alumni briefing paper 5 : 2011 Cambridge ST JOHNS INNOVATION CENTRE COWLEY ROAD CAMBRIDGE CB4 0WS info@marketingcollege.com

Transcription

1 alumni briefing paper 5 : 2011 Cambridge MARKETING COLLEGE GUIDE TO WEB ANALYTICS NIGEL BRADLEY CAMBRIDGE MARKETING COLLEGES ST JOHNS INNOVATION CENTRE COWLEY ROAD CAMBRIDGE CB4 0WS info@marketingcollege.com

2 For further information: Tel: Web: Published in the United Kingdom by Cambridge Marketing Colleges Cygnus Business Park Swavesey, CAMBRIDGE CB24 4AA Nigel Bradley 2011

3 CONTENTS page PREFACE 2 INTRODUCTION 2 SECTION 1 What Are Web Analytics? 4 1a. The Web Analytics Industry 4 1b. Logfile analytics 6 1c. Dashboards 7 1d. Important services today 9 SECTION 2 What Are The Opportunities For Marketers? 10 2a. Setting up your web pages for analytics b. Bounce Metrics 12 2c. Segmentation filters 12 2d. Conversion metrics and Testing of campaigns 16 SECTION 3 How Can I Find Out More? 17 3a. Links 17 3b. Books 18 3c. Glossary of terms 18 3d. Abbreviations 20 3e. Help 20

4 Preface The growth of the world wide web has been phenomenal. According to the Interactive Media in Retail Group (IMRG), the leading industry body for global e-retailing, some 4.8bn was spent in the UK online in September 2010, and 6.4 billion was expected to be spent online at Christmas. Government figures suggest that 7 out of every 10 businesses in the UK have a website and many invest significant sums in designing, developing and maintaining their sites. But how do you evaluate the effectiveness of your website? How do you improve its design to maximize sales? The answer lies in Web Analytics and a whole new industry has developed to measure and analyse internet data bringing with it a new set of tools and jargon. This Guide provides an introduction to Web Analytics. It explains the jargon, looks at the opportunities for marketers these tools offer and provides guidance on how and where to find out more. Whether you plan to undertake web analytics yourself or employ a specialist this Guide will help you understand what can be done and how to make the best use of the information available. 2 Introduction The Internet is a series of interconnected networks and can be traced back to the 1960s. The World Wide Web is more recent and can be traced back to 1990 when Tim Berners- Lee conceived the usefulness of connecting documents on the Internet. These documents needed to be linked by preparing each with hidden hypertext codes. With the arrival of the World Wide Web we saw the birth of web pages, which collectively became web sites. A massive industry therefore emerged; web site design and creation was big business. Every company, whether large or small, wanted a presence on the World Wide Web and the low cost made this possible. However early web sites were nothing more than brochures, hastily pushed onto cyberspace. Gradually expertise grew and design was more elaborate and well-considered. A new set of conventions and vocabulary emerged: linking, keywords, stickiness, FAQs, Search Engine Optimisation (SEO), Pay Per Click (PPC). The web site industry flowered surely. What is more fascinating is that silently an industry grew in its shadow, an industry designed to monitor the performance of these web sites. That industry has become known as Web Analytics. A professional body was created: The Web Analytics Association (WAA). This body holds conferences, has created standards for measures and has an ambitious education programme. The Official WAA Definition of Web Analytics is: the measurement, collection, analysis and reporting of Internet data for the purposes of understanding and optimizing Web usage.

5 z Highly numerate with extensive use of more than one web analytics software package. CAMBRIDGE MARKETING COLLege The Web Analytics industry has more jobs than available candidates: it is one of the few sectors still hiring! Here is a typical job description, this instantly gives an indication of the type of work and tasks involved. Web Analyst - London, c 50K The Role An outstanding individual with a real passion for analytics. Can translate complex information from disparate data systems. Must communicate simple insights to a variety of internal stakeholders in a non-technical manner. This self-starter must be able to work well with all levels of the business hierarchy, possess excellent presentation skills and be an enthusiastic and persuasive communicator. Not just a numbers person but an individual who can be focused on the customer experience whilst working to tight deadlines and managing internal stakeholder expectations. Key Responsibilities z To drive website optimisation recommendations through actionable insights based on data analysis. z To manage KPI dashboard production (daily, weekly, monthly) and development with key internal business stakeholders on an ongoing basis. This includes regular communication on key business metrics to the appropriate audiences in order to keep them informed and involved. z To assist with the planning and execution of analytics and optimisation initiatives and routinely audit the web tracking data to provide recommendations and best practice for data collection and web tagging. z To present analysis and findings on the key customer journeys to internal stakeholders and senior staff. 3 z To assist in vendor management periodically by monitoring usage fees and new product recommendations. Pre-requisite Knowledge, Skills and Experience z Proven track record of improving website conversion through analysis and iterative investigation. z Basic understanding of the company s business model and peer-to-peer proposition. z Understanding of data warehouse fundamentals combined with business intelligence software analysis tools. z Marketing campaign performance evaluation and exposure to Channel promotions (paid search, SEO, affiliates, etc) crucial. z Web technology fundamentals along with the evolving applications using in page design. Desirable technical skills z Intermediate level of understanding HTML z Exposure to SASS/SPSS software z A/B testing experience This guide aims to break through the jargon and show marketers the range of opportunities available to them when using Web Analytic tools and services. There are two options: first to buy the services from established buyers, second to do web analytics in house. This guide will identify the vendors offering services and it will also give introductory indications on

6 how web analytics can be carried out in house. These guidelines should help to build and support brands. Web analytics emphasises the potential of the website as a promotional tool and its use in business development. Section 1 - What Are Web Analytics? 1a. The Web Analytics Industry Let us start with a little history lesson and refer to Table 1. We start in 1990 when the World Wide Web emerged. Early web sites had a counter at the top or bottom of each page. This often boasted something along these lines: we have had 10,000 visitors! Table 1: A Brief History of Web Analytics Period Event Description 1990 Web counters easily available and visible Device which counts the number of page hits or visitors, this is usually displayed on the page for all to see Log File Analysis programmes created 2000 Key vendors based on log file analysis were firmly established: Accrue, WebTrends, WebSideStory, Coremetrics. Analog, an early log file analysis program, was written. WebTrends 1.0 took log file analysis further. Problems with log files were becoming clear...page caches by ISP, Search robots JavaScript Tags arrive. Four big vendors: Coremetrics, Omniture, WebTrends, WebSideStory and many mid-market names: Unica, indextools, ClickTracks etc. JavaScript Tags emerged as a new standard for collecting data from websites. A few lines of code are added to each page. When a page loads, these tags cause data to be sent to a data collection server. So work shifted from the IT Department to a web analysis software vendor Google Analytics offered free In 2005 Google purchased Urchin and after some development made the decision to offer the product free of charge. This left many of the established vendors without customers as anyone could perform web analytics easily and at no cost Yahoo! Web Analytics born Yahoo! acquired IndexTools and immediately offered the product free of charge but only to those paying to advertise on Yahoo! Networks. The early web counter can still be found on some pages, notably it is a badge of honour for You Tube who show the number of hits of the most popular videos. On corporate websites the counter is less common, as more complex information can now be derived from visitor activity.

7 Behind every web site there is a computer server which was normally located in the IT department at the company s offices but now may be in the cloud. This piece of hardware serves any requested information to the computer user. Inside every server there are log files which are a record of all web page activity. In 1995 a log file analysis program was written, this was called Analog and it took the position as the first major contribution to web analytics, not least because it is free. The first burst of growth in the web analytics industry took place between 1995 and 2000 where log file analysis was central to the operations of Accrue, WebTrends, WebSideStory, and Coremetrics. The limitations of using log files were becoming clear as the numbers of visitors was either overestimated or underestimated, this was due to such things as page caches carried out by Internet Service Providers (to avoid pressure on networks), by the activity of robots, and other issues. A second method called Page Tagging (or JavaScript Tags) emerged as a new standard for collecting data from websites. A few lines of code are added to each page. When a page loads, these tags cause data to be sent to a data collection server. So work shifted from the IT department to a web analysis software vendor. As a result Accrue, WebTrends, WebSideStory, and Coremetrics became stronger as they could complement their services. We also saw the arrival of smaller companies: Unica, indextools and ClickTracks to name just three. 5 It is worth noting that a series of mergers, takeovers and name changes has transformed these early pioneers: z IBM purchased SPSS (2009) Coremetrics (2010) and Unica (2010) z WebSideStory rebranded itself as Visual Sciences (2007), Visual Sciences was acquired by Omniture (2007) z Adobe Systems purchased Omniture (2009) z Accrue was bought by Datanautics (2003) now Datanautix z Microsoft bought DeepMetrix (2006) and develop Gatineau, change the name to adcenter Analytics then abandon the project (2009) z Urchin was bought by Google (2005) z IndexTools was bought by Yahoo (2008) z ClickTracks becomes part of Lyris (2006) z Comscore purchased Nedstat (2010) There are three major search engines with advertising linked. These are Google, Microsoft (with Bing) and Yahoo. It is important to look at their involvement in the Web Analytics industry.

8 First Google purchased Urchin in 2005 and after some development made the decision to offer part of the product free of charge in the next year under the name Google Analytics. This left many of the established vendors without customers as anyone could perform web analytics easily and at no cost. Second, Microsoft bought DeepMetrix in 2006 and developed Gatineau which changed its name to adcenter Analytics. Then, surprisingly, Microsoft abandoned the project in There are indications in 2010 the idea may have re-emerged as Campaign Analytics as part of the Microsoft adcenter initiative. Third, Yahoo! in 2008 made a similar move to both Google and Microsoft in that it acquired an established system, in this case IndexTools; this product was immediately offered free of charge, but only to those paying to advertise on Yahoo! Networks. This summary is intended to guide anyone intending to use external services. The names crop up frequently and it is likely that the industry will see further changes, so it is important to be aware of developments. 1b. Log File Analytics 6 Advantages of Logfile analysis. There are several advantages of Logfile analysis. The data is on the company s own servers, which makes it readily available. Also the format of the data is standard, so it can be manipulated more flexibly than if it was provided through a vendor with their own format. The benefit of this comes in the long term because the analysis of historical data can be carried out with whatever software is available. Some page tagging solutions have been accused of leading to vendor lock-in where it is difficult to switch from one provider to another without substantial work. Disadvantages of Logfile analysis. Conversely, Logfile analysis is criticised for providing inaccurate traffic figures. One reason for higher traffic readings is that pages are visited by robots in addition to humans. Software is programmed to trawl the worldwide web continuously, particularly to update search engine databases, and there are other reasons for spider activity which include experimentation, competitive intelligence gathering and so on. Logfile analysis software cannot always distinguish human from robot activity. A web page may not display fully before the user moves on to another website, this is not a view but it is still recorded; page tagging detects if the page is viewed properly.

9 Figure 1: Typical Topline Logfile Analysis 7 Source: AWStats demo at demo at A list of Free Log Analysers can be found at 1c. Dashboards Dashboards are an essential part of web analytics so let us take a few minutes to describe what they are. Dashboards are single computer screens containing visual displays of performance information, usually from several sources. They are important in Web Analytics because most services deliver information in the form of a dashboard, and this can be customised to meet your needs. The dashboard is personalised for one person but several versions can be created from the same data-set because several users may have different needs. The Web Analytics dashboard is generally in real-time, with a delay of seconds; non-real-time displays may have a delay of 24 hours or 48 hours depending on the information sources and service used.

10 The term dashboard was taken from the console found in every motor car. Here the marketing manager is in the driving seat and should be able to see his situation at a glance. The marketing manager is expected to choose Key Performance Indicators (KPIs) that help understand whether objectives are being met. There are several steps necessary for effective dashboard design: 1 Selecting correct metrics 2 Selecting correct visual displays 3 Arranging the metrics in a meaningful way 4 Ensuring that important details are emphasized 5 Installing alerts to indicate changes that may require attention Figure 2 shows the standard Google Analytics dashboard which will be familiar to users of this service. Note that a line graph indicates fluctuations in traffic over the last month, important numbers show the number of visits, the number of page views, the number of pages viewed per visit. There is a world map indicating that most visitors come from the USA. We can scroll down and see more information. There are numerous opportunities to personalise the displays offered. 8 With the many thousands of possible metrics available to the digital marketer it is important they focus on the measures and indicators from which they will be able to make strategic decisions and changes to their current plans. With this in mind a good starting point by which to decide the most relevant metrics to include in a dashboard is the Digital Marketing Plan. There are many frameworks available upon which to base a plan and a popular example is SOSTAC Situation where are we now? Objectives where do we want to be? Strategy what will it look like when we get there? Tactics what do we have to do to get there? Actions who will do what, when and how? Control What measures will we include in our dashboard? Such a framework should work as a continual feedback loop with the outcomes from measurement and analysis allowing the marketer to improve the situation by tuning their tactics and actions.

11 Figure 2: The Google Analytics Dashboard 9 1d. Important Services Today Because tagging is visible to the whole world we can go to any web site, inspect the code and discover which service is being used. The Vendor Discovery Tool by Web Analytics Demystified was written to automate the process. You can simply enter a web address into the software (perhaps one of your competitors) and the analytics vendor is identified. This is a free and transparent service so we can see the last 100 site enquiries made. This is useful to see the importance of the different services. From this we also know that at least 10 out of 100 companies use two or more analytic services, this is a minimum figure because log file analysis use is not detected by the Discovery Tool. It is clear that free Google Analytics dominates but that the paid-for Omniture has a strong hold on the market place.

12 Figure 3: 100 Queries at the Vendor Discovery Tool in October 2010 ( 10 Section 2 - What Are The Opportunities For Marketers? 2a. Setting up your Web Pages for Analytics For the purposes of this document we will examine the use of Java Tracking Code rather than log file analysis. Log File analysis requires a greater technical expertise, for example the page tagging service manages the process of assigning cookies to visitors; with logfile analysis, the server has to be configured to do this. For these reasons we will focus on page tagging. Furthermore we will use Google Analytics because we have seen that it is the most popular method. There are several steps involved in this: 1 Visit GOOGLE ANALYTICS at 2 Click SIGN UP NOW (you will need a Gmail account) 3 Follow the instructions and seek any TUTORIAL HELP during your journey There will come a point where you are instructed to copy some text and to insert this into the HTML of your web page. Java tracking code will work no matter where it s placed on the page. The reason it s recommended that JavaScript is placed at the end of the page is because javascript blocks the browser from rendering the page while it s executing, but if

13 it s at the bottom of the page (right above </body>) then the page will have already (mostly) rendered, thus improving the apparent load time of the page. After several hours the ready-made dashboard will start to fill with numbers, visual displays will come to life. This dashboard contains metrics that you are likely to use but it can easily be changed to accommodate your needs. On the dashboard prepared earlier, with extracts shown here we see, for the past month: z Number of visits as a line graph z Site usage: 1163 visits, 2517 page views z Site usage: 2 pages were seen per visit z 1 and a half minutes spent on the site z Visitors: just over a thousand 11 z Traffic sources: 11% came direct, 75% came via search engines and the rest were probably links on other sites z We can see which search engines were used (Google dominates) z We can see the keywords used in those search engines z We can even see which pages were seen the most (and least)

14 2b. Bounce Metrics For many years we have heard the term stickiness used in relation to web sites. If a web site is sticky then we attract a visitor and keep them looking at our site, hopefully they will take some action such as requesting further information or placing an order. Web Analytics has a word to describe the opposite of stickiness it is the word bounce. Bounce occurs when a visitor arrives at a web page then leaves the site, effectively a single visit. The dashboard extract below shows a bounce rate of 67%, it means that 67% of visitors took one look at my page and decided it wasn t for them. Only a third of visitors decided to look further into my site. 12 This can be interpreted in many ways. One interpretation is that I have been promoting my website to the wrong people, I may have paid for the wrong keywords or I may have given misleading descriptions in my pages or in the hidden messages I write for search engines. Another interpretation is that my entry pages are badly designed; I need to modify them to create something far more interesting. Avinash Kaushik, an authority on bounce rates, said It is really hard to get a bounce rate under 20%, anything over 35% is cause for concern, 50% (above) is worrying. So we can see I have a real problem that needs to fixed, and a simple number has allowed us to see the problem in the first place. It must be said that specific pages, such as blogs and case studies, especially when used as a landing page within a customer retention campaign, see high bounce rates because the visitor is already very familiar with the content on other pages on the site or is just dipping in for breaking news. It s therefore important to say that whilst bounce rate is a key performance indicator in website analytics it should be used in conjunction with other measures to give a well rounded view. 2c. Segmentation Filters As marketers we are all taught about Segmentation, Targeting and Positioning. We must divide the population into similar groups, only then can we decide what targets to select. Once we have a target or several targets, we can then position our product in the mind of the prospect. Web Analysts have taken the aspect of segmentation (although targeting and positioning are not mentioned in those words).

15 Table 2: Segmentation Bases Available or Unavailable through Web Analytics Base Standard Can be created with a form or adding service Demographic Language preference Age Sex Education TEA Occupation Social class Geographic Country Geographic region City Timezone Postcode area Zip code area Behavioural Online purchases Page visits Bounces Conversion behaviour Previous web site Referring website Browsing behaviour Keywords used Pages chosen Search engine used Hour of the day Mobile phone user Connection speed Browser used Benefit sought Loyalty 13 Psychographic Lifestyle Personality Geo-demographic ACORN MOSAIC PRIZM Dashboards have a set of pre-defined segments and the opportunity to build more. In Table 2 we use the traditional bases of segmentation from marketing to list some of the choices on offer by web analysts. Where segments are NOT available they can be created by presenting web site visitors with a form and asking them to select an option such as job title or hobby. Other non-standard segments can be created by purchasing and fusing data, as is the case with geo-demographic systems or some vendors have proprietary systems that extend segments. Adding segments to the dashboard is as easy as dragging or clicking as we see in Figures 4 and 5 (see next page). Figures 6-9 shows how segmentation by mobile phone use and then geography appear on the visual display. It is interesting to see that USA visitors have the average bounce rate (68%) but visitors from India are down at 50%. Here we see the value of applying the segmentation filter it shows that Indians are more interested in the pages than Americans.

16 Figure 4: How to Create Extra Segments (Yahoo Analytics) Figure 5: How to Create Extra Segments (Google Analytics) 14 Figure 6: Dashboard Showing Mobile Phone User Segments

17 Figure 7: Segmentation of Visitors by Country (map) Figure 8: Segmentation of Visitors by Country (table) 15 Figure 9 Dashboard showing USA/India visitor Bounce rate differences

18 2d. Conversion Metrics and Testing Campaigns It is important to remember that all web sites have a purpose, and usually it is to persuade the visitor to carry out some action. It might be: z To read an article z To sign up to a newsletter z To request a brochure z To make contact z To place an order When this objective is achieved, then we can call it a conversion. Yes, the term sales conversion springs to mind, but the web analytics use of the term can be stretched to other actions. One display option on most dashboards is the funnel. Figure 10 is a sales funnel used by the vendor Unica. If we follow it from the top we see that 2069 people entered an offer code, 473 started their shopping cart. There were some who abandoned their cart because 470 began the checkout but only 399 completed an order form. 16 We can substitute different actions at each point and instantly see the dropout at different points and fix the problem. In this case there was something after the offer code that caused over 75% of people to go away. Figure 10: A Sales Funnel used by the vendor Unica Finally it is important to mention A/B Testing. This is very simple, and direct mail marketers will be familiar with the idea. If we have two executions of a web page. Perhaps two different offers let s say one of the campaigns is centered on low price but the other on quality.

19 We simply call the first A and the second B. These two pages are created and the server is told to alternate the viewing: so A goes to the first visitor, B to the second, A to the third and so on. Then a conversion funnel is created for both options and we can see the difference. This is simple but effective experimentation. Another test you may come across is MVA which means Multi-Variate Analysis, it goes beyond testing two versions (bi-variate analysis) and tests many aspects. Clearly this is a specific study area with many guidelines. This guide has shown you the opportunities available to you when using Web Analytic tools and services. Clearly individual needs differ but this should be a starting point for you whether you want to buy vendor services, do it yourself or want to build a career in this new sector. Section 3 - How Can I Find Out More? 3a. Links Marketers should consider the following links to more detailed information references in this guide. The Web Analytics Association (WAA) 17 A Proposed Code of Ethics html Free Visitor Counters to add to your web pages Web Analytics Demystified: free cases, whitepapers and even books! Vendor Discovery Tool Web site health check up, free. Try it now Google Analytics Web site Yahoo Analytics Web site (Google) Insights for Search

20 Google Trends Microsoft Analytics links b Books Arikan, A. (2008) Multichannel Marketing. Metrics and Methods for On and Offline Success, Sybex Bradley, N. (2010) Marketing Research: Tools and Techniques, Oxford University Press, Oxford Clifton, B. (2008) Advanced Web Metrics with Google Analytics, Sybex, Wiley Kaushik, A. (2007) Web Analytics: An Hour a Day Sybex, Wiley Kaushik, A. (2010) Web Analytics 2.0 Sybex, Wiley 18 Mortensen, D. R. (2009) Yahoo! Web Analytics. Sybex, Wiley, Indianaplois, Indiana Peterson E.T. (2004) Web Analytics Demystified: A Marketer s Guide to Understanding How Your Web Site Affects Your Business. Celilo Group Media Peterson E.T. (2005) Web Site Measurement Hacks. O Reilly ebook Sterne, J. (2010) Social Media Metrics. How to measure and optimise your marketing investment, Wiley, New Jersey 3c Glossary of Terms These definitions were created by Nigel Bradley. You are also advised to consult terms created by the Web Analytics Association which act as an industry standard. Bounce Occurs when a visitor arrives at a web page then leaves the site, effectively a single visit. Click A positive and deliberate action made by a computer user, usually by depressing the left button of a mouse. Clickthrough rate (CTR) A measure expressed as a percentage. This is the number of clickthroughs divided by the total number of times the page in question has been served (the number of page impressions).

21 Dashboards Single computer screens containing visual displays of performance information, usually from several sources, a type of portal. E-metric Performance indicators derived from traditional research or web analytics. Log file A record of the download activity of a specific set of web pages. Log file analysers are used to make sense of the data. Useful to create online measures for marketing. Stickiness A phrase used to describe whether a site is able to hold a visitor. This can be quantified by the length of visit recorded in log files. Tagging Observing the movements and behaviour of people using cookies, RFID or other techniques, this may be online or offline. Usability tests Term used to describe the procedures used to discover whether hardware or software is easy to operate. Web analytics Also known as website analytics. The capture and processing of data from software and hardware that produces performance indications (e-metrics). Web servers Computers used to store web sites and their corresponding databases. 19

22 3d Abbreviations CPC CTR HTML http IP KEI KPI PPC SEM SEO URL URL WWW Cost per click Clickthrough rate Hypertext Markup Language HyperText Transfer Protocol Internet protocol Keyword Effectiveness Index Key Performance Indicator Pay Per Click Search Engine Marketing Search engine optimisation Uniform Resource Locator Universal Resource Locator World Wide Web 20 3e. Help Tutors at Cambridge Marketing College blog on this and other research topics at and through the College s Digital Marketing website at Cambridge Marketing College offers 2 digital marketing qualifications: the CIM CAM Diploma in Digital Marketing and the CIM CAM Diploma in Managing Digital Media. More information on these courses is available at:

23 About the Author This Guide was written by Nigel Bradley who is a Senior Lecturer in Marketing at the University of Westminster. With a Masters degree in Product Management and Marketing from Cranfield, Nigel spent his career in marketing research agencies before joining academia in Nigel s book Marketing Research: Tools and Techniques was published by Oxford University Press. He has been a Tutor with Cambridge Marketing College since

24 C A M B R I D G E M A R K E T I N G C O L L E G E S S T J O H N S I N N O V A T I O N C E N T R E, C O W L E Y R O A D, C A M B R I D G E, C B 4 0 W S I N F M A R K E T I N G C O L L E G E. C O M W W W. M A R K E T I N G C O L L E G E. C O M