Very quickly this technology has spawned a number of companies and investment in this area. Some of the more well known tag management tools are (in no specific order):

• Ensighten
• TagMan
• Tealium
• Satellite
• OpenTag

The rapid growth is validation that the simple act of tagging a site is complicated, and to do good measurement businesses need a faster, quicker and easier way to implement different types of tracking.

What is a Container Tag?

A container tag is simply a tag. It’s a line of JavaScript that you add to the pages of your site. But the special thing about the container is that it can hold other tags. Hence the name ‘container’.

In most cases you populate the content of the container tag using some type of web interface. Then the tag management vendor pushes out the content via their network (or CDN).

Tag Management tools add a "container" to your pages. You can then populate that container with various tags.

The beauty is that you remove the need for coding changes when you want to modify your analytics tags. Less work for the IT department!

The downside is that you can bypass some of the QA process that IT uses to insure the performance and safety of the website (or app).

But that’s the tradeoff.

Every business needs to evaluate the risk and rewards and decided if they should use a tag management solution.

I personally like container tags as long as they’re used carefully.

Rules: Making Tag Management Awesome

In my opinion, the really cool thing about tag managers is their ability to control which tracking code appears on a page. Most tag managers have some type of rules engine that can change the output based on the URL of the page or some other attribute.

For example, you could create a rule that insures your AdWords conversion tag only appears on your receipt page by specifying the container only show the AdWords tracking code when the URL of the page matches the URL of your receipt page.

Or you could customize the Google Analytics tracking code by including a custom variable based on a page attribute.

Dynamically Collecting Data with a Container

But wait, there’s more!

Many container tags can pull data from the HTML of a page and put it into your tracking tag. For example, some tag management tools have templates where you tell the container tag the name of an HTML element, like a DIV or anchor tag, and the container copies the data out of the element and puts it into the analytics tag.

This is especially handy when you’re coding Google Analytics events, custom variables or the ecommerce tracking code.

Many tag management tools let you extract data from the page HTML and pull it into the container tag.

I know what you’re thinking. “That’s pretty cool. It makes it REALLY easy to configure complex tagging situations.”

I agree, very cool!

But there’s a problem. What happens if the HTML of the page changes? What happens if your site gets redesigned?

You’ll need to tweak your container tag to make sure that it pulls the right data.

I know what you’re thinking again. “What a pain in the butt.” Agreed.

The Data Layer

There is a technique that can help you avoid ever changing your container tag. It’s called a data layer.

Simply put, a data layer is a JavaScript variable or object that holds all the information you want to collect in some other tool, like a web analytics tool. You add the data layer to every page on your site and then have the container pull data from the data layer.

Use a data layer to store the data you want to collect in your analytics tags.

This insulates the data collection tools by separating the data from the page structure. No matter what happens to the structure of the HTML page the data layer will always be the same.

That means your container tag will always have a consistent, unchanging source of data to pull from.

Ideas for Data Layer Content

So what should go into a data layer? There’s a lot you can stuff into one. Here are just a few ideas.

Page Attributes: Data about the pages that people look at.

• Page title
• Page URL
• Page category, sub category, etc.
• Page version

Visitor Information: Anonymous data about the visitor. This works best for websites that can maintain state or have a logged in experience. All of this data provides many opportunities for segmentation in your analytics tool.

• Visitor type
• Visitor state
• Visitor value
• First conversion date
• First conversion traffic source
• Historical information about key actions (did they leave a comment, etc.)
• Anonymous demographic data
• Social information (are they a follower, etc.)

Data for conversion pages: Conversion pages are slightly different. They need to include details about the transaction or the value of the conversion.

• Value of the conversion
• Product SKUs
• Product names
• Product categories
• Transaction ID
• Total purchase value
• Taxes
• Shipping cost
• Shipping method
• Currency
• Payment type
• Month & year of transaction
• Discount or promotion used

Your task is to convince your IT department to add a data layer to your pages. It may be a little work up front, but over time it will save them work.

And you don’t need to use all of the data on every page. It depends on what you want to track in your tool. But it’s better to have the data there and choose NOT to use it, rather than have to ask IT to add the data.

In reality you don’t need a container tag to implement a data layer. You can write your own custom JavaScript that translates data from a data layer and puts it into your analytics tag. But the container tag launched the need for the data layer.

Here’s a simple data layer example that might appear on a receipt page.

var dataLayer = {
"pageTitle" : "Receipt Page",
"pageURL" : "/pages/checkout/receipt",
"pageCat" : "Checkout Pages",
"PageCat2" : "",
"tranID" : "17658726382",
"tranTotal" : "34.95",
"tranTax" : "0.00",
"tranShipping" : "0.00",
"tranShippingMethod" : "USPS",
"tranCurrency" : "USD",
"tranProds" : "249|398",
"tranSKUs" : "249-32|398-12",
"tranProdNames" : "Kids Onsie|Kids Lava Lamp",
"tranCategories" : "Kids|Kids",
"tranPayMethod" : "VISA",
"visitorType" : "RETURN",
"visitorState" : "Logged In",
"visitorFirstPurchDate" : "20111205",
"visitorFirstProds" : "822"
}

Don’t Forget Mobile!

So now that we’ve got all the basics covered, don’t forget about mobile web and apps. There are a number of vendors that are doing some cool things with container tags and apps. And you can port the data layer to your mobile website or mobile app. It simplifies the data collection no matter what the platform.

As we move forward with an increasingly diverse number of content consumption devices, it will be important for businesses to publish first-party data in a way that’s easy to collect via tag management systems.

Make Analytics Better with Tag Management and a Data Layer is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Adding Business Data to Google Analytics Data
2. Event Tracking Pt. 1: Overview & Data Model
3. Merging Google Analytics with your Data Warehouse

Creating a social dashboard in Google Analytics is a lot easier now that we have specific social reports. This social dashboard outlined below will help you deep-dive into social data and understand social activities on, and to some extent, off, of a site.

Before we get into the details, I’d like to recognize Kayden Kelly for contributing some ideas used on this social dashboard. Thanks Kayden!

Social Dimensions & Metrics: Foundation for the Dashboard

To build a social dashboard you use the social dimensions and metrics. These are the same dimensions and metrics that generate the Google Analytics social reports. Here’s a quick overview:

Data Hub Activities: The social data hub is an open data collection platform. Any social network can send their social activity to Google Analytics. This metric is the total data hub activities for a given site.

Social Network: This dimension is a list of all the social networks that drive traffic to a site. These networks are automatically identified by Google Analytics.

Social Source Referral: This is a simple flag that indicates if the traffic source is from a social traffic source. This dimension is very useful if you want to create a widget that just contains data for social media.

Social Source & Action: This dimension is the name of a social network and an action that is specific to that social network. This track social sharing ON a site. GA will automatically track social interaction with Google + tools but needs to be configured to track other social sharing tools.

Social Entity: This is a URL that shared via social media. It’s any URL from your site.

Social Type: This is a simple boolean that indicates if a visitor is socially engaged, meaning they used a social sharing tool on your site. GA will automatically track social interaction with Google + tools but needs to be configured to track other social sharing tools.

The Social Media Dashboard

I’ve divided the dashboard into three sections: Off-site activity, On-site activity and Conversions/Outcomes. This makes it easy to evaluate user activity throughout the conversion process.

Feel free to download the Social Media Dashboard for Google Analytics and customize it.

It's easy to create a social media dashboard in Google Analytics.

Offsite Activities

This section is about what happens off of the site and some of the attributes of traffic that comes from social.

First is some basic context: total visits to the site. This puts all of the social data into context. You can quickly gauge when looking at a widget if social is a large or small percentage.

Next I wanted to get an idea of new traffic from social. So I included the % New Visits metric segmented for traffic from Social. When looking at this metric it’s a good idea to remember your social strategy. Are you trying to attract a new audience from social or trying to bring people back to the site? Your strategy will drive the context for this metric.

%New Traffic from Social Media

Now a widget to trend traffic and bounce rate from social. Here I can see how traffic from social changes over time. And we can use the total number of visits to the site to put this data into context. I also have bounce rate in this widget to gauge the quality of the social traffic. Do these people stick around or take off quickly?

A trend of traffic and bounce rate from social sources. How much traffic do you get from social and does it engage with your site?

The next widget is a plot of Social Data Hub Activities and Site Visits. I like this plot of offsite activities and site traffic. It’s a quick way to identify if any offsite actions resulted in traffic to the site. Many times with social media the activity happens somewhere else and there is no impact on the site.

Remember, this is activity from the social data hub partners, not the entire world of social media.

A plot of Google Analytics Data Hub Activities vs. Site Traffic. Is there any correlation?

Now let’s get a bit more specific about which social sources are driving traffic to the site. The final widget in this section is the top social sources based on their traffic. This is a classic segmentation of source. And I’m using bounce rate as a gauge to determine if people stick around or leave immediately.

This widget lists traffic from the most popular social networks. It also uses bounce rate an a gauge of quality from each network.

On-site Social Activities

Moving on to on-site activities we can include a number of things. Onsite activity is about what content people are interacting looking at and content that people might share using some type of social button (Google +1, Tweet, Like, etc.) This is a good way to understand which social networks people like to share content on.

A widget that measures on-site social actions. This shows how people are sharing content on your site.

I also like the value of visitors that are socially engaged. This segment of traffic is those that perform some type of social activity, like share using a tweet but or +1 button. I think a lot of people are trying to increase the sharability of their content. It leads to more traffic and, hopefully, more conversions. I would look for this metric to increase over time, depending on the tools that you give your visitors to share content.

Are those that engage socially on a site worth more? If they are, can you somehow increase social activity?

Another way to look at social sharing is to focus on which content people share. And we can do that using a widget with the Social Entity dimension. I find that it’s important to consider how you are promoting content when looking at this widget. It may be that you are constantly promoting certain content.

Which content gets shared on social media? This Google Analytics widget is a list of pages that get's shared on social media.

Another widget is the social traffic segmented by mobile device. Social and mobile are intimately connected. So much social content is consumed on various mobile devices. The goal of this widget is to get an understanding of which devices are popular with social users.

I like to view social traffic based on mobile devices. Is one device more popular than another? Are certain social actions popular on certain devices?

Outcomes & Conversions from Social Media

The last group of metrics focus on the outcomes from social. It focuses on goals and ecommerce (if you’re an ecommerce site). This is where you’ll probably need to adjust some of the widgets based on your goal configuration.

It starts with the value of traffic from social. I like the Per Visit Value metric. It’s a good way to compare the economic impact of different sources of traffic. It’s a single number that puts a value on traffic from different places. Some good context for this metric is the amount of effort (i.e. time and money) you spend to generate traffic from social. Do you employ a “social media guru?” If so, how much do you pay them, and how does this translate into revenue?

Measuring the per-visit-value provides an easy way to compare the value of different traffic source. How does social media compare to other sources for your site?

Now revenue! Here’s a simple widget with the revenue from various social sources and the per visit value for each source. Keep in mind which social networks you are focused on and the effort you put into each.

Tracking revenue from your social sources is critical. Are you getting a return on your investment?

NOTE: The one thing that I wish I could add to the dashboard is the Assisted Conversions metric for social. So often social media influences conversions higher up in the funnel. Unfortunately you can’t add the Assisted Conversions metric to the dashboard.

Now for more outcomes: conversion rate for various social sources of traffic. Remember, you’ll need to configure this widget to reflect your specific goal configuration. And you can certainly add more widgets for your various conversion activities. I’m just measuring the conversion rate for people reading an article.

Here's the conversion rate for various social sources. You can change this widget based on your goal configuration.

What would you put on a Google Analytics social media dashboard?

Remember, this is a shared dashboard, so you can add it to your Google Analytics account. You can keep it as-is or modify it to meet your needs.

A Social Media Dashboard for Google Analytics is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Full Funnel: Google Analytics Measures Social Media
2. Building a Mobile Ecommerce Dashboard in Google Analytics
3. The Google Analytics Social Data Hub – More than Meets the Eye?

But the biggest news, and the subject of this post, is the introduction of a new Event Flow report to track users through a series of events.

NEW! Google Analytics Event Flow Reports

When the world was focused on web analytics a common analysis technique was path analysis. The goal was to find the “magic path” through the website that lead to the most conversions and then force everyone through that process.

We almost never found the “magic path.” But there are some uses for path analysis, like looking at a defined path, for example a checkout process, and identifying where people leave the process.

But things have changed.

In the new world of digital analytics we collect more than just pageviews. We also collect events, which we use to track the minute details of how people interact with a website or app.

Until now we had no way to understand the sequence, or flow of these events. The Google Analytics Event Flow report provides a visualize of the user path between events.

If you’ve used the Flow Visualization reprots in Google Analytics then the Event Flow reports will look very familiar. It’s the same report. The only difference is that the event flows reports show events.

The event flow report can really be used for anything. It’s really up to you, and how you use events. A few that come to mind:

• If you’re an automotive site, you might use it to track people using a vehicle configuration tool
• If you’re a financial site, you might use it to track how people use a mortgage calculator
• If you’re tracking an app with Google Analytics, you might use this to track interactions
• If you’re tracking video streams with Google Analytics, you might use this to track how people flow from one stream to another

Before we get into an example, I should point out that this is all based on event data. If you don’t use event tracking this report will be useless. Or, if you have really bad events, this report will not work for you.

Make sure you have great events!

Overview

As is the case with the standard Flow Visualization report, the new Event Flow report consists of segments, nodes and paths. You can view the flow of a specific segment through the report using the segments on the left side. Or you can segment the entire flow using the drop down at the top of the report.

You can view various segments of traffic through the Event Flow or segment the entire flow.

Nodes represent either an event category, an event category/event action combination or a category/action/label combination.

Example

Last month I wrote about a new way to track content engagement with events. Using some custom code to can generate events as people scroll through a page and read content.

The data generates a model of the reading process. Events are generated at the following times:

• When an article is loaded in the browser
• When a visitor starts reading (by tracking scrolling)
• When a visitor gets to the bottom of the content
• When a visitor gets to the bottom of the page

We can now visualize the above process with the Event Flow report. I not only want to visualize the above process, but I want to see how people might jump steps in the process.

You can choose to view a different combination of events in the Event Flow.

Here’s a tip, the size of the nodes change depending on the event combination you choose to view. If you choose to only view the event categories, you’ll have a few, large nodes. But, if you choose to view the categories/actions/labels you’ll have LOTS of nodes.

I find that starting with the category/action combination is usually sufficient.

There are a lot of events here, so I’m going to click on a node and choose Highlight. This shows the traffic through the chosen node, which in this case is the category/action of Reading/ArticleLoad. Now I can see the path of all the traffic that loaded an article.

You can highlight traffic through a specific event node in Google Analytics.

The visualization shows traffic that came to the site, loaded an articles, read the article, hit the content bottom and hit the bottom of the page.

Using the Google Analytics Event Flow to visualize a series of event. In this case how people read content on a website.

I was able to see the percentages of traffic that move from step to step in the tabular data. But a visualization makes it easier to identify drop-offs.

Here’s something interesting. Using tabular data I learned that 67% of those that start reading get to the bottom of the content and 9% get to the bottom of the page.

Very few people read all the way to the bottom of the page. What happens after people get to the bottom of the content? Do they leave the site?

Using the flow I can easily get the answer.

I can literally see four actions when people hit the bottom of the content:

• Some people exit the site (the RED comments below
• Some move on to a new article (the BLACK comment below)
• Some read to the bottom of the page (the BLUE comment below)
• Some convert at a goal (the YELLOW comment below)

Analyzing what happens during a series of events.

You’re probably wondering how I can get more detail, perhaps view this flow on an article-by-article basis. Well, if I was tracking the article name in the event label I’d be able to see that! But I’m not doing that. Yet.

So there you have it. One application of the Event Flow reports.

How do you plan to use the Event Flow reports?

Go With The (Google Analytics Event) Flow is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Path Analysis in Google Analytics with Flow Visualization
2. Event Tracking Pt. 3: Reporting & Analysis
3. Event Tracking Pt. 1: Overview & Data Model

As analyst and marketers we’re trying to define the business value of social.

But the measurement is challenging. We’re trying to change that with Google Analytics and have introduced a set of Social Media reports to help measure the value of social.

I like to relate the new social reprots to the various stages of the conversion funnel. The goal is to help marketers understand how social impacts all phases of the funnel, especially the lower funnel: conversions, transactions and revenue.

The new Google Analytics Social Media Reports (listed on the right) map to the various stages of the funnel (listed on the left).

Let’s look at the reports starting at the top of the funnel and working towards the bottom.

Acquisition Reporting

The Social Sources report lists all traffic from different social sources. Google Analytics maintains a list of social networks and will automatically categorize incoming traffic based on the URL.

NOTE: This list has over 450 social networks from around the world and includes all the big ones, like Twitter, Facebook, Google+, etc.

This report has the standard web metrics like visits, pages per visit, avg time on site, etc.

The Google Analytics Social Sources Report will automatically identify traffic from social networks.

So far, so good.

But the Social Sources report holds a lot more.

When it comes to social, many activities happen off the site. But integrating the offsite actions with on-site behavior has been a manual process. That’s why Google launched the Social Data Hub.

This giant database (for lack of a better term) let’s any social network send social activity data, that occurs on a social network, to Google Analytics. The data has not been available until now.

You’ll also notice that there is a little icon next to some of the social networks in the Sources report.

I call it the “sleeping Mickey Mouse.”

This indicates that the social network is a Social Data Hub partner and is sending social activities to Google Analytics.

The Social Data Sources report shows traffic from social sources AND data from Social Data Hub Partners.

If you click on the icon and you’ll see a list of social content URLs. These are URLs, from your site, that people are sharing on the social network.

There are a couple of things to notice in this report. After you click on the data hub icon there will be a link to an activity stream at the top of the data graph AND a link to view the social network and actions at the top of the data table.

You can view social activities on a data hub partner using the Activity Stream tab.

If you click the “Originating Social Network and Action” link you’ll get a list of all the actions happening on the network that you selected.

Remember, these actions are defined by the social networks, not Google Analytics. GA simply takes the data that the network sends. It’s quite possible that you’re getting data from a social data partner and there are activities that you don’t understand. You need to take the time to explore these networks.

Social data from the Delicious bookmarking site.

For example, you might not be familiar with Del.icio.us, the social bookmarking site. As a result you might not have any idea what the social actions mean. Take the time to investigate these networks, play with them, and learn the various actions.

If you click on the Activities tab at the top of the data graph you’ll get a huge dump of activities on the social site. These activities are divided into two sections: Conversations and Events.

Social activities are divided into two groups: conversations (which are longer content) and events (which are shorter actions).

• Conversations are social activities that include free form text; things like a ‘comment’ or ‘share’.
• Events are social activities that do not include free-form text; thinks like a ‘bookmark’ or ‘+1′.

It’s easier to understand the data if it is separated into two groups. If it was in in a single table it would be a horrendous mess and too complex to understand.

Remember, you can view the activity stream for the entire social site OR for a single piece of content. This type of segmentation means you can approach your analysis different ways.

A quick note, the new Social > Pages report is similar to the Social > Sources report. The primary difference is that the Pages report starts with the Social content URL and not the Social Source. It a similar report, but starts with a different piece of data.

Engagement Reporting

The social engagement reporting focuses on two thing: how people use social media tools on your site and what happens to traffic from social media once it lands on your site.

GA previously launched social plugin code that let’s site users track how often someone clicks on a Tweet button, Like button or any other social button. This data is also in Traffic Sources > Social > Social Plugins Report.

You can track how visitors interact with social plugins on your site using the Social Plugins report.

If you decide to implement this code you can measure which content people share most on social media and which social networks they choose to share it on. You can use this knowledge to guide you in content creation decision and which social content is appropriate for which networks.

And remember, Google+ actions will be automatically tracked. You don’t need to do any additional coding. And please, no jokes about Google+… People do use it :)

There is one thing to be aware of. The social data can not be filtered. So if you have filtered profiles the social plugin data will not be filtered out.

In addition to measuring which social sharing tools people use there is a Traffic Sources > Social > Social Paths report. This report shows all social traffic sources and the path visitors take through the site. This makes it easy to identify drop-off points.

For example, looking at the data below I can see about 30% of traffic goes to a couple of landing pages. But most of this traffic exits the site from the landing pages. Looking at other landing pages, I’d be better served directing the traffic to a different starting page that is stickier.

The Social Flow report identifies visitor paths through the site based on originating social network.

If you like our flow visualization report then you’ll like the social flow report.

Conversion Reporting

This is where things get really good: bottom of the funnel measurement.

What is the value of social media? Everyone is asking this question. Social Media experts beg us to forgo ROI measurement and invest in relationships.

Old-school marketing folks demand an ROI from this new and evolving channel.

Who’s right? I always like measurement :)

Almost all social conversions still happen on a website. That means we can measure them with GA.

We need to measure how many conversions come directly from social media.

We also need to measure how many conversions are generated with the help of social media. We call these assisted conversions.

That’s where the Traffic Sources > Social Media > Conversions report comes in. It not only measures direct conversions (ie conversions where social media was the LAST referring source) but also conversions where social media generated a visit prior to conversion.

Use the drop down at the top of the report to choose a specific conversion you want to analyze. You can also use the link at the top of the graph to change the view from Direct Social conversions to Assisted Social conversions.

The Social Conversions report shows direct conversions and revenue from social as well as assisted value.

My favorite metric in the world is in this report. The Assisted to Last Conversions ratio. Remember, if this ratio is around 1.5 or greater it means that a given source of conversions is primarily an upper channel source. But if this number is around 0 then the source is primarily a lower funnel channel.

The Assisted Social Conversions report shows how upper funnel social activities lead to conversions.

Remember, these social sources are auto-identified by Google. You’ll start to discover all sorts of social sites that you never existed. That’s because they were previously grouped as referrals.

If you’re using campaign tracking to identify your campaigns you can find that data in the Multi-Channel Funnels reports.

One more thing.

Many of the social dimensions and metrics are available in the GA customizations. For example, you can use the social dimensions in the new Dashboard. This makes it easy to build out customizations that make your life easy.

I built a small Social Media Dashboard to keep track of which social networks drive traffic to my site, which onsite site social tools people use, and how social traffic impacts my conversion.

The Dimensions and Metrics will eventually be in the API as well.

So there you have it. My overview of the Google Analytics Social Reports.

Full Funnel: Google Analytics Measures Social Media is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. The Google Analytics Social Data Hub – More than Meets the Eye?
2. Google Analytics Real Time: Real Time Data for Real Time Decisions
3. Google Analytics Multi-Channel Funnels

Event tracking is a flexible, data collection mechanism that you can use to track almost anything. From video players, to mortgage calculators, to mobile apps, events are a way to collect and count visitor actions. While the data may seem simplistic, it’s valuable insight into visitor behavior.

But event tracking can be hard to implement, especially if you don’t know how to code. Even if you do know how to code, managing a larget amount of events can be a challenge. I wanted to push out a couple of tools to make implementing and maintaining events easier.

A Quick Review of Google Analytics Events

There are five parts to an event: category, action, label, value and a flag that controls interactivity.

• Category: The category is at the top of the hierarchy. It’s a way to bundle visitor activity together. It encompassed all of the other event data.
• Action: The action is literally what the visitor does. In our video player example some potential actions might be: play, pause, share, get embed link, etc.
• Label: Provides a bit more information about the visitor’s action. For example, if you are using events to track a video player you might record the movie name as the label when an action occurs. That provides more context to what the visitor is doing.
• Value: Any positive integer value. It’s a number. You can use it to count things, like dollars or seconds. If you choose to use your event as a goal, then you can specify that Google Analytics use the event value as the goal value.

The last event setting is a bit different. It’s called non-interactive and it is used to alter how Google Analytics interprets the event data. Normally, when an event is sent to Google Analytics, the system recognizes the user as interacting with the site. So it records that the visit is NOT a bounce, even if the visitor only sees one page.

But there may be a case where you want to track the visitor interactions with events AND bounces. In those cases you can set the non-interactive value to true and GA will still count a bounce.

Setting up an event is a four step process:

1. 1. Design your event data (ie write down you categories, actions, labels, etc.)
2. 2. Identify where you need to put the event code
3. 3. Add the code to the appropriate places of your site (or app, or whatever)
4. 4. Analyze data

My goal with the tools below is to help you with step 1, and to some extent step 2. But remember, you need to add this code to the correct parts of your site.

Event Creator Tool

Below is an Event Code creator. I wanted to create something where anyone can enter in their values for an event and have the code automatically generated. If you’ve ever used the Google Analytics Link Tagging tool then you’ll be able to use the Event Creator.

Remember, you’ll still need to attach this code to the appropriate place to capture the visitor action.

[iFrame below, if you can't see it please visit the site.]

In addition to the standard event data, you can select added a few formatting options:

• Force Lowercase: This setting forces the event values to lowercase. This can increase the consistency of your data by making everything the same case. You don’t need to force everything to lowercase, but make sure you’re consistent!
• Replace White Space: This setting will strip out all of your white spaces. Again, this is not necessary, but it can make your data more uniform.

This tool may seem very simplistic, but it is a very good learning tool. If you’re trying to show someone how to take an event from idea to code this can be very useful.

Event Management Worksheet

If you run a large site, then you probably work with a lot of events. As a result the event creator tool is not very useful. It just doesn’t scale.

A good alternative is using a Google Doc with an embedded formula. It does the same thing as the form above, but you can crag the formula to create a lot of code quickly. You can enter all the standard event values, then drag the forumla in column F to create the event code. You can also add a description of what the event does and a note about where in the code the event should be placed.

[iFrame below, if you can't see it please visit the site.]

I personally like to do this with a Google Spreadsheet, so I can share it with other people at a company. But you can use the “other” spreadsheet program if you like :)

Tools to Help Organize Google Analytics Events is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Tools for Debugging Google Analytics
2. Tools fo Debugging Urchin Software
3. Event Tracking Pt. 2: Implementations

I’d like to clarify how Google Analytics track time on page and visit length. It’s probably different than you think.

Understanding Engagement Hits

The key to understanding Google Analytics time calculations is understanding the data that is sent to Google. We affectionately refer to these data as HITS. I know, that’s a terrible term to use when talking about analytics. But they really are hits.

Data hits are the image requests sent to Google Analytics. Three are six different types of data hits in Google Analytics:

• Pageview hits
• Event hits
• Ecommerce transaction hits
• Ecommerce transaction item hits
• User defined hits (this is the predecessor of custom variables)
• Social plugin hits

While these are all data hits they are not all engagement hits.

An engagement hit is any hit that is not marked as “non interaction” and is not filled only with custom variable information. This means that the hit has at least page information, ecommerce transaction information, ecommerce item information, event information or social tracking information.

Filtering the above list of data hits using the engagement hit definition results in five types of engagement hits:

• Pageview hits
• Interactive event hits
• Ecommerce transaction hits
• Ecommerce transaction item hits
• Social plugin hits

You’re probably wondering, “what the heck does this have to do with time calculations?”

Most analytics tools use the time between pageviews to track time. But Google Analytics time calculations uses engagement hits to get a more accurate measure of time on page and time on site.

How Time on Page is Calculated

Time on Page is calculated in two different ways. The calculation depends if the visit has one pageview OR if the visit has multiple pageviews.

When There are Multiple Pageviews in a Visit

This is pretty easy to understand.

If there are multiple pageviews during a visit then the time calculation is based on the start time between the current page and the start time of the next page hit. Here’s a visual representation.

Image #1: When there is more than one pageview in a visit, Google Analytics uses the time between pages to measure time on page.

Pretty easy, right? Most tools work this way, measuring time based on pageviews. But this leads to trouble.

We can’t calculate the time on the last page of the visit because there is no pageview after the last page. Stay tuned, more on this later.

When There is Only One Pageview in the Visit

Most people believe that Google Analytics can not, and does not, calculate time on page when there is only one page viewed in a visit. This is partially correct.

If no other page is viewed during the visit, Google Analytics use the time between the initial page hit and the last engagement hit that follows the page hit.

Time on Page = (time of last “engagement hit” on page) – (time of first hit from page)

Let me make this clear, if a visit only contains one pageview (i.e. the visit is a bounce) then Google Analytics can track time on page by using other engagement hits.

If you add multiple engagement hits to a page you can get a more accurate measure of time on page and site, as shown in the image below.

Image #2: Google Analytics uses the last "engagement hit" to measure time on page when there is only one pageview during a visit.

This was the whole reason behind the posts Advanced Content Tracking Part 1 and Advanced Content Tracking Part 2.

How Visit Length is Calculated

Visit length also uses engagement hits to get a more accurate measure of time. Rather than use just the time between pageviews, the visit length measurement includes the time between the first hit and the last engagement hit of the session.

Visit Length = (time of last “engagement hit” of visit) – (time of first hit of visit)

Let’s look at a real example. Here we see a number of pages in a visit. Normally, if we only measured visit length based on page load time we’d miss all of the time on the exit page.

But because there is an engagement event on the exit page we get a more accurate measure of the visit.

Image #3: Google Analytics uses the last engagement hit of the visit to measure visit length.

It’s also importnat to note that in the previous example Google Analytics will also use the engagement hit to calculate the time on page for the exit page. In Image #1 it was not possible to calculate the time on the exit page because there was no final engagement hit after page 3.

But in Image #3 there is an engagement hit on the exit page. So Google Analytics will use that piece of data to calculate the time.

Don’t forget, there are other engagement hits, like the ecommerce transaction hit, that can also be used to more accurately measure the length of a visit.

I hope this post demystified how Google Analytics tracks time.

Let’s hear it for greater accuracy!

Understanding Google Analytics Time Calculations is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Understanding The Google Analytics Terms of Service
2. Understanding the New Google Analytics Interface
3. Understanding Google Analytics Custom Reports

As I mentioned in part one, this technique and concept was born from collaboration. There are a number of people that need to be recognized for contributing:

Nick Mihailovski – Developer advocate at Google (and the guys that sits across from me)
Thomas Baekdal – Smart guy and publisher of www.baekdal.com (you should subscribe)
Avinash Kaushik – If you don’t know Avinash…
Joost de Valk – Creator of the Google Analytics for WordPress plugin (you should use it)
Eivind Savio – Blogger and GA consultant (read his stuff)

Let’s look at some data. It’s all from this blog.

The Reports

This tracking technique uses event tracking to track how people scroll through pages on a site. So let’s start with the event tracking reports.

Here’s the Content > Events > Top Events Report.

Reading actions in Google Analytics

All the events are bundled in the Reading category. You’re probably wondering why the Value is so high. Remember the value is the number of seconds between certain actions. That’s why the event value is so high. More on this later.

Click on Reading and you can see all of the actions that we created within this category (ArticleLoaded, StartReading, ContentBottom or PageBottom).

Drill into the Reading event to view the specific reading actions.

Let’s take a look at this data and translate it. Of the articles that load, 82% actually start to read the article. I think that’s pretty good. I actually think that the 18% that do not start to read the article are actually visits that open the article in a new tab/window and then time out. They go away for 30+ minutes and break the tracking.

Of those that start reading the article, 63% make it to the bottom of the content. That seems pretty good. I don’t have any other reading benchmarks, so I’m content that more than half make it to the bottom of the article. But this is something that I’m going to trend over time. I’m also going to do some segmentation on this in a minute.

Of those that actually start reading an article, 18% actually make it to the bottom of the page.

Believe it or not, 18% is very high. On average the number of visits that make it to the bottom of an article is between 3% and 5%. I’m basing this observation on 5 days of data across a couple of sites.

UGH! To me that’s terrible! I thought more people read the comments. I guess I was wrong. Comments don’t seem to interest too many people.

But remember, this is top-line data. We need to get into some segments to understand what content drives these metrics.

Page Level Reading Metrics

Staying with the Top Events report, we can add a secondary dimension of Page to see which articles are loaded most often, which are read and which ones are finished.

NOTE: I’m going to filter the results below to include a single article, it will make it easier to see the report. You can use an advanced filter to focus on only the data that you’re interested in.

View engagement actions for individual content in Google Analytics.

Here you can see an article, how many times it was loaded, how many times people started reading it, how many times people got to the bottom of the content and how many times people made it to the bottom of the page.

Average amount of time it takes to interact with content.

This is pretty cool.

Comparing this data to the average I can see that it basically follows the same trend.

• About 82% that load the article start reading (daily avg = 82%)
• About 55% make it to the bottom of the content (daily avg = 63%)
• About 14% make it to the bottom of the page (daily avg = 18%)

I suggest looking at the article and checking things like how many comments the article has in order to further understand the data.

Let’s take a moment here and talk about event value. The event value measures time in seconds. These time measurements represent the following time intervals:

• Time between page loading and start reading (i.e. scrolling)
• Time between start reading and the bottom of the article
• Time between start reading and the bottom of the page

The first thing that jumps out is the amount of time before scrolling, It’s about 5 minutes! (time is in seconds, so I just divided by 60). After talking with a few people we believe that this time lag is caused by people opening articles in tabs and reading them later.

Once they start reading it takes about 6.5 minutes to get to the bottom of this article and 9.5 minutes to get to the bottom of the page. Again, this depends on a number of things, like comments, article complexity, etc. But you can compare this to the site average for context.

Or, better yet, create a average for the content category, so you’re comparing the time it takes to read a technical article to the avg time it takes to read a technical article.

Be aware, these metrics can easily be skewed due to outliers.

If a few people open tabs and then walk away the results can really throw off these metrics.

Ok, more reports.

Let’s check out the traffic sources report. Remember, I created goals out of these events. Now you can look at the Goals tab and instantly see how much traffic made it to the end of an article or page:

Measure traffic sources based on actual reading metrics.

Now you can really measure the value of a traffic source based on how many people actually read your content not just time on site/page or pageviews.

NOTE: GA only counts one conversion per visit. So as soon as someone reaches the bottom of an article or page the goal will be counted. Total events are the raw count and will be higher that the total number of goals. Unique events are the number of visits that included a specific event and should match the goal counts.

Don’t Forget Advanced Segmentation

This is just the beginning. You can do some serious analysis of the data. If you’re a publisher try segmenting by things like:

• Author
• Publication date
• Content category

Obviously you need these data points to create these segments. I have this data in custom variables (thanks to the Google Analytics plugin for WordPress) so I can do things like this:

You can segment your reading metrics using a Custom Advanced Segment.

The resulting data is useful, but working with it in the interface can be a bit challenging. Here, take a look.

Get some deeper insights by segmenting reading data based on category.

This would be easier in Excel.

Advanced Data Analysis

While you can get a lot of insights from the reports, this data begs for Excel. It’s actually a lot easier to export the data and filter it in Excel especially when you’re looking at the actions with a secondary dimension of Page.

In a spreadsheet, I want to see the Dimensions of Event Action and Page URL. I want to see the metrics Total Events, Unique Events, Total Event Value (all of our time measurements) and Avg. Event Value (which is the average time measurements).

Remember, the event actions are :

• How many times the page loaded
• how may times people started to scroll
• how many reached the bottom of the content
• how many reached the bottom of the page

The first thing you’ll want to do is convert the time measurements from seconds to minutes:seconds. Then start to build some averages for your site. Build benchmaks based on category, author, publication date.

You may want to include even more Dimensions, like category, author, etc. We’re getting into some pretty serious data here :)

You can pull data using any GA excel plugin like NextAnalytics or GA Data Grabber.

Working with content engagement data in Excel is really powerful.

If you’re really nerdy and want to pull the data using some code, here’s the API query that I used. You’ll just need to change the date range.

http://code.google.com/apis/analytics/docs/gdata/gdataExplorer.html?dimensions=ga%253ApagePath%252Cga%253AeventAction&metrics=ga%253AtotalEvents%252Cga%253AuniqueEvents%252Cga%253AeventValue&filters=ga%253AeventCategory%253D%253DReading&start-date=2012-02-20&end-date=2012-02-20&max-results=10000

Other Things to Note

In addition to the above data, there are a few more things to be aware of. First, your bounce rate is going to go down. Way down. The event that tracks scrolling counts as an interaction. So if the visitor does not view any other pages they will NOT be counted as a bounce.

Second, your time on site and page will go up. I’m not going to get into all of the details here but for visits that only include one pageview, Google will use the last engagement HIT of the session to calculate time. So this will be the time the visitor gets to the bottom of the content or the page.

If you use this technique expect your bounce rate to drop and your time on site to rise.

Your time measurements are going to be MUCH more accurate.

Before and After

To wrap up some analysis I thought it would be interesting to take a look at a few pieces of content and their perceived performance before this tracking technique.

Here’s a piece of content with data compared week over week. This week with the new content tracking vs. last week. Look at the HUGE difference in time on page and bounce rate.

There will be a huge change in your metrics after implementing this technique.

An obvious improvement right? I’m getting much better numbers now that the code has been changed.

What About Custom Variables?

If you read part one then you’re probably wondering about the custom variables. The code actually sets a custom variable when someone reaches the bottom of the page in more than 60 seconds. If they get to the bottom in less then 60 seconds then they are a scanner.

After seeing how few people actually make it to the bottom of the HTML page I think this code should be moved to the bottom of the Content section. So I moved the code. I also changed the code to be a page-level custom var.

Custom vars will give you much of the same data that you get from events. Analysis for custom vars will have to wait.

That’s the end… for now.

I hope you find this interesting. I certainly do. I think it opens the door to a different, more accurate, method to track content.

Again, thank you to all the people that have contributed.

Advanced Content Tracking with Google Analytics: Part 2 is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Advanced Content Tracking with Google Analytics: Part 1
2. Tracking Clicks with GA Pt. 3: Advanced Implementation
3. Google Analytics Advanced Segmentation

Do people actually read content?

The default content tracking content in Google Analytics is fairly straight forward. Using the standard page tag you can get all sorts of information like time on page, bounce rate and pageviews.

But sometimes this is not enough. For publishers and minor bloggers (like your’s truly) these metrics can be sub-optimal.

I want more detailed information about each article. Do people read the comments or do they just read the post? Do they open a lot of posts in tabs?

What would be better is a way to measure more detailed information about how website visitors interact with each page.

So that’s what this post is all about: measuring how people interact with content using custom tracking.

Some Thanks

Before we begin, this blog post, technique and concept was born from collaboration. There are a number of people that need to be recognized for contributing. You can read more about the genesis of this technique at the bottom of the post. Contributors include:

Nick Mihailovski – Developer advocate at Google (and the guys that sits across from me)
Thomas Baekdal – Smart guy and publisher of www.baekdal.com
Avinash Kaushik – If you don’t know Avinash…
Joost de Valk – Creator of the Google Analytics for WordPress
Eivind Savio – Blogger and GA consultant

Now, on the details!

Business Objective

As I mentioned above the objective here is to get a better understanding, on a page by page basis, of the content that visitors engage with. Using some objectives suggested by Thomas Baekdal here’s what we’re going to track:

• How many people scroll
• When a person starts to scroll
• When a person reaches the end of an article (not the end of the page, but the end of the article or post area)
• When a person reaches the botton of the page (the bottom of the HTML)
• Which website visitors are scanning my articles and which are reading my articles

Think about the value here! We will be able to get an accurate measure of which articles are actually read. We can even see which articles are so engaging that visitors continue through the comments to the botto of the page. Very useful stuff.

Tracking Technique

All of the above can be tracked with Event Tracking. The concept is that we will fire events when certain actions happen. Specifically we’re going to fire events based on visitors scrolling down the page.

Critical to any event tracking implementation is the data model. We need to define the data we want to see in Google Analytics.

All of the reading activities will be grouped together into a category named Reading.

Within this category there will be four main actions:

• Article load: Measure how many times the article loads in a browser. Basically another count of pageviews. This will provide context to the other events that we track.
• Start Reading: Track when a visitor starts scrolling down the page. This will be triggered after a visitor scrolls 150 pixels down the page. This value can be customized. I’m also tracking how much time it takes to start scrolling.
• Content Bottom: Track when a visitor reaches the end of the article content. And track how much time it took between the scroll start and getting to the bottom of the content.
• Page Bottom: Track when the visitor reached the botton of the page and how long it took.

Another piece of critical information is the page URL and title. We need this to segment the data and see which articles are most engaging to people. Google Analytics will automatically track the page URL and title so there’s no need to add it to the event.

We’re also going to use a Custom Variable to place this visitor in a bucket. If it took them less than 60 seconds to get to the bottom of the page then I will assume they are just scanning. We’ll put them in the Scanners bucket.

But, if they took longer than 60 seconds to get to the bottom of the page then we’ll put them in the readers bucket.

FInally, I can set these events up as goals. I’ll add one goal for those that make it to the bottom of the content and one goal for those that make it to the bottom of the page. This is an easy way to measure what percentage of visits complete these actions.

The Code

First, this code uses something called jQuery. It’s a special JavaScript library that makes it easier to program complex tasks. Almost every website is running jQuery these day. But make sure your site includes the library.

Here’s the code, feel free to copy, tweak and share. Just remember all the people that contributed to it!

[ If you don't see an iFrame below, please visit the site. ]

We start with some simple declarations. These control flow etc. But notice there are a couple values you can change.

// Debug flag
// CHANGE THIS TO false BEFORE INSTALLING
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;

// Set some time variables to calculate reading time
var startTime = new Date();
var beginning = startTime.getTime();
var totalTime = 0;

You can change the callBackTime variable and the readerLocation variable. callbackTime is the time (in millisecond) that the browser will wait before checking the scroll location. This eliminates any lag in scrolling.

readerLocation is the distance, in pixels, that the visitor must scroll before we fire an event and classify them as someone who starts reading.

Now we send off an event to track that the article has loaded:

// Track the aticle load
if (!debugMode) {
_gaq.push(['_trackEvent', 'Reading', 'ArticleLoaded', '', , true]);
}

Next comes the code that checks the location. First we gather where the visitor is on the page and how far they have scrolled.

bottom = $(window).height() + $(window).scrollTop();
height = $(document).height();

Then we start checking.

First, have they scrolled enough to fire the first event (150 px)?

// If user starts to scroll send an event
if (bottom > readerLocation && !scroller) {
currentTime = new Date();
scrollStart = currentTime.getTime();
timeToScroll = Math.round((scrollStart – beginning) / 1000);
if (!debugMode) {
_gaq.push(['_trackEvent', 'Reading', 'StartReading', '', timeToScroll]);
} else {
alert(‘started reading ‘ + timeToScroll);
}
scroller = true;
}

IMPORTANT: The above event WILL change your bounce rate. As soon as someone starts scrolling I consider them engaged and not a bounce. So this event will drop your bounce rate. Also note that these events WILL change your time on site calculations. You should see time on site increase.

Then, when they reach the bottom of the content area, this event fires marking their progress. I’m basically checking to see if the div that contains the article content has been reached. If so, fire the event.

// If user has hit the bottom of the content send an event
if (bottom >= $(‘.entry-content’).scrollTop() + $(‘.entry-content’).innerHeight() && !endContent) {
currentTime = new Date();
contentScrollEnd = currentTime.getTime();
timeToContentEnd = Math.round((contentScrollEnd – scrollStart) / 1000);
if (!debugMode) {
_gaq.push(['_trackEvent', 'Reading', 'ContentBottom', '', timeToContentEnd]);
} else {
alert(‘end content section ‘+timeToContentEnd);
}
endContent = true;
}

It’s really important to note that the above code looks for a div specific to my blog. On my site the div is named entry-content. It might be different on yours. Basically you’re looking for the container that holds the blog post or article.

Finally, we track if the visitor got to the bottom of the page. Here we do a few things.

1. We calculate how long it took them
2. We send an event
3. We set a custom variables to bucket our traffic. If the visitor took longer than 60 seconds to reach the bottom then we’ll put them in the reader segment using a visit level custom variable. If they take less than 60 seconds I’ll put them in the Scanner bucket.

I’m putting them into custom variable slot 5 because that’s the only slot that I have available. You may need to use a different slot. Dont know what a slot is? Read more about mastering custom variables.

// If user has hit the bottom of page send an event
if (bottom >= height && !didComplete) {
currentTime = new Date();
end = currentTime.getTime();
totalTime = Math.round((end – scrollStart) / 1000);
if (!debugMode) {
if (totalTime < 60) {
_gaq.push(['_setCustomVar', 5, 'ReaderType', 'Scanner', 2]);
} else {
_gaq.push(['_setCustomVar', 5, 'ReaderType', 'Reader', 2]);
}
_gaq.push(['_trackEvent', 'Reading', 'PageBottom', '', totalTime]);
} else {
alert('bottom of page '+totalTime);
}
didComplete = true;
}

Since we’re collecting the time spent on page, I’m going to use this data to adjust the threshold after I collect some data. I chose 60 seconds arbitrarily.

And finally, here’s the code that actually checks if the visitor has scrolled down the page:

// Track the scrolling and track location
$(window).scroll(function() {
if (timer) {
clearTimeout(timer);
}
// Use a buffer so we don’t call trackLocation too often.
timer = setTimeout(trackLocation, callBackTime);
});

So that’s the code. You can copy it from the iFrame above and place it on your site if you want to.

A Bonus For WordPress Users

All of this awesomeness will be added directly into the Google Analytics for WordPress plugin developed by Joost. Look for it soon.

Stay tuned for a post tomorrow about the resulting reports!

Advanced Content Tracking with Google Analytics: Part 1 is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Tracking Clicks with GA Pt. 3: Advanced Implementation
2. Event Tracking Pt. 2: Implementations
3. Content Optimization With Google Analytics, Part 1

So this post is all about my measurement plan. A quick caveat: there are lots of different sized ecommerce businesses, some more complex and advanced than others. This plan will work good for a mid-size business. We’ll get into more complex ecommerce measurement like lifetime value and cohorts some other time.

I’ve divided the ecommerce data into four categories, and created a small graphic (because everyone likes graphics):

The four types of ecommerce data.

* Acquisition Data: Information associated with getting traffic.
* Engagement Data: Info about how people interact with the website.
* Conversion Data: Info about business success (revenue, revenue, revenue…..)!
* Foundational Data: Un-sexy things that we should check every now and then.

The REI Ecommerce Website

And to make things even easier I’ll use REI as an example. They’re an ecommerce site with a substantial number of brick-and-mortar stores.

They use some neat selling features on their site. I’ve also included a reporting frequency to help describe how often I look at this data. Let’s face it, there’s a LOT of data and we can’t look at all of it all of the time!

Acquisition Data

Acquisition data is all about how we get customers to an ecommerce website. Marketers need to know which campaigns are working and which are not. There are lots of different types of campaigns, some are focused on spreading our brand while others are direct-response activities. How we measure these activities can differ.

With that said, Campaign Tracking is critical. This allows many types of segmentation so we can align our campaign measurement with the campaign objectives.

With a direct conversion campaign we might measure things like:

* Which marketing channels generate the most revenue?
* Which creative and messaging are most popular with consumers?
* Which marketing activities are effective at reaching people early in the buying process?
* Which marketing activities are effective at reaching people late in the buying process?
* What time of day are certain campaigns successful?

Reporting frequency: Daily for a tactical marketer. Less frequent for senior staff.

Engagement Data

Engagement can be a tricky thing to describe. There are some basic metrics, like Bounce Rate, that are very easy to understand. But I want to measure, at a deeper level, how people interact with an ecommerce site. And when you look at a site like REI.com there are lots of different ways to interact.

These interactions are commonly referred to as micro-conversions and, while they do not generate revenue immediately, they can lead to revenue in the future. Most of these activities are related to building a relationship with prospective customers.

Engagement metrics include:

* Bounce Rate: We’ve been talking about this one for years! Bounce rate measures the percentage of single-page visits. While it’s more-or-less useless at a site-wide level, it’s very useful when segmented by marketing campaign or channel.

* Newsletter signups: Email is still very important! Getting someone to sign up for a newsletter is HUGE.

* Store finder: This is a good one. Many sites have a store finder just in case you want to visit and physically examine an item. While measuring the number of times the store finder is used is not a direct indication of sales, it does yield interesting data. Especially when you collect which locations are most commonly searched for.

Subscribing to the newsletter or using the store finder are micro conversions.

* RSS subscriptions: I love subscribers. While we can’t control if they’ll actually read what you put out there, a subscriber is still someone interested.

* Add to Carts: This is an important part of the buying process. If people do not add items to their cart then how will they purchase them? It’s a critical step in the buying process, so we’re going to measure it.

* Save to Wish List: This is like a predictor of the future! How many people save an item to a wish list or save their cart? And then how many people actually complete the transaction later? These are both important things to know. If we can measure both we can actually get a sense for future revenue from saved carts.

* On-site social interactions: (Tweets, Likes, +1): Are people clicking these things? And is there something special about the people that do? Segmenting by this group is important.

More ecommerce micro-conversions for an ecommerce website.

* Product Information Interactions: Many ecommerce sites have TONS of content to help convince consumers to buy. This information is usually separated into separate tags, like here, on the REI site. These tabs need to be tracked so we can segment data based on those that use the tabs.

* Product Ratings: This is another great way that ecommerce companies can generate interaction with customers. Interaction = engagement = future revenue. Reviews can be a great source of traffic (hello, free SEO) and insight into what your customers like and do not like.

* Product Video: I LOVE product videos. There is no better way to get a sense for a product. Unless you can pick it up and hold it. But that’s impossible on the web. But we need to measure video! Do the videos make a difference? If so, which type and how do people interact with the video? So if I’m a company like REI, I want to track product videos.

* Look to Book: This is a metrics that I learned from Bryan Eisenberg during a Google+ Hangout, and it is FANTASTIC. Bryan recommends measuring how many people look at a product or category and then do not buy that product. Think of how useful this is. If people are looking at a product, but not buying it, there is some friction there. Remove the friction.

[ Don't worry, we'll cover the implementation in another post. ]

Reporting Frequency: Weekly for a tactical marketer. Less frequent for senior staff.

Conversion Data

Now we get to the heart of the business! Transactions! That’s the main focus, right? Everyone, from the top of the organization to the bottom, will want to see revenue. Luckily we can do that.

But it’s not just revenue, there are many revenue-related metrics that are important to an ecommerce business. Here are the ones that I put together:

* Revenue: Not much to explain here! You sell stuff, measure how much money you make!

* Return on Investment: ROI is a great metric, it helps us understand how much money we make based on how much money we spend. Cool, right? But Google Analytics is limited in it’s ability to track true ROI. IT can only track the ROI of AdWords. Why? Because it does not have any of the investment data. So if you spend $1500 on an email marketing campaign, that data is not in Google Analytics. Regardless, you should be thinking about ROI.

* Average Order Value: On average, what do people spend per transaction? We often try to get our customers to spend more per transaction and employ various techniques, like cross-selling, to increase AOV. We can also use AOV to identify high-value channels, like email, search and social.

* Revenue by Repeat Customers: Technically this is a segment, but I’m putting it here. Almost every business wants repeat customers. Why? It takes less effort to attract a repeat customer versus a first-time customer. It’ important to segment these two groups of customers and study their behavior.

* Revenue Per Visit or Per Visits Value: Per visit value is a great metric because it normalizes the value of traffic from different sources. It creates a good way let’s me compare the performance of different traffic sources.

* Internal Campaign Performance: Many businesses will run some type of promotion or campaign directly on their site. For example, they might put a banner on the homepage to liquidate seasonal merchandise. The ability to segment revenue and measure the effectiveness of these campaigns is performance.

* Visitor satisfaction and intent: Here we have some qualitative data. Are the people coming to the site happy with their experience? Is their visit a success? Even more important, why are they coming to the site? We can’t get a good answer using qualitative data. Quantitative data, usually from a survey, is the way to go here.

Reporting Frequency: I look at most of these daily.

“Foundational” Metrics
In addition to all of the data related to the purchase life-cycle, there are many other pieces of information that can help us understand the business performance.

* Time Before Purchase: Does it take one week to sell a customer a product or one month? It’s really important to understand this behavior so you can tailor your marketing campaigns to your customers.

* Website Visits before Purchase: How many times do we need to interact with the person (on the website) to drive a transaction?

* High-value Customer Behavior: What do my high-value customers do? By high-value I mean people that spend more than a certain amount of money. Where do the come from (i.e. geo-graphic location and marketing campaign) Almost everything we talked about so far has to do with running the business day to day. But there is an entire world of metrics that help us understand the technical health of our website.

* Site Performance Metrics: If your website is not fast then people will not use it. Seriously. While site performance metrics are not as sexy as some of the above, they’re still important. These are things that can impact revenue and should be monitored, just not every day.

* What is the average page load time for the site?
* Specifically, which pages take a long time to load? How does this impact bounce rate and revenue?
* What are the most common error pages on the site (404′s, etc.)?
* What is the technical profile for visitors (operating system, screen resolution) and how does this impact the business

Reporting frequency: Bi-weekly (for heavily trafficked sites) or monthly.

* Mobile App/Website Data: Related to site performance, but slightly different is device data. I usually put mobile devices into a separate category as the mobile space is evolving very quickly. Not only do we need to consider how the website works on a specific device, but potentially usability information about how people might use an app or the mobile site.

* Which devices are most popular?
* Which versions of the device are most popular?
* What carriers are people using?

Reporting frequency: depends on initiatives

* Site Search: I’ve been long extolling the value of site search, it’s just amazing data. Again, this isn’t a data set that I’m working with every day, but something that I look at weekly, bi-weekly or monthly.

You can gain a deep understanding of user behavior using Site Search data.

The search terms used in site search provide valuable insight into how customers think about products specifically the keywords they use to describe your products. Site searches that return zero results usually indicate a missing product, or that your site search is broken. Either way, interesting data.

Reporting frequency: Bi-weekly (for heavily trafficked sites) or monthly.

So that’s my general plan. In the next post we’ll get more tactical and talk about how to exactly track all of this stuff.

Did I miss anything?

I’d love to hear how others are tracking ecommerce.

Creating an Ecommerce Tracking Plan for Google Analytics is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Building a Mobile Ecommerce Dashboard in Google Analytics
2. 5 Google Analytics Custom Variables for Ecommerce
3. Google Analytics E-Commerce Tracking Pt. 4: Tracking Lead Gen Forms

For those that are unfamiliar with Feedburner, it tracks how many people subscribe to an RSS feed, the various applications that access your feed, and how many people take action on your content. It will even track how many people subscribe via email. All wonderful metrics related to interaction with your syndicated content.

Some people, like me, syndicate all of their content via RSS. But others will will only syndicated a snippet of content in the feed in hopes of driving people back to a website where the content lives. This is true for most publishers like Search Engine Land and the BBC.

In this case a feed becomes a source of traffic and must be measured as such. Luckily there is a simple integration with Google Analytics that makes it easy to see how much traffic is coming from a feed.

Feedburner will automatically inject the Google Analytics Campaign Tracking parmeters into your feed. Then, when someone clicks on a link in your feed, the traffic to your site will correctly identified as coming from your syndicated content.

(You can read all about campaign tracking in a previous post if you are not familiar with it.)

It’s important to note that Feedburner will only tag SOME of your links. More on this later.

You can find the campaign tracking settings in the Configure Stats” section of the Analyze tab. Here are the default settings:

Default Feedburner settings for Google Analytics.

Digging into this a bit more, here’s a description of values that Feedburner will insert into the various campaign tracking parameters:

• Source: This value is set to “feedburner” by default. It identifies that the visitor came from content that was syndicated by Feedburner. I would not recommend changing this.
• Medium: The channel where the feed was distributed. Remember, Feedburner can distribute your content via RSS, Twitter or Email. Feedber alters the value of medium depending on how the feed is accessed. The values will be either “feed”, “email”, or “twitter”.
• Campaign: This is just the feeduri and feed name. Because my feed name and URI are the same, I actually simplified this to just be the feed name. But no need to change this.
• Content: The application where a click request originates, e.g. Google Reader, Gmail. Can be interesting to see how people are consuming your content.
• Term: not used by default

So that’s the good news. We have a tool that measures the reach of our feed on a daily basis and the traffic back to our site from that feed.

Now the bad news.

There are some quirks when it comes to Feedburner. Specifically, not all of the links in a syndicated post are tagged with the Google Analytics campaign parameters. The result is incorrect traffic source data. Let’s look at some data to understand the impact.

Here’s a simple graph of direct traffic to my blog. All of the annotations with start (shown at the bottom of the graph) are dates when I published a new article. You can notice the spike in direct traffic on the day of, or the day after, that I publish the post.

Direct traffic to this site.

Now, let’s focus on Dec 8 and 9, the large spike of Direct traffic and look at some of the landing pages for direct traffic.

Top landing pages for direct traffic.

The top landing page for direct traffic is:

/blog/2011/12/08/the-google-analytics-social-data-hub-more-than-meets-the-eye/index.php

I don’t think anyone is going directly to that page. I do believe that a lot of people are going directly to /blog/index.php. But people are not typing /blog/2011/12/08/the-google-analytics-social-data-hub-more-than-meets-the-eye/index.php into the browser. This is clearly an issue.

Direct traffic can often be caused by links that are not tagged. Depending on server redirects, we often see untagged email traffic, especially from application like Outlook, incorrectly tracked as Direct Traffic. I’m guessing that’s happening here.

So let’s go one step further. Let’s go right to the source of the problem. Here is an email that Feedburner sends to those that subscribe to my feed via email. A quick investigation reveals that the link in the header, which links to the sire URL, is not tagged. So that would increase direct traffic to /blog/index.php.

However, the link to the article page is tagged, which is great news.

A blog post sent from Feedburner, some links are tagged and some are not.

A quick look at the links at the bottom of the page reveal that the links are NOT tagged.

Not all of the links in a Feedburner Email are tagged.

So that link at the bottom of the page, the one to the post “The Google Analytics Social Data Hub – More than Meets the Eye?”, that’s the link that’s responsible for driving a lot of direct traffic to the site.

You may be asking why doesn’t Google record this as referral traffic? Due to redirect, the value of document.referrer, which would normally be the referring site is blank. Resulting in direct traffic.

ARGH!

So there you have it. Feedburner is AWESOME. A fantastic way to track content syndication. But beware, there are quirks. Specifically not all of the links that are in an email are tagged.

Google Analytics & Feedburner: A Love-Hate Relationship is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. How Does Google Analytics Track Conversion Referrals?
2. Google Analytics Real Time: Real Time Data for Real Time Decisions
3. Welcome Analytics Readers!!!!