Web analytics: measuring marketing success

1. Web analytics: measuring marketing success MARK 430 Week 3

2. During this class we will be looking at: You can’t manage what you can’t measure (Bob Napier, ex CIO, Hewlett Packard) • Methods of measuring marketing success and analyzing problems • Web analytics • static (historical data) – server and browser based • Realtime (clickstream) analysis • Data Mining • Behavioural targeting Source: eMarketing eXcellence. 2008. Chaffey et al. BH

3. Web Analytics - definition • Techniques used to assess and improve the contribution of online marketing to a business • Includes the review and analysis of • Web site traffic volume • Referrals from affiliates • Clickstreams and clickpaths • Customer satisfaction surveys • Website usability testing • Leads and sales • Social media and mobile apps analysis • Purpose – to optimize websites and web marketing initiatives in order to meet business objectives Source: eMarketing eXcellence. 2008. Chaffey et al. BH

4. Technology-Enabled Approaches • The Web provides marketers with huge amounts of information about users • This data is collected automatically • It is unmediated (and therefore unbiased) • Server-side data collection • Log file analysis - historical data • Real-time profiling (tracking user Clickstream analysis) • Client-side data collection (page tagging and cookies) • Data Mining • These techniques did not exist prior to the Internet. • They allow marketers to make quick and responsive changes in Web pages, promotions, and pricing. • The main challenge is analysis and interpretation Source: eMarketing eXcellence. 2008. Chaffey et al. BH

5. First decision: What should we measure re the web channel? • Channel promotion – where did visitors come from? • Channel buyerbehaviour – what do they do when they get to the site? • Channel satisfaction – how happy are the visitors? • Channel outcomes – conversions • Channel profitability – online sales contribution – the primary aim of eCommerce Source: eMarketing eXcellence. 2008. Chaffey et al. BH Source: Chaffey et al 2006

6. Which site “referred” them Search engine Affiliate site Partner Advertisement Contribution to sales or other desired outcome Measures - allows the evaluation of the referrer What percentage of all referrals came from this source? Cost of acquisition of each visitor Web channel promotion – where did web site users COME FROM? Source: eMarketing eXcellence. 2008. Chaffey et al. BH

7. We can monitor Which content is accessed by users When they visit How long they stay Whether interaction with content leads to sales or other desired outcome Measures – eg. Bounce rates – proportion of visits to home page only Stickiness – how long a visitor stays on the site, and how many repeat visits they make Web channel buyer behaviour - what do people DO when they get to the site? Source: eMarketing eXcellence. 2008. Chaffey et al. BH Source: Chaffey et al 2006

8. Web channel satisfaction - how HAPPY are the visitors? • Customer satisfaction is vital, but hard to measure directly with technology • Can use survey tools etc • Can measure indirectly by testing • Ease of use • Site availability (down time) • Performance • Email response times Source: eMarketing eXcellence. 2008. Chaffey et al. BH Source: Chaffey et al 2006

9. Web channel outcomes • Measure sales, leads, and conversions from the web channel • Conversion rate • Percentage of site visitors who perform a particular action such as registering for a newsletter, subscribing to an RSS feed, or making a purchase • Attrition rate • Percentage of site visitors who are lost at each stage of a multi-page transaction (the “funnel”) • Related concept is “shopping cart abandonment” Source: eMarketing eXcellence. 2008. Chaffey et al. BH

10. Some terminology for key website volume measures Source: eMarketing eXcellence. 2008. Chaffey et al. BH See course website for link to glossary from WebTrends

11. Web analytics softwaretools available to the marketer to measure web site activity • Two main approaches • Server-based: analysis of automatically generated first-party server log files (ie. the server on which the site resides), using desktop software • Browser-based page tagging: uses JavaScript code embedded on each html page to let a third-party server know each time the page is loaded into a web browser. Source: eMarketing eXcellence. 2008. Chaffey et al. BH

12. Web server log files – basic metrics • All web servers automatically log (record) each http request • That request contains information about the requesting client computer and software • Most log file formats can be extended to include “cookie” information • This allows you to identify a user at the “visitor” level • Sample log file http://digitalenterprise.org/metrics/sample_log.txt Source: eMarketing eXcellence. 2008. Chaffey et al. BH

13. What server log files can record includes: • Number of requests to the server (hits) • Number of page views • Total unique visitors (using “cookies”) • The referring web site • Number of repeat visits • Time spent on a page • Route through the site (click path) • Search terms used • Most/least popular pages Source: eMarketing eXcellence. 2008. Chaffey et al. BH

14. Shortcomings of server log file analysis • Cannot identify individual people. The log file records the computer IP address and/or the “cookie”, not the user. • Information may be incomplete because of caching. • Proxy servers also skew numbers • This is why benchmarking is so important • trends rather than absolute numbers Source: eMarketing eXcellence. 2008. Chaffey et al. BH

15. Browser based page tagging • A service that relies on code embedded in each web page • See it on the course website (I use Google Analytics) • Each time the page is loaded in the browser, the JavaScript notifies the third-party analytics vendor • Cookies are also managed by the analytics vendor • This enables the analytics process to be managed remotely (and thus easily outsourced) • Many vendors offer both solutions (or hybrid solutions) Source: eMarketing eXcellence. 2008. Chaffey et al. BH

16. Advantages of server-based approach Data is always available from the server – no alterations to web pages needed Does not rely on JavaScript being enabled by the user Includes information about visits from search engine spiders and other automated robots Lets the firm know about potential problems with the site – eg. failed requests Can be analyzed in real time Advantages of browser-based approach Solves the page caching problem (page is counted each time it is reloaded) Manages the cookie process Available to firms without their own web server – attractive to small businesses Pay-as-you go pricing Server versus browser based analytics solutions Source: eMarketing eXcellence. 2008. Chaffey et al. BH

17. Web analytics software and reports • Format and sophistication of reports depends on software used (and the price paid) • Many software packages / hosted solutions available – one well-known example of each • Google Analytics (browser-based solution only, closely tied to its search marketing products) – see the analytics for the course website • WebTrends - offers both server and browser-based (hosted) solutions • And integrates metrics from other sources to help manage and measure integrated online campaigns • Several examples and case studies are available from Webtrends Source: eMarketing eXcellence. 2008. Chaffey et al. BH

18. Not just your website anymore • We also need to measure other digital channels : • Mobile Apps • Blogs • Facebook • Twitter • Email • Large software vendors such as Webtrends offer integrated tools to manage these – “dashboards”

19. How do you use web analytics effectively? • Identify leading indicators of business success • Identify the key performance indicators (KPI) with which to measure them • Establish benchmarks to track changes over time • Configure software and use settings consistently Source: eMarketing eXcellence. 2008. Chaffey et al. BH

20. Enhancing marketing tactics using web analytics - some examples • Identify point of drop-off in registration or purchasing process. • Pinpoint problem and concentrate efforts on the apparent trouble spot to improve conversion rates. • Maximize cross-selling opportunities in an on-line store • Identify the top non-purchased products that customers also looked at before completing the purchasing process. • Add these products in as suggestions • Refine search engine placements by implementing keyword strategy • Use referrer files to identify commonly used search terms and the search engine or directory that sent the customer. Source: eMarketing eXcellence. 2008. Chaffey et al. BH

21. Improve web site structure using web analytics - some examples • Analysis of search logs to improve findability on the web site. • Do people search by “category” rather than “uniquely identifying” search terms? • Redesign home page to enhance visibility of most commonly used links and therefore promote usability. • Demote least used items to “below the fold” • Analyze “click paths”, entry and exit points to trace most common routes around the site. • Identify areas where navigation seems unclear or confusing Source: eMarketing eXcellence. 2008. Chaffey et al. BH

22. Real-time profiling / behavioural targeting: building relationships with customers • Uses real-time Clickstream Monitoring - page by page tracking of people as they move through a website • Uses server log files, plus additional data from cookies, plus sometimes information supplied by user • Real time profiling entails monitoring the moves of a visitor on a website starting immediately after he/she entered it. • Can be served personalized content in real-time according to the “profile” • Very expensive to implement and do well Source: eMarketing eXcellence. 2008. Chaffey et al. BH

23. Behavioural targeting • Past actions determine the advertising or content you will see in the future • Onsite behaviour • Web analytics are used to identify customer profiles • The behaviour on the site is then tracked and appropriate content served • Network behaviour • Used extensively by advertising networks • Entails tracking across third party sites / use of ISP data • Many privacy concerns have been raised • We will look at these techniques in more detail when we look at online advertising Source: eMarketing eXcellence. 2008. Chaffey et al. BH

24. Data Mining Frontline (PBS): The Persuaders The Narrowcasting Future Source: eMarketing eXcellence. 2008. Chaffey et al. BH

25. Data Analysis and Distribution • Data collected from all customer touch points are: • Stored in the data warehouse, • Available for analysis and distribution to marketing decision makers. • Analysis for marketing decision making: • Data mining • Customer profiling • RFM analysis (recency, frequency, monetary value of customer) Source: eMarketing eXcellence. 2008. Chaffey et al. BH

26. Data mining • Data mining = extraction of hidden predictive information in large databases through statistical analysis. • Real-space primary data collection occurs at offline points of purchase with: • Smart card and credit card readers, interactive point of sale machines (iPOS), and bar code scanners • Offline data, when combined with online data, paint a complete picture of consumer behavior for individual retail firms. • Marketers are looking for patterns in the data such as: • Do more people buy in particular months • Are there any purchases that tend to be made after a particular life event Source: eMarketing eXcellence. 2008. Chaffey et al. BH