Measurement and evaluation: Web analytics and data mining. MGMT 230 Week 10

Size: px

Start display at page:

Download "Measurement and evaluation: Web analytics and data mining. MGMT 230 Week 10"

Adrian Stevens
5 years ago
Views:

1 Measurement and evaluation: Web analytics and data mining MGMT 230 Week 10

2 After today s class you will be able to: Explain the types of information routinely gathered by web servers Understand how analytics can be used to ensure the effectiveness of your website and track how well you are meeting your goals Understand the basics of data mining

3 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 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 (plus increasing consumer privacy concerns)

4 We will focus on website data analytics Web site activity can be analyzed to produce quantitative information about the activities and behaviour of web site users. Analysis of this freely available data should be a routine form of information for marketers and business managers for all sizes of organizations So much data is available that software is needed to assist in analysis Analysis can be performed at varying levels of sophistication (and cost)

5 Website 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 efficiency testing (dead links, speed of loading) Purpose to optimize websites and web marketing initiatives in order to meet business objectives

6 Two technical approaches to obtaining website analytics data First party server logs: All web servers automatically log (record) each http request That request contains information about the requesting client computer and software This allows you to identify a user at the visitor level 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 very similar data is collected

7 Web analytics software tools available to measure web site activity Analytics software available from a variety of vendors a couple of examples WebTrends enterprise software application used to analyze server log files (and other data about users) Now also tracks via browser tagging and offers a desktop and a hosted solution Google Analytics requires tracking code to be inserted into web pages then analyzed remotely (a hosted solution)

8 What is automatically recorded includes: Sessions and interactions Number of page views Total unique visitors (using cookies ) The referring web site Number of repeat visits Time spent on a page Visit duration Route through the site (click path) Search terms used (now no longer fully available from Google) Most/least popular pages Understanding Google Analytics: key metrics and dimensions defined (video 6 minutes)

9 Some commonly used indicators using this data Bounce rate Time on page Unique visitors Conversion rate Number of pages viewed Advertising success (via referrer data)

Remember this about web You cannot identify individual people. The log file records the computing device IP address and/or the cookie, not the user.

10 Remember this about web You cannot identify individual people. The log file records the computing device IP address and/or the cookie, not the user. Unless the user has logged in (for example, to Chrome) As the use of multiple devices grows, there are significant audience overlaps which might lead to double counting This is why benchmarking is so important trends rather than absolute numbers analytics

11 Steps to effective use of web analytics 1. Identify website goals 2. Identify the key performance metrics with which to measure business success how will you know how well you are doing? 3. Establish benchmarks to track changes over time 4. Configure software and use settings consistently

12 First decision before we start analytics? What are our business goals? What are our key performance indicators? In other words, what metrics can we use to measure success?

13 The big picture types of online business objectives and how they are measured Business Objective ecommerce Lead generation Content publishing Online info / support Branding Measurable outcome Sell products Contact information for sales prospects Ads shown to visitors Help customers find information Drive awareness, engagement, and loyalty The importance of digital analytics (Google)

14 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 Smith &Chaffey

15 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 Smith &Chaffey

16 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 Smith &Chaffey

17 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 Smith &Chaffey

18 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 Smith &Chaffey

19 MEASURING SOCIAL MEDIA IMPACT

20 Social media return on investment Any marketing activity costs money social media is definitely not free Direct expense Paid media eg advertising and promoted posts Owned media eg. Payments to an outside agency for setting up and managing a social media presence Indirect expense eg. Labour costs for employees who are managing earned media activities by engaging in social listening and engaging with people on social media

21 Indicators of success in owned and earned social media The main measures that businesses use: Mentions Sentiment Reach and exposure Engagement Additional indicators that marketers should use: Share of voice (% of mentions for your brand compared with your main competitors) Audience growth rate (% increase in followers over time) Influence

22 For earned media focus on ENGAGEMENT metrics Engagement metrics measure audience ACTION (more than just seeing a post) How much and how often does your audience interact with your social media content Likes, comments, shares, retweets, favourites, repins etc these all indicate engagement (but of varying VALUE) Sharing metrics can be separately identified, because they are also used to measure amplification and EXPOSURE of particular posts The Beginners Guide to Social Media Metrics (White Paper). Hootsuite. 2014

23 Engagement metrics

24 BEYOND WEB ANALYTICS - BIGGER DATA

25 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 Online data both actively and passively provided by internet users 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 available for analysis and distribution to marketing decision makers. How it works: Analytics (IBM SocialMedia) video

26 Big Data Analysis for Marketing Marketers are looking for hidden patterns in the data predictive analytics Analysis for marketing decision making: Customer profiling - How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did Behavioural targeting of advertisements: How internet advertisers read your mind 7m video (The Economist, 2014) Predicting behaviour

27 Real-time profiling Sophisticated and expensive! 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 - called sense and respond using a recommendation engine Amazon.com has invested heavily in this technology Called collaborative filtering when combined with data from other users to create profiles

Analytics: measuring web site success. MBA 563 Week 4

Analytics: measuring web site success MBA 563 Week 4 Overview: Methods of measuring marketing success You can t manage what you can t measure (Bob Napier, ex CIO, Hewlett Packard) 1. Data mining and predictive