1. ANALYTICS: MEASURING AND PREDICTING MARKETING SUCCESS MBA 563

2. Overview: Methods of measuring marketing success 1.Focus on web analytics static (historical data) – server and browser based Realtime (clickstream) analysis 2.Data mining and predictive analytics You can’t manage what you can’t measure (Bob Napier, ex CIO, Hewlett Packard) (Note: we will look at social media metrics later in the course)

3. Results of Accenture's 2014 CMO Insights Survey In what areas do you believe the marketing function will change the most? Accenture's 2014 CMO Insights Survey

4. FOCUS ON WEB ANALYTICS

5. Web Analytics - definition Techniques used to assess and improve the contribution of online marketing to a business or organization Onsite analytics – Web site traffic attributes and trends – Clickstreams and clickpaths – Website usability testing – Offsite analytics – Measurement of potential audience, social media activity, social “listening” and “buzz” Purpose – to optimize websites and web marketing initiatives in order to meet business objectives via data- driven decision making Source: eMarketing eXcellence. 2012. Smith &Chaffey

6. 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) Social media analysis 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. 2012. Smith &Chaffey

7. WEB ANALYTICS SOFTWARE

8. Web analytics software and reports The volume of data generated by even a small website is so large that human analysis would be impossible 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) Google Analytics – WebTrends - offers both server and browser-based (hosted) solutions WebTrends And integrates metrics from other sources to help manage and measure integrated online campaigns – Several examples and case studies are available from Webtrends Several examples and case studies are available from Webtrends

9. Web analytics approaches Two main approaches to obtaining website analytics data: 1.Server-based: analysis of automatically generated first- party server log files (ie. the server on which the site resides) 2.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.

10. 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 Sample log file http://www.jafsoft.com/searchengines/log_sample.html http://www.jafsoft.com/searchengines/log_sample.html

11. What server log files can record includes (amongst other things): Number of requests to the server (hits) – although you should NOT use this metric Number of page views Total unique visitors (using “cookies”) The referring web site Number of repeat visits Time spent on a page (key metric is “bounce rate”) Route through the site (click path) Search terms used (now no longer available from Google) Most/least popular pages

12. Browser based page tagging A service that relies on JavaScript code embedded in each web page Each time the page is loaded in the browser, the JavaScript notifies the third-party analytics vendor This enables the analytics process to be managed remotely (and thus easily outsourced) Many vendors offer both solutions (or hybrid solutions)

13. Server versus browser based analytics solutions 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 – Becoming the standard approach for analytics Source: eMarketing eXcellence. 2012. Smith &Chaffey

14. Remember this about web analytics You cannot identify individual people. The log file records the computer IP address and/or the “cookie”, not the user. – Unless the user has logged in! Information may be incomplete because of caching. This is why benchmarking is so important – trends rather than absolute numbers

15. USING WEB ANALYTICS EFFECTIVELY

16. First decision before we start analytics? What are our business goals and the goals of our users? How will we measure how well we have met those goals? What are our key performance indicators?

17. Second decision: What should we measure via the web channel? Channel promotion – where did visitors come from? Channel buyer behaviour – 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. 2012. Smith &Chaffey

18. Web channel promotion – where did web site users COME FROM? 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? – Calculation of the cost of acquisition of each visitor Source: eMarketing eXcellence. 2012. Smith &Chaffey

19. Web channel buyer behaviour - what do people DO when they get to the site? 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 rate: proportion of visitors to a page who leave immediately – Stickiness: how long a visitor stays on the site, and how many repeat visits they make – Conversion rate: % of visitors who perform a desired action Source: eMarketing eXcellence. 2012. Smith &Chaffey

20. Web channel satisfaction - how HAPPY are the visitors? Customer satisfaction is vital, but hard to measure directly with technology Stickiness is one indirect indicator of satisfaction Conversions are another Bounce rate is very important Can measure indirectly by testing and via survey tools – Ease of use – Site availability (down time) – Performance Source: eMarketing eXcellence. 2012. Smith &Chaffey

21. 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, posting a comment, downloading a file, signing a petition, 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. 2012. Smith &Chaffey

22. Some terminology for key website volume measures Measure Definition How many? (audience reach) Unique usersIP+User-agent Cookie and/or Registration How often? (frequency metric) Visit (user session)A series of one or more page impressions served to one user (gap of 30minutes=end of visit) How busy? (volume metric)Page impressionFile (or files) sent to a user as a result of a server request by that user What do they see?Ad impressionsA file (or files) sent to a user as an individual ad as a result of a server request by that user What do they do?Ad clicks?An ad impression clicked on by a valid user Source: eMarketing eXcellence. 2008. Chaffey et al. BH

23. Focus on Google Analytics Getting Started with Google Analytics (video from Google 29 minutes) Getting Started with Google Analytics YouTube Channel for Google Analytics

24. So….how do you use web analytics effectively? 1.Identify leading indicators of business success via the web channel 2.Identify the key performance indicators (KPI) and data points with which to measure them 3.Establish benchmarks to track changes over time 4.Configure software and use settings consistently Source: eMarketing eXcellence. 2012. Smith &Chaffey

25. Web metrics in-class exercise Take the Web site goal setting exercise that we did in Week 1. – Look at the success factors you used for each goal – Now add specific indicators from the data that is collected by web servers that will help you measure success Think about the kind of data that is routinely collected by web servers and that will be available to you via web analytics software

26. REAL-TIME ANALYTICS

27. Real-time profiling / behavioural targeting 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” : “sense and respond” – Very expensive to implement and do well Source: eMarketing eXcellence. 2012. Smith &Chaffey

28. 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 – Many privacy concerns have been raised Source: eMarketing eXcellence. 2012. Smith &Chaffey

29. Not just your website anymore We also need to measure offsite digital channels : – Mobile Apps – Blogs – Facebook – Twitter – Email Large software vendors offer integrated tools to manage these – “dashboards” We will look at this in a bit more detail later in the course when we look at social media

30. DATA MINING AND PREDICTIVE ANALYTICS

31. Data mining and “Big Data” 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. Data collected from all customer touch points are: – Traditionally stored in a data warehouse, – Available for analysis and distribution to marketing decision makers. – Increasingly analyzed in real-time for “in the moment” reporting Source: eMarketing eXcellence. 2012. Smith &Chaffey

32. Big data and predictive analytics How it works: Analytics How it works: Analytics (IBM SocialMedia) video Kenneth Cukier: Big data is better data Kenneth Cukier: Big data is better data (TED talk 2014)