Digital is a channel that can and should be tracked end to end – data capture starts from an impression and goes all the way to the final sale. As a company specializing in Search and Social Media, we regularly educate our clients on the importance of granular tracking and help them improve their existing tracking methods across platforms. With integrated in-house PPC, SEO and Analytics teams, we constantly seek to maximize our client’s campaign ROI through regular research, granular data capture and analytics.

This article’s focus is to showcase the importance of capturing paid search (PPC) keyword data through analytics. Google Analytics (GA) has been considered for this discussion. While you might have a different analytics tool, we believe that the information here still applies to your PPC campaigns and you should try and get similar data from your current analytics system.

Capturing Keyword Data through Google Analytics

When it comes to PPC, it is important to capture all the metrics accurately within the main user interface (Google Adwords) and the analytics system (GA). The metrics captured can be categorized as ‘front-end’ and ‘post-click’.

Front-end metrics cover data on keywords, ads and the landing or destination pages, and include clicks, Click through Rates (CTR), Conversions/Leads, sale value, Cost per Lead (CPL) etc. Post-click metrics include bounce rates, average time spent on site, % of new visitors and much more.

Adwords provides keyword information up to the point where the user clicks onto the website or the landing page. Post-click performance on GA shows user behavior on the destination page or the website. This information helps understand if users landing on the page stayed on it, if so, for how long, how many pages they visited etc.

Keyword level data can be obtained from GA in multiple ways. Keywords can be analyzed individually or at an aggregate level by defining segments. Data for one of Position²’s client in the B2B space is shown below as an example. An advanced segment named ‘Brand’ keyword aggregates data for a pre-defined set of corporate and brand name keywords. Similarly, the ‘Non-brand’ keyword segment excludes traffic from these keywords.

Table 1.0: Aggregate Branded VS. Non-Branded Keywords


Table 1.1: Keyword Level Performance

Regardless of the business type (B2B, E-Commerce etc), capturing keywords through analytics is crucial. Some of the key advantages of having keyword information through GA are provided below. The benefits should typically apply to any analytics platform you may be using.

• Keywords within GA help determine the performance at an aggregate level. In Table 1.0, aggregated performance of brand Keywords shows that they are producing 100% bounce rate and no conversions; an unusual occurrence for brand terms. This information indicates that the users landing on the destination page(s) are not finding what they are looking for. By extracting this data from our client’s GA account, we understood that those conducting branded searches could be doing so to sign-in to their online accounts. If the campaign objective is lead generation or form fills, the campaign may be better off without these specific terms.
• GA can be used to determine if non-performing keywords should be weeded out of the campaign or if new messaging should be tested. In Table 1.1 Keyword 5 and Keyword 6 are poor performers. These terms have 100% bounce rate despite driving new users and a reasonable time spent. For these keywords, it would be important to evaluate the ad messaging and the landing page copy.
• Keyword data from paid search can be compared with numbers Organic traffic. For example, if there is a drop in traffic and leads for a keyword in paid search, Organic traffic for the same keyword can be compared to check if it’s a seasonal trend.
• Destination URL performance can be evaluated down to a keyword level. This helps identify the best combination of Keyword-Destination Page and crucial information for campaign optimization.
• Performance for each keyword and post-click activity can be obtained in every match type (Exact, Broad and Phrase). Precise user queries that triggered the ads and their post-click performance can also be gathered. Again, important data for campaign optimization.
• The best position for keywords can be determined. As seen below, the position and post click performance for keywords can be captured. Crucial data to make decisions on keyword positions.

  

Passing the Keyword Data within GA

You can pass paid search keyword data to GA by:

1. Adding UTM parameters to destination URLs or
2. Linking the GA and AdWords accounts and enabling Auto-tagging in Adwords

Let’s now examine how you can pass keyword data to other analytics platforms.

AdWords ValueTrack allows passing detailed data to any tracking system to optimize campaigns. By adding parameters to the destination URL, it is possible to pass information on keyword, ads etc.

Here’s an example -

Destination URL tagged with parameters for keyword and match type:

http://www.example.com/?keyword={keyword}&matchtype={matchtype}

Actual URL post visitor click

http://www.example.com/?keyword=used%20cars&matchtype=p

Your tracking system will need to parse these parameters and allow for slicing and dicing of data based on these.

We recommend doing exhaustive testing to ensure all required information is captured accurately on your platform.

Conclusion

Keyword data helps optimize paid search campaigns down to the most granular level possible. This will in turn help generate efficient (low Cost per Lead) and quality leads. It will also provide multiple testing opportunities with messaging, positions and landing pages.

Not having keyword tracking within analytics will still allow for campaign management, but at an aggregate (ad group) level only. While not the optimal way to manage paid search campaigns, this will still help in optimizing campaigns possibly to increase leads.

At Position² we encourage all our clients and work closely with them to take advantage of granular information through their analytics platform. We use end-to-end performance metrics to optimize campaigns and work with our clients to improve not only the PPC performance, but also Analytics set-up, SEO performance and their sales process.

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Over the past 4 years, the Analytics team at Position² has consulted hundreds of companies on Google Analytics. The projects include fixing tracking code issues, setting up goals, e-commerce tracking and a variety of custom implementations.

Over time, we noticed that most of our clients faced a common set of issues or had a common set of errors in set-up. To give back to the online marketing community, Position² offered a free Google Analytics implementation audit in December 2010. The audit covered:

• Code check
• Multi-domain/Sub-domain tracking
• Tracking non-conventional goals
• Custom implementations

Over 100 sites signed up for the audit and free reports were sent to the first 25 sign-ups.

The exercise gave us insights on the state of Google Analytics implementation and the level of expertise users had achieved. For instance, we were surprised to see that only 66% of the users had migrated to Asynchronous version of the code since its launch almost two years ago in Dec 2009.

We used the sign-ups received for the free audit for a survey to understand the level of user expertise and identify the common errors in Google Analytics implementation. Totally, 134 sites were selected for this study.

As we did not have access to the Google Analytics accounts of these websites, the study was restricted to only code related issues.

By using the common errors from this study as a check list, web analysts can check the implementation on their sites. We hope you find this study informative.

Tracking Code Type

There was a lot of buzz and excitement in the web analytics industry around the asynchronous snippet launch in Dec 2009. The new code was touted to improve webpage load times and fine-tune the accuracy of data. In fact, test results published by Position² showed that the asynchronous version of the Google Analytics code was faster than traditional/ synchronous code by 17.03%.

After almost two years of its existence, it’s quite disappointing to see that only 66% of websites have migrated from the traditional code to the asynchronous code.

Code Accuracy

We looked at different aspects of the code to check if analysts were working with accurate data. Data accuracy depends on the right implementation of the correct code.

Code Syntax Errors – 91% of the sites were successful in implementing the right syntax. Some common errors included:

• Calling Trackpageview before making the call to SetAccount
• Use of the period ‘.’ in sub-domain tracking , pageTracker._setDomainName(”.domainname.com”);
  The period before domainname.com should be used in cases where there are lower level sub-domains involved, for example dogs.petstore.example.com.

Multiple Account IDs – Some websites were tracking the data into two different GA accounts and hence had placed two versions of the tracking code with different account IDs.

This is not supported by GA and can lead to one tracker overwriting the cookies of the other one. While it is possible to track data using two different account IDs, it requires a modification to the tracking code.

In most situations, different profiles can be set-up to manage reports better.

Code Placement and Coverage

Altogether, about 21% of the sites had pages that were missing tracking codes. Not implementing the tracking code across 100% of the pages is a major cause for data inaccuracy.

Common misses were non-conventional pages such as pop-ups, tools/calculators on sub- domains.

Placement

• Asynchronous – The main advantage of the asynchronous code was the ability to place the code higher on the page (hence capturing data more accurately) without delaying subsequent content from rendering. Surprisingly, only 50% of the sites using the async code had placed it in the <head>…</head> section, thereby not making use of its primary advantage.
• Synchronous – Another error in implementation that we noticed was that 15% of the sites using the traditional code had placed the code, not in the recommended body section, but in the <head>…</head> section. This could lead to sub-optimal user experience due to delay in page loading.

Non-conventional Goals

We studied the websites to see if they had implemented virtual page views or event tracking to track activities such as file downloads or clicks to external links.

• Downloads – While most of the sites were offering a download of some kind, only 6% of them were tracking the download action.
• Chat – Requires a simple modification of the link anchor of the ‘Chat Now’ button to include an onClick event handler to call a virtual page view or event. One can track the number of visitors using the chat option and also segment the data. For example, one can analyze the location from which conversations are initiated, the keywords resulting in chat (Organic, Paid), and most importantly, the contribution of chat to conversions. Here’s a case study on improving chat funnel conversion rate:

In our survey, only 2% of the sites were tracking chat using Google Analytics.

Sub-Domain Tracking

Another area where there were implementation issues was sub-domain tracking. Almost 48% of the site had not implemented tracking across its sub-domains.

Even among the sites which had sub-domain tracking implemented, most of them had errors.

Apart from syntax related issues (example in section 2, under Code Syntax), the most common error was that the sub-domain line was added to the sub-domain pages and not the main domain pages. A few sites did the opposite.

The objective of adding the ’setdomainname’ line is to pass the cookie values when the visitor moves from the sub-domain to the main domain and vice-versa. This requires that the line be added to all the pages of the main domain and all the sub-domains.

Conclusion

Analysts tend to spend a lot of time sifting through data without first making sure that they are working with the right data set. It is also important to ensure that tracking is optimized to measure ‘all that can be measured’. This means measuring across sites, across sub-domains and measuring non-conventional goals or visitor actions such as downloads, video plays and other interactive content.

Tracking that is complete, accurate and one which measures all visitor behavior is the starting point for all great analytics.

Contributed by Divya Krishnan, Assistant Manager – Analytics, Position²

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Segmentation is a feature offered by many web analytics tools. It allows grouping of website visitors based on certain common characteristics. Some examples of segments are:

• Visitors coming from paid search campaigns
• Visitors spending more than ‘x’ minutes on the site
• Visitors who download a product brochure, etc.

Segmentation leads to deeper insights about our web site visitors. In the words of Avinash Kaushik it helps us in becoming Analytics Ninjas!

The Position² Analytics Team segmented website traffic for one of our clients based on certain pre-defined criteria. This client offers privacy and security certification and compliance services for websites. Here is what we found:

Problem Statement

The client’s website was receiving a very high number of visits but their conversion numbers were extremely low.

Analysis

We started by defining a conversion on the site. To count as a conversion (lead), a visitor needs to sign up to ‘know more’ about the services provided. Rather than just looking at the aggregate traffic we segmented it as follows:

1. Visits by Source (Referring Sites were sending 81% of the traffic)
2. Conversions by Source (Paid Search traffic was the major contributor with 95% conversions)

We followed it up with a segmentation of visits by intent, using the ‘advanced segmentation’ feature of Google Analytics, and discovered that all referral visits can be classified into three C’s

1. Credential Verifying Visits: Verifying member site credentials.
2. Complaint Visits: Complaints about member sites regarding privacy violations
3. Content Visits: Visits related to site content

Visitors under categories 1 and 2 visited pages containing specific keywords in the URL (which were not present in URLs of other pages). This fact was used while creating the ‘advanced segments.’

We compared these segments on the basis of their ‘content engagement metrics:’

• Average Time on Site
• Pages Per Visit
• Website Bounce Rate
• Percentage of Returning Visitors

From this graph, it is clear that ‘Credential Verifying Visits’ fared poorly on all metrics as compared to ‘Content’ and ‘Complaint’ visits:

Inferences

• Referring Sites bring in 81% of the traffic, but these visitors do not convert, as they mostly visit to
• Verify the credentials of other sites
• File a privacy violation complaint
• More than 95% of conversions came from PPC campaigns, which account for just 0.2% of total traffic.

Based on these findings, the client immediately saw the value of increasing their spend on PPC campaigns as that is where they got the most conversions from.

Segmentation based on user intent helps you drill deeper into where your most important traffic comes from, enabling you to focus your efforts on the traffic that matters the most to you.

Contributed by Ravi Shukla, Analytics Team

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All products have a demand cycle of their own. Understanding this becomes important when making marketing decisions. Sales reports may give marketing teams plenty of data on seasonality and demand trends, but they may miss out on an important KPI in the online context – the amount of interaction a visitor has with a site before converting.

The way customers interact with a site is very complex. A visitor might search for a product, click on a PPC ad, enter your site, visit your competitor’s site, then come back to compare prices, postpone decision making, and come back after two weeks to complete the purchase.

At Position², we call this the Holy Grail of Web Analytics.

Google Analytics provides two metrics as part of their Ecommerce Reports – the ‘Days to Purchase’ and ‘Visits to Purchase’ reports. These pan-session metrics tell us how many visits have occurred (and days elapsed) since someone’s first visit, until a purchase action took place. You can get a good understanding of your customers’ behavior by analyzing these reports.

A Closer Look

To illustrate, let’s look at one of our clients in the manufacturing sector.

    

Less than half of the purchases occurred on the same day as the first visit, and almost 38% of them took place after 5 visits or more. This suggests a long sales cycle with multiple interactions with the site before conversion.

While this in itself is good to know, it is not actionable. What a marketer really needs to know is which customers convert on Day 1 and which ones need more handholding and cajoling before they convert.

The Next Step

We segmented the above data and looked at time to convert across different acquisition sources.

A look at ‘Days to Purchase’ data across sources shows that the site average has been influenced by ‘Direct’ visitors. More than 70% of PPC visitors converted with 48 hours, indicating a strong PPC strategy that targeted the right audience with the right communication. Direct visitors usually take the longest time to convert, whether they are existing customers or visitors from offline campaigns.

In contrast, the ‘Visits to Purchase’ data for PPC does not look as advantageous. Almost 44% of purchases ocurred after 3 or more visits. This could imply a not-so-attractive offer, or a Landing Page (LP) that does not work very positively with visitors. The numbers stack up very well, though, for Organic visitors, with 65% of sales occurring over 1 or 2 visits.

Delving Deeper

We went on and broke down the search visitors by keyword type to see if there was a difference in behavior for brand keyword visitors.

As expected, the brand keyword visitors converted much faster. The next logical step was to segment data by visitor source.
Interestingly, the Non-Brand visitors for both PPC and Organic took a longer time to convert, with one-third of Non-Brand PPC visitors requiring more than 9 visits to convert. One promising solution was to analyze the ‘Top Content’ of this set and design a separate landing page that addressed them better. Ideally, we would test this new LP against the existing one to compare their performance. Another possible recommendation would be to capture the visitor’s email ID in a form and send reminder emailers or enticing offers.

Conclusion

The above analysis reveals a cycle of first time visitors entering the site through PPC non-brand keywords and returning several times before converting. Since these visitors know the brand name from multiple visits, they use organic search (Segment-organic return visitor using brand keywords), or type the URL directly into the browser (Segment- PPC return visitor using brand keywords, or Direct visitor if the campaign cookie is deleted), to make the purchase.

This knowledge gets us closer to the Holy Grail in terms of better understanding our customers, and significantly provides us with actionable information to help win them over.

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Position² is delighted to announce the launch of a one-time free Google Analytics Implementation Audit Package. The audit evaluates the current Google Analytics set up for a corporate website and provides a score. Apart from the score, companies will also receive a report with recommendations on how to improve their analytics setup. This includes fixes involving advanced configurations.

Speaking about this package, Divya Krishnan, Manager Analytics Group at Position² said, “The need to launch such a package was felt when we realized that every one of our clients for online marketing services needed initial help in setting up Google Analytics either afresh, or in the form of tweaks to ensure data was being collected accurately. This is key for a company to measure ROI on all online marketing initiatives. While Google Analytics is free and easy to install – one needs to get expert help to really leverage it to the fullest.”

According to Vinod Nambiar, Head of Global Delivery at Position², “Our team works across platforms like Google Analytics, Webtrends, Omniture, and Clicktracks. They are well versed with the latest techniques and contribute regularly through articles in this space. Web analytics is a key service that is part of our Marketing Services offering which also include creative production, search and media.”

The Google Analytics audit covers code accuracy, multi-domain and sub domain tracking, tracking events and virtual pages and other custom implementations including ecommerce tracking. This service is being launched for a limited time and will be free for the first 25 requests till December 31, 2010.

To apply, fill up the request form here.

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Here is something really cool that we came across late last week. Google Analytics has introduced a new feature called “In-Page Analytics” under “Content” report though it is still in the BETA.

It provides insight on how visitors navigate on a given website page by visually displaying clicks on every link inside a bubble. In the earlier navigation report it was not possible for us to visualize visitors’ behavior and it was a cumbersome manual process that we used. In addition one can apply Advanced Segments and keyword filters to this report for effective comparative study of various traffic types. e.g.: Total traffic vs. Direct Traffic and traffic by keyword. This is much better than the site overlay report. Though this is still in BETA, we can expect this to be widely used by Analysts in the coming days.

“This is something one can find in Crazy Egg, so we have a strong feeling that the “Google Analytics” team was inspired by it!”

This is a quick note; do look out for a detailed analysis, once we start playing around with this feature.

Google Analytics In-Page Analytics Preview:



Contributed by Sasthi Sarma, Analytics Team, Position²

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A large number of websites have a chat feature to interact with their visitors. This allows them to

   - Offer real time assistance and advice

   - Promote a service/product to the visitor

Some chat applications offer integration with web analytics tools. The Google Analytics blog gives an example of such a tool here.

We tracked some chat conversations for a client using Google Analytics and analyzed the data obtained. Here is what we found.

Visitors to a site (xyz.com) can discuss their technical problems by chatting online with a support executive. The goal of this chat application is to solve technical queries as well as persuade the visitor to sign up for an annual service package.

Why is tracking important?: We recommended tracking ‘chats’ to analyze

1. Paid/Organic keywords resulting in chat
2. Locations from where people initiate conversations
3. Contributions of chat to conversions (sign ups)

Implementation: Clicking on a chat button on the website opened a new window, on a third party domain. This domain did not allow placing the Google Analytics Tracking Code (GATC) thereby ruling out cross domain tracking.

We recommended putting virtual pageview code linked to the ‘on click’ event of the “Chat Now” button. “Virtual pageview” was suggested instead of “event tracking” as we wanted to carry out a funnel analysis on the conversations.

Analysis: A funnel was set up in Google Analytics which tracked clicks on the chat button – from initiating a chat conversation to signing up for the annual service package. A majority of the conversations originated from a particular segment of the visitors, for which this analysis was done.

On analyzing this funnel we realized that the first step itself was a bottleneck in this funnel as only 9% of visitors proceeded to the next step (Registration Page) after the chat session. (First two steps Shown in Fig 1)


Fig 1: Funnel Report – Pre Analysis

A high drop rate could also indicate a technical issue with the chat application like,

1. Long loading time.
2. Long response times while chatting due to issues with the chat server.

Both the possibilities were evaluated and discarded after conducting several dummy chats from our side.

We investigated this matter and it was concluded that an inefficient sales process at the client’s end (during the chat) was causing the visitor drop off.

We recommended better sales training to the online support staff so that chat visitors could be converted at a better rate. Here are some of the inputs shared with the client:

• Asking for a phone number does ensure that the visitor initiating the chat is genuine, but this information should be asked only after a certain level of trust has been built during the chat session. The visitor needs to be assured of company credentials, only then he/she will be comfortable in providing their phone number. Building trust with a visitor could be a time consuming process.
• Analyze the chat scripts of all successful sales and then build a “model script” based on that.
• Identify the best chat representative (in terms of conversions) for managing shifts.

Results: Here is the funnel report after implementing our suggestions. (The duration of the funnel is same as Fig 1)


Fig 2: Funnel Report – Post Analysis

The results are tabulated below:

Supporting results

The average time spent on chat increased by 30% after implementing our suggestions. It indicates a better engagement with visitors (as building trust takes time).

The above experiment demonstrates the use of web analytics for finding and fixing issues in the chat conversion process. Here the issue is not related to website design or usability but can be addressed using insights from web analytics.

The analysis helped the client to increase their ROI. We look forward to sharing similar experiments in the future.

Contributed by Ravi Shukla, Analytics Team

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By “Stickiness” we mean:

• Someone visits your site for the first time
• Stays there to explore the content
• Returns later to look for more.

For most web sites, attracting quality traffic is the major goal. Stickiness provides information about visitors’ interest in your site and quality of your site’s content.

Measuring stickiness is important to a website with online advertising, as visitors are exposed to ads if they navigate through the site. For an ecommerce site, stickiness indicates that visitors navigate through the pages of the site and are more likely to purchase. The stickiness of a blog is a measure of the quality of its articles. But for a lead generation site, where the aim is to get the visitors to fill in a contact form, download a white paper or register, the measurement of stickiness is not as important as conversion data.

So, in order to know the quality of your site, the measurement of its stickiness is important. Google Analytics helps in measuring the stickiness of your site with the help of some Key Performance Indicators (KPIs).

KPIs play a significant role in web analytics. Analytics throw up a bunch of data, some of which may not be relevant for your site. To get the best out of any analytics tool, the first step is to define a set of KPIs for your site. Without proper KPI definition it is easy to get lost in a lot of irrelevant data. Once you define what you want to measure, finding the data relevant to your work becomes easy.

On the basis of the defined KPIs, it is possible to conduct a historical analysis of existing data to establish baseline measures. These baseline measures can be used to evaluate the future success of the website, business goals and marketing campaigns.

Listed here are some basic but important KPIs to measure the stickiness of your web site:

1. Average Pageviews per Visit
2. Percentage of New vs. Returning Visitors
3. Visitor Loyalty
4. Bounce Rate

1. Average pageviews per visit (Pageviews/Total visits):

This is a good indicator of site navigation as a visitor usually leaves your site early on if it’s difficult to navigate. It also indicates the quality of your content as a visitor reads more and visits more pages if it is well written. For a site with a CPM business model, high average pageviews per visit means an increase in the average value of each visit. Conversely some sites like support sites that want users to find information quickly would like this value to be lower.

For sites based on new technologies like AJAX and Ruby a low value of Average Pageviews per visits does not indicate poor quality of content because interaction on them can occur without any change in the URL. For a product site a high value of this KPI may indicate difficulty in navigating it.

This metric can be extracted from Visitors Overview report under Average Pageviews.

2. Percentage of new vs. returning visitors (New visitors/Total visitors):

This is a good indicator of your web site’s stickiness, as a high percentage of returning visitors means that your content is relevant and interesting to them. A high percentage of new visitors shows that you are successful in driving traffic to your site (through PPC, SEO, and referrals). For an e- commerce site returning visitors are important – they may have come back after some research and are now ready to buy, or they liked what they bought previously. This metric can be used to further explore the source of your visitors, as well as the keywords with most visits.

This metric is available in the Visitors report under New vs. Returning.

3. Visitor Loyalty:

For a pure content site like a blog or a site that features whitepapers, visitor loyalty gives an indication of stickiness of your site. This is measured by numbers for the following::

• Loyalty
• Recency
• Length of visit
• Depth of visit

Loyalty is how often visitors visit your web site within a given period of time. If this number is high, it means a large number of visitors are visiting your site repeatedly, and proves strong site engagement.

Recency is the time gap between two subsequent visits of a visitor to your site. For a content rich site, repeat visitors on the same day indicate quality, and relevance of your site’s content to their needs.

Length of visit is the time spent by a visitor in one session in your site, during a given period of time. It indicates the quality of a visit to your site in terms of time spent; how long visitors spend exploring your content. If the length of a visit is long, it means your content is engaging them.

Depth of visit is the number of pages navigated by a visitor on your web site, in one session in a given period of time.

Visitor’s loyalty data is provided in absolute numbers instead of averages. This helps you set a benchmark for your web site’s ‘stickiness’ goals.

This metric is available in the Visitors Overview under Visitors Loyalty.

4. Bounce Rate:

The bounce rate is the percentage of instances where visitors left your site right at the landing page – people that exited the web site soon as they entered it. It is the percentage of single page visits, or of visitors that stay on a site for a very short duration. A high bounce rate means your site’s content is not of relevance to visitors, or that it does not engage them.

This metric can be used to measure traffic quality , and can be analyzed across different data segments and across landing pages to identify poor performers. The definition of a high or low bounce rate depends on the purpose and type of web site. In an e-commerce site, a bounce rate of over 60% may indicate low interest in items for sale. However, a high bounce rate for a blog likely means visitors read the latest post and leave; they still find the content engaging.

This metric can be found in the Visitors Trending report of Visitors under Bounce Rate.

Contributed by Sasthi Sarma, Analytics Team, Position²

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Google Analytics introduced asynchronous tracking in Dec 2009 as an “alternate way to track your website”. According to Google, this code is processed separately and can execute without blocking other code or content. This allows the Analytics code to be placed higher in the page, unlike the traditional code.

The asynchronous tracking snippet is said to offer the following benefits:

• Faster overall page load time
• Improved data collection for short visits to rich media or script-heavy pages
• Collecting (and retaining) user clicks that occur before the tracking code loads

We decided to test Google’s claim that the new code snippet improves page load time. Here’s what we found.

Problem: To study the effect of using the asynchronous code as against the traditional code on page load time.

Hypothesis: Page load time with the traditional code will be higher (i.e. the page will take longer to load) than with the asynchronous code in all possible placements of the codes.

Limitation: The experiment does not attempt to study the effect of the codes on script-heavy or rich media pages. It also does not attempt to study tracking errors when the script has not fully loaded.

Methodology: Six simple HTML pages were created and tested on a local Apache server. Each page consisted of a single line of text and a Google Analytics code. The only difference between the pages was the version of Google Analytics Code being used and its placement.

The six combinations were:

1. Traditional/ Synchronous code placed just before the </head> tag.
2. Traditional/ Synchronous code placed just after the <body> tag.
3. Traditional/ Synchronous code placed just before the </body> tag.
4. Asynchronous code placed just before the </head> tag.
5. Asynchronous code placed just after the <body> tag.
6. Asynchronous code placed just before the </body> tag.

Ten readings of “page load time” were taken for each page using the Firebug add-on. Browser cache was cleared prior to each reading to ensure that page was not loaded from the cache.

Experimental Results

Observations:

1. Overall, the asynchronous version of the Google Analytics code is faster than Traditional/ Synchronous code by 17.03%.
2. Placing the asynchronous version in any part of the file, does not significantly change the load time unlike the synchronous version.
3. Putting the code on top of the page no longer slows the page. This is very important if tracking the click is as important as loading contents of the page. Incomplete page load is a major reason for disparity between GA visits and Google AdWords clicks.

Conclusion:

The overall results support Google’s claim that asynchronous Google Analytics code improves page load time. We recommend that the traditional synchronous code be slowly phased out and the asynchronous code be adopted

This is especially important in paid campaigns where cost per click is being analyzed and where the Google Analytics code should be placed in the header. In today’s internet scenario where users expect to see instant page loads, this is a welcome move by Google

We’ll be testing the other claims that Google makes about its asynchronous page tracking code. Watch out for the next one!

Contributed by Ravi Shukla, Analytics Team

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More than just a fancy graphic, the Analytics Funnel can provide you with clear, actionable insights. The big challenge for website owners when it comes to improving conversions are high bounce-rates, conversion path drop-offs and exits. A Funnel report can provide key insights into visitor behavior which helps to improve website metrics including conversion rates. There are many funnel reports in various analytics packages, but few have the capabilities of Lyris HQ (formerly ClickTracks). It is one of the favorites of our Analytics group at Position² because it has resulted in increased conversion rates of 15-40% for our clients.

In this report, we will not only discuss Lyris HQ Funnel Report key features, but best practices on how we have used it with our clients.

Multi Dimensional Funnel Definition

Most analytics tools assume that a visitor’s progression to a goal or conversion page is linear. The visitor starts at the Landing Page, clicks on ‘Buy Now’, fills the form, completes the purchase and reaches the ‘Thank you’ page. Studies have shown that visitor behavior is not linear, but far more complex. Visitors navigate back and forth between different sections such as ‘About Us’, ‘Privacy Policy’ or move between different product pages before choosing one. This behavior in the extreme is called Pogo-sticking which has been shown to reduce conversion rates. A multi dimensional funnel analytics tool like Lyris HQ can be configured to take this behavior into account. This leads to more relevant and results oriented insights. Analytics Pros such as Avinash Kaushik of Google have consistently rated this funnel report tool as one of the best.

How It Can Help You

A key function of the funnel report is to a visitor’s path from entrance to conversion. It sheds light on the following:

1. Paths that people take towards the goal. This can help eliminate redundant steps. For example, if the report reveals that most visitors tend to read the ‘About Us’ section, it might be useful to have the some summary information about the company on relevant pages.
2. Exit points in your site. These provide insights into reasons why visitors leave and which sites they go to. Exit points in the funnel are highlighted indicating where visitors drop out in the conversion process.
3. Click Patterns. For example, if you find that visitors click on the ‘Contact Us’ button across pages, then it may indicate that the relevant information is not easily accessible to the visitor. You can then modify your website accordingly.
4. Usability experiments: Another use for funnel reports is in usability experiments. You could create different versions of key pages like the ‘Landing Page’ or ‘Registration Form’ and see which design has the best visitor progression to deeper stages of the funnel.

Key Features

Let’s use a consumer financial tools site to highlight key features of the Lyris Funnel Tool like visitor progression and visitor segmentation.

Visitor Progression

To understand the flow from start to end, you need to map visitor progression across stages. Each stage is defined as a group of inter-connected pages, not just as an individual page. It displays which page groups enable visitors to progress to a deeper level by measuring the progression rate. For example, if a page group is requested in 100 visitor session and 50 of those sessions result in a visitor going deeper, the progression rate for that page group is 50%.

This is indicated in shades of blue as represented below. The darker it gets, the more influence the page has in driving visitors towards the goal. In the example below, the ‘Home Page’ and the ‘Sweepstakes Landing Page’ are the two most influential pages.

Visitor Segmentation

Segmentation is created by using labels. Using this mechanism, it is possible to study how different groups of visitors react on various page groups. Progression data for each label can then be viewed by clicking on the label name at the bottom of the report.

For example: Stage 1 can be defined as a group of landing pages. You can analyze which landing page had the highest visitor progression for all visitors as well as which page had the best progression for ‘PPC visitors’ vs. ‘Email campaign visitors.’

In the example shown below, ‘Social media” visitors (segment shown in Green) had the maximum progression from the ‘Home Page.’



Testing The Funnel

We conducted an experiment for a client who wanted to test the existing form completion (which was all on one page) against a registration process broken out over a couple of pages. The results were striking enough to re-evaluate the registration process. The current Registration Page was on a single landing page with 8 fields. The modified Registration Form (New Register) was split across 2 pages with 4 fields in each.

We found that at an overall level, the progression rate improved from 61% to 91% when we used the two step form.



However, this was not the same for all visitor segments. The Progression Rate for visitors landing on the home page via organic and social media sources was higher on the New Register form (two page form). In contrast, visitors who came in from paid advertising sources continued to show higher progression on the current Registration form.

Based on this critical insight, we recommended and helped implement different registration pages based on the visitor’s entry page. The result – overall conversion to registration increased by 23%.

Best Practices In Defining LyrisHQ Funnel

Defining the funnel correctly makes all the difference. Here are some best practices:

1. The first stage of the funnel must be your entry page. This includes the home page.
2. The pages should not be distinguished on the basis of their tracking URLs or parameters since that distinguishes a visitor source and not the page itself.
3. Use labels to create different groups of visitors and analyze visitor progression for each group.
4. Defined page groups should share common attribute. For example: A group of ‘Product pages’ or a group of ‘Registration pages.’ You should also analyze if page modifications have improved the progression rate.
5. Note that if a visitor visits two pages in the same page group, say Landing Page1 and Landing Page2, then the progression is assigned to the first page group (the one on the extreme left in our example). Thus interchanging the order of page groups can change the funnel data.

As you can see, analyzing visitor behavior on your site provides powerful insights on how visitors interact with your website. It can be a lot of fun as well.

If you have any questions or need help with your marketing analytics challenges, please contact your friends at Position².

Contributed by Divya Krishnan, Analytics, Position²

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