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Introduction to Online Marketing Intelligence

Zhangxi LinISQS 3358

Texas Tech University

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Outline

Online Targeted AdvertisingAbout Web miningDataKnowing your customerConsumer segmentation

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Online Targeted Advertising

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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 aren’t 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

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Online Marketing Technology

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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, China’s 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 MarketingWhen 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

Source: Advertising.com, 2005 Source: Advertising.com, 2004

Lift in CVR

Advertiser A 167%

Advertiser B 2,232%

Advertiser C 3,130%

Lift in CVR

Advertiser A 90%

Advertiser B 323%

Advertiser C 105%

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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%

PRODUCT PURCHASE

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.

1 out of every 2 people who

clicked on the ad completed a booking.

A hotel booking was generated for

every 2,000 impressions served.

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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 systemsStock quotes or financial servicesNews, weather, sports, traffic conditionsCelebrity or event photos and multimediaSearch enginesWeb site hosting or e-mailGames or contestsBeach 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 DataFields

User’s 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

WebServer

Browser

User requests index.htm.

Server sends copy of index.htm.

Browser parses index.htm,finds references to image files,and requests image files.

...

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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 limiteddata: referring URL, page selections, browser.

Different users within a household may beindistinguishable.

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What Is a Cookie?

Web ServerWeb Browser Client

Browser requests Web page

Web page is delivered withinstructions for creating cookie

Browser createscookie and writesto hard disk

Value of cookie sent to server

Custom content returned

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The Anatomy of a Cookie

Name Sequence of charactersuniquely identifying cookie

Value Stored information

Domain Domain name

Path Path within a site. Accessis restricted to this path.

Expires Expiration date in UTC

Secure Encryption flag

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A Sample Cookie

session-id103-0556164-3592039www.megastock.com/ 073071001630123554274210028829450847*

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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 cookiesper 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 thedocument 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/usage/ 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 registeredcustomers 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

ProductInfo

CustomerInfo

ProductSelection

Shipping Billing/CreditCardInfo

FinalDecision

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A Visitor Path

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3

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6

7

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EXIT

Path: 1 6 7 1 3 8

1

23

4

5

6

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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 data=crssamp.rlinks;run;

Produce Contents of the RLINKS Dataset

continued...

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SAS Code for Path Analysis

proc freq data=crssamp.rlinks;tables Category DollarCat

NumItems PurchaseStep

/list missing;run;

Produce Frequencies for Class Variables

continued...

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SAS Code for Path Analysisproc univariate data=rlinks; var TotalCost;run;

title2 "Total Cost when a Purchase is Made";proc univariate data=rlinks (where=(PurchaseSequence=7)); 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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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 VAR=VALUE. 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

HomePage(Point ofEntry)

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EXIT

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