Analytics

Introduction

Web analytics is the measurement, recording and analysis of web data to use for some significant purpose (e.g. digital marketing data)

Defining the Area


  • Analytics is the understanding and interpretation of patterns found in data. While it is similar to analysis, it is considered the next step. When using analytics, data is first analyzed, then broken down and interpreted to find patterns that can be used to locate consumer trends, increase cost efficiency, or simply plan for the future. For this reason, analytics is used predominantly in business and market settings.1
  • Analytics relies on statistics (preexisting data), computer programming (software and formulae used to observe patterns), and operations research (understanding and deciding the most viable direction for improvement once patterns have been discovered). Data visualization is also used frequently. Graphs, charts, and tables help portray the most important patterns in a user-friendly manner. This makes it possible for anyone to use analytical software.
  • Analytics platforms are mostly offered as SaaS (software as a service) in an open or closed source format. They aim to combine multiple tools to manage data more efficiently than relational database management systems (RDBMS). These tools can include an engine (an executive command system), a DBMS (database management system), data mining (the process of combing through "big data", large amounts of data found normally in business or marketing settings), and some method of retrieving data not stored on the cloud format. For example, some companies offer hybrid formats that provide a cloud-based format in addition to a physical store of data. Apache Hadoop is an example of a widely-used platform. It was created by a Yahoo employee, who named the program after his son's toy elephant. The platform is used by corporate giants like Facebook, Yahoo, Microsoft, Amazon, IBM, and Google.2

History


1993 - Log Files, Webtrends

Website “hits” are recorded into log files. As the internet grew, website owners began to focus on the hits on their sites. Soon, individuals started analyzing their log files to discern patterns that could help them develop their sites further. This is called log file analysis and was the base of modern day web analytics with the creation of
WebTrends.3

1995

Dr. Stephen Turner invented Analog, which was a completely free log file
analysis program.4 It simplified log file reports and made them applicable to marketers instead of exclusively those trained to decode the meaning behind log files. Urchin software was also released, which was later acquired by Google and is now known as Google Analytics.5

1996 - Hit Counters

Web-Counter was founded which was the first “ticker” of hits on a website6

1997 - Javascript Tags

Prior to 1997, most of the hits on a website were exclusively text requests. As the Internet developed, more diverse patterns began like image requests and videos. Javascript Tagging was developed to analyze web trends in a more advanced way. Today, we still use this method more than any other in web-analytics.

2004 - WAA

The Web Analytics Association, now known as The Digital Analytics Association was founded and led to another leap in analytics whereby greater amounts of data could be processed at quicker speeds.7

2005 - Google Launches Google Analytics

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The biggest web analytic company to date, Google Analytics found its roots in the acquisition of Urchin and made its focus on quantitative analytics.8

2006 - ClickTale and In-Page Analytics 

In-Page Analytics began and allowed owners to see the actual usage of their website by their consumers. Heatmaps also became popular which played directly into corporate analytic teams. In-Page or On-Site analytics are now the most used style of analytics as they measure user patterns once on the site in question.

2010 Onwards - Bit.ly

Bit.ly is the new and improved tiny.url. Its basic function is to shorten url length. It aids social media analytics for companies like facebook and twitter. Soon after, both Twitter and Facebook release their own optimized analytic programs specific to their sites like the Facebook App Analysis .9

Case Studies


  • Web Trends
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    • WebTrends was the earliest popular form of web analytics. Founded in 1993 by W. Glen Boyd and Eli Shapira, WebTrends was originally the most basic form of log file service.10 Its purpose was to monitor the amount of hits any given website, and its pages, was receiving. The service would then analyze the log files and produce a comprehensive review of page traffic that could enable website owners to have a greater depth of knowledge regarding their users habits. In 1999, WebTrends had its IPO and was valued at 45 million dollars. In 2001,NetIQ acquired WebTrends for around 1.1 Billion in shares and in 2005, sold to Francicso Partners for 94 million dollars.11 It is still operating as a web analytics service and offers both software and SaaS products. Last Feburary, WebTrends released WebTrends Explore, which is the newest version of the original idea WebTrends had envisioned. Explore now uses page tagging, log files, and more to provide their customers with what they refer to as “unlimited first party data.” In short, Explore further simplifies the technicalities behind web analysis in order for marketing teams and other consumers to gain a better understanding of what it is the data actually means. By continuously innovating the interface by which web analysis is monitored through, companies and website owners alike can respond to changes in consumer patterns in a more timely and precise manner. Now, WebTrends is partnered with some of the biggest corporations on earth like Microsoft, BMW, The Telegraph, and over 2000 more.12 WebTrends is a massive company, with a massive amount of data. To put it in perspective, the corporation boasts over 13 Billion transactions a day, has over 130+ Tech Patents, and 25,000+ Digital Properties. All that means that WebTrends, not even the biggest company in the market, collects, analyzes, and reports on what you do online. Once reported on, corporations use that data to advertise specifically to you. In short, your online presence acts like a revolving door between Web Analysis companies, Corporations, and Ad agencies.
  • TrackMaven
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    • TrackMaven is a local DC startup based out of DuPont circle. Founded in 2012 by GW graduate Allen Gannet, the company has already raised over 20 million dollars and has expanded their team to over 40 members.13 TrackMaven is a competitive intelligence platform for digital marketers. Essentially what this means, is that TrackMaven has developed a web analytics algorithm that allows digital marketers (e.g. people who post facebook ads) to track the digital marketing data of their competitors. TrackMaven tracks data on many digital marketing platforms including, Facebook, Twitter, YouTube, Instagram, Pintrest, and even digital marketing sent to your emails. Data generated may include things such as likes on their FB page, or views on a youtube video. This is incredibly valuable as it allows marketers to see trending content in various market segments and optimize their distribution based on the new information. Today organizations such as AOL, NPR and the NBA all use TrackMaven to help optimize their digital marketing.14
    • Utilizing social media data for web analytics has great potential for future use. With over 1 billion users now on Facebook, we are able to make sense out of social connections in ways we have never been able to before. We can see how the world interacts on a global scale and optimize our business's based of this information. Social media web analytics will continue to grow as more and more users come online and more social media platforms are created.
  • Urchin Software
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    • Urchin Software is an analytics company that created the program Urchin. Urchin uses the log files of web servers to measure a particular website’s traffic. Urchin can be used in two modes: log file analyzer or a hybrid model. The log file analyzer mode can process information from log files in various ways, even as a custom format. The hybrid mode attaches page tags and log file data to observe relationships between the two at the same time. Urchin was most popular when it was bought by Google in 2005 to create Google Analytics. However, demand was so high for the program when it first started that the software was unstable. Google developed a lottery draw for people who desperately wanted this new service until the program could be upgraded to handle more consumers. Finally, in August of 2006, Urchin and Google Analytics became fully operational for all users. Google Analytics used Urchin software until 2012, then discontinued the program and bought a new analytics company named Angelfish Software. Angelfish offers Urchin users a method for translating data from Urchin into Angelfish, for customers will soon be unable to use Urchin’s service at all.

Analysis


  • Strengths
    • Web analytics has already and will continue to optimize our organizations, and provide valuable data to scientist and researchers. The data generated through web analytics has the advantages of seeing large amounts of traffic, being non invasive, and requiring relatively little human input once the algorithm has been optimized.
  • Challenges
    • The Hotel Problem
      • The Hotel problem is when the amount of visitors logged on specific days or weeks, do not add up to the total amount for that month. The reason it is called the Hotel problem is that, for advanced analytics, you want to count the number of individual people that visit a web page, not the number of hits. In other words, I want to know a unique number, not a summation of the amount of times that number hit my page.
    • Cookies
      • Problems with cookies arise from people individual fears about third party cookies being logged and used across multiple sites. This allows for private information to be accessed on a site where the user had not authorized such information as long as both sites are maintained by the same host. Thus, users deactivate or delete their cookies, which causes analytics companies to have skewed statistics regarding total web traffic patterns.
    • Misinformation/skewed data
      • Another issue with web analytics in general is that their data is not always accurate. Just because a company has all the raw data, does not mean their analysis of the data is accurate. Moreover, the sheer amount of data can be overwhelming and lead to poor decision making on the client end. Timing is also a problem, as analyzing massive amounts of data takes considerable time, yet decisions based on that analysis is time sensitive. Thus, a decision can be made that will fix an accurately diagnosed issue based on data, but the data could simply be outdated. This is why efficiency and accuracy of web analysis are companies number one priorities.

Future


  • The field of web analytics has an interesting future. As computers become more powerful, algorithms become more in depth and complicated. As analytics platforms become more powerful, we are able to mine more in detailed data and gather more data points.
  • This allows the information to be more targeted towards a specific task and can have huge implications on things such as the way the stock markets behave or speculating votes for a presidential election. There is an incredible amount of potential use in the future including the way AI's might gather there information. The improvement of our demographic segmentation abilities will lead to a more accurate service that targets individuals.
  • Instead of focusing on the sheer number of hits a site may get, the focus has turned towards the unique user and his or her behavior once on the site. Thus, brand targeting will continue to become more and more personalized as our ability to monitor each individual strengthens.
  • Mobile analytics will follow a similar trend as web analytics did, but there will be more diverse data due to the use of applications. Because of the development in analytics, it follows that there will be a change in the way marketing agencies look and react to data.
  • Since analytics really are just a complicated extension of advertising, it is important to note that with the progression of analytics, comes a parallel progression of advertisement.15