ANALYTICS: MEASURING AND PREDICTING MARKETING SUCCESS

MBA 563

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)

Results of Accenture's 2014 CMO Insights SurveyIn what areas do you believe the marketing

function will change the most?

Accenture's 2014 CMO Insights Survey

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

– 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

Technology-Enabled Approaches • The Web provides marketers with huge amounts of

information about users Þ This data is collected automaticallyÞ 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.

Þ They allow marketers to make quick and responsive changes in Web pages, promotions, and pricing.

Þ 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 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.

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

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

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)

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

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

USING WEB ANALYTICS EFFECTIVELY

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?

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 – 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– 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

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, 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

Some terminology for key website volume measures

Measure Measure Definition

How many? (audience reach)

Unique users IP+User-agentCookie and/orRegistration

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 impression File (or files) sent to a user as a result of a server request by that user

What do they see? Ad impressions A 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

Focus on Google Analytics

• Getting Started with Google Analytics (video from Google 29 minutes)

• YouTube Channel for Google Analytics

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

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

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– Used extensively by advertising networks– Entails tracking across third party sites – Many privacy concerns have been raised

Source: eMarketing eXcellence. 2012. Smith &Chaffey

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

DATA MINING AND PREDICTIVE ANALYTICS

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

Big data and predictive analytics

How it works: Analytics (IBM SocialMedia) video

Kenneth Cukier: Big data is better data (TED talk 2014)