Download Data Mining for Customer Service Support

Data Mining for Customer
Service Support
Senioritis Seminar Presentation
Megan Boice | Jay Carter | Nick Linke | KC Tobin
Traditional Hotline Services
Problem
Traditional Customer Service Support (manufacturing)
Customer service DB stores 2 types
unstructured reports of problems and actions
structured data on sales, employees and customers
DB holds invaluable amounts of information
PROBLEM: how to best utilize information
SOLUTION: using data minding techniques to extract
knowledge regarding decision support and machine fault
diagnosis
Data Mining
Automates the detection of relevant
patterns in databases
Functions
summarization
association
classification
prediction and clustering
Applications
marketing
banking
finance
manufacturing
health care
Customer Service Support DB
each record contains customer account information and
service details
fault-condition
checkpoint information
Customer Service Support DB
also stores data related to sales, customers and employees
6 major tables
MACHINE_FAULT (unstructured)
CHECKPOINT (unstructured)
CUSTOMER (structured)
EMPLOYEE (structured)
MACHINE (structured)
SERVICE_REPORT (structured)
Mining Structured Data
multiple tools available for structured data
many data mining techniques supported
neural networks
regression tree
k-means algorithm
case-based reasoning
Mining Unstructured Data
text mining
case-based reasoning
relies on a large repository of diagnostic cases
learns by experience
depends on well organized cases
many techniques used
nearest neighbor algorithm
hierarchical indexing such as CART decision trees
neural networks
others: rule-based reasoning, fuzzy logic, genetic
algorithms, decision trees, inductive learning systems,
statistical pattern classification systems, and various hybrid
systems
Data Mining Process
Data Mining Process
Establishing Mining Goals
Marketing
Customer Support
Resource Management
Selection of Data
EMPLOYEE and CUSTOMER vs. MACHINE and
SERVICE_REPORT
Data Pre-Processing
remove noisy, erroneous and incomplete data
Data Mining Process Cont.
Data Transformation
reformat data
developing new attributes
Data warehousing
visioning
planning
building
using
managing
maintaining
enhancing
Data Mining Process Cont.
Data Mining - Example 1: summarization function
Data Mining Process Cont.
Data Mining - Example 2: association rules mining
Data Mining Process Cont.
Evaluating the mining results
Marketing
Clustering used to identify customers suitable for
cross sales
Customer Support
Identify customers who have been receiving support,
analyze geographical location and machine model
Resource Management
Identify expertise of service engineers and efficiency
Data Mining for machine fault diagnosis
integration of neural network, case-based reasoning and
rule-based reasoning
Two major processes
Off-line knowledge extraction
extracts knowledge from customer service DB
neural network models and rule-base work
On-line fault diagnosis
uses four cycles of CBR to diagnose
retrieve: problem description
reuse: closest previous fault condition
revise: based on user's feedback
retain: new result
Data Mining for machine fault diagnosis
Knowledge extraction process
2 major generation steps to retrieve information from the
unstructured data
neural networks: fault conditions
rule-based: checkpoints
Use of Neural Networks
extracts knowledge from the fault-conditions to train the
network and build neural network models from classification
and clustering
pre-processed to extract keywords
weight vectors initialize the neural networks
2 types
supervised learning vector quantization: classification
unsupervised Kohonen self-organizing map: clustering
Rule-based Generation
guides the reuse of checkpoint solutions
manually coded control rules
automatically generated checkpoint rules for specific
diagnostic instructions
results in a step-by-step guide
Fault Diagnosis Process
1. Pre-process of user input
inputs and keywords turned into input vector
2. Neural network retrieval
uses similar past fault-conditions
3. Reuse of service records
checkpoints revealed in order
4. Revise and retain user feedback
problem is resolved - rule-base updated
problem persists - user contacted
Revise and retain with user feedback
Performance Evaluation
performance was compared to k nearest neighbor clustering
technique
Conclusions
Different then traditional data mining
Advantageous to both businesses and customers
increased efficiency is solving issues
removes some of the human touch
improved accuracy
consistency
Source
Hui, S.C. and Jha, G. Data mining for customer service
support. Information & Management 38, 2000, pp. 1-13.