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?

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

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

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

But that’s OK, I use data alerts to stay on top of my data even if I can’t check analytics every day. Are you using alerts?

If you’ve never used Google Analytics alerts they’re fairly simple to set up. You can find them in the Profile Settings.

There are five things that you need to do to set up an alert:

1. Choose a profile to apply the alert to.
2. Select how often you want to monitor the data (daily, weekly or monthly).
3. Identify the segment of traffic you want to monitor (i.e. which group of visitors, like visitors from a particular campaign or geographic region).
4. Choose what metric you want to monitor (Revenue, Visits, Time on Site, etc.).
5. Set a threshold for the alert meaning how much does the metric need to change to activate this alert (ie increase, decrease, etc.).

I divide my alerts into two groups: those that insure I have good data and those that measure the performance of the business. This post deals with the former: alerts that will keep your data in tip-top shape.

Alert #1: 10% Daily Traffic Drop

This first alert is simply there to make sure that I’m collecting data. I’ve noticed, for my site, that my traffic usually never drops more than 10% for a given day when measured week-over-week. So if I see a drop of more than 10% I know that something happened and I need to investigate more.

A 10% traffic drop alert.

Alert #2: Flatline! i.e. No Data

This is a generic alert that identifies when a metric goes to zero. I can’t tell you how many times a client tries to analyze data at the end of a month only to find a data issue. This commonly happens because not every metric is analyzed every day, some metrics are reported monthly.

But I don’t want to wait until the end of the month to find a problem.

So for almost every important metric I will create a daily alert to insure that I’m getting data. Here’s an example alert that monitors an event.

A Google Analytics Alert that monitors an Event

Alert #3: Daily Error Pages

Another metric I like to monitor is error pages, more specifically 404 errors. I want to know if any of my content is generating an error for my readers. So I created a little alert that will alert me when the number of 404 pages is greater than 0.

Monitor 404 pages, and other website errors, with an alert.

Obviously you can change this alert to monitor almost any type of website error, it depends on which errors you are tracking with Google Analytics. The key is that you need to be able to identify your error pages in a Google Analytics report.

As you can see above, my error page has a unique URL (404.html) and a query parameter that identifies the page that was missing. You may need to use a virtual pageview or an event to track your error pages. Once you do you can create an alert.

Alert #4: Non-Domain Traffic aka The Poser Alert

The hostnames report in Google Analytics is a handy little report. It shows the hostname that is in the location bar of the visitor’s browser. On numerous occasions people have ripped off borrowed my content without my permission. They just copied my source code and posted it on their own domain.

I know, classy.

This alert will automatically let me know if my account is getting data from any domain other than my own.

The "Poser" Alert will let you know if someone is poaching your content.

In some cases this alert actually detects something good! I regularly see the hostname translate.googleusercontent.com, which is used when popele use Google Translate to translate the content on my site.

A note from your data therapist

All of these alerts can help. But you’ll still have inaccuracies in your data. The idea is to keep them to a minimum. If you try to eliminate all errors 100% of the time you won’t do anything else. It’a almost impossible. Do the best you can.

If you do have some data issues, and you will, you’ll have to spend some time resolving them when you do your analysis.

Save Your Ass With Google Analytics Data Alerts is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Adding Business Data to Google Analytics Data
2. Google Analytics Real Time: Real Time Data for Real Time Decisions
3. An Analysis of My Data

After the talk someone came up to me and asked if I could share more details on the dashboard. So here they are!

My goal with this dashboard is deep-dive on the mobile segment of the business: to focus on the business objectives (conversions), key actions that could lead to conversions AND ancillary data about the mobile experience. This will give us a wide range of metrics that provides a deep understanding of the mobile experience.

This is not a data puke.

Before we begin, some assumptions about the business that will make it easier to build the dashboard.

• This is an ecommerce business and revenue is queen!
• The business has launched an ecommerce website in hopes of diring more revenue via mobile
• There are a number of mobile marketing campaigns that are running and it is critical to measure their effectiveness
• The mobile site is ever evolving and the business is always looking for information on how to improve the site

A dashboard is a great way to pull all of this information together. Could you use a custom report? Sure.
But the dashboard does include some nice (but VERY basic) visualizations.

Here it is, the mobile dashboard.

Mobile Ecommerce Dashboard for Google Analytics

The key to creating tis dashboard is the filter option built into each widget. I’m basically segmenting each widget to include only mobile traffic. It’s not that hard to do, just set the Dimension to Mobile and the value to ‘Yes’. (The Mobile dimension is a yes or no flag.)

Filtering a Google Analytics Dashboard Widget for Mobile

That’s it. The widget will only contain data specific to mobile.

Now that we know how to manipulate the widgets so they only include mobile data it’s just a matter of putting the most appropriate widgets on the dashboard.

I’ve divided the mobile dashboard into four sections:

1. Revenue Measurement (outlined in green above)
2. Campaign Performance (outlined in red above)
3. Mobile Device Information (outlined in orange above)
4. Content Performance (outlined in yellow above)

Let’s look at each section.

Revenue, Revenue and Revenue!

The first section of the dashboard deals with revenue. Surprise! There’s not much to explain here. This is an e-commerce site and revenue is their key success metric. You could easily change this to conversion rate if you’re not an ecommerce site. I like the little spark line that shows me a trend.

Google Analytics Mobile Revenue Widget

Now this is where things get interesting, the next widget is Mobile Revenue vs Non-mobile revenue. This is cool if you want to see how mobile revenue compares to overall revenue.

Mobile Revenue vs. Non-Mobile Revenue

The way this widget is a pie-chart widget is to group the metric, in this case revenue, but the Mobile dimension. Like this:

Grouping a Pie Chart Widget

Channel & Campaign Segmentation

Next we continue to look at revenue but now we’re segmenting it my marketing channel. A bit more advanced, but very, very insightful.

This fictional company is leveraging a number of marketing channels, so let’s segment the revenue by medium to determine which ones are generating revenue.

Mobile Channel Revenue

Drilling a bit deeper we can include a table of the top 10 campaigns to quickly see which ones are successful and which ones are not. Again success is measured based on revenue. But if you are not an ecommerce site you can change the widget to measure almost any goal conversion.

Mobile Campaign Performance

Notice I’ve also included Bounce Rate in this table. This makes it easier for me to understand if the poor performance of a campaign might be due to the initial experience the visitor has on the site.

In this case every campaign has $0.00 and a 100% Bounce Rate. Not so good. Time to rethink my landing page experience.

Don’t forget, if you’re using Google Analytics you need to use Campaign Tracking to accurately track marketing activities.

Device Information

In addition to our key business metrics, let’s include some usability metrics that will help guide future development of the site. In this case, because we’re dealing with mobile, let’s include some device data.

Mobile Device Revenue

I know… not another pie chart. But it’s a fast way to show which devices are driving revenue on the site. 38% from the iPad! Maybe we should focus on the iPad user experience?

Just to provide a little context, let’s add a table with the top mobile devices.

Mobile Device Traffic and Bounce Rate

The table helps us understand two things. First, what are the most popular devices that people use to access our mobile site? This information is critical for future development work. Second, I’ve included bounce rate in the table. This metric will make it easier for us to determine if users of a certain device have trouble accessing the site.

Popular Content

The last part of the dashboard deals with content. I put the mobile site bounce rate just to keep it at the top of my mind. It’s basically useless without some segmentation, but good to keep an eye on.

Mobile Website Bounce Rate

Let’s segment it by landing page using a simple table widget. And to add more context, let’s add the number of Entrances for each landing page. Now I can see how popular a landing page is along with the bounce rate. Popular landing pages with really high bounce rates suck.

Mobile Landing Page Bounce Rate

Now let’s see what people actually like to read or interact with on the mobile site with the top mobile content. Using two simple metrics, pageviews and time on page, it’s easy to see what people like. And if I know what’s popular I might be able to promote it in new ways.

Most Engaging Mobile Content

Wrapping Up

I know that there are some serious shortfalls with the Google Analytics dashboard. BUT, let’s not look past certain things that are helpful. You get to add almost any metric and dimension. You can segment that data AND you can add some (VERY basic) visualizations. All in all, it doesn’t suck. Too much.

There are two things I wish the dashboard had:
1. The ability to add a simple text area where I can add my analysis and thoughts
2. The ability to add external data, like ad spend

Post Script

Google recently hosted a webinar with Avinash Kaushik promoting mobile marketing and, of course, measurement. You can watch the entire video below. It’s about an hour but well worth it.

Avinash also included a link to a custom report for mobile measurement. A nice compliment to the dashboard above.

Now it’s your turn. What would you add to a mobile dashboard?

Building a Mobile Ecommerce Dashboard in Google Analytics is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. 5 Google Analytics Custom Variables for Ecommerce
2. New GA Feature: Custom Dashboards
3. New GA Feature: Sticky Filters

Flow Visualization report in Google Anlaytics

For a long time click path analysis was not really possible in Google Analytics. Sure, we had the Navigation Summary and Entrance Paths reports, but these were very limited. This was by design. Most click path reports, no matter the analytics tool, has little to no value. They usually show 9,845 different paths to conversion. How does that help?

The new Flow Visualizations reports hope to remedy this problem and make it easier to understand visitor behavior regarding how they move through content.

Nodes: Foundation of Flow Visualizations

All flow visualization reports are built using nodes. Nodes are groups of pages. The cool thing is that the nodes are automatically created using an intelligence algorithm. For example, nodes will automatically group the same page that may only differ by a couple of query parameter.

Don’t worry, you can create your own nodes, and I’ll show you how to do that in a minute.

The new reports show visitor flow through nodes, and where people drop out of the flow (ie leave the site). The really important thing here is the visualization AND our ability to manipulate the visualization. So if you a specific issue or scenario that you are analyzing it’s easy to drill down on the scenario you want. But the visualization also supports ad-hoc analysis.

These new reports are in the Home > Intelligence > Flow Visualization section.

On to the Visits Flow report.

Visit Flow Report

The Visit Flow report is a nice refresh of the traditional click path report. Rather than looking at clicks from one page to another, we’re seeing visitors moving between nodes. The ‘story’ starts on the left side of the visualization where you can choose a to view a starting segment, like Campaign, Traffic Source, Country, Region, etc.

This is AMAZING! Now you can easily segment traffic through a site based on various dimensions of data! The next time someone asks what campaign traffic did, now you can provide some very detailed flow information.

Connections in blue represent the number of visits that move between the nodes.

Connections in red represent the drop off from a particular node.

How easy is that to understand? The brilliance is the simplicity.

Let’s say you want to focus on a particular segment of data, like the state of Texas. Just choose a dimension from the drop down, in this case Region. Then click on the Texas node at the far left and choose ‘Highlight traffic through here’. The path of traffic from Texas will appear a bit darker.

View the path for a specific dimension of data in the Flow Visualization report.

I know there can be a lot to take in. If you are a bit confused, then perhaps we should reduce the number of paths that are visible. Use the ‘Connections’ slider at the top to view more, or less paths and make the analysis easier.

Alter the number of paths visible in Flow visualization

The reason why click path reporting has sucked for SO LONG was the vast amount of data stuffed into a really crappy display. Google is trying to solve that using this new visualization, the Dimension drop down AND the Connections slider. You have control over the amount of data you are looking thus making analysis area.

Using this interface you can identify where people come from, follow them to various pages and evaluate the bounce rate, and then see what they look at after the landing page.

The interactivity is just awesome.

[ By the way, you can scroll to the right by dragging the visualization when you see the fist. You can also add more steps by clicking on the arrow at the far right. ]

Let’s say you want to learn more about a particular node. Just hover over a node and you’ll see something like this:

Node details in Google Analytics Flow Visualization

The first thing we learn is how many pages are in this node. Remember, an algorithm initially created this node. We can also see how many dropped off and how many moved on. Very useful.

Now, perhaps I want to understand this page a little better. I can click on the node and choose ‘Explore Traffic through here.’ That brings us to the next report

Navigation Flow Report

The Navigation Flow report is similar to the old Navigation Path report in Google Analytics. Now you can see the nodes before, and after, your chosen node.

Google Analytics Flow Navigation Report

Let’s talk about how we can create and edit your nodes.

Click on the small gear at the top of the node. You can use a reg ex, or other basic pattern, to group a set of pages together into a node.

For example, if I’m a publisher, I may want to group all my Sports pages together into a Sports node, like this:

The Name field is the name that will be displayed in the report, it’s nice and human readable :)

The value here is you can group your content together so you can analyze your site your way.

Ecommerce people, want to understand the flow of traffic through your product pages? Group them together in a node.

Publishers people, want to see where people go after the last page in all your articles? Group those pages together in a node.

Another really cool feature of the Flow Visualization reports is our ability to segment the entire graphic. Use the drop down at the top of the report to apply a segment, like new visits, return visits, etc.

Apply a segment to a Flow Visualization

Combine that with the dimension drop down and I can slice and dice the behavior of different groups as they move around the site. Crazy awesome.

Goal Flow Analysis

In addition to the basic flow reporting, there is also a Goal Flow report that provides insight into your conversion paths.

Goal Flow report in Google Analytics. Click to enlarge.

Again, you can choose a dimension of data, here I chose the medium, and view how that traffic moved through your funnels. I hope you have a funnel configured!

NOTE: At this time the Goal Flow report only works for URL based goals and funnels. Events will follow soon.

Notice the loops? Those show traffic that ‘loops back’ to other steps. I think this is a HUGE improvement over the existing Goal Funnel reports. It’s much easier to see holes in the funnel (drop offs) and behavior (looping).

Is this just the beginning?

Hopefully this gives you a basic idea of how the flow visualization reports work and how you can manipulate them. The key here is that you can segment the data to isolate the behavior that you want to investigate.

I’ve got to say, I think this is going to have a much bigger impact on Google Analytics than most people know. Sure, these reports are sexy. And we’ll start to do more path analysis.

But this visualization of data is the key change. I believe we’re going to see Google include more creative data visualizations to help aid analysis. That’s truly exciting.

Path Analysis in Google Analytics with Flow Visualization is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Event Tracking Pt. 3: Reporting & Analysis
2. Google Analytics Campaign Tracking Pt. 3: Reports and Analysis
3. An Analysis of My Data

The NEW Google Analytics Real Time Reports

Google Analytics now contains a series of real-time reports that measure what’s happening on your site in real time. These reports will roll out in the next 1 to 2 weeks.

I’ve long railed against real-time data. I’ve said that businesses don’t need real time data. It’s very hard to action on time data. I’ve said this on the record!

But here I am, with my tail between my legs, to admit that some businesses really do need real time data. I was wrong. I can absolutely see the need for real time data.

Chartbeat Real Time Report

And if you still need convincing look at the success of tools like Chartbeat. They provide some pretty awesome data that helps business make important decisions WHEN those decisions need to be made.

Real time data can be very helpfull in the following business scenarios:

• If you are a publisher, you often need real time data to understand what content is popular right now, so you can optimize the placement of that content, or publicize that content, to optimize ad revenue.
• If you are a retailer, you might want to understand, in real time, the launch of a new campaign. This is especially useful if you are using a channel, like social media, that has a very immediate impact.
• If you are holding a physical event, you might want to understand, in real time, how people in that location are using your website.

To fill this analysis hole Google Analytics now has a Real Time section of reports, creatively named Real Time. This series of repots offers a subset of data that is processed in real time, literally every second. You can see who’s on your site, how they got there and what they’re looking at in real time.

The data covers three dimensions:

• Geographic locations
• Traffic sources
• Content

I know, there’s no conversion data. Sucky. BUT let’s consider how much data is getting processed every second. Seriously, that’s a lot of data!

IMPORTANT: One thing to keep in mind is that the Real Time reports are NOT filtered. Even if you have a profile filter applied the data is not filtered. I guess that requires a bit too much processing power.

The real time reports are NOT real time data for all metrics, only a subset of metrics. But they are pretty good. You have access to real time stats for:

• Visitors
• % New visitors & returning visitors
• Pageviews
• Traffic sources (referrals, direct, campaigns, etc.)
• Active pages

Let’s look at the reports and how they solve the above business cases.

Real Time Overview Reporting

Everything starts with the Overview report. This is mission control for Real Time data and gives you a general overview of what’s happeneing.

The Google Analytics Real Time Overview Report

At the top of the report Google shows a second by second view of pageviews. It then aggregates this data and shows a minute-by-minute view. I know, this view is mesmerizing. Try not to wast too much time watching this.

The top of the report also shows the total number of unique visitors on the site (it counts the cookies). Google Analytics then segments this number to show you the amount of new visitors and returning visitors.

Moving deeper into this report we get information about what’s the most popular content right now, where visitors came from (both traffic source and keywords) and a geographic breakdown of where the visitors are located.

With this one report we’re able to answer the business questions we posed above.

Real Time Locations Report

The location report has basically the same geo data as the Overview report. It still show second-by-second data and a breakdown by Country.

The Google Analytics Real Time Locations Report

One thing that is different is the ability to view geography using Google Earth. Does this offer any additional insights? HELL NO! But it looks sexy. The Google Earth plugin will zoom you from from city to city.

Oooooooo! Zoom from city to city with the Earth View

Real Time Traffic Sources Report

The real time traffic sources is literally a dump of your top traffic sources. Again, not too different than the data on the Overview, the tabular data shows the Medium and Source dimensions.

The Google Analytics Real Time Traffic Sources Report

This is especially handy when you launch a new campaign and want to see how it’s working. Think about when that big tweet goes out and you want to see what’s happening on the site.

Or consider the case where you might have an unexpected spike in traffic. This report will help you understand if there is some referring site that is driving a lot of traffic. We still can’t see if there is a lot of conversions (grrrrrr) but at least we can see traffic.

Real Time Content Report

And finally we have a dedicated real time content report. Again, not much new here. You can view how many active users are viewing a specific page or get an agregate view of pageviews over the last 30 minutes.

The Google Analytics Real Time Content Report

Notice how some of the data rows are shaded green and red? Green indicates a row of data that has increased while a row of data that is red indicates a row of data that has decreased. This is a nice touch to make it easier to see things “move.”

So there you have it. Google’s new Real Time feature for Google Analytics. How do you plan to use this to optimize your business?

Google Analytics Real Time: Real Time Data for Real Time Decisions is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Adding Business Data to Google Analytics Data
2. An Analysis of My Data
3. Event Tracking Pt. 1: Overview & Data Model

For those that have not used custom variables before you can get read Mastering Custom Variables for overview.

Now on to the custom variables!

Tracking Coupon Codes and Promotional Codes

I think this is a pretty obvious use of custom variables. If you sell something you probably run various promotions. And you need to track these promotions to see how they perform. Some promotions can be tracked using campaign tracking. I’ve seen lots of people use the utm_content parameter to identify the promotion in a marketing campaign, like an email. But a custom variable works just as well.

When coming up with a strategy think about how you might scale this as time goes on. Do you want all of your promotional codes aggregated together under one variable? You could do something like this:

_gaq.push(['_setCustomVar',1,'PromoCode','<PROMO CODE ID>',3]);

Or you could get fancy and use different custom variables to bucket groups of promotions, like seasonal promotions. For example, if you lots of promotions during the holiday season you might want to create a custom variable named HolidayPromos and then a unique name for each of the codes, something like this:

_gaq.push(['_setCustomVar',1,'HolidayPromo','Free2DayShip',3]);
_gaq.push(['_setCustomVar',1,'HolidayPromo','FreeWrapping',3]);
_gaq.push(['_setCustomVar',1,'HolidayPromo','10perOff',3]);

The above code would need to appear on your receipt page. And remember, the slot, or index, in the code above may be different for you. It all depends on your use of custom variables.

You might be wondering why I decided to go with a page level custom variable here. Remember, Google Analytics will sessionize a page level custom variable. So even though the custom var will only fire on the receipt page the data will be applied to the entire visit.

Tracking Payment Method

Remember, we study visitor behavior to understand the impact on business. One of these behaviors is how people pay for a purchase. Is there a difference between a visitor who pays with a credit card vs one who pays with PayPal? Does one spend more, on average, than the other? You’ll never know unless you capture payment type and review metrics like average order value and conversion rate.

To capture payment type use a page level custom variable. The resulting GA code will look something like this:

_gaq.push(['_setCustomVar',2, 'PaymentType','<NAME OF PAYMENT>',3]);

Don’t forget to replace <PAYMENT TYPE> with the customer’s payment method.

Tracking Shipping Method

Like payment method, it’s interesting to see what shipping method people choose with various products. Why? Perhaps people who buy certain products choose certain types of shipping. If so, maybe you can build a new promotion using this information. Could there be no insights here? ABSOLUTELY! But you’ll never know unless you have some data and do a bit of analysis.

_gaq.push(['_setCustomVar',3,'CustType','Repeat',1]);

Tracking Repeat Customers

I remember reading Eric Peterson’s book Web Analytics Demystified a looong time ago. Besides being one of the first books on analytics it was exceedingly actionable. One of the ecommerce key performance indicators Eric talked about, and rightfully so, was the repeat-buyers.

The behavior of repeat customers is different than first time customers. This segment of customers usually takes less time to purchase due to a knowledge of your products and services. Normally it costs you less money to get these people to convert. So it’s a good idea to understand what marketing they respond to and how they use your website.

To track repeat customers in Google Analytics you must use a visitor scope custom variable. Remember a visitor scoped custom variable persists on the visitor’s computer as a cookie. The trick is when you set the custom variable.

Most people think you can set the custom variable when the transaction happens, like this:

_gaq.push(['_setCustomVar',4,'CustType','Repeat',1]);

But in reality this will not work. When you set a visit level custom variable the value gets applied to the CURRENT visit and all future visits. So you need to wait until the SECOND purchase before setting the above custom variable. Then it should work just fine.

[UPDATE: Elizabeth Brings up a good point in the comments. This method will only work if the visitor comes back and makes a second purchase on the site. You can tweak the above logic and set the custom variable on the visitor's return to the website, rather than when they make a second purchase. While this is not perfect it is a workaround.]

I should note that this is an AWESOME custom variable to use in an advanced segment. Actually, they’re all great to use in Advanced segments, but segmenting repeat buyers is especially useful

Purchase History

This is where things get a little more complicated. Tracking things like purchase history requires a bit of configuration on the server side, meaning you or your nerds need to create some code to categorize customers based on the number of purchases they’ve made in the past. Rather than track every single purchase I prefer to bucket the purchases, like this:

• 1 to 3 purchases
• 4 to 6 purchases
• 7 + purchases

You need to have the logic on the server to review the custom’er purchase history and set the custom variable accordingly. The GA JavaScript will look something like this:

_gaq.push(['_setCustomVar',5,'PurchHistory','1-3',1]);

Now you’re probably thinking that there is some duplication with the previous custom variable, and you’re right. The repeat customer variable is a bit more generic and gives you a quick view of new and returning customers. The purchase history variable is geared towards deeper analysis of customer behavior.

So there you have it, five custom variables for ecommerce websites. This is by no means an exhaustive list, but I think these can help any commerce based business to better segmentation and analysis.

Are you using custom variables on your ecommerce website? If so I’d love to hear how!

5 Google Analytics Custom Variables for Ecommerce is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Mastering Google Analytics Custom Variables
2. Google Analytics Custom Variables Overview
3. Google Analytics Configuration Mistake #2: Query String Variables

During our presentation Amy announced an amazing new feature called Multi-Channel Funnels. This awesome feature is a series of marketing attribution reports that will help marketers understand how different marketing channels lead to conversion. These reports are full of strategic and tactical data.

Note: Google Analytics has let us know that the feature called Multi-Channel Funnels discussed in this blog post is in limited pilot. That means that Google Analytics is testing the feature and its usefulness to a small group of trusted testers, and have not made any plans or a timeline for a full launch.

You’re probably thinking, “HOLY COW!” I know, this feature is really amazing. But I want to reiterate, this is NOT a formal launch. There is NO TIMELINE to roll these reports out to GA users.

Multi-channel funnels are completely new. That means a whole set of metrics to measure the interaction of all your online marketing channels. Before we begin I know there are some basic questions you might have:

1. What is the look-back window? These reports provide a 30-day look-back window. That means that Google Analytics will stitch together all the different traffic sources 30 days prior to a conversion.

2. Do these reports include view-thru data? View-through data from other systems, like DoubleClick, is NOT included in this data. The reports will only include data from visitors who visit the site.

3. Is there any attribution modeling? No.

4. Do I need to do anything special to set up these reports? You must be tracking conversions using Goals or tracking ecommerce transactions. But to really make these reports meaningful you should also be tracking your marketing campaigns with Campaign Tracking.

Let’s start with the beginning. Multi-channel funnels are part of the Conversion reports in the new GA v5 interface. There are 5 new reports and all are very useful.

One of my favorite new reports, and one that I’ve been asking for, is the Path Length report. This report shows the number of interactions that leads to a conversion. This is the next version of the Visits to Transaction report from the old Google Analytics.

The great thing about this report is that you can now choose to view the path length for ANY conversion, not just transactions. Use the drop down box at the top of the report to choose any conversion.

What are the insights? Now you know how many interactions you need to have with a visitor before they convert. If it takes a lot of interactions you can engage in different types of activities to engage prospects.

But let’s dig in and get more details about various marketing activities and where they fall in the funnel. That’s the job of the Assisted Conversions report.

This report shows a number of new metric, all your traffic sources, grouped by source/medium. The new metrics are:

Assisted Conversions: This is the number of conversions that contained this source/medium combination in the last 30 days.

Last Interaction Conversions: This is the number of conversions where this source/medium was the “last touch.”

Assisted / Last Interaction Conversions Ratio: This is the simple ration of the above two metrics.

The Assisted / Last Interaction Conversions Ratio is THE KEY new metric. I’ve come up with my own name for this metric: the Exposer to Closer Ratio. It’s way more catchy, don’t you think? This single number will tell you which traffic sources are Exposers, meaning they spread your brand message, and which ones are Closers, meaning the get people to convert.

Marketing activities that have a higher Exposer to Closer ratio are upper funnel activities. They get you prospects.

Marketing activities that have a lower Exposer to Closer ration are lower funnel activities. They close deals.

So if you Path Length report shows that you have a longer sales cycle, that takes a lot of interactions, you need to push upper funnel activities. What kinds? Look for marketing activities that have a higher Exposer to Closer ratio and start pushing those!

There is another view of this report that is actually very useful. Google has created traffic groups which bundle certain kinds of traffic together. For example, all referral traffic from Twitter and Facebook will be grouped as Social Media.

There are some cool features of the Assisted conversions report. Notice the Custom Dimensions item at the top of the page? Google will let you group your traffic together according to your marketing activities.

I think one report everyone wants to see is the Top Paths report. This is a list of every combination of marketing activities that led to a conversion. This is the one that people have been asking for.

Let’s just soak in the beauty of this one for a minute….

While I too like this report, it can be a bit overwhelming. Like click-path reporting, there can be a LOT of different marketing paths. This report will NOT show you the ideal way to mix and match marketing channels. But it will help you drill into your channels and uncover details. For example, we can use the tools at the top of the report to focus in on keyword paths.

The last report I’d like to share is a very strategic one. The Overview report in the Multi-Channel Funnel section actually allows you to mix and match different marketing channels and see which combinations result in the most conversions.

To me, this is the most advanced feature. This is the beginning of multi-channel modeling: a way to help marketers determine spend across multiple channel. Wow.

I’ve got a lot more to write about search funnels. I had to hammer them pretty hard last week to put together some slides for AdTech.

I’ll leave you with the official videos from Google:

Google Analytics Multi-Channel Funnels is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Google Tackles Campaign Attribution with AdWords Search Funnels
2. Tying clicks & content to conversion in GA
3. New Google Analytics Goals