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QR Code Analytics

March 30, 2011 By 5 Comments
QR codes have started to pop-up in lot of places such as store display, business cards, online ads, postcards etc. Whether QR codes are here to stay or not but from the measurement perspective they do present a huge opportunity in measuring advertising’s (particularly offline) effectiveness.

If you are one of those marketers who have embraced QR code or are thinking about it or just curious to know how QR code measurement works then this post is for you.

Measuring URLs in QR Codes

You won’t be able to measure the number of impressions of the QR codes if they are distributed offline. What you can measure is how much traffic those QR codes are driving to your site or to your pages on 3rd party sites like facebook page, twitter account etc.

  • Measuring QR code links to your site

    Measuring QR codes that sends user to your site is as simple as campaign tracking. Just add the campaign tracking variable to the URLs that you have in your QR Codes and treat it like any other campaign. Then you can use your campaign reports to see how much traffic QR codes are bringing and how valuable that traffic is.

    (Note: The tracking code, that you should append, depends on your Web Analytics tool.

    For Google Analytics, you need to append add at least 3 variables, Source, Medium and Campaign Name. to the URL for it to be tracked in the Google Analytics (Check out URL Shortner, http://clop.in as it’s URL builder let’s you append the variables for tracking in Google Analytics, Omniture, WebTrend and Unica NetInsights )

    Example
    Say I want to create a QR code to send people to
    http://webanalyis.blogspot.com

    Instead of simply creating a QR code to http://webanalyis.blogspot.com I appended Google Analytic campaign tracking code so my URL looks like the following http://webanalysis.blogspot.com?utm_source=qrcode&utm_medium=blog&utm_campaign=qrcodeblogpost

    Now I can use the campaign tracking in Google Analytics to see the stats on my QR code advertising.

  • Measuring QR links to offfsite URLs such as Facebook page

    Since you won’t have your own web analytics tool running on a Facebook page you can use a URL shortener like http://clop.in or http://bit.ly (or better yet get a URL Shortener for your own domain with built in analytics from http://clop.in) to shorten the destination URL and then build a QR code using the shortened URL. This way you can use the built in analytics functionality of the URL shortener.

    Example:
    Say I want to send user to my facebook page http://www.facebook.com/TheAnilBatra

    Rather than sending user to the facebook page, via my QR code, I created a short URL using http://clop.in, http://clop.in/PByJfv and then used this shortened URL to build my QR Code.

    Now I can use the analytics reporting of http://clop.in/short-url-clopin.aspx?utm_source=qrcode&utm_medium=blog&utm_campaign=qrcodeblogpost to see the stats on my QR code advertising.

Tracking Phone Numbers in QR Code

To Track phone numbers, that get dialed when someone scans a QR code, use a unique phone number that you have tracking for. If you don’t have unique phone number then you can use 3rd party services likes Marchex to get a unique phone number for each QR code that you publish.

Note: To create a QR code use a service like http://qrcode.kaywa.com/ 

Questions? Comments?

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Filed Under: google analytics, web analytics

Why is Google really rolling out Google Analytics opt-out plug-in?

March 30, 2010 By 9 Comments

Last week I wrote about a browser plug-in that will allow people to opt-out of the Google Analytics cookie. The release of this plug-in seems to be a proactive step towards another announcement.

Eric Peterson, on his blog, voiced his opinion on why Google is rolling out this plug-in. I think Eric has a point in his post about Google trying to make a push into Federal Government web sites and this roll out is a check-box to satisfy the needs of privacy advocates.

However, I am still not entirely clear on what Google will get if it can’t use all the data that it will collect on Federal Government website? It won’t be able to use in any way…well maybe at an aggregate level to compete with the likes of ComScore. So what do I think the reason is?

Behavioral Targeting

I think the real answer to Google Analytics’ rolling out Opt-out is “Behavioral Targeting”.
Google Analytics is deployed on thousands of websites and collects a wealth of data. However, so far this data is used by individual sites to optimize their ad spend and improve the site for customers while improving the websites impact on the bottom line. Google has not really used that data much other than providing benchmarking reports in Google Analytics. I think the time has come for Google to start using Google Analytics data to make more money.

Google Analytics Data Sharing

Remember, in 2006 Google Analytics started asking GA accounts to opt-in/opt-out of data sharing. As I wrote in my blog post on Google Analytics Data Sharing, “data sharing” was a step towards using Google Analytics data for behavioral targeting. This opt-out plug-in seems to be a next step in that direction.

Limited functionality of Adwords Remarketing

Last week Google Adwords announced the roll-out of “Remarketing” feature that will allow advertisers to remarket to the visitors (cookies) who came to their sites but left without taking the desired action i.e. without converting (this is also called Retargeting, a form of Behavioral Targeting). For this feature to work, Adwords advertisers will have to put a small script and identify the people (cookies) whom they want to target.
The functionality to identify the segment that you want to retarget seems to be very limited in Adwords.

Google Analytics role in enhancing Adwords Remarketing

Now imagine, you can create complex segment in Google Analytics and then use Adwords to target the visitors who fall in that segment. Won’t that be nice? Well that can’t happen unless you use Google Analytics. Here is an example to make this clear:
I want to target visitors who viewed more than 3 products and looked at the contact us page and then left without converting (or it could be more complex than this).
This is a segment that I won’t be able to create easily in Adwords without some coding on my pages. Using Google Analytics, advanced segment functionality this is a piece of cake. I create my segment then use Adwords to remarket to visitors who fall in that segment. Won’t that be more powerful? I think that’s where the money is.

Other Behavioral Targeting Scenarios with Google Analytics

Let’s take a hypothetical example to see how this will work.

Say I write a blog on luxury cars. I use Google Analytics as my web analytics tool (it is free, so why not). I don’t have real business model but I use Adsense ads to make some extra money. Since my blog is really popular I get lot of traffic. My blog reader are those who have interest in luxury cars because that’s all I write about.

Now imagine Google asks me to pool my visitors’ information, collected in Google Analytics, into a pool that they can use for advertising purposes. They tell me that by doing so I will be able to make some more money from my visitors even when the visitors have left my site. Hmm… free money. Sure :)

How will Google use that data?

Google will identify the visitors who come to my blog, read articles, write comments, come often etc. They will then put the visitors (cookies) who fulfill certain criteria (as defined by Google or the Advertiser) in a segment called “Luxury Car Enthusiast”. They will pool my data with other similar sites or similar pages on other sites to create a bigger pool than my site has to offer. Google now has a set of cookies who are interested in luxury cars.

BMW creates a campaign in Adwords to reach “Luxury Car Enthusiasts”. They are willing to play 2X-3X CPC or CPM to reach this target.

A visitor, who left my site (A) and lands on a site (B) that, has nothing to do with luxury cars. Google can identify that visitor because that visitor is in the pool of cookies that belong to “Luxury Car Enthusiasts” segment. Google shows this visitor an ad from BMW on Site B. Visitor sees the ad that matches his/her interest and the advertiser reaches their target.

Note: This all is a speculation by me and I have no official information.

Win-Win Situation

  • BMW finds its target
  • Google gets more money for the same ad slot.
  • Publisher of the ad makes more money.
  • Sites, which pool their visitors in this segment, get a cut.

What does GA Plug-in has to do with all this?

Behavioral Targeting has been under scrutiny for a long time and there is a big uproar about tracking people. Privacy advocates want an easy way for people to opt-out of Behavioral targeting and this plug-in seems to be a proactive step in that direction.

What do you think?

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Filed Under: Adwords, behavioral targeting, google analytics, privacy

Google Analytics Opt-out Browser Plug-in

March 22, 2010 By 1 Comment

Late last week Google Analytics announced a browser plug-in that will allow users to opt-out of Google Analytics tracking.

Here is an offical statement from Amy Chang, group product manager at Google Analytics:

As an enterprise-class web analytics solution, Google Analytics not only provides site owners with information on their website traffic and marketing effectiveness, it also does so with high regard for protecting user data privacy. Over the past year, we have been exploring ways to offer users more choice on how their data is collected by Google Analytics. We concluded that the best approach would be to develop a global browser based plug-in to allow users to opt out of being tracked by Google Analytics. Our engineers are now hard at work finalizing and testing this opt-out functionality. We look forward to make it globally available to our users in the coming weeks.

How does it impact the website stats reported by Google Analytics

This is the biggest question everybody is asking. Since I don’t have the full information on how this plug-in will exactly work, let’s take a look two potential ways this plug-in can work.

  • The plug-in will not collect any sort of data for the user
    Let’s assume that this plug-in will ensure that absolutely no data about the users behavior on any of the site that user visits will be collected. Which means that Google Analytics will completly ignore the presence of that person (computer) as if that person never visited the site. In that case all of the data, i.e. visitors, visits and page views, conversion etc., will be under reported.
  • The plug-in will collect the data but then destroy the cookie after every session (visit)
    In this case, a session level cookie will still work but this cookie will be removed from the users computer after every session. This will result in the accurate count of visits (sessions) and page views and all the data that is reported at visit or page view level. However, the visitors count will be inflated since same person (computer) will get a new cookie for every visits.

How big the impact will be?

Well, it is not clear how widespread the use of this plug-in will be. Currently, there are 3rd party tools available clear or block Google analytics cookies but the use of such tools do not seem to be an issue for Google Analytics tracking. So I wonder if this plug-in will have any huge impact on Google Analytics data. However keep in mind that there will be some impact.

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Filed Under: google analytics, privacy

Is Your Conversion Rate Wrong?

January 22, 2010 By 6 Comments

According to Web Analytics Association (WAA) standard definition, a conversion is a visitor completing a target action.

Conversion rate is calculated by total number of visitors completing a target actions divided by a “relevant denominator” (Sometimes it is total number of target actions divided by a “relevant denominator).

The key here is “relevant denominator”. This is often ignored by many organization and they tend to use the default denominator provided by their Web Analytics tool provides. The most common denominator used by most of the tools is either total visits or total visitors.

If you are an eCommerce site then potentially every visitors or visit is a target for conversion and “Total Visits” or “Total Visitors” as a denominator might make sense. However if your site is a non-eCommerce site and conversion to you means getting people to register on the site then using a denominator like total visits or total visitors to calculate conversion rate, wrongly assumes that all of the visits to sites have not previously converted (registered) and hence are potential conversion worthy. We all know that’s not the case. These sites get a mix of visitors, visitors who have registered in past and are not going to register again and, visitors who have not registered yet and hence are a potential target. Examples of such sites are facebook, WSJ.com, espn, yahoo etc.

Calculating the Right Conversion rate

In my examples I will assume that number of visits is equal to the number of visitors.

Hypothetical site in our examples is a portal that requires visitors to login (register) for some actions but also has content that does not require a login.

This sites gets an average of 1000 visits a day and gets 50 new registrations (conversions) a day. By most common way of calculating the conversion rate, the conversion rate for this site is 5% (i.e. Conversion Rate = Conversions/Total Visits = 50/1000 = 5%)

Let’s assume that every day this site gets 50% of the visits from the people who have registered in past. So that means 500 (50% of 1000) are registered and won’t register again. Since they are not going to register, why do we even consider them when calculating the conversion rate? By removing them from the calculation of conversion rate, the new conversion rate becomes:
50/500 = 10% (Immediately you doubled your conversion rate)

Why is this important?

Let’s look at two scenarios to illustrate the importance of choosing a relevant denominator.

  1. Scenario 1
    One day the customer retention department sends some emails to previously registered visitors that result in 500 more visits from people who had previously registered. As a result the site got 1500 instead of 1000 visits it used to get every day. Considering that nothing else changed on the “not-registered” visitor base, the site got 50 conversions, just like any other day.

    Using “Total Visits” as the denominator, it appears that the conversion rate drops to 50/1500 = 3.33%

    It looks bad, and might cause you to panic. Won’t it? However if you choose the right denominator you will find out that nothing really changed. The pool of people who had not previously registered is still 500, so the true conversion rate is still the same
    50/500 = 10%.

  2. Scenario 2
    The “customer acquisition” department bought a new email list and sends emails to this list which resulted in 500 extra visits. Overall the site got 1500 customers, 1000 who had never registered and 500 who had registered previously. That day the site got 60 registrations, resulting in a conversion rate of
    60/1500 = 4%.

    Look like our email list did not work because it caused the conversion rate to go down.

    This might cause you to wrongly assume that the email list that the retention department bough was not as good as the other traffic that you have been getting. However when you calculate the true conversion rate, it turns out that the email list actually worked; it resulted in a conversion rate of 6% (60/1000).

Note: Some might argue that why won’t this company tag all the emails with proper campaign identifiers so that we can track the performance of the emails. In theory this sounds great and that’s how it should be. In reality though, many departments work in isolation and never interact with the ‘Web Analytics” or follow their best practices. Now you know why marketing departments should share the marketing calendar/activities with “Web Analytics” group and involve them before running any campaign.

Hope this gives you an a reason to investigate what denominator you are using in your conversion rate.

In the next post, I will show you how you can calculate the “True Conversion Rate” using Google Analytics.

New Job: Analytics Associate at Huge (Brooklyn, NY)

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Filed Under: conversions, google analytics, web analytics

Google Analytics Motion Charts Overview with a PPC Example

February 25, 2009 By 3 Comments

Most people look at click through rates, clicks or visits when analyzing their keywords. Some people will go a step further and look at the conversion rates. If you have a content site with no end conversion you might look at bounce rate to figure out what keywords are working or you might look at the page views/visit or look at some other form of engagement metrics. To look at all the data points you need to do full analysis, which requires you to look at a few reports and maybe use excel to pull all that data together in one view. Google Analytics Motion Charts make it really easy to visualize several data points in one screen.

In this post I will show:

  1. Step-by-step overview of how motion charts work.

  2. An example of how I used Google Analytics motion charts to figure out which keyword to spend money on.
    Note (I could have got similar information from a few reports in Google Analytics, but motions charts just made it easier and faster).

Let’s get started

To access motion charts,

  1. Click on your report (keywords report in this case).

  2. Select the date range that you want to analyze.

  3. Click on Visualize button on the top. This will bring the motion chart (see below).

In the motion charts interface you can choose up to four dimensions that you can plot your data point against and see it in this graph. The four dimension areas are circled in the image below. Two dimensions can be plotted on the chart on X-axis and Y-axis, one using the color and the fourth using the size of the bubble that represents the data point.

For the dimensions on X-Axis and Y-Axis you have the option to plot the data either on “Linear” or “Log” scale. I chose linear in this case. (Note: You should choose log when your data points have too much variations in their values from one day to another. Using a linear scale will make it hard to draw them on one chart but log scale will make the chart much cleaner.)

You can select the dimensions by clicking on appropriate section (see below)

After you have selected your dimensions and chosen the scale (log or linear) you want, click on the data points (bubbles in the middle of the chart) to show the name of the data point. Also, those data points are will be tracked when the “Trails” option is enabled (discussed below).

Once all your selections are done you can check the “Trails” check box to track the progression of each data point on each of the dimensions you selected over the time period that you selected. Even though “Trails” is optional, I have found it to be an important feature to explore the full power of motion charts.
Note: if you select too many bubbles (keywords in this case) along with “Trails” then the motion charts become very crowded. You should limit selection to no more than 3 bubbles at a time.

In this case I chose two keywords, Keyword A and Keyword B since I wanted to see how they compare to each other on the dimensions that I chose. (Actual keywords are disguised for this example).

So why did I choose those two keywords? I actually chose a lot of keyword combinations. I was trying to find the keywords which I should buy on search engines to drive more traffic. Since the budget is limited (isn’t that the case everywhere?) I had to pick the keywords that will give us the biggest bang for our buck.

This site had two main site goals:

  1. Convert visitors to registered members (Registration).

  2. Drive more page views/visit to increase the ad inventory and hence ad revenue.

Based on these goals we needed to see following metrics:

  1. Conversions driven by keywords

  2. Visits Driven by Keywords

  3. Page views/Visit

  4. Bounce Rate

Since both the keywords had about same conversion rate, it was not important for us to plot it on X and Y-axis and it was chosen to be represented by the bubble size. I plotted metrics 1 and 2 (see above) on Y-axis and X-axis respectively since I wanted to see how the traffic is driven and how many pages people view when they visit the site. Bounce Rate was chosen to show different colors.

In my option, any critical metrics should go on X-axis and Y-axis. You should play with plotting all different metrics on different axis, color and size to see which one provide you more meaningful view. Plotting them in different ways provide different views and ideas of additional metrics that you might want to look at.

After all the dimension selections were made, I checked the “Trails” option and hit the “Play” button to see the motion chart in action (You can also manually move the slider next to play button).

Here is the final Motion Chart:

Looking at this it is clear that Keyword A is driving more traffic but lower Pages/Visit than Keyword B. The bounce rate is also higher for keyword A. The conversion rate is about the same for both keywords.

It is clear that we should put more dollars behind Keyword B as it helps us achieve goal number 2 better than Keyword A. Goal 1 is about the same for both these keywords.

Hope this post was helpful in demystifying Google Analytics Motion Charts.

For any questions on Motion Charts and Google Analytics please visit Ask an Expert section on my site.

Comments? Questions?

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Looking to fill your Web Analytics or Online Marketing position?Post your open jobs on http://www.web-analytics-jobs.com/
Sr/Lead Analytic Warehouse/Java Engineer at Saas Company In the SF Bay Area (Emeryville, CA)
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Site: AnilBatra.com
Twitter: http://www.twitter.com/anilbatra
Filed Under: actionable web analytics, google analytics, Google Analytics Tips, SEM, SEO

Things I have been working on

February 18, 2009 By 1 Comment

There has been a lag in my blog posting lately, and it’s gone on longer than I like. Lately, things have been hectic at work leaving very little time for blogging. Any time left has been consumed the following activities:

  1. Working on my Site, AnilBatra.com
    1. Ask an expert – I started a section for you to ask questions on Web Analytics, Behavioral Targeting and Web Analytics tools, particularly Omniture, WebTrends and Google Analytics. The questions will be answered by one of the people who have volunteered, one of my team members at Ascentium or me personally. So if you have a question please go ahead and fill out the form and someone will get back to you within 48 hours. If you would like to be one of the experts please email me a brief description about your skills and experience.

    2. Twitter – Google Analytics URL Builder – Nothing fancy here but a tool that allows you to create short URLs that you can post in Twitter and track them in your Google Analytics. It allows you to create short URLs for both your internal links (links to you own site) and external links (links that reside outside your site).

    3. Calculators – I am working on some basic and advanced calculators related to Web Analytics and Online Marketing. So far I have added a CPM, CPC, and CPA calculator (will be adding few more optional fields to it). More calculators will be added to this one. If you have suggestions on what calculators I should add then please send them to me.

  2. Industry Average Bounce Rates – I conducted a survey to understand what Bounce Rates to expect for various types of sites. I added a pdf that shows the results of this survey.

  3. Google Analytics Session for Seattle Tech Startup Group – I, along with Loren Bast, conducted a Google Analytics training session for the Tech Startup community of Seattle. Download the Seattle Tech Startup Google Analytics Presentation.

  4. Future of Web Analytics Panel at SDMA – I have been busy putting together a panel for the Seattle Direct Marketing Association to be held on March 11.

  5. Preparing for Breakfast Event in San Francisco – Gary Angel, Akin Arikan and I will be speaking at a breakfast event in San Francisco on March 9th. You can find more information and registration on my site.

  6. Neel – Neel is the name of my 5.5 month old son. He is growing and getting more and more active every day and needs more of my time.

I expect to resume my blog writing schedule very soon.

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Looking to fill your Web Analytics or Online Marketing position?
Post your open jobs on http://www.web-analytics-jobs.com/

Sr/Lead Analytic Warehouse/Java Engineer at Saas Company In the SF Bay Area (Emeryville, CA)

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Site: AnilBatra.com
Twitter: http://www.twitter.com/anilbatra
Filed Under: bounce rate, google analytics, web analytics

Social Network in Action: How Twitter Helped Me

February 9, 2009 By 1 Comment

Twitter has become a tool that I have come to love. It has provided me a way to connect with people I have never met, and it has helped me find information that I might have missed otherwise. A recent experience on Twitter showed me how this tool can be used to gather customer feedback and also to collaborate with others to solve a problem.

Last week I wrote a small tool to help me create short url’s with Google Analytics Tracking embedded in them. This tool allows me to create short URLs for links that are both external (not residing on my site) as well as internal (residing on my site). The tool is open to the public so please give it and try and send me your feedback. It is nothing fancy but something that I needed for my own use. (More on this in another blog post). (Also see, Twitter Analytics and Google Analytics Twitter URL Builder)

At 11:04 p.m. on February 5th I tweeted about this tool.

Within minutes I got my first feedback in a direct message (also called “DM” in twitter language).

Yes, I had not tested the tool in other browsers and O/S but my Twitter friends came to my rescue and provided me valuable information. Several other people jumped in and provided feedback. I immediately jumped into action to fix this issue. Had it not been for Twitter I don’t think I would have been able to get such feedback so quickly.
Next I was able to isolate the issue, which was a JavaScript problem. The JavaScript I had on the page worked fine on IE but not on Firefox. Since I am rusty with my JavaScript I had 4 options

  1. Search on a search engine and try to sort out all the content and find what I was looking for.

  2. Call one of friends/colleagues/Freelancers who are JavaScript gurus

  3. Buy a JavaScript book and try it on my own

  4. Try to find if my Twitter friends (people I might not have met in person but are following me on twitter) will help again

Given that it was late, I went to bed and thought about fixing the JavaScript issue the next morning. The next morning I decided to give option number 4 a try, and posted the following message in Twitter:

I posted the message on Twitter at 7:25 a.m. and within minutes I got reply from my Twitter friends, who helped to debug or find a solution for me.

Isn’t Twitter a powerful tool for feedback and collaboration? Despite all the noise that happens on Twitter, it can be a very valuable tool, as I have found it on several occasions.

Following are my Twitter Friends, who provided me the valuable feedback, helped with the javascript issue and/or help spread the word via their ReTweets (Thank you all).

Comments? Questions?

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Looking to fill your Web Analytics or Online Marketing position?

Post your open jobs on http://www.web-analytics-jobs.com/
—————————————————————————-
Site: AnilBatra.com
Twitter: http://www.twitter.com/anilbatra

Filed Under: google analytics, social networking, twitter, web analytics

Google Analytics: Creating Advanced Segments and an Issue

November 12, 2008 By 5 Comments

Last month Google Analytics released Advanced Segmentation functionality. I am very impressed with it so far, however this functionality is still in beta, which means it could potentially have some issues. In this post I will show you how to create an advanced segment and one issue that I found with it.

Business Problem: I want to see all the data for visits that originate from Google.

Solution: Create an Advanced Segment that will have all the visits that has the source as google.

Let’s see how to create such a segment. (Not this is a very simple segment but the same steps can be used to create more complex segments).

Creating an “Advanced Segment”

  1. Click on the “Advanced Segments” link on the left navigation bar under the “Settings” section.

  2. In the next screen you will see all the “Advanced Segments”. In this screen you manage all the advanced segments. Google Analytics has predefined some of the segments and they are grouped under “Default Segments”. The segments that you create will be listed under “Custom Segments”. To create a new segment, click on the “Create new custom segment” link on the top right hand corner.

  3. The next screen is where you create the segment. The segments can be created by using one or more dimensions and metrics. On the left hand side you have 2 sections “Dimensions” and “Metrics”. I chose a dimension of “Source” listed under “Traffic Source” as I wanted to see all the visits which originated from Google (i.e. the source was Google). I chose “contains” as the condition as I wanted to get all the visits that originated from anything that contained Google in the source. Finally I entered the word “google” in the value.

  4. Click on “Test Segment” button to do a sanity check and see if the segment size is as expected. Once you are satisfied with the segment, give it a name in “New Segment” filed and click “Save Segment” to Save the segment.

  5. Once you the save the segment you will be taken back to “Mange Segments” where your new segment will appear. See below, a new segment called “Google Visits” show up. The new segment is now ready to be applied to various reports.

So far so good. However, I found one potential issue with the data.

The Issue

I applied this segment to one of the pages in my Content Report (see below) and chose the date as Nov 3rd.


As you can see my “Google Visit” segment is reporting 328 pageviews while my “All Segments” is reporting only 175 pageviews. That does not seem right. Similarly Unique Views is 112 for “All Visits” while 201 for “Google Visits”. As you can also see from the graph, “Google Visits” are higher than “All Visits” on several dates not just November 3rd. Has anyone else seen something similar? Am I not reading these reports correctly?

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Site: AnilBatra.com
Twitter: http://www.twitter.com/anilbatra
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Looking to fill your Web Analytics or Online Marketing position? Post your open jobs on http://www.web-analytics-jobs.com/

Filed Under: google analytics, Google Analytics Tips, web analytics

Goal Attribution to Organic Keywords – Google Analytics Tips and Tricks

July 23, 2008 By 1 Comment

In April I wrote a blog post to show you why some of your keywords show 0 Visits and 0 page views in your Google Analytics Report. In this post I am going to show the attribution of goal to the search engine keywords, when a user searches multiple keywords on the search engines to visit your site (all within the same session) and converts from one of the keywords.

Note: The past post and this one are both based on the Organics keywords searches and clicks.

I conducted two following two experiments

  1. Converted on the Last Keyword

    1. Searched “1 page no register seattleindian” on Google, arrived on http://www.seattleindian.com/ , viewed one page and existed the site by typing Google.com in the browser address bar

    2. Searched “2 pages no register seattleindian” on Google, arrived on http://www.seattleindian.com, viewed 2 pages and then exited the site by typing Google.com in the browser address bar

    3. Searched “4 pages register seattleindian” on Google, arrived on http://www.seattleindian.com , registered on the site (converted, Goal 1), viewed total of 4 pages and then exited the site by closing the browser

    4. All of the above was done within 30 minutes and using the same browser session

    Visit and Page View Attribution

    As you can see my visit resulted in 3 keywords, total of 7 page views and 1 visit (visit time out is 30 mins and all of it was done in 30 minutes). As I showed you in the last post, 1 page view is shown and attributed to 1st keyword. The other keywords do not get visits or page views attribution (Figure 1). The total pages are accounted and counted in the keyword report even though 6 pages were not attributed to any particular keyword (Figure 2)


    Figure 1 (click on the image to enlarge it)


    Figure 2 (click on the image to enlarge it)

    Goal Attribution

    In this scenario, the Goal is attributed to overall search engine keywords but not to any particular keyword.


    Figure 3 (click on the image to enlarge it)

  2. Converted on the First Keyword

    1. Searched “SeattleIndian 4 pages register test 3” on Google, arrived on http://www.seattleindian.com , viewed 4 pages, registered on the site (converted, Goal 1) and then exited the site by typing in Google.com in the browser address bar

    2. Searched “SeattleIndian 3 pages no register test 3” on Google, arrived on http://www.seattleindian.com, viewed 3 pages and then exited the site by typing Google.com in the browser address bar

    3. Searched “SeattleIndian 1 pages no register test 3” on Google, arrived on http://www.seattleindian.com , viewed 1 page and then existed the site by closing the browser

    4. All of the above was done within 30 minutes and using the same browser session

    Visit and Page View Attribution

    In this case I converted (Goal 1) when I arrived via the first keyword. When I look at the Site Usage of keywords, the first keywords is credited with 1 visit and 4 pages, the other two keywords did not get any credit of the visit or the pages that were viewed as a result of click on those keywords. So the 3 pages are not attributed to any keyword. This is what I showed in my last post.


    Figure 4 (click on the image to enlarge it)


    Figure 5 (click on the image to enlarge it)

    Goal Attribution

    In this scenario when the conversion happens from the first keyword, the goal is properly attributed to that keyword.

Conclusion

When a user searches multiple keywords to arrive to the site,

  1. The visit is attributed to the first keyword only

  2. The page views directly related to the first keyword are attributed to that keyword and other keywords show 0 visit and 0 page views

  3. Total page views from all the keywords are counted in the overall keyword report

  4. If the conversion happens as a result of the first keyword then it is attributed to that keyword

  5. If the conversion happens as a result of any of the keyword other than the first one, then the conversion is not attributed to any of the keywords

  6. The conversion from any keyword is counted in the overall keyword report

What’s next? I will be testing how attribution works when a user clicks both Organic (SEO) and Paid PPC (search result) within the same visit.

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Filed Under: google analytics, Google Analytics Tips, SEO, web analytics

Web Analytics Buyer’s Guide, Assessing Vendors’ Competencies and Value

July 17, 2008 By 1 Comment

John Lovett from Jupiter Research released his new report titled Web Analytics Buyer’s Guide, Assessing Vendors’ Competencies and Value. In this report he analyzed the web analytics market and various vendors, and concluded:

The Web analytics feature race is largely over and we are left with Web Analytics providers competing on Price and Flexibility.

John provides great analysis and insights in this report but I do not fully agree with his conclusion that feature race is largely over. I believe that as new trends in web continue to emerge so is the need for new features. As Phil Kemelor, another great analyst, writes that audio and video are becoming an important part of the web, so there are clearly features needed to support their tracking. Other features currently missing are for Mobile Analytics. Mobile web is clearly gaining momentum and so is a need for Mobile Analytics. I am sure Product Managers of these tool providers are not ready to quit yet.

I do agree that most of the vendors are mostly competing on Price and Flexibility (post-data-capture segmentation, reporting on custom data elements etc.) at this time. (In my view, flexibility is also a feature though). However competing on price when there is still a lot of room for innovation and differentiation is a very myopic view by current web analytics vendor and will provide a way for someone to disrupt their business very quickly.

Satisfaction with the current tool

One of the surprising results in this report was that more than 69% of the web analytics clients have decided to stay with their current web analytics tool. This is a big change from the trend that we have seen in past. A lot of this has to do with education about web analytics tools, what they can do, how they operate and the fact that several most used features and reports are comparable in various tools.
72% of those 69% where completely satisfied with their current provider.
47% of the customer said that the biggest challenge they were trying to solve with Web Analytics tools was Vistor Segmentation. 47% also said that customer engagement was their biggest challenge.

And the Winner Is?

This report has a great 3 dimensional chart showing how different tool vendors rate in terms of business value, market suitability and breadth of company.
Omniture, Unica, and Coremetrics emerged as industry leaders for large enterprises while WebTrends, Google Analytics, IndexTools and Lyris HQ ClickTracks emerged as industry leader for small-to-midsize businesses along with the three listed for large enterprise customers.

It was surprising to see that WebTrends was not considered an enterprise tool anymore, though WebTrends was the only company to score 100% on availability of basic features. (Sidenote: A customer of ours is replacing Coremetrics and going with WebTrends).
Another surprising result was that Omniture was the overall winner even for small-to-midsize businesses.

You can get the complete report at http://www.jupiterresearch.com/bin/item.pl/research:vision/79/id=100411

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New Position
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Filed Under: coremetrics, google analytics, IndexTools, Jupiter Research, omniture, unica, web analytics, webtrends
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