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Chapter 8:

Web Analytics, Web Mining, and Social Analytics

Business Intelligence and Analytics: Systems for Decision Support

(10th Edition)

Business Intelligence and Analytics: Systems for Decision Support

(10th Edition)

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Learning Objectives

Define Web mining and understand its taxonomy and its application areas

Differentiate between Web content mining and Web structure mining

Understand the internals of Web search engines

Learn the details about search engine optimization

Define Web usage mining and learn its business application

(Continued…)

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Learning Objectives

Describe the Web analytics maturity model and its use cases

Understand social networks and social analytics and their practical applications

Define social network analysis and become familiar with its application areas

Understand social media analytics and its use for better customer engagement

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Opening Vignette…

Security First Insurance Deepens Connection with Policyholders

Situation

Problem

Solution

Results

Answer & discuss the case questions.

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Questions for the Opening Vignette

What does Security First do?

What were the main challenges Security First was facing?

What was the proposed solution approach? What types of analytics were integrated in the solution?

Based on what you learn from the vignette, what do you think are the relationships between Web analytics, text mining, and sentiment analysis?

What were the results Security First obtained? Were any surprising benefits realized?

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Web Mining Overview

Web is the largest repository of data

Data is in HTML, XML, text format

Challenges (of processing Web data)

The Web is too big for effective data mining

The Web is too complex

The Web is too dynamic

The Web is not specific to a domain

The Web has everything

Opportunities and challenges are great!

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Web Mining

Web mining (or Web data mining) is the process of discovering intrinsic relationships from Web data (textual, linkage, or usage)

Is it the same as data mining on data generated on the Internet?

Web data?

Content, Link, Log, …

Web Mining versus Web Analytics

Look at the simple taxonomy on the next slide

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Web Mining

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Web Content/Structure Mining

Mining the textual content on the Web

Data collection via Web Crawlers/Spiders

Web pages include hyperlinks

Authoritative pages

Hubs

hyperlink-induced topic search (HITS) alg.

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Application Case 8.1

Identifying Extremist Groups with Web Link and Content Analysis

Questions for Discussion

How can Web link/content analysis be used to identify extremist groups?

What do you think are the challenges and the potential solution to such intelligence gathering activities?

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Search Engines

Google, Bing, Yahoo, …

For what reason do you use search engines?

Search engine is a software program that searches for documents (Internet sites or files) based on the keywords (individual words, multi-word terms, or a complete sentence) that users have provided that have to do with the subject of their inquiry

They are the workhorses of the Internet

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Structure of a Typical Internet Search Engine

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Anatomy of a Search Engine

Development Cycle

Web Crawler

Document Indexer

Steps

Step 1 – Pre-Processing the Documents

Collecting, organizing, and storing

Step 2 – Parsing the Documents

Step 3 – Creating the Term-by-Document Matrix

How to represent the values (numeric, binary, …)

Term Frequency / Inverse Document Frequency

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Anatomy of a Search Engine

Response Cycle

Query Analyzer

Document Matcher/Ranker

How does Google do it?

Googlebot

Google indexer

Google Query Processor

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Technology Insights 8.1 PageRank Algorithm

PageRank is a link analysis algorithm

 Larry Page

Outcome of a research project at Stanford University in 1996

The “secret sauce” in Google

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Application Case 8.2

IGN Increases Search Traffic by 1500 Percent with SEO

Questions for Discussion

How did IGN dramatically increase search traffic to its Web portals?

What were the challenges, the proposed solution, and the obtained results?

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Search Engine Optimization (SEO)

It is the intentional activity of affecting the visibility of an e-commerce site or a Web site in a search engine’s natural (unpaid or organic) search results

Part of an Internet marketing strategy

Based on knowing how a search engine works

Content, HTML, keywords, external links, …

Indexing based on …

Webmaster submission of URL

Proactively and continuously crawling the Web

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Top 15 Most Popular Search Engines (by eBizMBA, March 2013)

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Methods for Search Engine Optimization

Search engine recommended techniques (White-Hat SEO)

Producing results based on good site design, accurate content (for users, not engines)

Search engine disapproved techniques (Black-Hat SEO)

Spamdexing? (search spam, search engine spam, or search engine poisoning)

Deception (what is shown is different to human and machine/spider)

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Application Case 8.3

Understanding Why Customers Abandon Shopping Carts Results in $10 Million Sales Increase

Situation

Problem

Solution

Results

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Web Usage Mining

 Web Analytics!

Extraction of information from data generated through Web page visits and transactions…

data stored in server access logs, referrer logs, agent logs, and client-side cookies

user characteristics and usage profiles

metadata, such as page attributes, content attributes, and usage data

Clickstream data, clickstream analysis

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Web Usage Mining

Web usage mining applications

Determine the lifetime value of clients

Design cross-marketing strategies across products

Evaluate promotional campaigns

Target electronic ads and coupons at user groups based on user access patterns

Predict user behavior based on previously learned rules and users' profiles

Present dynamic information to users based on their interests and profiles

…

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Web Usage Mining (Clickstream Analysis)

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Application Case 8.4

Allegro Boosts Online Click-Thru Rates by 500 Percent with Web Analysis

Questions for Discussion

How did Allegro significantly improve clickthrough rates with Web analytics?

What were the challenges, the proposed solution, and the obtained results?

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Web Analytics Metrics

Provides near-real-time data to deliver invaluable information to …

Improve site usability

Manage marketing efforts

Better document ROI, …

Web analytics metric categories:

Web site usability: How were they using my Web site?

Traffic sources: Where did they come from?

Visitor profiles: What do my visitors look like?

Conversion statistics: What does all this mean for the business?

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Web Analytics Metrics - Web Site Usability

Web Site Usability

Page views

Time on site

Downloads

Click map

Click paths

Traffic Source

Referral Web sites

Search engines

Direct

Offline campaigns

Online campaigns

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Web Analytics Metrics - Web Site Usability

Visitor Profiles

Keywords

Content groupings

Geography

Time of day

Landing page

Conversion Statistics

New visitors

Returning visitors

Leads

Sales/conversions

Abandonment rates

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A Web Analytics Dashboard

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Web Analytics Maturity Model

Maturity  degree of proficiency, formality, and optimization of business models

Business Intelligence Maturity Model (TDWI)

Management Reporting ➔ Spreadmarts ➔ Data Marts ➔ Data Warehouse ➔ Enterprise Data Warehouse ➔ BI Services

Business Analytics Maturity Model (INFORMS)

Descriptive Analytics ➔ Predictive Analytics ➔ Prescriptive Analytics

Web analytics maturity model  next slide…

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Web Analytics Maturity Model

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Web Analytics Tools

Plenty of them exist, and numbers are increasing (Web-based versus downloadable)

Google Web Analytics (google.com/analytics)

Yahoo! Web Analytics (web.analytics.yahoo.com)

Open Web Analytics (openwebanalytics.com)

Piwik (PIWIK.ORG)

FireStats (firestats.cc)

Site Meter (sitemeter.com)

Woopra (woopra.com)

AWStats (awstats.org)

Snoop (reinvigorate.net) …

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Putting It All Together—A Web Site Optimization Ecosystem

Two-Dimensional View of the Inputs for Web Site Optimization

Goal:

Customer Experience Management (CEM)

Voice of Customer (VOC)

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Web Mining Success Stories

Amazon.com, Ask.com, Scholastic.com, …

A Process View of the Web Site Optimization Ecosystem

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Voice of the Customer Strategy Framework (Attensity.com)

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Social Analytics Social Network Analysis

Social Network - social structure composed of individuals linked to each other

Analysis of social dynamics

Interdisciplinary field

Social psychology

Sociology

Statistics

Graph theory

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Social Analytics Social Network Analysis

Social Networks help study relationships between individuals, groups, organizations, societies

Self organizing

Emergent

Complex

Typical social network types

Communication networks, community networks, criminal networks, innovation networks, …

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Application Case 8.5

Social Network Analysis Helps Telecommunication Firms (TELCOs)

Questions for Discussion

How can social network analysis be used in the telecommunications industry?

What do you think are the key challenges, potential solution, and probable results in applying SNA in telecommunications firms?

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Social Analytics Social Network Analysis Metrics

Connections

Homophily

Multiplexity

Network closure

Propinquity

Segmentation

Cliques and social circles

Clustering coefficient

Cohesion

Distribution

Bridge

Centrality

Density

Structural holes

Tie strength

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Social Media Definitions and Concepts

Enabling technologies of social interactions among people

Relies on enabling technologies of Web 2.0

Takes on many different forms

Internet forums, Web logs, social blogs, microblogging, wikis, social networks, podcasts, pictures, video, and product reviews

Different types of social media

Based on media research and social process

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Different Types of Social Media

Collaborative projects (e.g., Wikipedia)

Blogs and microblogs (e.g., Twitter)

Content communities (e.g., YouTube)

Social networking sites (e.g., Facebook)

Virtual game worlds (e.g., World of Warcraft), and

Virtual social worlds (e.g., Second Life)

--Kaplan and Haenlein (2010)

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Social versus Industrial Media

Web-based social media are different from traditional/industrial media, such as newspapers, television, and film

Differentiating characteristics

Quality

Reach

Frequency

Accessibility

Usability

Immediacy

Updatability

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How Do People Use Social Media?

Different engagement levels

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Application Case 8.6

Measuring the Impact of Social Media at Lollapalooza

Questions for Discussion

How did C3 Presents use social media analytics to improve its business?

What were the challenges, the proposed solution, and the obtained results?

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Social Media Analytics

It is the systematic and scientific ways to consume the vast amount of content created by Web-based social media outlets, tools, and techniques for the betterment of an organization’s competitiveness

Fastest growing movement in analytics

Social Media

Tweeter

Facebook

LinlkedIn

…

Insights

Solutions

Course of Actions

…

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Social Media Analytics

HBR Analytic Services survey (HBR, 2010)

75% of the companies did not know where their customers are talking about them

31% do not measure effectiveness of social media

only 23% are using social media analytics tools

7% are able to integrate social media into marketing

Measuring the Social Media Impact

Descriptive analytics – simple counts/statistics

Social network analysis

Advanced analytics – predictive analytics, text mining

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Best Practices in Social Media Analytics

Think of measurement as a guidance system, not a rating system

Track the elusive sentiment

Continuously improve the accuracy of text analysis

Look at the ripple effect

Look beyond the brand

Identify your most powerful influencers

Look closely at the accuracy of your analytic tool

Incorporate social media intelligence into planning

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Application Case 8.7

eHarmony Uses Social Media to Help Take the Mystery Out of Online Dating

Questions for Discussion

How did eHarmony use social media to enhance online dating?

What were the challenges, the proposed solution, and the obtained results?

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Social Media Analytics Tools and Vendors

Attensity360

Radian6/Salesforce Cloud

Sysomos

Collective Intellect

Webtrends

Crimson Hexagon

Converseon

SproutSocial …

Twitter

Facebook

YouTube

Flickr

…

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Social Media Analytics

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End of the Chapter

Questions, comments

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All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America.

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360 Customer View

Log Analysis

Marketing AttributionCustomer Analytics

Social Media Analytics

Search Engines Optimization

Page RankInformation Retrieval

Search Engines

Social Network Analysis

Clickstream Analysis

Social Analytics

Semantic WebsWeb Analytics

Graph Mining

Sentiment Analysis

Web Structure Mining

Source:the unified

resource locator (URL)

links contained in the

Web pages

Web Content Mining

Source:unstructured

textual content of the

Web pages (usually in

HTML format)

Web Usage Mining

Source:the detailed

description of a Web

site’s visits (sequence

of clicks by sessions)

Data

Mining

Text

Mining

WEB MINING

Query Analyzer

Document

Matcher/Ranker

Web Crawler

Document

Indexer

Scheduler

Cashed / Indexed

Documents DB

User

World Wide Web

Responding CycleDevelopment Cycle

Weblogs

Website

Pre-Process Data

Collecting

Merging

Cleaning

Structuring

-Identify users

-Identify sessions

-Identify page views

-Identify visits

Extract Knowledge

Usage patterns

User profiles

Page profiles

Visit profiles

Customer value

How to better the data

How to improve the Web site

How to increase the customer value

User /

Customer

Web

Analytics

Voice of

Customer

Customer Experience

Management

Customer Interaction

on the Web

Analysis of Interactions

Knowledge about the Holistic

View of the Customer

Creators

Critics

Joiners

Collectors

Spectators

Inactives

Time