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.