Web Usage Mining for EBusiness Applications

1
Web Usage Mining an overview
Bettina Berendt
Humboldt-Universität zu Berlin, Institute of
Information Systems http//www.wiwi.hu-berlin.de/
berendt/ Talk at Universidad Politecnica de
Madrid, 24 February 2005
2
Acknowledgements

• Andreas Hotho
• Ernestina Menasalvas
• Bamshad Mobasher
• Myra Spiliopoulou
• Gerd Stumme
• Max
  Teltzrow

3
Agenda
Introduction
Data Acquisition and Data Preparation
Forms of analysis mining techniques
A case study
Outlook
4
Data mining a definition

• the process
• of exploration and analysis,
• by automatic or semi-automatic means,
• of large quantities of data
• in order to discover meaningful patterns and
  results.
• (Berry Linoff, 1997, 2000)

Picture from http//www.smithsonianmag.si.edu/smit
hsonian/issues98/jan98/mining_jpg.html
5
Data Mining and Knowledge Discovery

• Knowledge discovery
• the non-trivial process of identifying valid,
  novel, potentially useful, and ultimately
  understandable patterns in data. (from Fayyad,
  U.M., Piatetsky-Shapiro, G., Smyth, P.,
  Uthurusamy, R. (Eds.) (1996). Advances in
  Knowledge Discovery and Data Mining. Boston, MA
  AAAI/MIT Press.)
• Data mining
• sometimes refers to the whole process of
  knowledge discovery and sometimes to the specific
  machine learning phase.
• (from Kohavi Provosts glossary at
  http//robotics.stanford.edu/ronnyk/glossary.ht
  ml)

6
What is Web Mining?

• Despite its success, one problem of the current
  WWW is that much of this knowledge lies dormant
  in the data.
• Web mining tries to overcome these problems by
  applying data mining techniques to the content,
  (hyperlink) structure, and usage of Web resources.

• Goals include
• the improvement of site design and site
  structure,
• the generation of dynamic recommendations,
• and improving marketing.

Web Mining Areas Web content mining
7
Application examples eCommerce
8
Knowledge discovery multi-stage and iterative
The CRISP-DM process model
More on CRISP-DM as a framework for Web
mining Berendt, Menasalvas,
Spiliopoulou, Tutorial at ECML/PKDD 2004
http//www.crisp-dm.org/Images/187343_CRISPart.jpg
9
Agenda
Introduction
Data Acquisition and Data Preparation
Forms of analysis mining techniques
A case study
Outlook
10
Web Usage Mining

• Discovery of meaningful patterns from data
  generated by client-server transactions on one or
  more Web servers
• Typical Sources of Data
• automatically generated data stored in server
  access logs, referrer logs, agent logs, and
  client-side cookies
• e-commerce and product-oriented user events
  (e.g., shopping cart changes, ad or product
  click-throughs, etc.)
• user profiles and/or user ratings
• meta-data, page attributes, page content, site
  structure

11
Data collection
Web server
Client (Browser)
Proxy
12
Whats in a typical Web server log
(Requests to www.acr-news.org)
ltip_addrgt - - ltdategtltmethodgtltfilegtltprotocolgtltcodegt
ltbytesgtltreferrergtltuser_agentgt
203.30.5.145 - - 01/Jun/1999030921 -0600
"GET /Calls/OWOM.html HTTP/1.0" 200 3942
"http//www.lycos.com/cgi-bin/pursuit?queryadvert
isingpsychology-maxhits20catdir"
"Mozilla/4.5 en (Win98 I)" 203.30.5.145 - -
01/Jun/1999030923 -0600 "GET
/Calls/Images/earthani.gif HTTP/1.0" 200 10689
"http//www.acr-news.org/Calls/OWOM.html"
"Mozilla/4.5 en (Win98 I)" 203.30.5.145 - -
01/Jun/1999030924 -0600 "GET
/Calls/Images/line.gif HTTP/1.0" 200 190
"http//www.acr-news.org/Calls/OWOM.html"
"Mozilla/4.5 en (Win98 I)" 203.252.234.33 - -
01/Jun/1999031231 -0600 "GET / HTTP/1.0" 200
4980 "" "Mozilla/4.06 en (Win95
I)" 203.252.234.33 - - 01/Jun/1999031235
-0600 "GET /Images/line.gif HTTP/1.0" 200 190
"http//www.acr-news.org/" "Mozilla/4.06 en
(Win95 I)" 203.252.234.33 - - 01/Jun/19990312
35 -0600 "GET /Images/red.gif HTTP/1.0" 200 104
"http//www.acr-news.org/" "Mozilla/4.06 en
(Win95 I)" 203.252.234.33 - - 01/Jun/19990312
35 -0600 "GET /Images/earthani.gif HTTP/1.0" 200
10689 "http//www.acr-news.org/" "Mozilla/4.06
en (Win95 I)" 203.252.234.33 - -
01/Jun/1999031311 -0600 "GET /CP.html
HTTP/1.0" 200 3218 "http//www.acr-news.org/"
"Mozilla/4.06 en (Win95 I) 203.30.5.145 - -
01/Jun/1999031325 -0600 "GET
/Calls/AWAC.html HTTP/1.0" 200 104
"http//www.acr-news.org/Calls/OWOM.html"
"Mozilla/4.5 en (Win98 I)"
13
and what does it mean?
(Requests to www.acr-news.org)
ltip_addrgt - - ltdategtltmethodgtltfilegtltprotocolgtltcodegt
ltbytesgtltreferrergtltuser_agentgt
203.30.5.145 - - 01/Jun/1999030921 -0600
"GET /Calls/OWOM.html HTTP/1.0" 200 3942
"http//www.lycos.com/cgi-bin/pursuit?queryadvert
isingpsychology-maxhits20catdir"
"Mozilla/4.5 en (Win98 I)" 203.30.5.145 - -
01/Jun/1999030923 -0600 "GET
/Calls/Images/earthani.gif HTTP/1.0" 200 10689
"http//www.acr-news.org/Calls/OWOM.html"
"Mozilla/4.5 en (Win98 I)" 203.30.5.145 - -
01/Jun/1999030924 -0600 "GET
/Calls/Images/line.gif HTTP/1.0" 200 190
"http//www.acr-news.org/Calls/OWOM.html"
"Mozilla/4.5 en (Win98 I)" 203.252.234.33 - -
01/Jun/1999031231 -0600 "GET / HTTP/1.0" 200
4980 "" "Mozilla/4.06 en (Win95
I)" 203.252.234.33 - - 01/Jun/1999031235
-0600 "GET /Images/line.gif HTTP/1.0" 200 190
"http//www.acr-news.org/" "Mozilla/4.06 en
(Win95 I)" 203.252.234.33 - - 01/Jun/19990312
35 -0600 "GET /Images/red.gif HTTP/1.0" 200 104
"http//www.acr-news.org/" "Mozilla/4.06 en
(Win95 I)" 203.252.234.33 - - 01/Jun/19990312
35 -0600 "GET /Images/earthani.gif HTTP/1.0" 200
10689 "http//www.acr-news.org/" "Mozilla/4.06
en (Win95 I)" 203.252.234.33 - -
01/Jun/1999031311 -0600 "GET /CP.html
HTTP/1.0" 200 3218 "http//www.acr-news.org/"
"Mozilla/4.06 en (Win95 I) 203.30.5.145 - -
01/Jun/1999031325 -0600 "GET
/Calls/AWAC.html HTTP/1.0" 200 104
"http//www.acr-news.org/Calls/OWOM.html"
"Mozilla/4.5 en (Win98 I)"
14
Data Preprocessing (1)

• Data cleaning
• remove irrelevant references and fields in server
  logs
• remove references due to spider navigation
• remove erroneous references
• add missing references due to caching (done after
  sessionization)
• Data integration
• synchronize data from multiple server logs
• Integrate semantics, e.g.,
• meta-data (e.g., content labels)
• e-commerce and application server data
• integrate demographic / registration data

15
Data Preprocessing (2)

• Data Transformation
• user identification
• sessionization / episode identification
• pageview identification
• a pageview is a set of page files and associated
  objects that contribute to a single display in a
  Web Browser
• Data Reduction
• sampling and dimensionality reduction (ignoring
  certain pageviews / items)
• Identifying User Transactions (i.e., sets or
  sequences of pageviews possibly with associated
  weights)

16
Why sessionize?

• Quality of the patterns discovered in KDD depends
  on the quality of the data on which mining is
  applied.
• In Web usage analysis, these data are the
  sessions of the site visitors the activities
  performed by a user from the moment she enters
  the site until the moment she leaves it.
• Difficult to obtain reliable usage data due to
  proxy servers and anonymizers, dynamic IP
  addresses, missing references due to caching, and
  the inability of servers to distinguish among
  different visits.
• Cookies and embedded session IDs produce the most
  faithful approximation of users and their visits,
  but are not used in every site, and not accepted
  by every user.
• Therefore, heuristics are needed that can
  sessionize the available access data.

17
Mechanisms for User Identification
18
Sessionization strategiesSessionization
heuristics
(Heuristics used in, e.g., CMS99, SF99,
formalized in BMSW01)
19
Path Completion

• Refers to the problem of inferring missing user
  references due to caching.
• Effective path completion requires extensive
  knowledge of the link structure within the site
• Referrer information in server logs can also be
  used in disambiguating the inferred paths.
• Problem gets much more complicated in frame-based
  sites.

20
Sessionization Example
21
Sessionization Example
1. Sort users (based on IPAgent)
22
Sessionization Example
2. Sessionize using heuristics (h1 with 30 min)
The h1 heuristic (with timeout variable of 30
minutes) will result in the two sessions given
above.
23
Sessionization Example
2. Sessionize using heuristics (another example
with href)
In this case, the referrer-based heuristics will
result in a single session, while the h1
heuristic (with timeout 30 minutes) will result
in two different sessions.
24
Sessionization Example
3. Perform Path Completion
AgtC , CgtB , BgtD , DgtE , CgtF
Need to look for the shortest backwards path from
E to C based on the site topology. Note, however,
that the elements of the path need to have
occurred in the user trail previously.
EgtD, DgtB, BgtC
25
Why integrate semantics?

• Basic idea associate each requested page with
  one or more domain concepts, to better understand
  the process of navigation
• Example a shopping site

From ...
p3ee24304.dip.t-dialin.net - - 19/Mar/20021203
51 0100 "GET /search.html?lostsee20stran
dsyn023785ordasc HTTP/1.0" 200 1759
p3ee24304.dip.t-dialin.net - -
19/Mar/2002120506 0100 "GET
/search.html?lostsee20strandplowsyn023785or
ddesc HTTP/1.0" 200 8450 p3ee24304.dip.t-dialin.n
et - - 19/Mar/2002120641 0100 "GET
/mlesen.html?Item3456syn023785 HTTP/1.0" 200
3478
To ...
Refine search
Choose item
Search by category
Search by Categorytitle
Look at indiv- idual product
26
Ontology-based behaviour modelling basic ideas
(1)

• Atomic application events The request for a Web
  page signals interest in the concept(s) and
  relations dealt with in this page interest in
  the obtained content as well as in the requested
  service.
• Formally a request as a (multi)set, or as a
  vector, of concepts/relations.

27
Ontology-based behaviour modelling basic ideas
(2)

• Composite application events Sequences, regular
  expressions, etc., that consist of atomic
  application events.
• Ex. Spiliopoulou, Pohle und Teltzrow (Proc.
  Wirtschaftsinformatik 2002) modelled the customer
  buying process known from marketing. Depending on
  which of its phases a user passes through, and in
  which order, (s)he can be assigned to a user type
  (Moe, J. Consumer Psychology 2002).
• Example knowledge builders

28
Basic Framework for E-Commerce Data Analysis
Web Usage and E-Business Analytics
29
Agenda
Introduction
Data Acquisition and Data Preparation
Forms of analysis mining techniques
A case study
Outlook
30
Web Usage and E-Business Analytics

• Session Analysis
• Static Aggregation and Statistics
• OLAP
• Data Mining

Different Levels of Analysis
31
Session Analysis

• Simplest form of analysis examine individual or
  groups of server sessions and e-commerce data.
• Advantages
• Gain insight into typical customer behaviors.
• Trace specific problems with the site.
• Drawbacks
• LOTS of data.
• Difficult to generalize.

32
Static Aggregation (Reports)

• Most common form of analysis.
• Data aggregated by predetermined units such as
  days or sessions.
• Generally gives most bang for the buck.
• Advantages
• Gives quick overview of how a site is being used.
• Minimal disk space or processing power required.
• Drawbacks
• No ability to dig deeper into the data.

33
Online Analytical Processing (OLAP)

• Allows changes to aggregation level for multiple
  dimensions.
• Generally associated with a Data Warehouse.
• Advantages Drawbacks
• Very flexible
• Requires significantly more resources than static
  reporting.

34
Data Mining Going deeper
Markov chains
Prediction of next event
Sequence mining
Discovery of associated events or application
objects
Association rules
Discovery of visitor groups with common
properties and interests
Clustering
Discovery of visitor groups with common behaviour
Session Clustering
Characterization of visitors with respect to a
set of predefined classes
Classification
Card fraud detection
35
KDD Techniques for Web Applications Examples (1)

• Calibration of a Web server
• Prediction of the next page invocation over a
  group of concurrent Web users under certain
  constraints
• Sequence mining, Markov chains
• Cross-selling of products
• Mapping of Web pages/objects to products
• Discovery of associated products
• Association rules, Sequence Mining
• Placement of associated products on the same page

36
KDD Techniques for Web Applications Examples (2)

• Sophisticated cross-selling and up-selling of
  products
• Mapping of pages/objects to products of different
  price groups
• Identification of Customer Groups
• Clustering, Classification
• Discovery of associated products of the
  same/different price categories
• Association rules, Sequence Mining
• Formulation of recommendations to the end-user
• Suggestions on associated products
• Suggestions based on the preferences of similar
  users

37
Agenda
Introduction
Data Acquisition and Data Preparation
Forms of analysis mining techniques
A case study
Outlook
38
A multi-channel retailer, its business goals, and
analysis questions

• General goals Standard e-tailer goals
  attract users/shoppers and convert them into
  customers
• Specific goals assess the success of the Web
  site in relation to other distribution channels

• ? Questions of the evaluation
• What business metrics can be calculated from Web
  usage data, transaction and demographic data for
  determining online success?
• Are there cross-channel effects between a
  companys e-shop and its physical stores?

Background Internet market shares BCG 2002
TB03,TBG03
39
Outline of the KDD process

• Business underst. customer buying process
• Data
• Web server sessions, transaction info.
• Data understanding main step
• modelling the semantics of the site in terms of a
  hierarchy of service concepts

• Data preparation
• Session IDs usual data cleaning steps
• Linking of sessions transaction information
  (anonymized)
• Modelling / pattern discovery
• Web metrics, cluster analysis, association rules,
  sequence mining correlation analysis,
  questionnaire study, qualitative market analysis
• Evaluation Interesting patterns

40
Agenda Case Study
Business Understanding
Data understanding and preparation
Pattern discovery evaluation Success metrics
Pattern disc. eval. Behavioural patterns
Pattern disc. eval. User types
Pattern disc. eval. Behaviour demographics
41
Description of the site and its services

• The retailer operates an e-shop and more than
  5000 retail shops in over 10 European countries
• It sells a wide range of consumer electronics
• Online customers can pay, pick-up/deliver and
  return both online and offline
• Web pages provide for all tasks in the customer
  buying process

42
Purchase Phases (Page Concepts) at Large MC
Retailers
Home (Acquisition)
1. Acquisition (home) All Web pages that are
semantically related to the initial acquisition
of a visitor
43
Purchase Phases (Page Concepts) at Large MC
Retailers
Home (Acquisition)
Product Impression
2. Catalogue information pages providing an
overview of product categories.
44
Purchase Phases (Page Concepts) at Large MC
Retailers
Product Click-Through
Home (Acquisition)
Product Impression
3. Information product (infprod) pages
displaying information about a specific product
45
Purchase Phases (Page Concepts) at Large MC
Retailers
Offlineinfo
Home (Acquisition)
Product Click-Through
Product Impression
4. offline information (offinfo) All pages
related to any offline information store locator
(pages for finding physical stores in ones
neighbourhood), information about offline
services, offline referrers etc.
46
Purchase Phases (Page Concepts) at Large MC
Retailers
Transaction
Offlineinfo
Home (Acquisition)
Product Click-Through
Product Impression
5. transaction (transact) steps before an actual
purchase, starting with a customer entering the
order process check-out, input of customer data,
payment and delivery preferences (online or
offline), etc.
47
Purchase Phases (Page Concepts) at Large MC
Retailers
Transaction
Purchase
Offlineinfo
Home (Acquisition)
Product Click-Through
Product Impression
6. purchase indicates if a visitor completed the
transaction process and bought a product, e.g.
invocation of an order confirmation page.
48
Agenda Case Study
Business Understanding
Data understanding and preparation
Pattern discovery evaluation Success metrics
Pattern disc. eval. Behavioural patterns
Pattern disc. eval. User types
Pattern disc. eval. Behaviour demographics
49
Data and data preparation

• Data sources and sample
• 92,467 sessions from the companys Web logs from
  21 days in 2002
• anonymized transaction information of 13,653
  customers who bought online over a period of 8
  months in 2001/02.
• 621 transaction records (21 days) were linked to
  Web-usage records
• Data preparation
• Sessions were determined by session IDs
• Robot visits eliminated, usual data cleaning
  steps
• Each URL request mapped to a service concept from
  c1,...,cn
• Session representation s w1, ...wn, with wi
  weight of ci, indicating whether or not the
  concept was visited (1/0), or how often it was
  visited
• Customer record feature vector incl. session and
  transaction data

50
Site semantics A service concept hierarchy
760,535 page requests were mapped onto the
concepts from this hierarchy
Any
Services
Game
Offline Service and Support
Acquisition
Registration
Company Infos
Offline Referrer
Advertiser
Other
Home
Other
Transaction
Information
Fulfillment/ Service
Customer Data
Shopping Cart
Payment
Store Locator
Information Catalog
Information Product
Multi-Channel Concept
51
Types of patterns

• Conversion rates ( confidence of
  content-specified sequential association rules)
  for assessing business success
• Association rule and sequence analysis for
  understanding online/offline preferences and
  their temporal development
• Cluster analysis for customer segmentation
• Correlation analysis for investigating the
  relationship between demographic indicators and
  online/offline preferences

52
gtgt Session representation

• Each session represented as a feature vector on
  the multi-channel concepts
• Two methods used for definition of new conversion
  metrics
• ? weighted-concept method (number of visits to a
  concept)
• dichotomized concept method (whether or not
  concept was visited)

53
Agenda Case Study
Business Understanding
Data understanding and preparation
Pattern discovery evaluation Success metrics
Pattern disc. eval. Behavioural patterns
Pattern disc. eval. User types
Pattern disc. eval. Behaviour demographics
54
Life Cycle Metrics

• Developed integrated scheme for formalizing both
  the life-cycle metrics of (Cutler, Sterne 2000)
  and the micro-conversion rates of (Lee et al.
  2001)

W (target market)
S (suspects / site visitors)
nS
nP
P (prospects / active investigators)
C (customers)
Cb (abandon cart)
nC
CR (repeat customers)
CA (attrited customers)
C1 (One time Customers)
1 M Marketing Data, C Cookies SI Session IDs,
TA Transaction Data
55
Micro Conversion Rates
Cutler and Sterne (2001)
W (whole population)
S (suspects / site visitors)
nS
P (prospects / active investigators)
nP
C (customers)
Cb (abandon cart)
nC
CR (repeat customers)
CA (attrited customers)
C1 (One time Customers)
56
Micro Conversion Rates
P
nM1 nC
M1 (saw a product impression)
nM2 nC
M2 (performed a product click through)
nM3 nC
M3 (effected a basket placement)
nM4 Cb
M4 (made a product purchase) C
57
Multi-Channel Metrics
C
WM5 (paid online)
SM5 (paid in store)
WM5 (belong to SM5 in at least one following
transaction)
WM5 (belong to WM5 in every following
transaction)
C
WM6 (direct delivery)
SM6 (pick up in store)
WM6 (belong to SM6 in at least one following
transaction)
WM6 (belong to WM6 in every following
transaction)
58
gtgt Conversion Formalization
Dichotomized concept conversion rate from concept
ci to concept cj
Weighted concept visit rate
Offline Conversion Rate (OCR)
59
gtgt Metrics Results
Time frame May 2001 to May 2002
60
Agenda Case Study
Business Understanding
Data understanding and preparation
Pattern discovery evaluation Success metrics
Pattern disc. eval. Behavioural patterns
Pattern disc. eval. User types
Pattern disc. eval. Behaviour demographics
61
Internal consistency of preferences payment
and delivery preferences

• Online payment ? Direct delivery (s0.27, c0.97)
  lt 1/3 traditional onl.users!
• Online payment ? In-store pickup (s0.02, c0.03)
• Cash on delivery ? Direct delivery (s0.02,
  c0.03)
• In-store payment ? In-store pickup (s0.69,
  c0.94)
• ? Site is primarily used to collect information.

s support, c confidence of the sequence
62
Internal consistency of preferences return
preferences
s support, c confidence of the association rule

• Return ? In-store (s0.06, c0.87)
• Return ? Mail-in (s0.04, c0.13)
• ? Customers may wish personal assistance.
• (a result supported by the service mix analysis
  of different multi-channel retailers and by
  questionnaire results)

63
Development of preferences over time
s support, c confidence of the sequence

• Direct delivery ? In-store pickup in ?1 following
  transaction (s0.001,c0.15)
• Direct delivery ? Direct delivery in all
  following transactions (s0.003,c0.85)
• In-store pickup ? Direct delivery in ?1 foll.
  transaction (s0.001, c0.10) ()
• In-store pickup ? In-store pickup in all foll.
  transactions (s0.004, c0.90)
• Results for payment migration are similar.
• ? 90 of repeat customers did not change
  transaction preferences at all.
• ? Rule () as an indicator of the development of
  trust?!

64
Agenda Case Study
Business Understanding
Data understanding and preparation
Pattern discovery evaluation Success metrics
Pattern disc. eval. Behavioural patterns
Pattern disc. eval. User types
Pattern disc. eval. Behaviour demographics
65
Market segments
Largest group visits all concepts except offline
information
Cluster centers of the weighted purchase
sessions with direct delivery preference
Cluster centers of the weighted purchase
sessions with pick-up in store preference
Tend to arrive with prior knowledge
Tend to be "true multi-channel users"
Tend to be "true online users"
66
Agenda Case Study
Business Understanding
Data understanding and preparation
Pattern discovery evaluation Success metrics
Pattern disc. eval. Behavioural patterns
Pattern disc. eval. User types
Pattern disc. eval. Behaviour demographics
67
Shop and Customer Distribution
Customers
Shops




68
Impact of demographics and of the offline
distribution channel ?!

• A significant Pearson correlation exists between
• the number of customers per zip code area,
  normalised by the number of residents/zip code,
  and the distance to the next store (r -0.3, p lt
  0.001).
• number of residents/zip code and distance to
  store (r -0.01, plt0.001)

69
Agenda
Introduction
Data Acquisition and Data Preparation
Forms of analysis mining techniques
A case study
Outlook
70
Many things to do, including ...

• Deployment of Web mining results
  personalization
• Architectures for Web mining
• Methodology integration for treating Web mining
  as a project

71
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References

• BMNS02 Berendt, B., Mobasher, B., Nakagawa, M.,
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