business intelligence & data mining-14

8/10/2019 Business Intelligence & Data Mining-14

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Lessons & Challenges

from Mining Retail E-Commerce Data

Kohavi et. al (2004)


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Motivation

n Important domain of data miningn Massive amounts of data is collected

n Data collection is automatic not prone to errors

n Data is Rich has a lot of potential for discoveringpatterns

n Three types of Data: Clickstream data, Transactionaldata and User Profile data

n Combined mining of these 3 types of data is possible

90%

10%


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The E-Commerce Data Mining Suite

n E-Commerce data mining suite developed byBlue Martini Software

n Purchased and used by many Brand Nameretailers: Debenhams, Harley Davidson,Sainburys, Sprint etc.

n System designed specifically for BI

n End-to-end solution:

n Data Collection

n Data Warehousing

n Data Transformations

n Visualization

n Data Mining


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The Business Intelligence Process

Data Cleaning

Data Integration

Data Sources

Data Warehouse

Task-

relevant Data

Selection and

Reduction

Data Mining

Pattern Evaluation


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The Experience Shared

Business Lessons & Technical Lessons have been shared Data Miningprojects executed for more than 20 clients

Clients from different industry verticals with varying

business models

Clients spread over: US, Europe, Asia & Africa

Data Sizes upto 100 million records Diverse data:

Clickstream

User Profile

Demographic

Response to Mail CampaignsOrders Placed through website / telephone / in-store


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Business Lessons


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Requirements Gathering is Challenging

n Clients are reluctant to list business questionsn They may not know what questions to ask

n They do not understand the underlying technologyand how much it can do

n Clients present standard reporting type

questions, e.g.n What is the gender-wise distribution of customers?

n What is the region-wise response rate of the mailcampaign?

n Instead of asking questions like:n What are the characteristics of customers who spend

more than $500?

n What kind of people responded to the mailcampaign?


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Educating the Users

n Involving the users is critical for success

n Understanding the business

n Uncovering the real needs

n Users will have to educated

n What can be achieved by BI

n Prototypes / Demo Systems

n

Case studies


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Business Events

n The architecture recordsn Every customer search and number of results returned:

Too many rows, No rows

n Shopping cart events: Add to cart, Change Quantity,Delete

n

Registration, log-in, checkout, payment, orderconfirmation

n Any failure / crashes

n Users timezone

n Technical capabilities of the users computer

n These details are collected particularlybecause they are useful for ANALYSIS


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Data Collection

n

Usual methods of data collection:n Stateless Http requests from multiple web servers

n Parsing and loading them session-wise and user-wise

n Difficult Web logs were designed for debugging

web servers not to provide data for BIn Blue Martini architecture was designed for BI

n Session & user data collected and linked togetherat Application Server level

n Transactions automatically tied to sessions

n All data automatically recorded in a databasen Pre-processing and data cleaning is not required


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Data Collection Lessons

n Collect the right data upfront

nAll data that could be useful should becollected and integrated

n Stored in a database / data warehouse

n Integrate with External Events

n Marketing events like promotions

n Cannot be captured by the data collectionsystems


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Creating the Data Warehouse

n DW creation requires substantial datatransformations

n Can take 80% of the time taken to thecomplete BI exercise

n Requires integration of several data sources:

n Website

n Payment gateway

n Call center

n POS terminals / shops systems

n External systems / inputs (e.g. promotions /campaigns data)


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Logical DW Architecture


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Data Warehousing: Challenges

Loading and Maintaining Consistent Data

Loading and Storing Large Volumes of Data

Coping with Changes in Operational Definitions

Providing Reasonable Response Times

If it is an E-Commerce site the website itself will

be outside the Firewall, so data will have to be

copied across the Firewall


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Business Intelligence Tools

n The software provided: Reports, Visualizationand Data Mining

n Data Mining algorithms included:

n Rule Inductionn Anomaly (outlier) detection

n Entropy-based statistics

n Association Rules


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Business Intelligence Lessons (1)

n Operational transactions have higherpriority than BI

nBI can be taken up after the systemstabilizes

n Can take several months to get startedn Users are happy with basic reports /

MIS

n Unexpectedly insightful findings capture their

interest

n This can start the BI process


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Business Intelligence Lessons (2)

n Trained Data Analysts are requiredn Domain knowledge is important

n Technical know-how is essential

n

Terminology needs to be Definedn Users can misinterpret results

n Potentially useful findings may be ignored orunrealistic expectations can arise


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Business Intelligence: Challenges

Designing user-friendly interactive interface

Automatic Feature Construction

Building models that users can interpret

Making users understand that correlation does not

imply causality

Explaining insights

Linking ROI to insights


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Deployment

n Insights need to be shared

n Insights obtained by Data Mining needs to beshared across the organization

n Easy to use tools for capturing andcommunicating (e.g. by E-mail) will help

n Taking Action

n Business users must see the value

nActing on the results may be difficult (e.g.

designing a campaign for a special segmentof customers)

nA good architecture would help


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Technical Lessons


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Data Collection and Management Lessons

n Collect data at the right leveln Data was collected at the Application Server

level

n Reduced pre-processing of weblog data

n Design the GUI with Data Mining in mindnAll useful data can be captured

n Default values should be avoided

n

Validate data to reduce cleaning effort


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Data Collection and ManagementChallenges

n Should data be sampled?n E-Commerce data is huge in volume

n Is it necessary to store all the data?

n Will rare events be missed if sampling is done?

n

Slowly changing dimensionsn Customers evolve (e.g. lifetime changes, lifestyle

changes)

n Products evolve (e.g. new lines, new technology)

n Frequency of DW uploads

n DW uploads take time and processing power

n Should not disrupt BI analysts work


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Data Cleaning and Pre-processingLessons & Challenges

n Time-outs, incomplete sessions, crashesn Needs to be detected

n What to do with such data?

n Duplicates

n

Same customer with more than one IDn Same account used by multiple customers

n Guest log-ins

n Missing, unknown, not applicable or default

valuesn Hierarchical Attributes

n Most algorithms cannot handle hierarchical attributes


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An Attribute Hierarchy

all

Europe North_America

MexicoCanadaSpainGermany

Vancouver

M. WindL. Chan

...

......

... ...

...

TorontoFrankfurt

all

region

office

country

city


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Analysis Lessons & Challenges

n Enriching the Datan Add demographic attributes

n Create derived attributes

n Calculate weighted averages, moving averages

n Exploration

n Visualization

n

Domain knowledge can help in gaining insightn Customer propensity scoring

n Building Models

n Start with simple models (easy to explain to users)

n Build models at the right level of the attribute hierarchy

n

Address scalability issues (to maintain users interest andconfidence)

n Test and validate the models

n Estimate accuracy levels

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