Introduction to Online Marketing Intelligence

1
Introduction to Online Marketing Intelligence

• Zhangxi Lin
• ISQS 3358
• Texas Tech University

2
Outline

• Online Targeted Advertising
• About Web mining
• Data
• Knowing your customer
• Consumer segmentation

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Online Targeted Advertising
4
Marketing Technology Adoption

• In December 2005, Forrester surveyed 371
  marketing technology decision-makers and
  influencers to investigate trends in marketing
  technology adoption and spending.
• Respondents hail from six major industry groups,
  and two-thirds work for firms whose annual
  revenues in 2005 exceeded 1 billion.
• Marketing technology adoption is widespread.
• Marketers say they need a more comprehensive
  application suite.
• Vendors arent delivering yet.

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Marketing Technology Spending

• Since 2003, budgets have crept steadily upward
  and, on average, 2006 budgets are up 7 over
  2005. But spending varies significantly by
  company size and industry. Specifically
• The largest and smallest firms are scaling back
  slightly.
• Technology followers are putting cash behind
  their intentions.
• As a percentage of revenue, retailers spend the
  most on marketing technology.
• B2B firms are growing marketing technology spend
  aggressively.

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Marketing Technology Spending
7
Online Marketing Technology
8
Online Advertising Market Status

• In 2006, the advertising spending was 16.8
  billion an increase of 34 from that of 2005 (IAB
  2007).
• According to DoubleClick (2005)
• Limited online advertising publishing resources
  because of limited online users capability to
  view growing number of web pages (DoubleClick
  Research 2005)
• Online targeted advertising is a seller market
• Online targeted advertising is emerging as a new
  trend.
• In March 2007, Chinas largest advertising
  company by advertising revenue, Focus Holding Ltd
  agreed to buy Chinese leading online firm Allyes
  Information Technology Co. Ltd for 225 million.
• In April 2007, Google Inc. announced a definitive
  agreement to acquire DoubleClick for 3.1
  billion.

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Targeted Marketing

• Users know what they want
• Users purchased certain items from certain
  websites
• We can apply real-time customized marketing
  solutions (see the process map later)
• Users did not purchase, but click through some
  links
• Mining the click streams of the customers, and
  figure out the needs----behavioral targeting
• Users do not know what they want---behavioral
  targeting
• Collecting information online (such as the blogs,
  discussions boards in a community)
• Segment/target/position strategy
• We can potentially build a database profiling the
  online users
• How to design (create) ads to make it appeal to
  end users

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Implications of Targeted Marketing

• For advertisers
• Help to drive immediate responses (or increased
  sales) to their advertisements
• Help to build branding for the advertisers
• For publishers
• Maximize the value of high-quality ad inventory
  space (differential services for different site
  sectors)

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Effectiveness of Online Marketing
When executed properly, behavioral marketing is a
highly effective means of reaching and
converting your target audience.
Network Behavioral Targeting vs. Non-Targeted
Advertising
Behavioral Re-Targeting vs. Non-Targeting
Advertising
Lift in CVR Lift in CVR
Advertiser A 167
Advertiser B 2,232
Advertiser C 3,130
Lift in CVR Lift in CVR
Advertiser A 90
Advertiser B 323
Advertiser C 105
Source Advertising.com, 2005
Source Advertising.com, 2004
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PRODUCT PURCHASE
This travel advertiser targeted consumers who
previously visited its website in order to drive
actual reservations.
Campaign Results Behavioral Targeting
Impressions 99 million
Clicks 92,223
Bookings 52,936
Conversion Rate 57.4
Visitors who had not booked a reservation
received custom ads highlighting guaranteed
rates, seasonal discounts, new hotel perks and
free gifts with an online booking.
A hotel booking was generated for every 2,000
impressions served.
1 out of every 2 people who clicked on the ad
completed a booking.
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About Web Mining
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Web Mining

• When online users browse web pages, their
  activities could be recorded. Using data mining
  techniques to analyze these activities will
  enable more accurate web-based online
  advertising?
• The possible web mining applications may include
• Consumer Profiling
• Purchase propensity analysis
• Web page effectiveness evaluation
• Online recommendation
• Realtime advertising
• Others

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Some Business Questions

• Who is visiting my Web site?
• Who is buying my product(s)?
• Who are my repeat buyers?
• Which customers are churning?
• Which Web design produces the most purchases?
• What campaign strategies are most effective in
  increasing Web site visits?

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Business Questions

• What factors influence product purchases?
• Time-of-day effects
• Gender, Age, Income, and so forth
• Latent factors e-shopper, Web expert, and so
  forth
• Which sales channels produce the most profitable
  customers?
• Do any site-visit patterns correlate with
  outcomes that can be exploited for business
  advantage?
• How can I forecast peak usage and future usage to
  ensure I have the hardware and technology to keep
  my Web site running?
• How can I monitor my Web site to prevent
  inappropriate access and malicious activity?
• How can I manage purchases, returns, and
  exchanges to avoid fraud and reduce waste?

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Web Mining for Profitability

• Increase viewing, navigation, and transaction
  efficiency.
• Improve the customer experience.
• Add services and features that promote
  cross-selling and up-selling opportunities.
• Identify problem areas.
• Improve security.
• Attract more high quality customers.

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Customer Relationship Management (CRM)

• Making the right offer to the right customer at
  the right time.
• One-to-one marketing. Peppers and Rogers
• TQM (Total Quality Management) with new buzz
  words.
• The practice of annoying customers for short
  term profits. Herb Edelstein

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Examples of Web Site Services
Recommender systems
Stock quotes or financial services
News, weather, sports, traffic conditions
Celebrity or event photos and multimedia
Search engines
Web site hosting or e-mail
Games or contests
Beach cams, space cams, hot spot cams
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Internet Commerce Challenges

• 24/7 operations
• International scope
• Non-standard media
• Many browsers
• Different display monitors and graphics adapters
• Different window geometry
• Different computers and operating systems
• Different customer concerns
• Secure transactions
• Privacy and confidentiality
• Legitimacy

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Data Collection and Preparation
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Data Collection Methods

• Web logs
• Cookies
• Forms
• Java applications
• Other applications

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Web Log Data

• Fields
• Users IP address, also called
• Remote host name
• Client IP address
• User name, also called
• Remote user log name (may be different)
• Authenticated user name
• Date and time of request, with or without a UTC
  offset
• Request type, also called method
• HTTP request with (CLF) or without (IIS) argument
• Status HTTP three digit status code
• Number of bytes sent to client

continued...
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Web Log Fields

• The URL path requested, if request type has no
  argument
• The port to which the request was served
• The name of the server
• The IP address of the server
• The time taken to serve the request
• Number of bytes in the request received from the
  client
• User agent, which is usually a text string with
  the name and version number of Web browser used
  by the client and the operating system of the
  client machine
• The domain name or IP address of the referring
  URL
• Query information in a text string
• Cookie information in a text string

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The User Session
User requests index.htm.
Server sends copy of index.htm.
Browser parses index.htm, finds references to
image files, and requests image files.
Web Server
Browser
...
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Three Popular Web Log Formats
NCSA Common Log Format
Microsoft IIS Format
W3C Extended Log File Format
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Web Logs May Be Inadequate for Data Mining
Limitations exist with respect to defining
users, sessions, and page hits.
User preferences must be inferred from
limited data referring URL, page selections,
browser.
Different users within a household may
be indistinguishable.
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What Is a Cookie?
Browser requests Web page
Web page is delivered with instructions for
creating cookie
Browser creates cookie and writes to hard disk
Value of cookie sent to server
Custom content returned
Web Server
Web Browser Client
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The Anatomy of a Cookie
Name
Sequence of characters uniquely identifying cookie
Value
Stored information
Domain
Domain name
Path
Path within a site. Access is restricted to this
path.
Expires
Expiration date in UTC
Secure
Encryption flag
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A Sample Cookie
session-id 103-0556164-3592039 www.megastock.com/
0 730710016 30123554 2742100288 29450847
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Limitations of Cookies
Can only be accessed by the domain name that
created them (which is a GOOD thing)
Are restricted to a maximum number of cookies per
Web site (20 with Netscape Version 0)
Are limited in size (4K with Netscape Version 0)
Have an expiration date
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Microsoft Internet Explorer Cookie Options
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Client-Side Cookies for Personalization
Deployed using JavaScript or VBScript
Implemented through the document.cookie property
Can be maintained using frames or the document
object model
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Server-Side Cookies
Can be used to restrict access
Support shopping cart applications
Help track user activity on the Web site
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Server-Side Data Collection
Maintaining user information
Collecting and updating information
e-Commerce strategies
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Evaluating Visitor Behavior
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Some Common Web Log Statistics

• Most popular pages
• Frequency of referring sites
• Page count statistics means, percentiles,
  variation
• Session count statistics
• Frequency of Web browser usage
• Frequency of operating systems
• Frequency of error types
• Check web log statistics http//www.commerx.com/u
  sage/
• This website is the business site of IMW
  (http//www.inetworks.com) headquartered in
  Austin, Texas.

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Baselines and Comparisons

• Which statement is more informative?
• Our Web server recorded 11,000 page views
  yesterday.
• Our Web server recorded an increase of 1000 page
  views yesterday compared to the previous day.
• Our Web server recorded a 10 increase in page
  views yesterday compared to the previous day.

continued...
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Baselines and Comparisons Good or Bad?
We converted 25 of our registered customers to
premium account status this month.
We converted 50 more of our registered
customers to premium account status this month
compared to last month.
Last month we converted 2 registered customers
to premium account status, and this month we
converted 3.
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Methods of Evaluating Visitor Behavior

• Web Stats
• Path Analysis
• Link Analysis
• Stochastic Process Methods
• Page transition probabilities
• Probability of site abandonment

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Path Analysis for an E-tailer
Final Decision
Product Selection
Customer Info
Shipping
Billing/ Credit Card Info
Product Info
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A Visitor Path
Path
1
6
7
1
3
8
1
5
1
4
2
6
3
2
3
7
5
4
8
6
EXIT
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Path Analysis Example Results

• Sixty percent of site visitors leave after
  viewing the home page.
• Seventy-three percent of customers who purchase
  product X do not access the product X information
  page.
• The highest probability of abandonment occurs on
  the shipping page.
• Sixty-three percent of consumers who purchased
  product X viewed warranty information, while
  twenty-seven percent of consumers who abandoned a
  shopping cart containing product X viewed
  warranty information.

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Path Analysis E-tailer Example

• Data
• Only sessions with shopping carts are included
• All paths up to checking out are condensed into
  a single Product Selection state
• Each session consists of 1 to 7 states, number of
  items selected, value of all items in the
  shopping cart, and time each state is entered.
• Purpose investigate the abandonment of shopping
  carts and exiting the site without making a
  purchase.
• Analysis group shopping carts by value, perform
  a sequential association analysis, and plot
  confidence as a function of state.

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SAS Code for Path Analysis
ods html path'C\workshop\winsas\CCWEB'
body'rlnkstat.html' title1 "Path Analysis of
E-tailer Data" proc contents datacrssamp.rlinks
run
Produce Contents of the RLINKS Dataset
continued...
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SAS Code for Path Analysis
proc freq datacrssamp.rlinks tables Category
DollarCat NumItems
PurchaseStep /list missing run
Produce Frequencies for Class Variables
continued...
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SAS Code for Path Analysis
proc univariate datarlinks var
TotalCost run title2 "Total Cost when a
Purchase is Made" proc univariate datarlinks
(where(PurchaseSequence7)) var
TotalCost run
Basic Descriptive Statistics
continued...
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Link Analysis

• Link analysis is the examination of the linkages
  between effects in a complex system. (SAS Help
  screen)
• Analysts try to discover the relationships
  between states in a complex system.
• A link analysis may employ a variety of
  techniques including OLAP, associations,
  sequences, clustering, and graphics.
• The path analysis performed on the e-tailer data
  may be viewed as a link analysis performed on a
  rather simple retail system.

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(No Transcript)
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SAS Link Node

• C1U -- the unweighted first-order Centrality
  measure.
• C2U -- the unweighted second-order Centrality
  measure.
• C1 -- the first-order Centrality measure.
• C2 -- the second-order Centrality measure.
• VALUE -- the value of the class variable, or the
  midpoint of the bin of the interval variable that
  constitutes the node.
• VAR -- the variable that constitutes the node.
• ROLE -- the variable role.
• COUNT -- the node count. The number of
  observations that are represented by the level of
  the variable.
• PERCENT -- the node count divided by the total
  number of observations.
• ID -- the node ID.
• TEXT -- the text variable, represented as
  VARVALUE.
• X -- the X-coordinate of the node in the Link
  Graph.
• Y -- the Y-coordinate of the node in the Link
  Graph.

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C1 and C2

• The values C1 and C2 are measures of node
  importance.
• C1 is the first-order undirected centrality
  measure, which attempts to measure the importance
  of the node in the network as a function of how
  often it directly links to other nodes in the
  network.
• C2 is the second-order undirected centrality
  measure, which attempts to measure the combined
  importance of all nodes that are directly linked
  to the node.
• In a social network, C1 would measure How many
  people (nodes) are my friends? C2 would measure,
  How many people are friends of my friends?
• The centrality measures can be weighted or
  unweighted.
• A weighted first-order centrality measure would
  be analogous to measuring, How many people with
  many friends are my friends? Thus, a node with
  many direct links that is linked to the target
  node would receive a higher weight than a node
  with few direct links.

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The Web Stochastic Process
1
4
Home Page (Point of Entry)
3
EXIT
5
2
States
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Consumer Segmentation
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Discussion How to segment

• Dataset Commrex web log dataset
• Two levels of granularity to aggregate the
  transaction records
• Per session
• Per user
• Identify the interested pages and extract the
  information to be mined
• Combining clustering and classification How?
  Referring to the case of INSSUBRO in Text Mining
• Step 1 clustering
• Step 2 Using Data Set Attribute node to choose
  the target variables and change status of other
  variables
• Step 3 Classification based on the target
  variable