Download Analysing the footprints of your customer - A case study by ask|net and SAS

Analysing the footprints of your
customer - A case study by ask|net and
SAS
Klaus-Peter Huber
Christiane Theusinger
Copyright © 2000 SAS EMEA
e-intelligence
Web Optimization
! Clickstream Reporting and Analysis
! Web Mining
!
Copyright © 2000 SAS EMEA
Web Mining
Data
Mining
Models
Copyright © 2000 SAS EMEA
Challenge and Profit
Information about user profiles, customer
types, purchase behaviour (CRM)
! Better internet offering by analysis of
customer behaviour
! Optimization of online marketing activities
with fast built response models etc.
! Scoring, to present the right offer or
advert at the right time (One-To-One
Marketing)
!
Copyright © 2000 SAS EMEA
Goals with web mining
Customer based optimization of the
internet shop or offering
! Segmentation of customers
! Analysis of purchase behaviour
! Personalized offerings or adverts
!
Copyright © 2000 SAS EMEA
ask|net GmbH
Internet shop
! sells software
! mainly LOTUS
!
Copyright © 2000 SAS EMEA
Project goal
Modelling and implementing
a Business Information System
for sales and marketing department
Data Warehousing & Data Mining in the
Internetshop of ask|net GmbH
Copyright © 2000 SAS EMEA
Steps
!
System analysis
!
Data Warehousing (Oracle8)
!
OLAP (SAS/EIS)
!
Data mining (Enterprise Miner)
Copyright © 2000 SAS EMEA
OLAP information structure
Copyright © 2000 SAS EMEA
Web mining questions at ask|net
!
Can the behaviour of a customer
(clickstream) be used to predict purchase
behaviour with data mining?
!
Can we define / derive user profiles out of the log
file?
!
Which factors influence a purchase in the online
shop SoftWarehouse?
!
How can we optimize the shop?
Copyright © 2000 SAS EMEA
e-cycle in e-CRM
Web
LogfileData
Customer-Data
Application:
Scoring offline
and online
Copyright © 2000 SAS EMEA
e-Intelligence Solution
include,
consolidated,
cleansing,
Data Mining ...
Analyses:
paths,
clickstreams,
cluster,
who buys,
cross selling ...
read,
transform,
aggregate,
schedule,
...
Reporting:
responsetime,
browsetime,
pagehits ...
Raw Data
Variable
Transformation
& Integration
Sequence
Analysis
targetvariable
variable
11target
Data Warehouse
Flat File
51
Variables
Logfiles
variables
66variables
Web Server
10 sequence variables
Copyright © 2000 SAS EMEA
Data Mining
Models
SEMMA
Raw Data
Variable
Transformation
& Integration
SequenceAnalysis
targetvariable
variable
11target
Data Warehouse
Flat File
51
Variables
Logfiles
variables
66variables
Web Server
10 sequence variables
Copyright © 2000 SAS EMEA
Data Mining
Models
SEMMA
How are the raw data generated
?
“Click - Session“
www.SoftWarehouse.de ...
Copyright © 2000 SAS EMEA
Copyright © 2000 SAS EMEA
Copyright © 2000 SAS EMEA
Copyright © 2000 SAS EMEA
Oracle database
C_VALUE C_CALLER
C_FQDN
C_REFERER
C_TIME
L_ID
...
...
...
...
...
...
a68adce1ab...
home
xyz.sas.com
www.softwarehouse.de
20-8-99:14:01
DE
a68adce1ab...
catalog
xyz.sas.com
home
20-8-99:14:02
DE
a68adce1ab...
program
xyz.sas.com
catalog
20-8-99:14:03
DE
a68adce1ab...
product
xyz.sas.com
program
20-8-99:14:05
DE
a68adce1ab...
login
xyz.sas.com
product
20-8-99:14:07
DE
...
...
...
...
...
...
Copyright © 2000 SAS EMEA
Raw Data
Variable
Transformation
& Integration
SequenceAnalysis
targetvariable
variable
11target
Data Warehouse
Flat File
51
Variables
Logfiles
Variables
66Variables
Web Server
10 sequence variables
Copyright © 2000 SAS EMEA
Data Mining
Models
SEMMA
Sequence analysis of the clickstreams
!
"
Copyright © 2000 SAS EMEA
Find weaknesses in web structure
Create new variables for data mining
! results
(sequence analysis)
#
Support (%)
Confidence (%)
1
15.5
36.8
login ⇒ register
2
13.4
31.9
login ⇒ login
3
12.3
38.5
addcart ⇒ login
4
11.2
28.1
addcart ⇒ register
5
0.7
4.6
pay_req ⇒ help
6
0.3
3.6
news ⇒ pay_res
Copyright © 2000 SAS EMEA
Rule
" results
(sequence analysis)
Support (%)
Confidence (%)
Rule
seq_0
28.7
74.6
program⇒product⇒p_info⇒product
seq_1
26.4
60.2
program⇒product⇒product⇒product
seq_2
17.5
80.3
program⇒product⇒addcart⇒freeze
seq_3
17.1
82.6
home⇒catalog⇒program⇒product
seq_4
13.5
95.6
logpost⇒catalog⇒program⇒product
seq_5
12.7
92.2
login⇒catalog⇒program⇒product
seq_6
12.6
93.0
logpost⇒product⇒addcart⇒freeze
...
...
...
...
seq_9
6.7
67.3
product⇒ login⇒register⇒regpost
#
Copyright © 2000 SAS EMEA
Raw Data
Variablen
Transformation
& Integration
Sequence
Analysis
targetvariable
variable
11target
Data Warehouse
Flat File
51
Variables
Logfiles
Variablen
66Variablen
Web Server
10 sequence variables
Copyright © 2000 SAS EMEA
Data Mining
Models
SEMMA
integration of ‘sequence
variables‘
C_VALUE C_CALLER
c_value
col1
col2
col3
col4
col5
...
...
...
...
...
...
a68adcelab... home
catalog program product
login
...
...
...
...
...
a68adce1ab...
home
c_value
a68adce1ab...
catalog
...
a68adce1ab...
program
a68adce1ab...
product
a68adce1ab...
login
...
...
...
...
concat_column
...
a68adcelab...
...
...
homecatalogprogramproductlogin
...
program⇒product⇒p_info⇒product
transformed into
‘%program%product%p_info%product%‘
Copyright © 2000 SAS EMEA
Integration of the transaction
table
C_VALUE C_CALLER
C_FQDN
C_REFERER
C_TIME
L_ID
...
...
...
...
...
...
a68adce1ab...
home
xyz.sas.com
www.softwarehouse.de
20-8-99:14:01
DE
a68adce1ab...
catalog
xyz.sas.com
home
20-8-99:14:02
DE
a68adce1ab...
program
xyz.sas.com
catalog
20-8-99:14:03
DE
a68adce1ab...
product
xyz.sas.com
program
20-8-99:14:05
DE
a68adce1ab...
login
xyz.sas.com
product
20-8-99:14:07
DE
...
...
...
...
...
...
Copyright © 2000 SAS EMEA
Input Variables
Number of clicks
! Duration of session
! pages involved
! User profile information
! Referer adress
! Country code
! target variable: purchase
!
Copyright © 2000 SAS EMEA
Raw Data
Variable
Transformation
& Integration
Sequence
Analysis
targetvariable
variable
11target
Data Warehouse
Flat File
51
Variables
Logfiles
Variables
66Variables
Web Server
10 sequence variables
Copyright © 2000 SAS EMEA
Data Mining
Models
SEMMA
Predictive Modelling
Which factors influence the purchase
of software at the Softwarehouse ?
Copyright © 2000 SAS EMEA
Predictive Models used
Decision Trees
! Regression Analysis
! Neural Networks
!
Copyright © 2000 SAS EMEA
Underlying Data
Number of clicks
! # sessions :
! total number of purchases:
! response:
! percentage training data:
! percentage validation data:
!
Copyright © 2000 SAS EMEA
>= 5
22527
1642
7.29%
60%
40%
LOG-transformation of interval
variables
Distribution bevor transformation
Copyright © 2000 SAS EMEA
Distribution after transformation
Process Flow Diagram
.....
Copyright © 2000 SAS EMEA
Copyright © 2000 SAS EMEA
Results of the Analysis
Copyright © 2000 SAS EMEA
Results of the Analysis
Neural network is the best predictive
model
! Decision Tree best for analysing which
clickstreams result in a purchase of a
product
! Regression analysis useful for analysing
relationships and verify results of the
decision tree
!
Copyright © 2000 SAS EMEA
Results of Data Mining
Copyright © 2000 SAS EMEA
Results of Data Mining
!
Order procedure too long and
complicated
⇒ Sequence analysis
‘addcart⇒login‘ or‘addcart⇒register‘
!
Click behaviour is representative
⇒ Split variables ‘seq4‘, ‘clcklgth‘ and ‘clicks‘
- typical buyer paths
- ‘readers‘ will buy
Copyright © 2000 SAS EMEA
Next Steps
!
Usage of more data from the customer
warehouse for
!
!
!
!
better analysis of behaviour
cross- and up-selling chances
multi channel integration
Personalized offerings and advertisement with
offline-scoring
Copyright © 2000 SAS EMEA
Actions (Data Mining)
!
Monitor registration/login behaviour ...
⇒ Sequence analysis
‘login⇒register‘ or ‘login⇒login‘
... because multiple registrations/user
complicate basket analysis
Copyright © 2000 SAS EMEA
Conclusions
Sequence Analysis can find weaknesses
in web design
! Predictive Modelling finds customer
segments and predicts probabilities
! Web-Mining is useful to analyze logfile
data
! Ask|net redesigned web design to better
fit customer needs
!
Copyright © 2000 SAS EMEA