ANALYTICS: MEASURING AND
PREDICTING MARKETING SUCCESS
MARK 430
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
– (we will look at social media metrics later in the
course)
You can’t manage what you can’t measure (Bob
Napier, ex CIO, Hewlett Packard)
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
DATA MINING AND PREDICTIVE
ANALYTICS
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:
– Stored in the data warehouse,
– Available for analysis and distribution to marketing decision
makers.
Source: eMarketing eXcellence. 2012.
Smith &Chaffey
Data trails…..
• 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
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
– Predicting behaviour
– RFM analysis (recency, frequency, monetary value
of customer) Source: eMarketing eXcellence. 2012.
Smith &Chaffey
Data mining and
predictive analytics
How it works: Analytics (IBM SocialMedia) video
Frontline (PBS): The Persuaders
The Narrowcasting Future video
FOCUS ON WEB ANALYTICS
Web Analytics - definition
• Techniques used to assess and improve the contribution
of online marketing to a business or organization
• Onsite analytics
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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 datadriven decision making
Source: eMarketing eXcellence. 2012.
Smith &Chaffey
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
WEB ANALYTICS SOFTWARE
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
Web analytics approaches
• Two main approaches to obtaining website analytics
data:
1. Server-based: analysis of automatically generated firstparty 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.
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
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)
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)
– 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
What is automatically recorded includes:
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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)
Remember this about web analytics
• 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!
• Information may be incomplete because of
caching.
• This is why benchmarking is so important
– trends rather than absolute numbers
USING WEB ANALYTICS EFFECTIVELY
First decision before we start
analytics?
• What are our business goals?
• What are our key performance indicators that
will tell us whether or not we have reached
those goals?
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
Web channel promotion – where did web site
users COME FROM?
• Which site “referred”
them
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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
Web channel buyer behaviour - what do
people DO when they get to the site?
• We can monitor
• Measures – eg.
– Which content is
accessed by users
– When they visit
– How long they stay
– Whether interaction
with content leads to
sales or other desired
outcome
– 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
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
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. 2012.
Smith &Chaffey
So….how do you use web analytics
effectively?
1. Identify leading indicators of business
success via the web channel (ie. set goals)
2. Identify the key performance indicators
(KPI) 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
REAL-TIME ANALYTICS
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
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
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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: eMarketing eXcellence. 2012.
Smith &Chaffey
GOOGLE ANALYTICS SOFTWARE IN
PRACTICE
Focus on Google Analytics
• Google Analytics Tutorial for Beginners 2014
(video)
– see the analytics for the course website (I need to log
in for you to view this)
• YouTube Channel for Google Analytics