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Analytics: measuring and predicting marketing success

MBA 563 Week 2. Analytics: measuring and predicting marketing success. Overview: . Data mining and predictive analytics Methods of measuring marketing success Focus on web analytics static (historical data) – server and browser based Realtime (clickstream) analysis

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Analytics: measuring and predicting marketing success

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  1. MBA 563 Week 2 Analytics: measuring and predicting marketing success

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

  3. Before we start….. • Write down all the ways in which you have left data behind you this week. Both actively and passively. • Where, how, what kind of data? "Information is flowing like mighty rivers from a trillion connected and intelligent things . . .“ IBM SocialMedia

  4. Data mining and predictive analytics

  5. Data mining (aka “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: • Stored in the data warehouse, • Available for analysis and distribution to marketing decision makers. Source: eMarketingeXcellence. 2012. Smith &Chaffey

  6. Data Analysis for Marketing • Marketers are looking for hidden patterns in the data • Analysis for marketing decision making: • Customer profiling • Predicting behaviour • RFM analysis (recency, frequency, monetary value of customer) Source: eMarketingeXcellence. 2012. Smith &Chaffey

  7. Data mining and predictive analytics How it works: Analytics (IBM SocialMedia) video Frontline (PBS): The Persuaders The Narrowcasting Future video

  8. Focus on Web analytics

  9. 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 • Referrals from affiliates • 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: eMarketingeXcellence. 2012. Smith &Chaffey

  10. 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: eMarketingeXcellence. 2012. Smith &Chaffey

  11. Web analytics software

  12. 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) • 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

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

  14. 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

  15. What server log files can record includes (amongst other things): • 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 (key metric is “bounce rate”) • Route through the site (click path) • Search terms used • Most/least popular pages

  16. Browser based page tagging • A service that relies on code embedded in each web page • Use view source and scroll down to the bottom of the page to 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 • This enables the analytics process to be managed remotely (and thus easily outsourced) • Many vendors offer both solutions (or hybrid solutions)

  17. 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 Server versus browser based analytics solutions Source: eMarketingeXcellence. 2012. Smith &Chaffey

  18. 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

  19. Using web analytics effectively

  20. First decision before we start analytics? • What are our business goals? • What are our key performance indicators?

  21. Second decision: What should we measure via 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: eMarketingeXcellence. 2012. Smith &Chaffey

  22. 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 Web channel promotion – where did web site users COME FROM? Source: eMarketingeXcellence. 2012. Smith &Chaffey

  23. 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 Web channel buyer behaviour - what do people DO when they get to the site? Source: eMarketingeXcellence. 2012. Smith &Chaffey

  24. 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: eMarketingeXcellence. 2012. Smith &Chaffey

  25. 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: eMarketingeXcellence. 2012. Smith &Chaffey

  26. Some terminology for key website volume measures Source: eMarketing eXcellence. 2008. Chaffey et al. BH

  27. Focus on Google Analytics • Beginning Analytics: Interpreting and Acting on Your Data (video 9 minutes) – see the analytics for the course website (I need to log in for you to view this) • YouTube Channel for Google Analytics

  28. So….how do you use web analytics effectively? • Identify leading indicators of business success via the web channel • 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: eMarketingeXcellence. 2012. Smith &Chaffey

  29. In-class exercise: goal setting and evaluation • A strong link to next week’s topic of web site planning and design

  30. Real-time analytics

  31. 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: eMarketingeXcellence. 2012. Smith &Chaffey

  32. 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 • We will look at these techniques in more detail when we look at online advertising Source: eMarketingeXcellence. 2012. Smith &Chaffey

  33. 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 • Local Vancouver firm HootSuite is a relatively recent startup in the field of social media measurement • Marketers still working on understanding the significance of social media indicators • Does it matter how many Facebook fans a firm has if those fans never do anything else? • Problem with “bought” LIKES

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