A version of the following post originally appeared on the Inside AdWords Blog.
What was your business’ New Year’s resolution, and how do you plan to keep it? At Google, ours is to help make the web work for you. Our new series of Learn with Google webinars will teach you how to use digital to build brand awareness and give you the tools you need to drive sales. By tapping into technology that works together across your business needs, you can resolve to win moments that matter in 2013.
Check out our upcoming live webinars:
Build Awareness
02/12 [Multiscreen] Brand Building in a Multiscreen World
02/20 [YouTube] How to Build your Business with YouTube Video Ads
03/05 [Social] How to Use Google+ and Make Social Work for You
03/12 [Mobile] Understanding Mobile Ads Across Marketing Objectives
03/27 [Wildfire by Google] The Call for Converged Media
Drive Sales
02/07 [Search] Your Shelf Space on Google: Get Started with Google Shopping
02/26 [YouTube] From Awareness to Sales: Making the Most of Video Remarketing
02/27 [Search] What's New and Next in AdWords
03/06 [Display] Biggest Loser: Digital Ad Spend Edition
03/13 [Mobile] The Full Value of Mobile
03/20 [Display] Getting Started with Dynamic Remarketing
Visit our webinar site to register for any of the sessions and to access past webinars on-demand. You can also stay up-to-date on the schedule by adding our Learn with Google Webinar calendar to your own Google calendar to automatically see upcoming webinars.
During our last series of webinars, attendees had the chance to win a Nexus 7. Our lucky winner was Donella Cohen, who is happily enjoying her new tablet. Check out our upcoming webinars for another chance to win!
Learn with Google is a program to help businesses succeed through winning moments that matter, enabling better decisions and constantly innovating. We hope that you’ll use these best practices and how-to’s to maximize the impact of digital and grow your business. We’re looking forward to seeing you at an upcoming session!
We’ve heard you loud and clear that getting started on Google Analytics can be challenging. It’s such a robust tool with a variety of reports, filters, and customizations that for a new user it can be overwhelming to figure out where to look first for the data and insights that will enable you to make better decisions. For more advanced users it can be time consuming to build out different variations of reports and dashboards to get the clearest snapshot of your performance. That is why we’ve created the Google Analytics Solution Gallery.
The Google Analytics Solution Gallery hosts the top Dashboards, Advanced Segments and Custom Reports which you can quickly and easily import into your own account to see how your website is performing on key metrics. It helps you to filter through the noise to see the metrics that matter for your type of business: Ecommerce, Brand, Content Publishers. If you're not familiar with Dashboards, Advanced Segments and Custom Reports, check out these links to our help center for detailed descriptions on how they work and the insights they can help provide.
Solution examples Here are a few examples of the solutions that you can download into your account to see how the analysis works with your data.
Social sharing report - Content is king, but only if you know what it's up to. Learn what content from your website visitors are sharing and how they're sharing it.
Publisher dashboard - Bloggers can use this dashboard to see where readers come from and what they do on your site.
Engaged traffic advanced segment - Measure traffic from high-value visitors who view at least three pages AND spend more than three minutes on your site. Why do these people love your site? Find out!
How do I add these to my account? We’ve designed it so it’s easy to get started. Simply go to the Google Analytics Solution Gallery, pick from the drop drown menu the solutions that will be most helpful for your business. Select from Publisher, Ecommerce, Social, Mobile, Brand, etc.. . Hit “Download” for the solution you want to see in your account. If you are not already logged into Google Analytics we’ll ask you to sign in. Then you’ll be asked if you want to accept this solution into your account and what Web Profile do you want to apply it to. After you select that it will be in your account and your own data will populate the report.
We’re planning on expanding on this list of top solutions throughout the year so be sure to check back and see what we’ve added. A big thank you to Justin Cutroni & Avinash Kaushik for supplying many of the solutions currently available.
This article describes the statistical engine behind Google Analytics Content Experiments. Google Analytics uses a multi-armed bandit approach to managing online experiments. A multi-armed bandit is a type of experiment where:
The goal is to find the best or most profitable action
The randomization distribution can be updated as the experiment progresses
The name "multi-armed bandit" describes a hypothetical experiment where you face several slot machines ("one-armed bandits") with potentially different expected payouts. You want to find the slot machine with the best payout rate, but you also want to maximize your winnings. The fundamental tension is between "exploiting" arms that have performed well in the past and "exploring" new or seemingly inferior arms in case they might perform even better. There are highly developed mathematical models for managing the bandit problem, which we use in Google Analytics content experiments.
This document starts with some general background on the use of multi-armed bandits in Analytics. Then it presents two examples of simulated experiments run using our multi-armed bandit algorithm. It then address some frequently asked questions, and concludes with an appendix describing technical computational and theoretical details.
Background
How bandits work
Twice per day, we take a fresh look at your experiment to see how each of the variations has performed, and we adjust the fraction of traffic that each variation will receive going forward. A variation that appears to be doing well gets more traffic, and a variation that is clearly underperforming gets less. The adjustments we make are based on a statistical formula (see the appendix if you want details) that considers sample size and performance metrics together, so we can be confident that we’re adjusting for real performance differences and not just random chance. As the experiment progresses, we learn more and more about the relative payoffs, and so do a better job in choosing good variations.
Benefits
Experiments based on multi-armed bandits are typically much more efficient than "classical" A-B experiments based on statistical-hypothesis testing. They’re just as statistically valid, and in many circumstances they can produce answers far more quickly. They’re more efficient because they move traffic towards winning variations gradually, instead of forcing you to wait for a "final answer" at the end of an experiment. They’re faster because samples that would have gone to obviously inferior variations can be assigned to potential winners. The extra data collected on the high-performing variations can help separate the "good" arms from the "best" ones more quickly. Basically, bandits make experiments more efficient, so you can try more of them. You can also allocate a larger fraction of your traffic to your experiments, because traffic will be automatically steered to better performing pages.
Examples
A simple A/B test
Suppose you’ve got a conversion rate of 4% on your site. You experiment with a new version of the site that actually generates conversions 5% of the time. You don’t know the true conversion rates of course, which is why you’re experimenting, but let’s suppose you’d like your experiment to be able to detect a 5% conversion rate as statistically significant with 95% probability. A standard power calculation1 tells you that you need 22,330 observations (11,165 in each arm) to have a 95% chance of detecting a .04 to .05 shift in conversion rates. Suppose you get 100 visits per day to the experiment, so the experiment will take 223 days to complete. In a standard experiment you wait 223 days, run the hypothesis test, and get your answer.
Now let’s manage the 100 visits each day through the multi-armed bandit. On the first day about 50 visits are assigned to each arm, and we look at the results. We use Bayes' theorem to compute the probability that the variation is better than the original2. One minus this number is the probability that the original is better. Let’s suppose the original got really lucky on the first day, and it appears to have a 70% chance of being superior. Then we assign it 70% of the traffic on the second day, and the variation gets 30%. At the end of the second day we accumulate all the traffic we’ve seen so far (over both days), and recompute the probability that each arm is best. That gives us the serving weights for day 3. We repeat this process until a set of stopping rules has been satisfied (we’ll say more about stopping rules below).
Figure 1 shows a simulation of what can happen with this setup. In it, you can see the serving weights for the original (the black line) and the variation (the red dotted line), essentially alternating back and forth until the variation eventually crosses the line of 95% confidence. (The two percentages must add to 100%, so when one goes up the other goes down). The experiment finished in 66 days, so it saved you 157 days of testing.
Figure 1. A simulation of the optimal arm probabilities for a simple two-armed experiment. These weights give the fraction of the traffic allocated to each arm on each day. Of course this is just one example. We re-ran the simulation 500 times to see how well the bandit fares in repeated sampling. The distribution of results is shown in Figure 2. On average the test ended 175 days sooner than the classical test based on the power calculation. The average savings was 97.5 conversions.
Figure 2. The distributions of the amount of time saved and the number of conversions saved vs. a classical experiment planned by a power calculation. Assumes an original with 4% CvR and a variation with 5% CvR. But what about statistical validity? If we’re using less data, doesn’t that mean we’re increasing the error rate? Not really. Out of the 500 experiments shown above, the bandit found the correct arm in 482 of them. That’s 96.4%, which is about the same error rate as the classical test. There were a few experiments where the bandit actually took longer than the power analysis suggested, but only in about 1% of the cases (5 out of 500).
We also ran the opposite experiment, where the original had a 5% success rate and the the variation had 4%. The results were essentially symmetric. Again the bandit found the correct arm 482 times out of 500. The average time saved relative to the classical experiment was 171.8 days, and the average number of conversions saved was 98.7.
Stopping the experiment
By default, we force the bandit to run for at least two weeks. After that, we keep track of two metrics. The first is the probability that each variation beats the original. If we’re 95% sure that a variation beats the original then Google Analytics declares that a winner has been found. Both the two-week minimum duration and the 95% confidence level can be adjusted by the user.
The second metric that we monitor is is the "potential value remaining in the experiment", which is particularly useful when there are multiple arms. At any point in the experiment there is a "champion" arm believed to be the best. If the experiment ended "now", the champion is the arm you would choose. The "value remaining" in an experiment is the amount of increased conversion rate you could get by switching away from the champion. The whole point of experimenting is to search for this value. If you’re 100% sure that the champion is the best arm, then there is no value remaining in the experiment, and thus no point in experimenting. But if you’re only 70% sure that an arm is optimal, then there is a 30% chance that another arm is better, and we can use Bayes’ rule to work out the distribution of how much better it is. (See the appendix for computational details).
Google Analytics ends the experiment when there’s at least a 95% probability that the value remaining in the experiment is less than 1% of the champion’s conversion rate. That’s a 1% improvement, not a one percentage point improvement. So if the best arm has a conversion rate of 4%, then we end the experiment if the value remaining in the experiment is less than .04 percentage points of CvR.
Ending an experiment based on the potential value remaining is nice because it handles ties well. For example, in an experiment with many arms, it can happen that two or more arms perform about the same, so it does not matter which is chosen. You wouldn’t want to run the experiment until you found the optimal arm (because there are two optimal arms). You just want to run the experiment until you’re sure that switching arms won’t help you very much.
More complex experiments
The multi-armed bandit’s edge over classical experiments increases as the experiments get more complicated. You probably have more than one idea for how to improve your web page, so you probably have more than one variation that you’d like to test. Let’s assume you have 5 variations plus the original. You’re going to do a calculation where you compare the original to the largest variation, so we need to do some sort of adjustment to account for multiple comparisons. The Bonferroni correction is an easy (if somewhat conservative) adjustment, which can be implemented by dividing the significance level of the hypothesis test by the number of arms. Thus we do the standard power calculation with a significance level of .05 / (6 - 1), and find that we need 15,307 observations in each arm of the experiment. With 6 arms that’s a total of 91,842 observations. At 100 visits per day the experiment would have to run for 919 days (over two and a half years). In real life it usually wouldn’t make sense to run an experiment for that long, but we can still do the thought experiment as a simulation.
Now let’s run the 6-arm experiment through the bandit simulator. Again, we will assume an original arm with a 4% conversion rate, and an optimal arm with a 5% conversion rate. The other 4 arms include one suboptimal arm that beats the original with conversion rate of 4.5%, and three inferior arms with rates of 3%, 2%, and 3.5%. Figure 3 shows the distribution of results. The average experiment duration is 88 days (vs. 919 days for the classical experiment), and the average number of saved conversions is 1,173. There is a long tail to the distribution of experiment durations (they don’t always end quickly), but even in the worst cases, running the experiment as a bandit saved over 800 conversions relative to the classical experiment.
Figure 3. Savings from a six-armed experiment, relative to a Bonferroni adjusted power calculation for a classical experiment. The left panel shows the number of days required to end the experiment, with the vertical line showing the time required by the classical power calculation. The right panel shows the number of conversions that were saved by the bandit. The cost savings are partially attributable to ending the experiment more quickly, and partly attributable to the experiment being less wasteful while it is running. Figure 4 shows the history of the serving weights for all the arms in the first of our 500 simulation runs. There is some early confusion as the bandit sorts out which arms perform well and which do not, but the very poorly performing arms are heavily downweighted very quickly. In this case, the original arm has a "lucky run" to begin the experiment, so it survives longer than some other competing arms. But after about 50 days, things have settled down into a two-horse race between the original and the ultimate winner. Once the other arms are effectively eliminated, the original and the ultimate winner split the 100 observations per day between them. Notice how the bandit is allocating observations efficiently from an economic standpoint (they’re flowing to the arms most likely to give a good return), as well as from a statistical standpoint (they’re flowing to the arms that we most want to learn about).
Figure 4. History of the serving weights for one of the 6-armed experiments. Figure 5 shows the daily cost of running the multi-armed bandit relative to an "oracle" strategy of always playing arm 2, the optimal arm. (Of course this is unfair because in real life we don’t know which arm is optimal, but it is a useful baseline.) On average, each observation allocated to the original costs us .01 of a conversion, because the conversion rate for the original is .01 less than arm 2. Likewise, each observation allocated to arm 5 (for example) costs us .03 conversions because its conversion rate is .03 less than arm 2. If we multiply the number of observations assigned to each arm by the arm’s cost, and then sum across arms, we get the cost of running the experiment for that day. In the classical experiment, each arm is allocated 100 / 6 visits per day (on average, depending on how partial observations are allocated). It works out that the classical experiment costs us 1.333 conversions each day it is run. The red line in Figure 5 shows the cost to run the bandit each day. As time moves on, the experiment becomes less wasteful and less wasteful as inferior arms are given less weight.
Figure 5. Cost per day of running the bandit experiment. The constant cost per day of running the classical experiment is shown by the horizontal dashed line.
1The R function power.prop.test performed all the power calculations in this article. 2See the appendix if you really want the details of the calculation. You can skip them if you don’t.
Posted by Steven L. Scott, PhD, Sr. Economic Analyst, Google
Last Wednesday we held a webinar on Remarketing with Google Analytics. We launched this feature earlier this year to help you reconnect with your site visitors in relevant ways. Remarketing with Google Analytics lets you show ads to website visitors who have shown an interest in your site as they browse other sites on the Google Display Network (GDN). So you can reach the right audience with the right message at the right time.
Watch the webinar video here to learn more about:
The overall benefits of Remarketing with Google Analytics
See a live demo of the product
Understand how to set this up for your business
And see some key examples of what’s possible
Read on for responses to some of the top questions we received during the webinar:
Any quick tips for getting started? Yes, our help center includes a great guide with everything you need to know to get started.
Is there a limit on the number of lists that you can create in your Google Analytics account? No! We want to encourage you to create as many lists as you need to run an effective remarketing campaign.
How should I set “membership duration” for my lists? The default membership duration is 30 days, but we recommend choosing a duration related to the length of time you expect your ad to be relevant to the user. Learn more about membership duration in this article in the AdWords Help Center.
How can remarketing lists in Google Analytics be edited or deleted? It’s easy to edit existing lists by clicking on the name of the list in the main table. Visitors who have already been added to the list will be removed from the list when the list duration for those visitors expires.
Both AdWords and Analytics save lists for historical campaign reporting purposes, so it’s not currently possible to delete lists -- but often you can simply edit your old lists so they continue to be useful. That said, we are looking into ways to provide better controls for managing lists that are no longer in use such as providing ways to hide or archive old or unused lists.
Can you use Google Tag Manager with Remarketing with Google Analytics? Yes! Google Tag Manager fully supports Remarketing with Google Analytics. When you are setting up your “Google Analytics” tag templates in the Google Tag Manager User Interface, you can choose to enable the “Add Display Advertiser Support” check box-- this will make all the tagging changes necessary to use Remarketing with Google Analytics.
Can you share lists between Google Analytics profiles? What about across different AdWords accounts? When you create a remarketing list in Google Analytics, you must choose to base it off of a single, specific Profile (a Google Analytics Profile determines which data from your site appears in the reports; it may, for example, include filters to eliminate traffic from internal users). If you want to create a list that’s based off of two profiles, you must create that list twice -- once for each Profile. Similarly for AdWords accounts, if you want to share a list with more than one account, you must create the list once for each account you want to share it with.
Do you have examples of remarketing lists I might consider creating with Google Analytics? Yes, you can find some examples in the webinar video and in on our product fact sheet, and we’re working on providing more examples and tips. Stay tuned!
We hope you found this webinar useful -- and that you go start creating your first remarketing lists using Google Analytics now.
Posted by Jesse Savage, Product Manager, Google Analytics
With the holiday shopping season in full swing, it’s important to ensure your website and digital marketing are running on all cylinders. Your potential customers should be able to find what they need on the digital shelf as easily as in real life. Sadly, many sites leave visitors frustrated - losing potential customers. However, the advantage of your online storefront is that you can understand where you’re losing customers and work to improve your shopping experience.
For the holiday season, our team at Google Analytics thought it would be helpful (and fun) to demonstrate how missteps on the digital shelf play out in real life.
What’s distracting your customers? Have you accidently placed obstacles directly in the path of your customers buying what they really want on your site? Watch Nick's journey to finding what he wants. Play Video
Improvement Tip: Always make sure your landing pages meet your users' expectations. Be sure your ad text leads visitors to a page that matches what was featured in the ad. Here is a helpful article on ways to improve the performance of your landing pages.
How can it be so challenging to find your favorite type of milk?
Are you making it difficult for users to browse or search your site by the way you categorize your products? Watch as Oli struggles to find his breakfast essentials. Play Video
Improvement Tip: A search box can be a goldmine of information because each time visitors search your site, they tell you in their own words what they are looking for. Here is an article on insights available from your Google Analytics Site Search reports to learn what your visitors want so you can improve your website to better meet those needs.
When do visitors check out from your online buying process? We shared this last year, but it’s too much fun not to share again. Great example of the importance of having a simple easy to use checkout process on your website. Watch for the humor, stay for the insights. Play Video
Improvement Tip: Are there some product pages that consistently send higher traffic through your shopping cart than others? See if there are differences between the page designs that might be driving the difference in traffic volume. Do the better performing pages offer more information about their products, more customer reviews, explain shipping options or provide more options for visualizing the products before adding them to the shopping cart? The Google Analytics goal flow visualization can help to identify these better performing pages to repeat their success.
We hope this helps you to find more way to use Google Analytics to make your customers' lives easier, and generate more happy and loyal customers for you - now that’s a holiday present worth giving.
Posted by Clancy Childs, Google Analytics Product Manager & Jon Day, Google Product Marketing Manager
The following is a guest post from Jeff Sauer, Vice President at Three Deep Marketing, a Google Analytics Certified Partner. Jeff recently started a website dedicated to advancing digital marketing knowledge called Jeffalytics.
Lead generators know that the combination of Google AdWords + Google Analytics is a winning combination for generating an inflow of high quality leads. They are like peanut butter and jelly, Forrest Gump and Jennay, Mel Gibson and Danny Glover.
What many users may not realize is that there are many features that they can unlock in Google Analytics to make their lead generation campaigns perform better while becoming more transparent and accountable. What follows is a series of tips, trips and hacks that you can use to make your lead generation campaigns work even better. I have broken this down into three sections: Configuration, Integration,Analysis.
Configuring Analytics for Lead Generation Websites
Set Up Goals in Google Analytics
Yes, this is a very elementary step in your Google Analytics evolution. You surely configured goals on your site years ago, right? Well, let's make sure you didn't miss anything:
Navigate to the URL of your 'thank you' page shown after a lead is generated. Make note of the URL of this page.
Make your best guess as to the value of each lead that you generate (note: you can have multiple lead values, and multiple goals).
Configure your goals in Google Analytics, assigning the proper goal value for each lead you generate.
Unlock a new world of reports in Google Analytics and see the real value of your lead generation efforts.
Bonus tip: There's absolutely nothing wrong with measuring micro conversions on your lead generation site. Have a PDF that someone can download freely? Set a goal and assign it a modest value (even if it's $5, the impact can be huge). Have a 2 minute video? Give it a value as well, even if it's just a dollar or two. Both PDF downloads and video plays can be tracked using GA event tracking - and you can configure goals around events.
Track Visitors Across Domains
Many lead generation sites use third party forms and services to capture leads, whether as part of an affiliate program or a third party CRM site. While this acts as an excellent conduit to lead delivery, it can often result in missing data in Google Analytics reports. Depending on the services used, there is still a way to retain this data in Google Analytics by tracking your visitors across domains. Here's how this is done:
On your primary website, add the _gaq.push(['_setDomainName', 'PRIMARY DOMAIN']); and _gaq.push(['_setAllowLinker', true]); methods.
When linking to your external domain, add an onclick element as follows: onclick="_gaq.push(['_link', 'THE LINK']); where THE LINK is your external page
Add the GA Tracking Code to your third party hosted page, being sure to use the _gaq.push(['_setDomainName', 'PRIMARY DOMAIN']); and _gaq.push(['_setAllowLinker', true]); methods on this page as well. It is important to make sure you are setting your primary domain here as well.
Configure your goals to match the thank you page URL on the third party domain (or on your own site if you can redirect visitors back to your domain)
By linking visits across domains, your reports will accurately attribute visitors and goals to their proper source and medium instead of treating them as direct visitors.
Integrate with Google AdWords Both Ways
Most of us know to share data between AdWords and Analytics and enable the Google AdWords report in Analytics, but many times this is not done properly. In addition, not enough marketers seem to take advantage of Google Analytics' ability to push conversion data back into AdWords. You really have nothing to lose when you integrate these two Google products both ways, but you have many insights to gain. Start off by making sure you configure these integrations properly:
Share Google AdWords data with Google Analytics. This may seem easy, but is often incomplete when implemented. Make sure that you 1) Turn on Auto Tagging in AdWords, 2) Enable Data Sharing and 3) Apply Cost Data into Google Analytics
Configure your goals in Google Analytics as outlined above
As soon as data starts to collect for these goals, you will see the option in AdWords to import your goals from Google Analytics
Enjoy consistent conversion data between both products and ensure that leads are being properly attributed
Using your goals in Google Analytics for your Google AdWords campaigns can come in handy when you don't have the ability to add a traditional JavaScript based conversion code onto your thank you page. In addition, importing goals from Google Analytics allows you to track some of the advanced conversions mentioned below in Google AdWords. The result? Better analysis capabilities, more advanced conversion rate optimization strategy and more credit for the leads you generate!
Integrating Analytics into Lead Generation Efforts
Phone Call Tracking
One thing that marketers may not realize is that for many industries, the majority of leads will come in through the phone instead of through a web form. Google AdWords understands this and now offers a robust system for tracking phone leads generated by AdWords. But how do you properly track and attribute phone calls generated from your site to a particular traffic source? You integrate Google Analytics with your call tracking provider.
This sounds complicated, but it really is not too bad. In fact, many phone tracking vendors offer a Google Analytics integration option as part of their service. For example, this works well with products like Marchex Voicestar and Mongoose Metrics among others.
Here are the basics of how this process works:
Sign up with a phone call tracking service, create tracking numbers and appropriate campaigns
Place tracking phone numbers on your website
Specify a post-back URL to be visited when a successful phone call occurs
Your phone tracking system will send a visit to the post back URL, complete with all Google Analytics cookie values for the visitor who saw that exact tracking number on your lead generation site
Please note that if you drive a lot of traffic to your website, it can take a lot of phone numbers and extensions to fully attribute phone calls to users. As such, you may want to start implementing this method for a small segment of your traffic and then building up to all visitors when this data proves useful.
Also note that even if you don't link calls back to Google Analytics, phone call tracking is still an imperative part of any lead generation campaign, because it's common for 30-70% of the leads you generate to come from the phone in certain industries.
Offline Marketing
Believe it or not, in many industries leads are still generated offline. Examples include trade shows, neighborhood canvassing (going door to door promoting a product or service), print and television advertising. These are activities that companies have been doing for years, but the problem that they run into when using these mediums to drive traffic to their website is that they don't register the traffic source properly in Google Analytics. The result: many direct visitors without proper attribution.
How do we fix this? By following this simple process:
Create a vanity URL that is unique to your campaign (can be a sub folder or new domain)
301 redirect your vanity URL to the tracking URL (this preserves your campaign attributes)
Learn about how each traffic source performed by viewing your favorite reports in Google Analytics and paying attention to the source/medium/campaign
Now you can put your offline and online leads on a level playing field and compare the effectiveness of both side by side.
CRM Integration
For companies that are generating several leads a day, a Customer Relationship Management (CRM) system becomes imperative for keeping up with the leads coming in the door. Unfortunately, most CRM implementations are not integrated fully with the website and useful data is not shared between the two systems. This can create friction between sales and marketing, while making it nearly impossible to close the loop on what lead generation efforts are working the best.
Fortunately, people smarter than myself have found a way to solve this problem, and this solution for CRM integration by Justin Cutroni has become my gold standard for how to pull information out of Google Analytics cookies and attach to the lead record you enter into your CRM system.
While Justin's post goes into great detail, the basic premise is this:
A visitor comes to your website and has source/medium/campaign/keyword information assigned to them in their Google Analytics cookie
This information is accessible to your website by pulling cookie values out of Google Analytics using JavaScript
Once this information is pulled out, you enter the values into hidden form fields underneath where your lead enters their contact information
The vital information (source/medium/campaign/keyword term) is passed into your CRM system alongside the lead record
Your sales team can now have deeper understanding of what type of traffic generates the best leads, all the way down to a keyword level
You can use this information to refine your marketing efforts and campaigns to focus on your top performers
Sharing information between your website and your CRM system is an imperative step for making your marketing data actionable to the rest of the business. Without integrating, decisions are made based on faith and HIPPOs, instead of actionable data. As a note, with the advent of Universal Analytics this is likely to get even easier.
Analyze the Results and Make Your Site Even Better
How you analyze your site is a very personal thing, and your mileage may vary, so there isn't a magic bullet to ongoing success with your lead generation programs.
With that said, there are several reports that can be extremely useful in Google Analytics for lead generation campaigns. I would start by paying attention to the following:
Use an advanced segment of paid search traffic and then navigate to the Conversions > Goals report. Compare the goal values you created recently with a similar time period in the past. Are your results improving?
Navigate to the Multi Channel Funnels report and either use standard or custom channels. What is the most common first click channel? Are you giving it enough credit in your reporting?
Compare direct traffic before and after implementing the integrations suggested above. Do you start to see more activity with proper attribution? Are you more confident analyzing with less of a grey area?
Have you been receiving all of the credit you deserve for leads you generate over the phone?
When a salesperson tells you that the leads you generate "suck" are you able to match their lead close rate to the source/medium/keyword that generated the lead?
Instead of presenting raw lead numbers in a vacuum are you starting to factor in appointments issued, quotes given and sales made? Can you calculate the true cost of sale from keyword to purchase?
When configured properly, you can use Google Analytics and residual data from GA to perform some in depth closed loop analysis on how your lead generation campaigns are performing. Savvy lead generation experts have figured out how to deliver maximum value to their clients and constituents using the capabilities built into Google Analytics. Now it's your turn.
There you have it, the three pillars to getting the most out of Google Analytics for your lead generation website. Have any cool integrations yourself? Let's talk in the comments below.
Posted by Jeff Sauer, Google Analytics Certified Partner
On Friday, November 2, following our public whitelist of the Attribution Modeling Tool, Bill Kee (Product Manager, Google Analytics) and Neil Hoyne (Global Program Manager, Attribution), came together to lead the 5th and final webinar in our series on marketing attribution. They identified opportunities in the customer’s journey from introduction to conversion, including:
Google’s recommendations for how companies should structure their own attribution programs.
Basics on the methodology and configuration of the Attribution Modeling Tool, and how to create custom models that can improve your business’ performance.
Identifying specific opportunities in attribution from brand-to-generic trends to position-based weighting.
If you weren’t able to attend the live webinar, Attribution for Digital Success, you can view a recording here:
You can also catch up with our entire attribution webinar series, which included:
an overview of our research on how the industry approaches attribution (watch here),
the foundational steps for attribution using Google’s tools (watch here),
intra-channel attribution with Search Funnels in Google AdWords (watch here),
cross-channel measurement with Multi-Channel Funnels (watch here),
and finally, our most recent webinar on strategies for the Attribution Modeling Tool (watch here).
We’d like to thank all of our users who have joined us for some or all of these attribution webinars. You have provided invaluable questions, ideas and feedback to help shape the next generation of our product. Some of these requests have already been addressed, including the public availability of the Attribution Modeling Tool (now available via whitelist), longer lookback windows, and cost-data import, and others are sure to come in the future. Stay tuned and stay in touch!
As has been our tradition throughout this webinar series, we’d also like to provide responses to some of the most common and most interesting questions we received during the webinar.
Questions
What business variables influence the decision on an Attribution Model? Any factor that could influence your business or marketing efforts, including weather, pricing and competitive behavior, could have an impact your attribution decisions. Still, we suggest that advertisers focus on those efforts that could have the largest effect on their business, usually by conversion volume as well as those that they can more easily control (paid search vs. organic search or direct traffic) for the basis of experimentation.
How is the social engagement metric calculated? Social engagement is measured any time a user clicks from a known social network, such as Facebook, Twitter, Google+ or over 400 others, to the advertiser’s website. At this time, no interactions that occur within the networks themselves, such as a “like” are presented within the Attribution Modeling Tool.
Could you further elaborate on how conversion paths are presented when a user converts multiple times within the 30-day lookback window? Each conversion has a unique path, which includes all of the interactions the converting user had in the 30 days leading up to the conversion. When the same user converts multiple times, the conversions are treated separately. For example, is a user clicked through from Display, and completed conversion #1, this conversion would have a path length of one from the channel “Display.” If the same user subsequently clicked through from Paid Search, and completed conversion #2, assuming the original Display interaction occurred within 30 days prior to conversion #2, a second conversion path would be recorded with a path length of two: Display, followed by Paid Search.
If we submitted our account to the Attribution Modeling Tool whitelist, how long will it take until we begin to see this feature available in our Google Analytics account? We understand how important attribution is to your business, and are incredibly grateful for all of the interest that has been shown in the modeling tool since the announcement of the public whitelist. As such, we are working as quickly as we can to add new customers to the tool and will continue to post any available updates directly on the signup form. Once your account has been whitelisted, you’ll see the Attribution Modeling Tool listed within the Multi-Channel Funnels reports, under Conversions.
Could you provide step-by-step details on how to build the models Bill described during the webinar? We created two custom models to show examples of the types of weighting you can apply using the model builder. The first model, called “Upper Funnel” emphasizes interactions earlier in the path, from channels that are focused on introducing and informing customers, and discounts channels that may be more navigational, like branded search. The second model, called “Lower Funnel” gives more weight to marketing interactions at the end of the conversion path, but does not solely give credit to the last interaction, and excludes direct interactions that are last in the path, giving credit instead to other marketing touch points toward the end. By comparing both models to the Last Interaction model, you’re able to see the contrasts in credit given to channels, and see whether marketing efforts play the roles you think they do or not.
Here are the rules for the “Upper Funnel” model.
Upper Funnel Model, step 1: Click on the model selector then “create new custom model” to open the custom model builder, and enter details as pictured (click to enlarge the image):
Upper Funnel Model, step 2: Turn on “apply custom credit rules” in the custom model builder, then enter model details as pictured (click to enlarge the image):
And here are rules for the "Lower Funnel" model.
Lower Funnel Model, step 1: Click on the model selector then “create new custom model” to open the custom model builder, and enter details as pictured (click to enlarge the image):
Lower Funnel Model, step 2: Turn on “apply custom credit rules” in the custom model builder, then enter model details as pictured (click to enlarge the image):
Marketing attribution is a challenging yet worthwhile pursuit. Our hope is that this webinar series will help you as you begin (or continue) your attribution journey. For more information on the Attribution Modeling Tool, please visit our website and the Google Analytics help center.
Happy analyzing! Posted by Sara Jablon Moked, Product Marketing Manager for Conversion and Attribution
Just over a month ago, we launched Google Tag Manager, a free tool that makes it easy for marketers to add and update website tags—including conversion tracking, site analytics, remarketing and more—with just a few clicks. Since then, we’ve released the product in 35 languages, we’ve added new tagging capabilities for Google Analytics, and we’ve been hard at work building more enhancements.
To help you get the most out of Google Tag Manager, we’ve scheduled a webinar next week with Product Manager Laura Holmes to walk through the tool and go over implementation basics:
Webinar: Getting Started with Google Tag Manager Date: Tuesday, November 13, 2012 Time: 10am PST / 1pm EST / 6pm GMT Register:goo.gl/YTulu
We’ve also been hearing great feedback from our users, including GoPro, the world’s leader in wearable and gear-mountable cameras and digital devices. With the growing popularity of GoPro products and accompanying complexity of their digital marketing activities, GoPro found itself with dozens of tags measuring countless engagement activities across its web properties. It was critical to find a way to implement and maintain marketing tags that would scale with the marketing organization. Analytics Pros, a Google Analytics Certified Partner and Google Tag Manager specialist, led a comprehensive migration to Google Tag Manager -- and GoPro stakeholders were delighted with the results:
“Google Tag Manager centralizes our tags into a single location that gives our marketing and analytics teams the flexibility to make tagging updates within minutes without burdening IT.” - Lee Topar, Director of Online Marketing, GoPro
We hope you’ll join us at the webinar next Tuesday the 13th. If you’re not able to attend, we’ll be posting a recording of the webinar about a week afterwards here on the blog and on YouTube, and you can also read more about Google Tag Manager on the website or the help center.
Posted by Sara Jablon Moked, Product Marketing Manager
Conversion is rarely straightforward, especially for products or services with lengthy or complicated buying cycles. Working for a college has made it clear to me that every consumer is different, and so are their research needs as they navigate their unique buying process.
It takes a holistic content strategy to address the extensive information needs of potential students, and rarely do blogs and other types of content marketing get the credit they deserve for the role they play in influencing conversion.
Luckily, Google Analytics Multi-Channel Funnels provides marketers with amazing new ways to see how users interact with web content on their path to conversion and to prove the value of content marketing.
Introducing Google Analytics Multi-Content Funnels
Consumers begin any major investment in the awareness/discovery phase, are triggered into a search/consideration phase, and finally end up at their buy/close phase when they take the conversion action. Imagine how your content strategy could perform if you understood how consumers interact with your website content as they navigate their investment decision.
That’s where the idea of Multi-Content Funnels started. To be clear, Multi-Content Funnels is not a new Google Analytics feature, but rather a specific application of the existing Multi-Channel Funnels reporting features that illustrates the direct and indirect effects of your website content instead of your marketing channels.
Multi-Channel Funnels launched a little over a year ago as a way to help show how users interact with your marketing efforts over multiple visits. By default, these reports are configured to report the relationships between marketing channels (e.g., paid search, social media, email), but we’re going to modify them to demonstrate the value of content marketing.
The key to this type of analysis is being able to use the Landing Page URL data attribute when you create Channel Groupings and Conversion Segments within a Multi-Channel Funnel report. When I first wrote on their inbound marketing benefits, Multi-Channel Funnels didn’t support this deep dive into your website content because they didn’t include landing page in the source data.
Turns out the Google Analytics team had it on the road map and added it to Multi-Channel Funnel reports within the last few months. Content marketers, get ready to geek out with these content-based applications of the Google Analytics Multi-Channel Funnel reports.
Building Content-Based Channel Groupings
The first major application of Multi-Channel Funnels for content marketing is to create Channel Groupings based on your content, which will demonstrate the most common content paths users take to conversion over the course of multiple visits.
Start off by creating a new Channel Grouping within the Top Conversion Paths report. You’ll want to group the major content sections of your website together into channels.
For example, here I’ve created this Channel Grouping that corresponds to the Degrees Catalogsection of our website that includes any landing page URL containing “/degrees.”
Creating a Channel Grouping in Multi-Channel Funnels:
I also included channels that correspond to each of the major content sections of the website as I built out this content-based Channel Grouping. This is what the content-based Channel Groupings of a college websitelooked like when I was done with them:
Content-Based Channel Grouping:
Your own content-based Channel Groupings will likely be different for every website, but each should include major product directories or service listings, blogs, sections that answer specific questions or solve specific problems, whitepapers, ebooks, etc.
Top Content Conversion Paths
Once the content-based Channel Groupings are set up, we’re able to access the Top Conversion Paths report, which instantly becomes the content marketer’s best friend because it shows how many visits it takes before visitors convert, and how they start their website experiences for each visit.
You can use the Channel Groupings that correspond to specific content sections as with the screenshot above, or you can apply even broader Channel Groupings to provide a high-level view of the most common content paths towards conversion by marketing intent, consumer action, or both.
Channel Groupings Based on Buying Cycle Path
Creating Channel Groupings based on marketing intent and the consumer buying cycle requires a deep understanding of how consumer interact with your website. These Channel Groupings can be created by combining multiple sections of the website when constructing each Channel Grouping, depending on which phase of the buying process they facilitate:
Pairing this information with traffic and conversion data makes it clear where to focus resources for new types of content, content edits, and expansion of existing website content, as well as demonstrates which parts of our content marketing strategy are driving results.
(Fascinating side note: Looking beyond the most popular conversion paths, some degree seekers’ research processes can see them returning to the website 50 times or more before they are confident in their conversion decision. As a student of web analytics, the next question is whether this conversion path is long because it should be, or is it fraught with unnecessary abandonment that can be overcome with improvements to the content?)
A Long Conversion Path:
Determining the Value of Specific Content with Conversion Segments
Channel Groupings are half the fun because they can only help to organize and present data. To determine the value of specific types of content, we need to create custom Conversion Segments to pair with Channel Groupings.
Content-Based Conversion Segments in Multi-Channel Funnels:
Custom Conversion Segments are easy to create and work just like any other segments in Google Analytics, however, these also include the ability to segment-based interaction: First interaction, last interaction, any interaction, and assisting interaction.
Custom Conversion Segment Setup:
This segment captures conversions where the last visit on the conversion path landed on the blog. Most of Google Analytics conversion reports are based on the last interaction, but this segment allows you to explicitly specify between first interaction, last interaction, any interaction, and assisting interactions.
As a content marketer, discovering some blogs assist 150 percent more conversions than they produce directly was a powerful revelation, one that was made possible by content-based Channel Groupings and Conversion Segments applied to Google Analytics Multi-Channel Funnels.
The Many Uses of Multi-Channel Funnels for Inbound Marketing
Understanding how consumers interact with your website content is the first step in providing them with the best experience possible – the primary goal of every modern SEO and content marketer. Those who understand and execute content strategy with this knowledge in mind continue to drive highly efficient campaigns.
The Google Analytics Multi-Channel Funnels with content-based segments and groupings, or Multi-Content Funnels as I like to call them, provides you with several new ways to leverage these amazing reports, boost your content marketing efforts, and better serve your current and potential consumers.
How have you used Multi-Channel Funnels in your content strategy?
(Note: Some screenshots were edited to remove site details.)
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