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Web Analytics Implementation Process Labels: analysis, Omniture, plan, QA, webanalytics


My take on 404 Error Page Naming and Analytics 


Small change with a huge impact

Recently I was involved in changing the layout of a website and measuring the impact of that change. We changed the Top navigation on this website and changed the color of a link to Red/Bold. It was a very minor change with respect to the whole website as the Top navigation menu only contributed to less than 5% of users engaging in the website. We wanted to make this change to enhance the Top navigation and entice more clicks on the edited link (Coupons). P.S. We leveraged Google Analytics to measure this change. Below is a screenshot of the previous Top Navigation menu: After a week, I pulled the ‘Top Content’ report and filtered on the Top menu Coupons link. I was pleasantly shocked to notice the results. There was a 65% increase in User Engagement (Clicks) on the Top navigation Coupons link clearly due to changing the link color to Red. From the context of the website, this page amounts only to a small proportion of traffic but this change has paved the way for similar changes which can be replicated on others pages in the future. Below is a screenshot of the change we made on the Coupons link: Immediately after noticing this change I sent a tweet in excitement: ‘Wow! Top menu navigation link text change resulted in over 60% increase in user clicks. Changed the font of the link to Red/Bold #ga #wa’. Surprisingly, I got a response from a Twitter user: ‘So funny. We also changed the nationalgeographic.com top nav to red/bold in Dec 07 for commerce promo. It stuck.’ The user mentioned that Nationalgeographic also made a similar change back on 2007 and they too noticed an increase in clicks on the button. Isn’t it coincidental?

Going forward we plan to replicate the same exercise on the Side menu. We will also be performing AB Tests on the Top navigation menu and compare it with newer menus. P.S. It is always a good practice to add a query string parameter in the URL. E.g. Add ‘menu=top’ (http://www.seattleindian.com/seattle/indian-restaurant-coupons.asp?menu=top) to distinguish this URL as a Top navigation link.

Hope you like this article. Please comment and let me know if you’ve done similar exercises and noticed a considerable impact.
Posted by Rohan Kapoor at 6:46 PM No comments: Labels: AB Testing, analysis, multivariate testing, web analytics


My take on Social Media Analytics

Social Media is one topic which I haven’t written about in the past. I am still coming to grips with Social Media as there’s so much to learn but am convinced that it is something which just cannot be ignored. I say this because websites like Facebook with 200 Million active users (Source: Facebook), LinkedIn with 39 Million users as of May 2009 (Source: Wikipedia) and Twitter with 7 Million users as of Feb 2009 (Source: Nielsen) are at the peak of their popularity with more users being added by the hour. P.S. Population of United States is 304 Million as of May, 2009. In this article I will write about my experience with Social Media Analytics and the mediums to track it. Twitter will be covered in detail.

Social Media measurement:

1) Twitter Campaign Analytics: It is very easy to measure traffic from Twitter with Campaign Tracking parameters. There is an article which I wrote on Email Marketing which brushed on the concept of Campaign tracking. Here is an example of a Twitter Campaign URL which can be measured in Google Analytics: http://rkapoor.blogspot.com/?utm_source=twitter&utm_medium=googleurlbuilder&utm_campaign=socialmediaarticletweet. This URL was created from the Google Analytics URL Builder. These campaigns can be attributed to Web Analytics metrics like Bounces, Time spent on Site, Visitor Type and Conversion once we start getting traffic from Twitter.

2) Short URL Analytics: There are also various short URL websites which can shorten the campaign URL to be placed on websites like Twitter (Maximum Twitter Tweet length is 140 characters so short URLs are very efficient). One of the websites is Clop.in which offers you an interface where you can easily create campaigns for Google Analytics, Omniture and WebTrends. This tool will allow you to create a custom campaign URL string and will shorten it. I leveraged this tool to shorten my Twitter campaign URL to http://clop.in/PWGCto. Other short URL websites are tinyURL, Cli.gs etc. Some of these websites will also report the total clicks, referrers and location of the users who clicked on the short URL along with measuring metrics on the destination website.

3) Reputation tracking tools: A new variety of tools have appeared which allow companies to analyze what consumers are saying about their brand (Customer Sentiment). I tried my hands on Radian6 and SM2 which offer a great interface built out of data captured from blogs, forums, review websites microblogging and news websites. These results are captured based on the keywords which users or companies search for. SM2 has a great graphical interface whereas Radian6 shows keyword search results via Widgets. For e.g. Microsoft can search for ‘Zune’ and analyze consumer feedback/mood for this product. The possible data points available from this search keyword can be segmentation of Media type with the location, sentiment and engagement. Based on this data, companies could even contact sources/people that have a negative taste about the brand to improve their reputation. Other reputation tracking tools are Buzzlogic and Buzzmetrics.

4) Twitter specific tools: As Twitter is the hottest from of Social Media today, companies are putting in more effort to know about their competition and the most popular trends in real time on Twitter. Some tools which I have used and report data from Twitter are Tweetvolume (Competition comparison tool) and Twitscoop (Current buzz on Twitter). There are some websites like Tweetvalue which even assign a price to your Twitter profile and offer to buy your Twitter account (I haven’t really tried selling it yet).

5) Widget Analytics: Another form of Analytics which comes under the Social Media umbrella is Widget Analytics. Widget is a snippet of code which can be embedded in Web pages and can be used to display Videos, Ads, News, and Weather etc. Widget integration has recently exploded with Social networking sites, blogs, and Ecommerce websites etc. Some metrics which can be measured on Widgets are clicks, impressions, install conversion, widget stickiness, installs by country and time spent on widget etc. Some tools which provide widgets and an interface to measure them are Gigya and Clearspring etc.

Through this article I am able to share my experience with Social Media measurement but there are lots of other tools which I haven’t used or mentioned. Please let me know if I need to add any more tools as many might have been missed. I will be writing more about Social Media in the future.
Posted by Rohan Kapoor at 4:11 AM 2 comments: Labels: campaign tracking, facebook, linkedin, social media analytics, twitter, widget analytics


Should a Web Analyst have development skills? - Part 2

I wrote an article in 2007 about Web Analysts having development skills and my conclusion was that it would be an add-on to have basic skills. I got a comment from a user who thought that only having analytics skills are suffice and I somewhat still agree with him. I have seen a lot of Web Analysts who are perfect in analyzing data but don’t have development skills. They tend to do very well in their usual job but lack context pertaining to the implementation of code. This article will cover what I’ve learnt about the Web Analytics job market since then and what makes an ideal Web Analyst.

I have been analyzing the Web Analytics job market and have noticed that almost 90% of the listed profiles have a mention of Web languages like HTML, JavaScript or Flash. (P.S. I had predicted such a trend) This wasn’t so common a couple of years back when companies mostly looked for people who are simply ‘Analysts’. In my opinion, it is very important to know how the Web Analytics code works and the technology behind capturing data. My take on the Web Analytics data capture is explained here. In one of my assignments, I was involved in configuring the Web Analytics tracking code to track features which were not possible through the generic snippet. A JavaScript Wrapper had to be created and added in the code. This asset helps a Web Analyst to stand out and is often the path to rise in the organization as a multi-talented contributor. Apart from knowing programming, it is also helpful for a Web Analyst to know basic SQL as most companies have an in-house reporting system which might need to be extracted for analysis.

We can have hours of discussion on whether the above skills are really necessary for a Web Analyst but based on the current market situation, extra skills other than analysis will be more than useful. Below are a few skills which I think will be make a very good Web Analyst in the order of priority:

1) Analytical skills, drawing conclusion from data and offering recommendations to improve the business (Presentation skills and Excel knowledge included)
2) Client interaction and excellent interpersonal skills (Requirement gathering and building relationships)
3) Statistics knowledge (Ensuring whether data is ready for analysis and concepts like Confidence level)
4) Basic Programming skills (Understanding Web Analytics code and ability to enhance it)
5) Basic SQL skills (Ability to pull data from the backend databases if necessary)

I will appreciate if you can share your views in case you agree/disagree with this article.
Posted by Rohan Kapoor at 1:33 PM 2 comments: Labels: javascript, SQL, web analysis, web development


My take on Web Analytics Testing/Quality Assurance

When we discuss Web Analytics, we talk about Implementation, Reporting/Analysis and offering recommendations on how to improve a website. QA/Testing is something which is often put in the backburner. Companies focus on the Analysis of data but don’t usually concentrate on the validity of data. This often leads to reimplementation of code and the data being inaccurate. This is not good news for companies tagging their website with Web Analytics code and paying for Implementation and Analysis. This article at a high level explains some of the things which may help improve the Web Analytics QA process of organizations. These are some of the points which can be useful for Web Analysts looking to setup a Web Analytics QA process.

1) Spread the word: Spread awareness about Web Analytics QA benefits to your Manager/Stakeholders specifying how thorough Web Analytics QA can help them save cost in the future. For e.g. you can tell them that proper Web Analytics of the code/data can help companies reduce repetitive fixes/patches of code or resultant skewness of numbers while performing Analysis.
2) Include in Release cycles: Try to incorporate Web Analytics QA in the weekly/monthly release cycles by bringing into perspective all stakeholders from QA Analysts, Developers to Program/Project Managers. It might involve lot of patience, persistence and convincing but it is worth the time. Come up with a process document/Flow chart depicting the QA process and share it with the concerned teams.
3) Performing QA/Knowledge Transfer: Once the Web Analytics QA process has been approved, start off by performing QA yourself and transition this responsibility to the resident QA Analysts as a Web Analyst should be involved more with Analysis/client interaction etc. Sharing tutorials about different Web Analytics tools might be helpful to pass on to the QA Analyst.
4) Create a reusable Test document: Once the training has been imparted, the QA Analyst or Web Analyst can create a test document to perform Web Analytics testing. The QA document should leverage a Packet Sniffer (explained in my previous article) and should also validate data in the Web Analytics tool. For e.g. the below screenshot makes use of conditional formatting to color incorrect values (not matching requirement) as Red and correct values as green. This is a screenshot taken from a QA document which I created in one of my assignments where we were QA’ing Omniture data. This document helps in validating the code as well as checking the output and should be reusable. Hope you like this post. Please comment in case you agree/disagree with my analogy about Web Analytics QA process being an integral part of a Web Analytics assignment.
Posted by Rohan Kapoor at 7:29 PM 2 comments: Labels: packet sniffer, QA, validation, web analytics testing


Vote for Anil Batra running for the WAA board of directors

Please cast your vote for Anil Batra (webanalysis.blogspot.com) at http://cli.gs/Y2PpTH running for the Web Analytics Association Board of directors.

P.S. You can only vote if you are a Web Analytics Association member.
Posted by Rohan Kapoor at 10:15 PM No comments: Labels: Anil Batra, WAA election My Profile on Linkedin