into sessions, let’s look at how sessions are grouped based on users.

Users

Here’s where things start to get interesting. A user is the tools best-guess of an anonymous person. Users are identified using an anonymous number or a string of characters. The analytics tool normally creates the identifier the first time a user is detected. Then that identifier persists until it expires or is deleted.

The identifier is sent to the analytics tool with every hit of data. Then the analytics tools can group hits (and thus sessions) together using the identifier in the hits.

Make sense?

Sessions from the same user can be grouped together as long as each hit has the same user ID.

Sessions from the same user can be grouped together as long as each hit has the same user ID.

Here’s how users are detected on some of today’s most common digital platforms.

Website Users

To measure a user on a website almost all analytics tools use a cookie. A cookie is a small text file. The cookie contains the anonymous identifier. Every time a hit is sent from the browser back to the analytics server identifier stored in the cookie is sent along with the data.

When measuring a website, the analytics tool usually uses a first party cookie to store an anonymous ID.

When measuring a website, the analytics tool usually uses a first party cookie to store an anonymous ID.

Now let’s have the cookie talk.

Google Analytics uses a first party cookie. A first party cookie is connected to the domain that creates it. A first-party can only be used by the domain that sets it. So on this site, the cookie has a domain of cutroni.com and can only be used by this website.

In Universal Analytics the cookie is named _ga and lasts for two years. In the previous version of Google Analytics the cookie was named __utma.

The good thing about a first party cookie is that almost all browsers will allow a first party cookie. It’s a very reliable piece of technology.

First party cookies are challenging when your site spans multiple domains. When a user leaves your site, and traverses to another site that you own, they do not take their first party cookies. In most situations, unless you configure analytics correctly, analytics will set another cookie when the user lands on the second domain.

Analytics uses a first party cookie to maintain a user ID.

Analytics uses a first party cookie to maintain a user identifier.

Now you have one user with two cookies. That could lead to double counting of users. Plus, if we want to create really cool metrics, like Revenue per user, it becomes very, very hard because we don’t know the true number of users.

The other type of cookie, a third-party cookie, can be set and accessed by domains other than the domain that creates it. Some analytics tools will let you use a third party cookie.

The value of a third party cookie is that the analytics tool can use a third party cookie to identify a user as they move from one domain to another.

A third party cookie can be used by multiple domains.

A third party cookie can be used by multiple domains.

However, third-party cookies are not permitted by most browsers – that means no data.

Google Analytics does not use a third party cookie. You can read all about the Google Analytics cookies in the developer documentation.

So what’s the solution here? How do you correctly identify a user if your website spans multiple domains? In the Google Analytics world we use a feature called Cross Domain Tracking. I’m not going to talk about it in this post, but you can read about it in our support documentation.

Mobile Users

Now let’s move on to mobile platforms – something that is very popular :)

Mobile tracking is similar to web tracking. There is an anonymous identifier stored on the device. The identifier is generated every time the app is installed. So if a user deletes the app the identifier will also be deleted. But if a user updates the app the identifier will not change.

The big difference between mobile and web is that the identifier is not stored in a cookie. It’s stored in a database on the mobile device – but it basically functions the same way as a cookie. The identifier is sent on every hit back to the analytics server. The analytics server then uses the identifier to create metrics like unique users.

Here’s one challenge with user measurement on an app. Many apps are not just an app. They’re a hybrid app/website. They use a browser within the app to “frame” content from a website. This can mess up the data collection.

In this situation we have two technologies with two different user identifiers. The app will measure a user based on the ID stored on the device and the website will use a cookie when a page loads in the app.

Mobile apps that "frame in" content from a website, might be sending duplicate hits to the analytics tool.

Mobile apps that “frame in” content from a website, might be sending duplicate hits to the analytics tool.

There are some ways around this, but it’s a long solution that need it’s own blog post. But just be aware of this potential data issue.

Ok, so now we know about website users and mobile users. But what about other digital touch-points, like a kiosk?

Other Digital Touch-points

In today’s world a user can interact with your digital content on lots of different devices (computers, mobile, kiosks, set top boxes, etc.). And that can cause a lot of issues as tools try to de-duplicate users and get an accurate count of users.

One of the key features of Universal Analytics is the ability to track users on devices other than websites and mobile devices, things like a point-of-sale system or a kiosk. It does this using a technology called the measurement protocol.

But how does it actually work?

The measurement protocol works by – wait for it – collecting hits :) These are the same hits that I described above. The big difference is that you must manually build the hits. So if you want to implement analytics on a kiosk, you must create MORE code to build the hits that are sent to Google Analytics.

But what about measuring users when you use the measurement protocol?

When you create the hit you must insert a user identifier into the hit. Google Analytics will then use this identifier as the unique identifier when it processes the data.

To measure users when tracking other devices, like a kiosk, you must insert your own identifier and generate your own data hits.

To measure users when tracking other devices, like a kiosk, you must insert your own identifier and generate your own data hits.

Unlike websites and mobile apps, there is no cookie or database to store the identifier. So the ID does not persist from one hit to another, or from one session to another. You must manually insert the identifier into every hit in every session.

Your code must control the generation of the identifier and the storage of the identifier.

Let’s end it there. That’s a pretty good overview of digital analytics data.

I know this was a really geeky post, but it’s an important subject and will become more and more important.

Now it’s your turn. Thoughts? Please feel free to leave a comment.

Hits, Sessions & Users: Understanding Digital Analytics Data is a post from: Analytics Talk by Justin Cutroni

The post Hits, Sessions & Users: Understanding Digital Analytics Data appeared first on Analytics Talk.

The Ultimate Guide to Email Marketing on Valentine’s Day

According to US National Retail Federation, in 2012 alone people were willing to spend on average $126.03 on Valentine’s Day for their significant other. In 2013, the spend went up to $130.97, so the total spend related to Valentine’s Day … Continue reading

According to US National Retail Federation, in 2012 alone people were willing to spend on average $126.03 on Valentine’s Day for their significant other.

In 2013, the spend went up to $130.97, so the total spend related to Valentine’s Day in the US was estimated at 18.6 billion dollars. In 2012 it was 17.6 billion dollars.

Just like Christmas, Valentine’s Day is a holiday celebrated by pretty much everyone nowadays. Actually, it isn’t just an american tradition anymore, countries all over the world are celebrating Valentine’s Day, and businesses are looking to leverage this.

As always, email marketing is  probably one of your best bets to capitalize on people’s willingness to spend money on Valentine’s Day.

Add some creative advertising and some engaging social media content to the mix and you got yourself a winning strategy.

Opportunity: checked.

Strategy: this article will help you out with that.

What To Do to Win Valentine’s Day

Your Valentine’s Day to do list should look something like this:

  1. add more subscribers to your email lists
  2. segment your list
  3. schedule when you are going to send the emails
  4. write great subject lines
  5. design amazing emails

Valentine’s Day is all about love and that means you shouldn’t have a “one night stand” with your email list. . (tweet this)

Treat this holiday right, and it can bring you HUGE benefits on long term.

Also, don’t make this holiday about you: make it about your customers and about your audience.

Help them experience a day they’ll never want to forget.

How to grow your email list very fast

I know, Valentine’s day is almost here. Getting more email leads & subscribers in such a short time is not an easy task.

Don’t freak out though, everything is not lost. We’ve got a few tips that can earn you some serious Valentine’s business.

Create a valentine’s Day Business Venture

Partner up with other companies, find companies that are not selling products or services suitable for Valentine’s Day. However, they might have bigger email lists than you do.

By partnering up with them you’ll get to increase the sales of your products and they’ll get a partnership commission out of a holiday which otherwise would bring them no extra sales.

Closing a partnership might take time so get it done right now.

ADD BUcketloads of subscribers by using behavioral targeting

If you are not using PadiAct, maybe this is the best opportunity for you to try it out.

You can start by defining a campaign and target people exclusively for the Valentine’s Day lists.

Here are a few tips to get loads of email subscribers from Day 1:

  • identify the most successful products of last year’s Valentine’s Day and ask people to subscribe while visiting similar products
  • target visitors after spending at least 2 minutes viewing the products (they are still in the researching phase)
  • offer them the best reason to subscribe, so they can’t refuse you: let people know about the Valentine’s Day exclusive emails with tips, gifts and promotions.
Setting up targeting rules inside PadiAct.

Example: How to target visitors with PadiAct.

Segment and clean your lists

On the romantic 14th of February and the days before it, the subscribers are going to receive loads of emails with Valentine’s offers. For them, it will be a tipping point and people will probably “abuse” the Report as spam or Unsubscribe buttons.

That’s why, you need to be one step ahead.

The week of 14th of February should be a week when open rates, click rates and purchases are at their best.

Here are a few segments that you should look into:

  • gender: craft emails respecting the fact men & women have different needs
  • activity: the closer you get to the last emails of your Valentine’s Day campaign, the more you should refrain yourself from sending emails to inactive subscribers
  • interest: use different call to actions in emails to identify user interest. Based on the links they’ve clicked, add subscribers in different buckets
  • age: a 40 year couple will probably have a different idea on how to spend that day compared to an 18 year one

A great way to keep your lists clean, is to allow people to opt-out of the Valentine’s Day emails, but to stay subscribed toyour other lists. A visible copy like the following one with the appropriate link should do the job:

I already found a gift and no longer want to receive Valentine’s Day emails.

Drip your emails

We already told you that love is in the air, a “one night stand” is very a bad idea.

Don’t plan a single email for Valentine’s Day. Avoid the “hit and run” approach.

Build a campaign that reminds people about the big day, a campaign with ideas on how to surprise their partners, makes it easy for them to find a gift and then follow up to make sure everything went well.

Is it your first Valentine’s Day email campaign?

Feel free to use the following Valentine’s Day Marketing Calendar:

20th-22ND January – announce Valentine’s Day

Send an email to all your subscribers reminding them that Valentine’s Day is coming. Tell them to hop on the exclusive Valentine’s day email list.

Give a few hints of presents or things they can do for their special one.

Add up to 3 call to actions in the email, each one of them describing a different user intention.

Future campaigns should be sent to visitors based on what link they clicked.

January 27th – 30th – content aware campaign

Based on expressed intention through the links they’ve clicked in the previous email, send them suggestions of gifts.

Make sure to add a story to each gift idea.

People would rather relate to stories than to product descriptions, so you’ll make their choice easier.

February 3RD – 5th – launch THE OFFER

The period when most people do the purchases. The more they delay, the harder it will be to find and have the perfect gift delivered on time.

Be creative about your incentive. Go beyond the classic discounts.

Discounts are always welcomed, but extravagant wrapping of purchased products can make a strong impression, especially on Valentine’s Day.

10th of February – continue with the promotion

Only send to people that opened at least one email in the last 3 months.

From this moment on, you are playing the safe card and you are keepings unsubscribes and spam reports as low as possible. This email should speak of urgency. It’s the last chance of buying a gift, if it’s not too late already.

13th of February – the day before

It’s probably too late to sell anything and deliver it on time. To the subscribers that did not purchase anything for their loved ones, offer them some tips on how to save the day. To the ones that did purchase, give them tips on how to surprise without actually giving them a gift.

This one email might be the one that actually helps you WIN your email list. If you provide them with great ideas on how to save the day, your subscribers will remember you and they will recommend you to all their friends.

15th of February – the day after

Encourage subscribers to write back their stories. Maybe even through in a prize or some gift cards.

Get as many testimonials & stories as possible.

Create content around your customers’ stories (it can be an article, a video or an infographic), and post it on Pinterest, Facebook and, what the heck, release it even on Google+.

Make it a Valentine’s Day that everyone remembers, and promote it heavily so that your competition will be jealous because they’ve decided to have a “one night stand” instead of actually providing help to their audience

Subject lines your email list will not ignore

One of your biggest challenges will be to get as many subscribers to open your emails. Here is what Experian found in a study they’ve conducted last year:

experian

As you see, subject lines are crucial, so here’s a list that can get you started on writing great emails:

  • Hurry, Cupid’s Counting Down! Send a Special Greeting Card
  • 3 Days Until Valentine’s Day. Find The Perfect Gift Today Or It Could Be Your Last.
  • Order now to avoid heartbreak this Valentine’s
  • For love or money?
  • Get gifts as extraordinary as your Valentine
  • Valentine’s Day gifts for your Rebecca
  • You’ll ❤ Key Pieces for the Season
  • We’ve got a crush (or two). How about you?
  • Ten Ways to Say “I Love You”
  • Wine, Chocolates & $0 Shipping
  • Will You Be Our Valentine? Sweepstakes, News, & More To Show Our LUV
  • Last Chance to Get the Look You Love Before V-Day

By sending up to 6 emails ’till the big day, you’ll have a chance to test a few sets of subject lines to find the best one for you.

Design amazing emails

Once you have subscribers open your emails, the copy and the design are going to be decisive in having people become customers as well.

Check out the following designs that were shared heavily on the web. Click on the images to get the full newsletter:

01-apple

02-perfume

03-pink

04-starbucks

05-restaurants

06-trips

07-jewels

08-furniture

 

How will you approach valentine’s day in 2014?

What are your plans for 2014’s Valentine’s Day?

What worked for you the last year and what do you plan to do better in 2014?

Did we miss something? We would love to hear your opinion on this article.

Collect Email Subscribers & Leads Using BlackMail

Here at PadiAct we are committed to growing your email leads & subscribers lists. This is what we know best, this is what we do for hundreds of online businesses. With this mission in mind we are constantly testing and … Continue reading

Here at PadiAct we are committed to growing your email leads & subscribers lists.

This is what we know best, this is what we do for hundreds of online businesses.

blackmail

With this mission in mind we are constantly testing and experimenting with features, pop-ups and targeting rules to find new and effective ways to get you more emails.

Recently, we developed a design that we think will skyrocket your subscription rates.

We introduce you: BlackMail

Hihi! Gotcha!

I had you worried there for a second.

Blackmail_form

BlackMail is a slick, yet elegant, predesigned subscription form that you can use as a popup, left to right slider and bottom slider, just like the other 2 predesigned styles (Default and Good Old Mail).

Adding the image is very easy, you just need to paste the URL where you hosted the image.

Recommended image width is 200px.

How to Start Using BlackMail

  1. Go and edit your campaign.

  2. Select the style to be BlackMail.

  3. Paste your image URL in the field

  4. Personalize the Interaction Look & Feel

  5. Quick preview it to see if you are happy with the results

  6. Fine tune your copy

  7. Save the campaign.

You’re done. Congratulations.

That’s it. Easy, right?

Now, just sit back, relax, and enjoy “the view” (the reports).

From now on, you will collect email leads and subscribers using “BlackMail”.

How to Use Google Analytics Content Grouping: 4 Business Examples

Content Grouping is a useful feature that let’s you group your website or app content together and view aggregate metrics for each group. This is particularly useful if you have a lot of content to analyze. Rolling up your content, based on your specific business structure, is very helpful when creating dashboards and other custom […]

How to Use Google Analytics Content Grouping: 4 Business Examples is a post from: Analytics Talk by Justin Cutroni

The post How to Use Google Analytics Content Grouping: 4 Business Examples appeared first on Analytics Talk.

Content Grouping is a useful feature that let’s you group your website or app content together and view aggregate metrics for each group. This is particularly useful if you have a lot of content to analyze. Rolling up your content, based on your specific business structure, is very helpful when creating dashboards and other custom reports.

In this post I’ll talk about how to actually use the data and walk through some examples for various business types.

If you have not set up content groupings, please check out my post on how to set up Google Analytics content groupings.

Standard GA Reports

Your content groupings are available in Google Analytics behavior reports. Navigate to the Behavior > Site Content > All Pages report. Notice at the top of the data table there is a selector for the primary dimension. This drop down list all of the content groupings that you added to Google Analytics.

Use the selector to choose a specific content grouping in your Google Analytics Content reports.

Use the selector to choose a specific content grouping in your Google Analytics Content reports.

This selector also exists in the navigation flow, so rather than viewing how users move from page to page, you can view how users move between the different types of content on your site.

You can also use your content groupings in the Navigation Summary report.

You can also use your content groupings in the Navigation Summary report.

Very handy for understanding the behavior of users!

It also exists in many other content reports, like the Landing Pages report and the Site Speed Page Timings report.

But who uses the standard reports these days? :) Analysis driven organisations use Custom Reports and Dashboards. Let’s look at how you can use content groupings in both features.

Custom Reports & Dashboards with Content Groupings

When you create a content grouping, Google Analytics will create a dimension for each content grouping.

Remember, a content grouping contains a number of groups, and each group can contain a number of pages or screens.

Each content grouping contains multiple content groups. A content group contains multiple pieces of content.

Each content grouping contains multiple content groups. A content group contains multiple pieces of content.

This means that the values for the content grouping dimension will be all of the content groups that you created within that grouping.

You can create up to five content groupings in Google Analytics, therefore you could have five new dimensions, one for each content grouping.

Use the content grouping dimensions just like you would any other dimension. Here’s a simple custom report that shows some a potential content grouping for a blog.

You can use your content groupings in a Google Analytics custom report.

You can use your content groupings in a Google Analytics custom report.

Then, when you look at the report, you’ll see something like this:

When you add a content grouping to a Google Analytics custom report, the data will be aggregated based on content group.

When you add a content grouping to a Google Analytics custom report, the data will be aggregated based on content group.

Note: I added this custom report to the Google Analytics solutions gallery. You can add it directly to your account here.

You can also use the content grouping dimension in your dashboards. Here is a very simple example using the page value metric and the content grouping dimension.

You can also use the Content Grouping dimension in a Google Analytics Custom Dashboard.

You can also use the Content Grouping dimension in a Google Analytics Custom Dashboard.

That’s really all there is to using content grouping in Google Analytics custom reports and custom dashboards. No go and give it a try!

One other note – the content grouping dimensions are hit level dimensions. This means that you can only use them with hit level metrics, like pageview, time on page, etc. You can not use them with session level metrics, like conversion rate, or revenue per visit.

Content Grouping Strategies

To really take advantage of content groupings you need to plan your content grouping carefully. You need to understand how your organization wants to analyze this data. So let’s look at a how different types of businesses might use content grouping.

Ecommerce: Patagonia.com

Patagonia sells outdoor equipment for men, women and children. They’re known for their ethos that you should travel “fast and light” when in the outdoors – take only what you need. They’re also known for their environmental advocacy. They incorporate both of these messages into their marketing stories.

Effectively breaking down the content structure could help each department at Patagonia better understand their marketing initiatives and site optimization efforts.

So how might we create a content grouping strategy based on their business?

Google Analytics Content Grouping can be used to organize the content on an ecommerce website.

Google Analytics Content Grouping can be used to organize the content on an ecommerce website.

Product pages: I would start by grouping all product pages together. It’s really important to understand what percentage of your users are making it to product pages. If people don’t look at product pages then they usually can’t buy something. And I’d take it one step further – group product pages by product line. I’d also be sure to differentiate category pages from the generic product pages.

You can mimic your product architecture with your content groupings.

You can mimic your product architecture with your content groupings.

Special selling tools: One cool feature that the Patagonia site has is the ‘kit builder’. This is a tool that let’s a customer build the best clothing combination for different conditions or activities. This is another section that could really use it’s own content group.

Special shopping tools can be categorized in their own group.

Special shopping tools can be categorized in their own group.

Checkout pages: Next I’d group all checkout pages together. These are all the pages in your checkout process. The percentage of people that see checkout pages might be very small, but I like to put these pages in their own group. They’re not product related, and they’re not marketing related. So they need their own group.

Account management pages: Many ecommerce sites let customers manage account settings, check the status of their order, manage returns, etc. I would lump all of these pages together in an Account Management group.

Marketing pages: Now we get into a large chunk of the content – marketing pages. Patagonia has a lot of information about their brand, and initiatives. Rather than lump all of this together as just Marketing pages, I would actually break all of this up into groups based on the different initiatives.

In the case of Patagonia I would use all of these different groups that you can see in the navigation.

Use a Google Analytics Content Grouping to categories marketing pages.

Use a Google Analytics Content Grouping to categories marketing pages.

Support pages: Business is all about relationships – and that’s represented by different types of support content. We can create a support group that containing any materials related to support. Again, you can create sub-groups for different types of support content (product support, order support, etc.)

Error pages: I like to group all error pages into a single group, then I can drill into the group and view the specific errors. This group can contain all different kinds of errors, depending on your personal preference. It could be technical errors, like 404 or 502 errors. Or it could be more functional errors, like when a user adds an incorrect credit card number during their purchase.

Software as a Service: Mailchimp.com

Mailchimp is a popular service that helps businesses manage their email marketing initiatives. Like all SaaS sites it’s primarily divided into two sections: a marketing section and an application section. The content grouping will mimic this general structure of content.

Product marketing pages: If people are going to sign up for the Mailchimp service then they need to know about the features! Product marketing page are pages dedicated to product information, this includes information about price, features, etc.

For a SaaS site, create groups for different kinds of marketing content.

For a SaaS site, create groups for different kinds of marketing content.

In addition to specific product information, there’s also a lot of thought leadership material to help drive marketing.

Marketing content pages: These pages are non-product marketing pages that help you demonstrate your thought leadership. It may be blog pages, or other content. In our example of mailchimp.com, there might be multiple groups. For example, they have a blog, but they also have a ton of research about email marketing. I would put this material in a marketing content group. Or even better, in the Reports group!

I would create a Google Analytics content group for the research reports on the MailChimp site.

I would create a Google Analytics content group for the research reports on the MailChimp site.

Application pages: The other side to a SaaS site is the actual application. This is the section of the site where you log in and actually use the product. Like the marketing pages, there can be many different types of application pages. Let’s go back to our example of Mailchimp.com. I would break down the content based on product features.

Perhaps we could use the application navigation as a template for the content structure.

You can create different groups for each part of the online application.

You can create different groups for each part of the online application.

Account management pages: Here’s another example of grouping different parts of the application together. We could easily group together the pages that control account management. And you can see from the image above that there are sections of the app dedicated to other functionality – all should be grouped accordingly.

Error pages: Like other types of sites it’s a good idea to group all error pages together. See the ecommerce section above for more details. These groups can be both website errors or application errors – like a login error page.

Gaming Application: Clash of Clans

We all use our mobile devices for incredibly important things, like waging medieval warfare on other clans! HA! Anyone out there like Clash of the Clans?

You can categorize app content using Google Analytics Content Groupings.

You can categorize app content using Google Analytics Content Groupings.

In reality, gaming apps are very similar to other business models – like publishing and commerce. Some games generate revenue from in-game ads while others up-sell users on features, like new levels. Some do both. We can group games content together just like we do ecommerce.

Game level screens: Most of the content for a game is probably level based. We can replicate this base structure in Google Analytics. If you’re a fan of Clash of the Clans then you there are other parts to the game in addition to levels. There are attack screens, chat windows, etc. All of these screens can be added to groups to roll-up the data.

Ecommerce screens: These screens are used to sell the user on pay features. In the case of Clash of Clans you can buy more gems, which can then be used to purchase other items, like more armies!

I would put all ecommerce app screens into a separate content group.

I would put all ecommerce app screens into a separate content group.

Configuration screens: Most apps have a configuration section. This is where the user can change everything from the language, to colors, etc.

Error screens: Last but not least we have error screens. Again, these can be technical app errors or functional errors, like login issues.

For Publishers: MarketingLand.com

Let’s face it, content grouping was made for the publishing industry! They’re the ones that have to organize thousands of pages of content. I don’t want to dwell on publishing too much, but let’s take a look at MarketingLand.com, a popular destination for anyone working in the digital marketing world.

I’ve actually written about how to customize google analytics for publishing sites in the posts Custom Variables for Publishers and how to measure how far users scroll down a page. I think both of those techniques still apply.

But now, if you’re a publisher, you can also use content groupings to organize the data about your content. This provides one more way to roll up data for analysis.

Content Category: Almost all publishers group content by category – and now this can be done with the content grouping feature.

Publishers can create content groups based on the organization of their content.

Publishers can create content groups based on the organization of their content.

Some publishing sites organize content in other ways, like by author or publication date. I would suggest creating content groups for topic categorization, and custom dimensions for any secondary organization (author, date, etc.)

Account pages: Some publishers, like the New York Times, offer a premium membership service. This is not the case with MarketingLand.com. But, if it did have a member’s section, you could group all of those pages together.

Error pages: Do I need to go over this again :)

I hope this post provides some inspiration for how you might use Content Grouping for your business. Ultimately how you organize your content groupings will be based on your organization. There is no right or wrong – just use a structure that is useful.

Questions or comment? Leave a note below – and happy grouping!

How to Use Google Analytics Content Grouping: 4 Business Examples is a post from: Analytics Talk by Justin Cutroni

The post How to Use Google Analytics Content Grouping: 4 Business Examples appeared first on Analytics Talk.

How to Set Up Google Analytics Content Grouping

Today everyone is creating content – lots and lots of content. Measuring that content can be a challenge given the sheer volume that’s out there. That’s where Google Analytics Content Grouping can help. This feature let’s you categorize your content based on your own business rules. Then, rather than view your data based on page […]

How to Set Up Google Analytics Content Grouping is a post from: Analytics Talk by Justin Cutroni

The post How to Set Up Google Analytics Content Grouping appeared first on Analytics Talk.

Today everyone is creating content – lots and lots of content. Measuring that content can be a challenge given the sheer volume that’s out there. That’s where Google Analytics Content Grouping can help.

This feature let’s you categorize your content based on your own business rules. Then, rather than view your data based on page URL or screen name, you can view based on your specific groups.

In this post I’m going to talk about how content grouping works and how you set it up.

Key Vocabulary: Groupings and Groups

There is a little terminology we need to cover before we get into the setup: groupings and groups.

You can create multiple content groupings in Google Analytics.

Within a grouping you can create multiple content groups.

A group is a collection of content. It could be pages in a certain section of your website. Or it might be screens from a certain part of your app. It can be just about anything.

A grouping is just a bunch of groups.

Each content grouping contains multiple content groups. A content group contains multiple pieces of content.

Each content grouping contains multiple content groups. A content group contains multiple pieces of content.

You can create multiple content groupings in Google Analytics and switch between them in the reports.

Here’s an example. For my blog I created a grouping called Blog Content Categories.

Within that grouping I create a number of groups to categorize the different types of content on my blog. There’s a group for posts, a group for about me pages, a group for error pages, etc. In the configuration I created a rule that puts each page in a group based on the structure of the URL.

You can view your content data based on groups, rather than URL, screen name or title.

You can view your content data based on groups, rather than URL, screen name or title.

Any item that is not added to a group will appear in the (not set) content group.

It’s important to know that there is not one specific report where you access this data. When you create a grouping it’s literally becomes a new dimension of data. You choose to view that dimension in almost all of the content reports.

Let’s take a look at how you actually create a grouping and groups.

Creating Groupings & Groups

Google Analytics does not automatically create content groupings – you must configure the tool to do that. Navigate to the settings for a specific view and choose Content Groupings.

Content Grouping is a view level setting.

Content Grouping is a view level setting.

Here you will see a list of all your groupings. You can choose to create a new group or edit an existing group.

Here's a list of your Google Analytics content groupings. You can add or edit groupings here.

Here’s a list of your Google Analytics content groupings. You can add or edit groupings here.

There are three methods you can use to create a content group – let’s take a look at each.

Tracking Code Method

This method requires you to add a small piece of code to each page on your site or in your app. The code will literally set the name of the content group when the page or screen renders. Here’s how the code would look for Universal Analytics:

ga('create', 'UA-XXXXXXXX-Y', 'example.com');
ga('set', 'contentGroup5', 'Group Name');
ga('send', 'pageview');

Or, if you’re working in iOS the code might look like this:

id tracker = [[GAI sharedInstance] trackerWithTrackingId:@"UA-XXXX-Y"];
[tracker set:[GAIFields contentGroupForIndex:5]
value:@"Group Name"];

The code for a content group is similar to the code for a custom dimension. You can set 5 content groups using the tracking code. Each group is associated with a number, one through five, as shown in the example above.

Check the Google Analytics support documentation for more code examples.

Basically this method let’s you suck in the group name, via code, from some other system. It might be a CMS, a data layer, or just the HTML of the page.

The key is that you somehow add the name of the group to the Google Analytics code.

Pros: Using the tracking code method you can use code to automatically adjust to changes in your content and new content groups.

Cons: It requires IT involvement to set up. But once it’s configured very little IT support.

I should also mention that content grouping is coming to Google Tag Manager. This will provide another way to programmatically set the content group – so stay tuned.

Extraction Method

The extraction method extracts (get it) the name of your content groups from an existing dimension of data. The idea is that you use a regular expression to parse the dimension and automatically extract the name of your group.

For example, the name of your content groups might be in the page title, like this:

Your website might use the name of the content in the Page Title or Screen Name dimension.

Your website might use the name of the content in the Page Title or Screen Name dimension.

I would need to specify that my group name is in the Page Title dimension, and then provide a regular expression that extracts the appropriate value.

The content grouping extract method will automatically pull the name for a content group from a dimension of data.

The content grouping extract method will automatically pull the name for a content group from a dimension of data.

For those of you that do not use regular expression, the value in the parenthesis will automatically be extracted. Google Analytics will then use the value as the group name.

You can see that this one rule will work for every product page on my site – as long as they are well formatted.

Pros: No coding involved. Flexible collection.

Cons: You might need to update your regular expressions when you add new content to your site or app. Specifically something that does not match your existing rules. Believe me – updating settings SUCKS. People forget to do it all the time.

In you’re new to regular expressions check out this reg ex tutorial in the Google Analytics help center.

Rules Method

The rules method is almost exactly like the extract method. The ONLY difference is that you have to MANUALLY name the group. The value for the name is not automatically pulled from a dimension of data.

The content grouping extract method will automatically pull the name for a content group from a dimension of data.

The content grouping extract method will automatically pull the name for a content group from a dimension of data.

Like the extract method you can create rules based on different dimensions of data- the page title, page url or the screen name. If the dimension value matches the rule then the content is added to the group.

Pros: No coding. Don’t need to know regular expressions.

Cons: You need to remember to update your rules when you add new content or if your site urls or app screen names change. Again – updating your analytics settings SUCKS. People forget to do it all the time.

Which method should you use?

That’s a tough question. Personally, I think page category is a critical piece of data that should be added to a page data layer. If you take this approach then using the tracking code method is very scalable.

I also like the extract method. It’s very flexible and reliable – as long as you have processes in place to maintain your implementation :)

Important things to know

Ok, so here are a few very important things to know.

You can use all three methods for creating groups within the same content grouping.

The grouping logic is applied to your data sequentially. That means that Google Analytics first applies the tracking code method first. Then it applies the extraction method. And finally it applies the rules method. You can use all three methods for your implementation.

When a page or screen matches a rule it is added to that group.

A page or screen can only be in ONE content group at a time! That means that an page or screen can only belong to one group at a time.

And finally, content groups are NOT applied to historical data. They are only applied from the moment you configure the feature.

A Best Practice

Because Google Analytics applies all grouping methods to your data, it is possible to use a combination of grouping methods.

But, because they they are applied SEQUENTIALLY, it’s a good idea to put your very specific grouping rules first, followed by your general rules. This way the later, general rules will catch anything that slips through the early, specific rules.

Content Group methods are applied sequentially.

All three content grouping methods are applied to each piece of content. They are applied sequentially.

It’s really, really important to try and get your groups right the first time. While you can edit your groups, there is no way to change the data that has already been processed.

Make sure you test your groups first before announcing them to your entire team.

It’s also a good idea to add an annotation to Google Analytics so everyone knows when the data was added.

Ok, I think that’s it for how to implement this feature.

Don’t worry – I’ll explain how to use content groups in a couple of days.

How to Set Up Google Analytics Content Grouping is a post from: Analytics Talk by Justin Cutroni

The post How to Set Up Google Analytics Content Grouping appeared first on Analytics Talk.

Dimension Widening: Import data directly into Google Analytics

There are lots of different ways to put data in Google Analytics. You can collect data from a website with JavaScript. You can collect data from an app using an SDK (Android or iOS). Or you can collect data from any network connected device using the measurement protocol. But there’s another way to add data […]

Dimension Widening: Import data directly into Google Analytics is a post from: Analytics Talk by Justin Cutroni

The post Dimension Widening: Import data directly into Google Analytics appeared first on Analytics Talk.

There are lots of different ways to put data in Google Analytics. You can collect data from a website with JavaScript. You can collect data from an app using an SDK (Android or iOS). Or you can collect data from any network connected device using the measurement protocol.

But there’s another way to add data to Google Analytics – you can import data using a feature called Dimension Widening.

You can add data to Google Analytics a number of ways - including Dimension Widening.

You can add data to Google Analytics a number of ways – including Dimension Widening.

With Dimension widening you can import additional dimensions and metrics directly into Google Analytics via a CSV upload or programmatically import data via an API.

Let’s take a look at how you might use Dimension Widening to augment the data in your account, and ultimately do better analysis.

Why Add More Data?

Analytics is more valuable when you can align the tool more closely with your business strategies and tactics. Adding additional data, like customer history, content publishing information, advertising cost data, etc. can help provide context to your data, thus making it easier to gauge performance and identify opportunities for improvements.

Adding additional data can also streamline your reporting (yes, basic reporting still happens) by consolidating all of your data in a single system that everyone has access to.

That’s where Dimension Widening comes in.

It is a mechanism to move data into Google Analytics.

How Dimension Widening works

You can upload two types of data to Google Analytics: Dimensions and Metrics.

A dimension is an attribute of a user or the sessions she creates.

A metric counts something – like time, money, clicks, etc.

When you use Dimension Widening you are uploading values for one or more dimensions or metrics.

You can upload values for existing dimensions/metrics or you can upload values for new dimensions/metrics that do not exist in Google Analytics.

When Google Analytics process the data it will join your custom data to the the existing data using something called a key.

The key binds your data, the data uploaded in a CSV file or sent programatically, to the Google Analytics data. When Google processes the custom data it will look at the value for the key, and then try to find the same value in the Google Analytics data.

If Google Analytics finds a matching keys then it will take the data in that row of the custom data and pull it into Google Analytics.

The key links your custom data to the data in Google Analytics.

The key links your custom data to the data in Google Analytics.

There are four basic steps to configuring Dimensioning Widening.

1. Identifying the data you want to import.

Step one is really simple, identify the data that you want to add to Google Analytics.

Remember, you can import a value for any dimension or metric that currently exists in Google Analytics. OR you can import values for custom dimensions and custom metrics that are not normally found in GA – more on this below.

When choosing the data you want to import ask yourself this – what data to I need to understand the behavior of my users? How can I make my analytics life easier by consolidating data in Google Analytics?

You also need to define your key. This is obviously critical. If you can’t define a key then you can’t import data.

2. Create the schema in Google Analytics.

Once you define your key and the dimensions/metrics you want to import it’s time to add the schema to Google Analytics. Think of this step as telling Google Analytics how to interpret the CSV file (or data feed) that you will import.

Choose a property in the admin section, then choose Data import and Dimension Widening.

The Dimension Widening settings are in the Data Import section of a property.

The Dimension Widening settings are in the Data Import section of a property.

To begin you need to name the data set you will import. You can actually upload multiple data sets (more on this later), so make sure you name it something very descriptive, like “Campaign Data” or “Content Information”.

Then choose the view where you would like the data applied.

Every data set must have a name, and you must specify which views to apply the data to.

Every data set must have a name, and you must specify which views to apply the data to.

TIP: Dimension widening will permanently change the data in a reporting view! It’s a good idea to test your dimension widening on a TEST view before applying it to your main reporting view.

Now add the schema. First, add the key that you’ve defined for your data.

Next, specify the dimensions and metrics that you want to add.

You must enter a schema into Google Analytics. Add the key along with the dimensions you would like to widen.

You must enter a schema into Google Analytics. Add the key along with the dimensions you would like to widen.

Here’s something cool – as you choose your key and dimensions Google Analytics will automatically show you the column headings that you will need to add to your CSV file.

As you add your schema Google Analytics will provide the column headers for your CSV file.

As you add your schema Google Analytics will provide the column headers for your CSV file.

Notice that they’re not the names that appear in the drop down boxes. They’re the dimension/metric names that are used in the API. Fear not – you don’t need to understand what they mean.

3. Build your CSV file.

Once you finish defining your schema choose save.

You’ll be presented with two options: get more details of your CSV file OR get an API key to upload your data programatically. Let’s focus on the Get Schema option.

Once you define your dimension widening schema you can download a CSV template or get an API key.

Once you define your dimension widening schema you can download a CSV template or get an API key.

Click the Get Schema button.

This window contains some really useful information. First, a list of the column headers that you need to add to your CSV file. This includes your key and all the other dimensions that you are adding to Google Analytics.

There’s also a way to download a CSV template for your specific data. The template is just an Excel file with the headers added to the first row.

Google Analytics will provide the column headers for your CSV and provide a CSV template that you can fill with your data.

Google Analytics will provide the column headers for your CSV and provide a CSV template that you can fill with your data.

4. Upload your CSV file or Send Data via API

Remember, there are two ways to add your data – via an API or manually via a file upload process. Let’s focus on the later – the file upload.

This isn’t too complicated, just click upload :) Once the file is uploaded Google Analytics will widen your data as it is processed.

You can check on the processing of your data using the Refresh button.

You can check on the processing of your data using the Refresh button.

NOTE: when you use Dimension Widening the data you import is NOT applied to historical data. Your data is only applied going forward.

I find that GA can process the file very fast (minutes). You may want to refresh your list often to determine if the new data has been added.

That’s it! That’s the basic process.

But you probably want to use Dimension Widening to import custom data, not data that’s already in Google Analytics. Let’s take a look at how to do that.

How to add Custom Data

You can also add custom dimensions and custom metrics to Google Analytics via dimension widening. The process is almost exactly the same. The only difference is that you must first define your custom dimensions or metrics in the Google Analytics admin section.

To upload a dimension or metric that does not exist in Google Analytics you must first define those custom dimensions or metrics.

To upload a dimension or metric that does not exist in Google Analytics you must first define those custom dimensions or metrics.

There’s not a lot of configuration here. Just give your dimension a name and choose a scope.

NOTE: You can only widen between dimensions and metrics of same scope. For example, you can’t widen from user scope Key to Hit scope dimensions. Check out this (somewhat old) article on Custom Variables to learn more about scope.

That’s it. Now you can choose these custom dimensions (or metrics) when you add your schema for Dimension Widening.

Once you define a custom piece of data it will be available in the Custom Data schema interface.

Once you define a custom piece of data it will be available in the Custom Data schema interface.

Then create your CSV file with the correct headers and upload your data.

Note: Custom Dimension and metrics are only available in Google Analytics customizations – this includes custom reports, custom segments and dashboards. They can also be used in certain analysis tools, like secondary dimensions.

An Example: Uploading simple publisher data

Let’s say I’m a publisher. I want to add the publication year, author for each article. My key to join my data with GA data is the URL of each page. I already defined two custom dimensions, one for page publication year and one for page author.

I’m going to define my data schema in Google Analytics.

Defining a custom data in your dimensions widening schema.

Defining a custom data in your dimensions widening schema.

Now I build my CSV file using the correct headers for my key and dimensions that I would like to widen.

A sample CSV file with custom dimensions.

A sample CSV file with custom dimensions.

Next I upload my file…

And finally, I have data in my custom dimensions. Here I can see the data in a Custom Report.

Custom Dimensions can be used in a Custom Report, Unified segment or other customization features.

Custom Dimensions can be used in a Custom Report, Unified segment or other customization features.

Best Practices for Managing CSV files

You might want to widen your data based on multiple keys. For example, you might want to widen your product data (using the product ID as a key) and your campaign data (using campaign name as a key).

In this case you’ll need to define two different schemas and upload two different CSV files. Make sure you name them something logical!

Another thing to consider is when to update your CSV files.

For example, let’s say that you’re a publisher, and you’re uploading new data about your content. But you’re publishing new content every day. And probably multiple times a day. You would need to upload a new CSV file every time you publish content. This is too manual. In case you probably want to consider a programmatic solution.

Use the CSV file for things that do not change often. Use the API for things that change a lot!

What about JavaScript and real-time collection?

Given my previous example, you may be asking yourself, “can’t I just collect custom data in real-time using JavaScript?”

Absolutely!

You could do something fancy, like add the data to a data layer, then pull it into some custom dimensions. No problem!

The point is that you don’t always have the time or the IT resource to implement the data collection. Even if you use a cool technology like tag management, it may be that the data you want to add comes from an isolated system. And that it would take too much effort to transport the data from it’s home all the way to the web server.

Dimension widening can be seen as a somewhat faster, less IT intensive way of joining your data together.

Things to be aware of…

Ok, a few things that you need to be aware of when using Dimension widening.

1. Your data is NOT applied to historical data. Your data is only applied going forward.

2. You can NOT widen on ALL dimensions. You can NOT widen on the following dimensions:

  • custom variables
  • product dimensions and metrics
  • campaign dimensions
  • time-based dimensions (hour, minute, etc)
  • geo-dimensions (country, city, etc)

3. If you would like to expand your dimensions and populate Custom Dimensions you MUST use Universal Analytics. The reason is that Custom Dimensions only exist in Universal Analytics. They do not exist in the previous version of Google Analytics.

4. You can not change a schema once it has been entered into Google Analytics. You must delete the schema and then define your new schema.

I know some of these caveats may seem limiting, but remember, this is just the initial version. I know the team is working hard to expand the functionality.

Do you think you will use Dimension widening? If so how? Feel free to share your examples below!

Dimension Widening: Import data directly into Google Analytics is a post from: Analytics Talk by Justin Cutroni

The post Dimension Widening: Import data directly into Google Analytics appeared first on Analytics Talk.