A version of this post originally appeared on Google Analytics Certified Partner InfoTrust's site.

by Nate Denlinger, Web Developer at GACP InfoTrust, LLC

One of our specialities here at InfoTrust is helping ecommerce businesses leverage their web analytics to make better data-driven marketing decisions. This typically starts with installing Google’s Universal Analytics web analytics software and utilizing all of the functionality that is offered with Enhanced Ecommerce tracking capabilities.

Enhanced Ecommerce provides you with a complete picture of what customers on your site are seeing, interacting with and purchasing.

One of the ways you track what your customers are seeing is with product impressions (whenever a user sees an image or description of your products on your website).

Normally, you track what products users see or impressions by simply adding an array of product objects to the DataLayer. These represent the products seen on the page, meaning when any page loads with product images/descriptions, data is sent to Google Analytics that a user saw those specific products. This works well.

However, there is a major issue with this method.  Sometimes you are sending impressions for products that the user never actually sees. This can happen when your page scrolls vertically and some products are off the page or “below the fold”.

For example, lets take a look at a page on Etsy.com:

Sample page on Etsy.com (click for full size)

Here are the results for the search term “Linens”. Currently, you can see sixteen products listed in the search results.  However, in the normal method of sending product impressions, a product impression would be sent for every product on the page.

So, in reality this is what we are telling Google Analytics that the user is seeing (every single product on the page):

Sample page of Etsy.com (click for full-size)

Obviously, no one's screen looks like this, but by sending all products as an impression, we are effectively saying that our customer saw all 63 products. What happens if the user never scrolls past the 16 products shown in the first screenshot?

We are greatly skewing the impressions for the products on the bottom of the page, because often times, users are not scrolling the entire length of the page (and therefore not seeing the additional products).

This could cause you to make incorrect assumptions about how well a product is selling based off of position.

The solution: Scroll-based impression tracking!

Here is how it works at a high level:

1. Instead of automatically adding all product impressions to the DataLayer, we add it to another variable just for temporary storage. Meaning, we do not send all the products loaded on a page directly to Google Analytics, but rather just identify the products that loaded on the page.
2. When the page loads, we actually see what products are visible on the page (ones “above the fold” or where the user can actually see them) and add only those products to the DataLayer for product impressions. Now we don’t send any other product impressions unless they are actually visible to the user.
3. Once the user starts to scroll, we start capturing all the products that haven’t been seen before. We continue to capture these products until the user stops scrolling for a certain amount of time.
4. We then batch all of those products together and send them to the DataLayer as product impressions. 
5. If the user starts to scroll again, we start checking again. However, we never send the same product twice on the same page. If they scroll to the bottom then back up, we don’t send the first products twice.

Using our example on the “Linen” search results, right away we would send product impressions for the first 16 products. Then, let’s say the user scrolled halfway down the page and stopped. We would then send product impressions for products 18 through 40. The user then scrolls to the bottom of the page so we would send product impressions for 41 through 63. Finally the user scrolls back to the top of the page before clicking on the first product. No more impressions would be sent as impressions for all products have already been sent.

The result: Product impressions are only sent as users actually navigate through the pages and can see the products. This is a much more accurate form of product impression tracking since it reflects actual user navigation. 

Next steps: for the technical how-to guide + code samples, please see this post on the InfoTrust site.

Personalization is a hot topic for today’s marketers, a group that spends nearly half of their budget attracting new prospects. But customer expectations have risen; content must be relevant to acquire new customers and move them to convert.

Some pioneering marketers are seeing better performance by using real-time personalization and remarketing simultaneously. Knowing who a customer is and what they do is a big step toward providing the hyper-relevant content that customers crave.

Join Marketo’s Mike Telem and Mike Tomita on January 15th at 10am PT/ 1pm ET as they discuss the importance of real-time personalization for marketing results. Google’s own Dan Stone will give an overview of the ways Google Analytics technology can be used to power advanced remarketing, while the Marketo team will share the ways their company uses real-time personalization and Google Analytics to generate more leads at a lower cost.

Looking for tips on how to get your organization started? Reserve your spot today!

Posted by Kelley Sternhagen, Google Analytics Marketing

Last year we introduced “The Customer Journey to Online Purchase” -- a tool that helped marketers visualize the roles played by marketing channels like paid search, email and display ads in their customers' journeys.

The goal was to help marketers learn more about the customer journeys for their industries. If social makes your customers aware, and email makes them convert -- or vice versa -- you can make sure you're in both places with the right kind of message.

Today we're happy to introduce a new improved version of the Customer Journey to Online Purchase, with a few key enhancements.  We’ve refreshed the data based on millions of consumer interactions, updated the industry classifications, and we’ve split out paid search so you can see the influence of brand and generic search terms on the purchase decision.

In each industry you can now see journeys for small, medium and large companies, which can often be quite different.

Click to enlarge image

For instance, the above image shows the journey for customers of small businesses in the shopping industry. Note that organic search is very often an "assist" interaction for these customers.

Click to enlarge image

Now here's the same journey for large shopping businesses. Note that display clicks and social are strongly assisting interactions -- while display didn’t even appear for the small businesses above. For both small and large businesses, a direct website visit is most likely to be the last interaction. Across industries, the differences from small to large businesses illustrate how different marketing strategies and customer profiles may lead to different buying behavior.

And there's more! Now you can drill down into each marketing channel for a closer look at the role it plays based on its position in the purchase path. Channels that occur more frequently in the beginning of the path are more likely to help generate awareness for your product, while the end of the path is closer to the customer’s purchase decision.

Click to enlarge image

In these charts, for example, we see the different roles that different channels play in the Shopping industry. One interesting insight is that all channels -- even those traditionally thought of as “upper funnel” or “lower funnel” -- occur throughout the purchase path, but a given channel may be more common at particular stages depending on its role (and depending on the industry).

Each marketing campaign and channel may have a different impact on customers depending on when they interact with it. Using what you learn from this tool, you can help adapt your marketing messaging to be more relevant and useful for your customers.

Try the Customer Journey to Online Purchase today. And for more helpful marketing insights, check out Measure What Matters Most: our new guide chock-full of suggestions on how to measure the impact of your marketing -- across channels -- to complement what you learn from the Customer Journey tool and take action to improve your marketing.

Happy analyzing!

The Customer Journey to Online Purchase »

Posted by Sara Jablon Moked, Product Marketing Manager, Google Analytics

Companies use many systems to run their business. These may include multiple web advertising networks, CRM and content publishing systems, point of sale systems, inventory databases, etc. Integrating the data from these systems with Google Analytics provides a better understanding for how your customers behave on the web. 

At the 2014 Analytics Summit we announced the new Data Import. Data Import helps unify data from your different business systems, allowing you to organize your data the same way your business is organized. This will allow for more accurate analysis and bringing together previously disparate datasets into one complete picture. Using Data Import, you can upload your brand’s existing data into Google Analytics and join it with GA data for reporting, segmentation and remarketing.

By using the Data Import functionality in Google Analytics Premium and with the help of Analytics Pros, consumer legal services brand Avvo created clear, accurate data, which continues to impact decisions across their organization. While Avvo already had a successful and fast-growing business, the lack of visibility into advertising success made it hard to align key revenue opportunities with actual site usage. Read the full case study here.

“We’ve been very pleased with the results that were realized using Data Import in Google Analytics to analyze client behavior on our website. This exercise has given us better insight into valuable data that will ultimately impact how we segment the market for legal services.” 

- Sendi Widjaja, Co-Founder & CTO, Avvo, Inc.

Data Import also now supports a new Query Time mode that allows you to join your data with historical GA data. With this mode you can:

• Enhance existing, already processed GA data with imported dimensions and metrics.
• Upload calculated values after a transaction occurs, like total customer spend, last transaction date, or a loyalty score.
• Correct any errors in data you have uploaded to GA in the past.

Query Time mode is currently in whitelist release for Premium users. For more information, contact your Premium account manager. You can learn more about Premium here.

Illustration of a new Google Analytics report with data from multiple sources 

We are also introducing a new version of Cost Data import that provides more versatile support for importing historical data. Additionally, cost data  can now be uploaded directly  through the Google Analytics web interface (previously, data import  required using the GA API). Note: Users of the original cost data import  must migrate to the new version. Details can be found in the cost data migration guide.

How to get started using Data Import

For more information, read Data Import on the Google Analytics Help Center. Also check our new developer Data Import guides that will get you up and running in no time. Some features are currently not rolled out to all users. If you’d like to join the beta for full-access, sign-up here.

Posted by Nick Mihailovski, Jieyan Fan, Richard Maher, Rick Elliott and the Google Analytics Team 

Back in August, 2012, we launched Remarketing with Google Analytics, which enabled advertisers to create sophisticated remarketing lists using Google Analytics’ 250+ dimensions and metrics. 

Today, we’re excited to announce a deeper remarketing integration between AdWords and Google Analytics. 

A single set of tags can now power both Google Analytics and Dynamic Remarketing on the Google Display Network using the Google Merchant Center. Retailers (with other verticals in beta) will gain power and precision for their remarketing along with access to detailed product level reporting through this integration.

What is Dynamic Remarketing?

[original article here]

Every customer is unique. Dynamic remarketing takes this into account, letting you create and deliver beautiful customized ads that connect visitors with their past shopping experiences on your site. If you’re a retailer with a Google Merchant Center account, you can use dynamic remarketing to construct remarketing ads on the fly with the products and messages that are predicted to perform best based on visitors’ past actions on your site.

For example: Customers who browsed the winter tires category on an advertiser’s website might see an ad that includes the exact products they’ve already viewed, in addition to related products from the company’s catalog. In the Tirendo example above, the ad also shows details of recently viewed tires, including the prices.

Early users are seeing great results

"We've been thrilled with the performance of Dynamic Remarketing with Google Analytics and Conversion Optimizer, which has so far driven a 203% increase in conversions and 100% increase in conversion rates vs. our display average. Combined with Google Analytics' powerful reporting on these same metrics, we've been able to derive actionable insights which we've put to good use throughout our other campaigns."

- Janina Rix, SEA Manager, tirendo.de

To begin using Dynamic Remarketing

1. Create one or more remarketing lists using Google Analytics
2. Update your tags to track Product ID, Cart Value, and Page Type as custom variables (or dimensions)
3. Enable the Dynamic Link in Admin > Property > Dynamic Attributes. This will let Google Analytics send attributes to your AdWords account. [more below]
4. Create a Dynamic Remarketing Display campaign in AdWords

Here’s a quick visual guide to the new interface.

Step 1: Configure account details

Step 2: Make sure the attribute names match your custom variables

Step 3: Click ‘Save’ and finish by creating your first Dynamic Remarketing campaign in AdWords

Want more help? Download a remarketing starter pack from the solutions gallery. 

Please stay tuned for more remarketing-related updates in the near future!

Happy Analyzing, Dan Stone and Lan Huang on behalf of the Google Analytics Team

The following is a guest post from GACP Michael Loban, CMO at InfoTrust.

The quality of the website analytics data we have is directly related to the tag management processes adopted by an organization. Most likely, you can remember days when the following incidents may have occurred:

1. You find that one (or several) of the pages on your site is missing Google Analytics, or some pages had Google Analytics deployed twice causing duplicate pageviews and inflating traffic.
2. Google Analytics custom variables were inconsistent or missing on some portions of the site, leading to data quality issues.
3. An unauthorized marketing tag was piggybacking off of another tag.
4. One of the tags on an international site you managed did not follow the new EU Cookie Laws related to privacy.

Adopting a Tag Management System like Google Tag Manager is a great way to go, but having a great tool to organize and deploy your tags is often not enough. You still need a system, a process, and ongoing review. Here are the steps for creating a tag management policy for your company:

1. Know where you are – what tags are currently firing, where and how? Whether you have a small site with a few hundred pages or an international publication with thousands of pages, it is important to assess your current tag deployment. 

Can you say, with 100% confidence, that your analytics tag are located on every page?  Are you sure the cookies set by your analytics tag/tool are accurate and not over-writing each other?

Regardless of whether you are confident or not, I suggest using a tool like TagInspector.com (Tag Inspector is an InfoTrust product). It will help you locate:

1. All the tags on your site, split up by specific pages’ tags, and even pages they are missing from.
2. Cookies set by various tags and what pages they are set on.
3. How the tag is deployed – through a tag management system or directly from a page source.
4. Instances of tag piggybacking – one tag being loaded by another tag.

Here is a screenshot from an example scan. It shows how tags load (commonly referred to as tag hierarchy). We have removed the website URL, but as you can see there are instances when Google Analytics is being loaded by the TMS, and instances where Google Analytics is being loaded directly from the source of the page. 

2. Document all approved tags. The average enterprise website might have 25-50 marketing tags. Not all of them have to be present across all pages. However, even if you are considering moving to a Tag Management System, or already are using one, it is not a bad idea to have the following documented and categorized:

1. Tag name and functionality
2. Pages or the category pages the tag needs to be on
3. Information collected through the tag about visitors (cookies set)
4. Firing rules

Check out Tagopedia – a wiki of tags to learn more about the many different types of tags.

3. Consider the implementation of a Tag Management System. There is a reason this is step three, and not step one or two. A lot of companies jump to this step first, thinking that a new technology will miraculously make all tagging issues disappear. The first step in moving to a TMS is knowing what tags you need to keep, and where they are or how they are loaded on your site so you can remove them from the source after the tag management system is deployed.

When considering the implementation of a tag management system, think about your team. Every website of a TMS vendor says you will no longer need your IT team to make changes to the tags thus simplifying and expediting the process. I have met plenty of marketers who do not want anything to do with a TMS. Even though you will free up your IT resources, you will still need a person or team with the technical training to manage your tags. 

Naturally, your first step in evaluating Tag Management vendors should be outlining what features you really need. Google Tag Manager is free, and is one of the few TMS systems that works for both mobile websites and native mobile applications. 

NOTE:  If you do decide to migrate to a TMS or if you have already done so, you still should scan all the pages across your site to ensure that your tags fire correctly, such as, once per page for analytics tags – and only from your TMS. You certainly want to avoid having a tag in the source of your page and inside a TMS – this will inflate your data and cause data quality issues.

4. Run ongoing site audits to ensure correct tags are deployed across correct pages. Ideally, this will only serve as the insurance. However, ongoing site scans or audits can help you avoid the moments when you realize you did not capture AdWords conversions because your GA or AdWords conversion tag was removed from the conversion page. Keep in mind certain tags might only fire when a user looks at your website on a mobile device, and your scan might need to simulate different user agents.  Doing this manually for all the sites you manage, or across one very large site, can be quite challenging. Again, TagInspector.com can help speed up this process and dramatically reduce the effort required. Here is an example screenshot of the scanning options:

5. Think ahead – will you be able to innovate? Complete lock down is in nobody’s best interests. What happens if there is a new platform for A/B testing that you would like to try? How long will it take you to get the tag approved, implemented on your site, verify its performance, and launch a campaign? Keep innovation in mind and make it relatively easy for marketers in your company to adopt new technologies.

One way to go about this is having an application that needs to be completed and approved prior to implementing a new tag. This will help you ensure only tags that meet company standards are implemented on your site. 

At the end of the day, tag deployment and data collection will only get more complex. If you do not have any process for managing your tags, it is time to start. If you have some kind of process, perhaps it is time for optimization. Get all the stakeholders in the room, and decide who will be your tag management team, and what the first step will be to ensure tag accuracy. You can’t do analysis if the data isn’t accurate. And your data won’t be accurate if your marketing tags aren’t implemented correctly. 

If you would like to learn more about implementing a tag management policy, we would like to invite you to attend a free webinar on March 26^th at 1:00PM EST where we will discus items outlined in this post, and a lot more. 

Posted by Michael Loban, CMO at Google Analytics Certified Partner InfoTrust

The following is a guest post from Google Analytics Certified Partner Feras Alhlou, Partner & Principal Consultant at E-Nor Inc.

Marketers and sales professionals want to know who’s visiting their site, what content the target audience is consuming and what converts site visitors to paying customers. 

In a B2B environment -- where long sales cycles and multiple stakeholders affect sales decision -- “knowing who’s coming to your site” takes on another dimension. 

Say you’re in charge of marketing an eLearning system, and your target market includes telecom, hi-tech/software companies and universities. Your sales cycle could span several months, and there are multiple personas/stakeholders who will evaluate your company and your product. 

Some key personas include:

• Trainers, professors and teachers evaluating user experience and ease of uploading curricula and content 
• Management/administrators evaluating your company, pricing, client testimonials, case studies, etc.
• IT assessing technical aspects of products, maintainability, your technical support processes, etc. 

As a marketer, your job is to ensure your site addresses the needs of each stakeholder, while realizing that the interests/questions each group of stakeholders are likely to be different. It’s critical that the message and content (that you invested so much in creating) “sticks” with the unique personas in each market segment. 

Easier said than done; measuring and optimizing all the above isn’t for the faint of heart.

But don’t fret. Integrating Google Analytics with Account-Based Marketing and Firmographic data has come to the rescue. 

B2B Measurement Framework

Let’s walk through a typical scenario and highlight key performance indicators (KPIs). The measurement framework our eLearning marketing manager has in mind includes (and yes, they follow GA’s ABC!):

Acquisition

1. What percent of my traffic comes from industries I target
1. Telecom
2. Hi-tech/software companies
3. .edu’s
2. Percentage increase or decrease in traffic from industries I’m not targeting 
3. Traffic volume and frequency from organizations our sales team targets offline

Behavior

1. Landing page stickiness by industry and organization
2. What content is very popular
3. What content is most shared
4. All the above segmented by the three targeted industries

Conversion

1. Number of whitepaper downloads by industry and company
2. Number of demo requests
3. Sales follow-up call requests 
4. All the above segmented by the three targeted industries

If your site visitors aren’t providing you with company and industry data, it’s not possible to report on this data in Google Analytics. Hello Insightera, a marketing personalization platform, enables your to enrich customer’s onsite journey with firmographic data in a seamless integrated fashion (note, another product in the Google Analytics app gallery offering similar functionality is Demandbase).

Rich Firmographic Data in Google Analytics

Insightera’s firmographic data is organized by 1) deriving information from site visitors by identifying their ISP 2) determining that organization’s information, including location, industry (and soon company size and company revenue will also be available). 

With easy-to-navigate firmographic readily available, analytics data takes on a new dimension; advertising dollars can be better targeted, and you have the ability to customize a visitor’s experience in several new ways.

Here’s a few examples of the rich and super cool data you have access to with Insightera, nicely integrated in the Google Analytics Reports (in Custom Variables):

1- Traffic Distribution by Industry  

Within the GA interface you have a nice presentation your traffic by industry. Telecom seems to be strong (24.1% of traffic) in the report below, while Education could use some love from your marketing team. 

2- Engagement By Industry

You can also report on your KPIs by industry (e.g. see how “Education” is the number 2 industry in the report below)

3- Traffic & Engagement By Organization

This report below shows the platform’s ability to take data segmentation a step further, and highlights specific organizations within the industry visiting the website (e.g. Yale University)

With firmographic data integrated into Google Analytics, it is possible to optimize paid campaigns such as Google AdWords, LinkedIn, banner ads, etc., and pinpoint how many companies from a specified list visited your site, which industries and what size companies visited the site. It provides the opportunity to then target paid campaigns to those visitors and channels, or increase efforts to reach untapped segments of a targeted audience. 

Technical Considerations 

Not a whole lot of considerations. Insightera makes it easy to plug and play. In your ‘Admin’ interface, select your Custom Variables slots for the ‘Industry’ and ‘Organization’ -- and let the rich data flow. Double check that the selected custom variable slots are empty and that you’re not already using them for something else in your Google Analytics implementation. 

Content Personalization

Equipped with this new data, you can automate and personalize remarketing efforts and create targeted ads based on any given criteria. In the example above, the education-specific whitepaper can be presented to your higher-ed visitors, while hi-tech/software related content can be presented to your hi-tech/software visitors. 

Insightera’s recommendation engine filters visitors by location and industry, content preferences and CRM data and digital behavior patterns. This process then predicts which content or channel works best for each visitor.

Increase the Value of Universal Analytics with more User Centricity 

If you’re an early adopter of Universal Analytics or planning to migrate to Universal Analytics, Insightera will soon have you covered. The same method described above can be applied and firmographic data can be integrated into Custom Dimensions. 

With some additional customization, and if you are (and you should be) user-centric, you can take up your implementation a notch up and report on visitors, not just visits, across web, mobile and other devices. Examples include where you have premium/gated content behind registration, user logins or when users self-identify. In these examples, a user-id is associated with each authenticated visitor and stored in a Custom Dimension. Measuring user behavior across multiple sessions and across multiple devices will then be available and you’ll be able to stitch data from different data sources including Insightera as well CRM systems such integrating GA with SalesForce.

Conclusion

As advertising and remarketing efforts reach new levels of focus, site owners have the most relevant information to meet their needs thanks to account-based marketing. Combining the power of Google Analytics with the new scope of firmographic data allows a new level of Performance Analytics. This set of tools offers deeper analytic insights into who your potential customers are, what they do, where they come from and what they consume.

Posted by Feras Alhlou, Principal Consultant, E-Nor, a Google Analytics Authorized Premium Reseller

The following is a guest post by Benjamin Mangold, Director of Digital & Analytics at Loves Data, a Google Analytics Certified Partner.

Universal Analytics includes new JavaScript tracking code for websites and new mobile SDKs. But Universal Analytics is a lot more than that - it also gives us the Measurement Protocol, which allows us to send data to Google Analytics without the need to use the tracking code or SDKs.

Earlier this year, the team at Loves Data used Universal Analytics and the Measurement Protocol to measure their caffeine consumption and tie it to the team’s productivity. Our next challenge: measuring our team’s movement into Google Analytics. With the help of an Xbox Kinect, movement recognition software, and of course the Measurement Protocol, we started getting creative!

Business Applications and Analysis Opportunities

So measuring movement is fun and although we can measure total and unique dance moves you might be wondering about the business applications. This is where the power of measuring offline interactions can really start to be seen. The Measurement Protocol enables business applications such as:

• Measuring in-store purchases and tying purchases to your online data
• Understanding behaviour across any connected device, including gaming consoles
• Comparing offline billboard impressions to online display ad impressions
• Getting insights into your audience’s online to offline journey

Once you have tied your online and offline data together you can begin to analyze the full impact of your different touch points. For example, if you are collecting contact details online, you can use Google Analytics to then understand who actually converts offline, whether this conversion is attending an information session or making a purchase at a cash register. The analysis possibilities made available by the Measurement Protocol are truly amazing.

The following is a guest post contributed by Dan Wilkerson, marketing manager at LunaMetrics, a Google Analytics Certified Partner & Digital Marketing Consultancy.

A core issue with measuring social media is that due to the way that traffic migrates around the web, there are lots of situations where we lose referrer information and those visits end up being labeled as 'Direct' inside of our analytics.

This can happen for a variety of reasons, but the most common situations where this kind of erroneous attribution occurs are:

• When a user clicks an untagged link inside an email
• When a user visits from a mobile application
• When a user clicks a link shared to them via an instant message

If a visitor has come to your site previously, Google Analytics will simply apply the same referral information it had for their previous visit, which it retrieves from the UTMZ cookie it previously saved on the visitor's browser. But, if there are no cookies, Analytics has no information, and buckets the visitor into Direct.

Obviously, this is problematic; 'Direct' is supposed to represent visitors who bookmark or directly type in our URL. These users are accessing our site through a shared link, and should be counted as referrals. Thankfully, we have some tools at our disposal to combat some of these scenarios, most notably campaign parameters. But campaign parameters only help with links that you share; what about when a visitor comes to your site and shares the link themselves?

These visits can cause serious problems when it comes time to analyze your data. For example, we offer Google Analytics & AdWords training. Most of our attendees are sponsored by their employers. This means they visit our site, scope out our training, and then email a link to a procurement officer, who clicks through and makes the purchase. Since the procurement officer comes through on the emailed link and has never visited our site, the conversion gets bucketed into 'Direct / None' and we lose all of the visit data for the employee who was interested in the first place. This can compound into a sort of feedback loop - the only data we see would be for individuals who buy their own tickets, meaning we might optimize our marketing for smaller businesses that send us less attendees. In other words, we'd be interpreting data from the wrong customers. Imagine how this kind of feedback loop might impact a B2B trying to generate enterprise-level leads - since they'd only see information on the small fry, they could wind up driving more of the wrong kind of lead to their sales team, and less of the right kind.

For a long time, this has been sort of the status quo. Now, with new features available in Universal Analytics, we have some tools we can employ to combat this problem. In this post, I want to share with you a solution that I've developed to reduce the amount of Direct traffic. We're calling it DirectMonster, and we're really excited to make it open source and available to the Google Analytics community.

What is DirectMonster?

DirectMonster is a JavaScript plug-in for Google Analytics that appends a visitor's referral information as ciphered campaign parameters as an anchor of the current URL. The result looks something like this:

When the visitor copies and shares the URL from the toolbar, they copy that stored referral information along with it. When someone without referral information lands on the site through a link with those encoded parameters, the script decodes that information as campaign parameters to pass along to Google Analytics, waits until Analytics writes a fresh UTMZ cookie, and then ciphers, encodes, and re-appends the visitors current referral information. It also appends '-slb' to the utm_content parameter. That way, those visits can be segmented from 'canonical' referrals for later analysis, if necessary. The visitor who would have had no referral information now is credited as being referred from the same source as the visitor who shared the link with them. This means that visits that normally would have been erroneously segmented as 'Direct / None' will now more accurately reflect the channel that deserves credit for the visit. 

At first, this might seem wrong - shouldn't we just let Analytics do its job and not interfere? But, the fact is that those visits aren't really Direct, at least not in its truest interpretation, and having 'assisted referrer' channel information gives you actionable insight. Plus, by weeding out those non-Direct scenarios, your Direct / None numbers will start to more accurately represent visitors who come to your site directly, which can be very important for other measurement and attribution. It's actually better all the way around. After all, if a Facebook share is what ultimately drove that visitor to your site, isn't having that information more valuable than having nothing at all? This way, you'll have last-click attribution for conversions that otherwise would have simply been bucketed as Direct. Of course, you won't have the visit history of the assisting referrer, but... well, more on that soon.

We've been fine-tuning this on our site for the past few months, and we've been able to greatly enhance our conversion attribution accuracy. In our video case study, I mentioned that we enhanced attribution by 47.5%; since that time, we've seen the accuracy of our data continue to climb; whereas before, we were seeing 'Direct / None' account for 45.5% of our conversions, it now accounts for just 20.6% - a decrease of 54.7%. Better yet, look at what it's done to all of our traffic:

We've gone from having about 20-25% of our traffic come in 'Direct / None' to just under 15%, and I anticipate that number will continue to fall.

DirectMonster and Universal Analytics

One of the coolest features that Universal Analytics has given us is Custom Dimensions. If you're not familiar with them, take a minute and read the Google Developer Resources page about what they are and how they work. Although initially designed for the asynchronous code, Universal Analytics has allowed us to put DirectMonster on steriods. 

In our Universal implementation, we store the visitors CID as a visit-level custom dimension, and we add their CID to the hashed parameters we're already storing in the anchor of their URL. 

When a visitor comes through on a link with a CID that differs from their own, we capture the stored CID as the Assisted Referrer. Then, we can open up our Custom Reports later on and view what visitors were referred to our site by whom, and what they did when they got there.

What does this mean? If a celebrity tweets a link to your product, you can discover exactly how many visitors they referred, and how much revenue those visitors generated. 

By cross-referencing the Assisted CID for single-visit 'Direct / None' purchases, you can discover the true visit history of a conversion.

Since it takes advantage of advanced Universal Analytics functionality, DirectMonster 2.0 requires some advanced implementation as well. Unlike its cousin, you'll need to adjust your Analytics tracking code to include a few functions, and you'll need to configure the Custom Dimensions you'll be storing a visitors CID and assisted referrers CID inside of. For a full reference on how to get either version of DirectMonster and configure it for your site, check out our blog post covering the topic in detail here or visit our GitHub page and get DirectMonster for yourself. 

I hope that you're as excited as I am about this development and all of the things Universal Analytics is enabling us to do. Think of a use case I didn't mention? Share it with me in the comments!

Posted by Dan Wilkerson, marketing manager at LunaMetrics

I recently wrote a post on the Google Analytics + page about monitoring error pages on websites. The post was well received and generated a healthy discussion on Google+, so I decided to write a more detailed article on the subject here.

First of all, what exactly is an error or 404 page? According to Wikipedia “The 404 or Not Found error message is a HTTP standard response code indicating that the client was able to communicate with the server, but the server could not find what was requested.” Or, in more general terms, the 404 is the error you get when the page you are looking for does not exist, usually because the link you clicked was broken.

Another important question is: why should I care? Often times the 404 is forgotten and no one cares to prioritize its optimization. I believe the answer to prioritization lies on section 2 of this post: by monitoring the percentage of users that arrive at this page you will be in a better position to know if (and how quickly) you should optimize your 404 page. In any case, even if the number of people viewing this page is low, you should at least have a page in the lines of your brand and try to add the elements described in section 1 below; after all, you don’t want to disappoint your customers!

In this post I propose a few techniques to help improve error pages, engage visitors and improve the website experience. The questions I will try to answer are the following:

1. How to build your 404 page?
2. How to monitor your 404 page traffic efficiently?
3. How to analyze & optimize 404 page success?

1. Error Pages Best Practices

Before we dive into Google Analytics, let’s take a look into some ways to create a great 404 page from the beginning. Following are some good usability insights proposed in a book called Defensive Design for the Web. The authors advise us to offer customized "Page Not Found" error pages; and they provide an interesting insight into how to create error pages:

Instead of merely saying a page is not found, your site needs to explain why a page can't be located and offer suggestions for getting to the right screen. Your site should lend a hand, not kick people when they are down. Smart things to include on your 404 page:

1. Your company's name and logo
2. An explanation of why the visitor is seeing this page
3. A list of common mistakes that may explain the problem
4. Links back to the homepage and/or other pages that might be relevant
5. A search engine that customers can use to find the right information
6. An email link so that visitors can report problems, missing pages, and so on

2. Monitoring Error Page Traffic

Suppose a prominent blog links to your site and the link is broken, this will cause a negative experience to users (which will not find what they expected) and to search engines (which will not crawl the right content). How long will it take until you notice it? How often do you check the traffic to your 404 page? Chances are you don’t do it every day, but you should! Or at least you should have someone look at it: why not let Google Analytics do it for you? 

Create an Alert on Google Analytics

In the screenshot below you will see how to set an alert on Google Analytics that will let you know each time your 404 pageviews increases above a certain threshold. This will enable you to do the work once and be alerted every time there is a problem. 

The alert below is based on the increase in error pageviews, but if you decide to create a goal (as suggested below) you could create the alert based on the goal too. Note that you can opt in to receive an email or a text message when the condition is met (404 pageviews increases more than 15% compare to previous day). Also note that I decided to define the 404 page based on the title of the page, very often there is no indication of an 404 page on the URL (read more about this below). 

To learn how to set a Custom Alert check this help center article.

Measure your 404 Page as a Goal

Setting the 404 page as a goal on Google Analytics will surface important information that can be achieved only through goals, e.g. the last three steps before getting to this page. Below is a screenshot on how to do it, but note that you would need to have an identifier on your URL (or trigger an event) in order to set your 404 as a Goal.

Add Your 404 Content Report to Your Dashboard

Every report on Google Analytics can be added to the dashboard. By adding the 404 page to your dashboard you will be able to constantly monitor the trend of visits to your 404 page. Learn more about customizing dashboards.

3. Analyzing & Optimizing Error Pages

Monitoring your 404 pages is important, but useless if you don't take action using this information. Taking action means doing all you can to decrease the number of people getting missing pages. Below I provide a few tips on how to find and fix both internal and external broken links.

Check Your Navigation Summary Report

This will help you understanding where did visitors come from from inside your site, i.e. it will tell you which pages contain internal broken links. You will also be able to understand what is the percentage of visitors that arrive to the 404 page from internal and external sources; the internal sources will be listed on this report. See navigation summary screenshot below:

Check 404 Page URLs

Learning which URLs are producing the errors is a great way to get rid of them. If you learn, for example, that 100 visitors a day get an error when they visit the page “/aboutS” you can infer that there is a broken link leading to it; sometimes it might not be possible to find the source of the error to fix the link, but you can add a redirect from that page to “/about”, which looks to be the right page. 

In order to do that you will need to find the report below, but please keep in mind that some assumptions were made to arrive at it. Most CMS (Wordpress, Drupal, and others) will return an error for non-existing pages on the actual content section, but they will keep the original URL; however, they will have a page title with the word 404 in it. So check your site to know if that is the case before you try the report below.

Once you find this report, click on the first entry and you will get a list of all the URLs that triggered an error page. Good luck with the redirects!

Measure Internal Searches From this Page

If you do not have a search box on your 404 page, you should seriously consider adding one. Through searches performed in this page you will be able to understand what people were expecting to find there and you will get insights on which links you should add to the page. If you don’t have Internal Site Search enabled on Google Analytics check this help center article.

Below are the metrics you will be able to analyze if you use this feature:

• Total Unique Searches: the number of times people started a search from the 404 page. Duplicate searches within a single visit are excluded.
• Results Pageviews/Search: the average number of times visitors viewed a search results page after performing a search.
• % Search Exits: the percentage of searches that resulted in an immediate exit from your site.
• % Search Refinements: the percentage of searches that resulted in another search (i.e. a new search using a different term).
• Time after Search: The average amount of time visitors spend on your site after performing a search.
• Search Depth: The average number of pages visitors viewed after performing a search.

Closing Thoughts

As we mentioned above, errors happen, and we must be prepared for them. We must give a hand to our visitors when they are most frustrated and help them feel comfortable again. The level of online patience and understanding is decreasing and users have a world of choices just one click away, so website owners cannot let one small error get on their way.

Posted by Daniel Waisberg, Analytics Advocate

As we’ve discussed in many previous posts, the customer journey is evolving — most consumers will interact with many different marketing channels before a sale or conversion. And marketers are recognizing this shift in consumer behavior. Instead of “last click” measurement, a strategy that only gives credit to the final interaction, they’re turning to full credit measurement. To help you make sense of the full customer journey, we’ve been focused on bringing you the very best full credit measurement tools in Google Analytics.

Nearly two years ago, we announced our first Google Analytics attribution product, Multi-Channel Funnels. With its ability to measure customers’ different paths to conversion, it quickly became one of our most popular reports for advertisers and publishers alike. We’ve seen great results from our users, including online travel agency On the Beach, who used data from the Multi-Channel Funnels reports and AdWords Search Funnels to explore and adjust their strategy for generic keywords. These attribution adjustments helped On the Beach to drive a 25% uplift in ROI — see the full case study here.

Beyond Multi-Channel Funnels, we also wanted to provide our users with an advanced platform for testing entirely new, more robust attribution strategies, including the ability to test alternative models or understand how metrics, such as site engagement, could impact their existing investments. So last year we released our Attribution Modeling feature — the Model Comparison Tool.

After several months of testing on a public whitelist, we're now in the process of rolling out the Attribution Model Comparison Tool to make it generally available to Google Analytics users without whitelist.  To reflect the importance of attribution, we also created a new “Attribution” section under the “Conversions” reports, so the tool will be found there.

Be sure to check out a previously recorded webinar with Product Manager Bill Kee for a complete walkthrough of the Attribution Model Comparison Tool, or view our multi-part attribution webinar series covering our entire selection of full-credit measurement tools.

Posted by Sara Jablon Moked, Product Marketing Manager, Google Analytics

Every customer journey is different — a customer may see your display or video ads, receive an email, and then click through to your site from a search ad or organic search listing. Often, viewing display ads can attract your clients’ interest in your product and brand even if no click occurs. Traditionally, measurement technology separated out impressions or “view throughs” from clicks, but this separation missed out on valuable data on the impact of display advertising.

Thanks to our integration with the Google Display Network (GDN), Google Analytics can now break down the separation between clicks and impressions and give a more complete view of the customer journey. When a user views display ads on the GDN, or video ads on YouTube, and later visits your website and converts, these interactions with your brand can now be captured in Google Analytics Multi-Channel Funnels reporting.

GDN Impression Reporting is now available through limited whitelist. Reach out to your Google Analytics Premium point of contact to request feature activation. Please note that we cannot guarantee access, but we will do our best to provide this feature to as many users as possible. Please also note that this data will only surface in the Multi-channel Funnels reports in Google Analytics. For more information on how to enable the feature in GA please see our help center article.

Read on below for more tips on how to make the most of this new feature.

How does Display fit on the conversion path?
By enabling GDN Impression Reporting in Google Analytics, you can learn how your display impressions assist your conversions.


In the Multi-Channel Funnels Overview Report you will see two additional conversion metrics. Impression Assisted Conversions shows how many of your conversion paths were touched by a display impression. Rich Media Assisted Conversions shows how many of your conversions had a rich media interaction on the path to conversion. Rich media interactions are user interaction with YouTube or rich media ad formats, such as ad expansion, video control (such as play, pause, and resume), or switching a video ad to full screen.

With the new Interaction Type selector you can now immediately filter your reports based how your users interacted with your marketing.

• Select Impression to see conversion paths from customers who saw your GDN display ads but did not click on them.
• Add Direct to the mix, to see who saw an ad and then visited your site directly to convert on a relevant transaction or Goal.
• If you want to focus on Rich Media interactions, you can select this interaction type to see how your users convert after interacting with your rich media and YouTube ads.

How do I quantify the impact of display on the conversion path?
In the Multi-Channel Funnels Top Conversion Path report you can see two new path elements, which indicate the presence of a display interaction. The “eye” symbol indicates a pure display impression from a non-interactive display image. This means a user has been exposed to your display ad on the journey to conversion, without clicking on it. The “movie” symbol indicates a user has interacted with one of your Rich Media ads, such as a YouTube video ad.

Now you can see how many conversion paths, and how much associated value, has been driven through paths which benefited from a display impression or rich media interactions. To better quantify your brand targeted display efforts, consider breaking out these campaigns using custom channel grouping.



Assigning partial credit to valuable display interaction touchpoints
You can use the custom model builder from the Attribution Modeling tool to assign partial credit to these display events. Consider giving these events on the user’s conversion path more credit, and compare this against your baseline model.

We also added a new set of dimensions to help you define valuable custom segments for your analysis. Want to see how many users are watching your TrueView video ads fully? Just create a custom segment using one of our new dimensions, TrueView. The full list of new dimensions is:

• Above the Fold: This dimension uses the Google Above the Fold measurement solution. The value is “Yes” if the ad was in the visible area of the screen when the page was loaded.
• Video Played Percent: The value can be “>=25%”, “>=50%”, “>=75%”, and “100%”, allowing you to see how much of a video ad was watched.
• TrueView: If a user has watched more than 30 seconds of an ad, or watched the ad completely, this will have a value of “Yes.” This is a payable event.

Enabling GDN Impression Reporting in Google Analytics
Once we have whitelisted your account, please ensure you have successfully linked your AdWords account to your Google Analytics account. Linking accounts takes just a few moments. Under ‘Data Sources’ > ‘AdWords’ you can then see an entry for each linked AdWords account. In the row there is a toggle switch named ‘GDN Impression Reports’, which turns the display impression data from the Google Display Network On and Off. Data is recorded from the time the switch is turned On.


We hope these new tools will help you understand the full impact of your display campaigns through Multi-Channel Funnels and Attribution. Please reach out to your Google Analytics Premium point of contact to request feature activation.

Posted by Stefan F. Schnabl, Product Manager, Google Analytics