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PolyAnalyst Web Report Training

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Presentation on theme: "PolyAnalyst Web Report Training"— Presentation transcript:

1 PolyAnalyst Web Report Training
Analysis of Call Center & Warranty Data PolyAnalyst Web Report Training Megaputer Intelligence © 2014 Megaputer Intelligence Inc.

2 Objectives Automate the analysis of Call Center and Warranty Data
Determine issues of interest for improving customer satisfaction and reducing costs

3 Objectives Automate the analysis of Call Center and Warranty Data
Determine issues of interest for improving customer satisfaction and reducing costs What issues are customers complaining about the most? What issues are the most costly? Can we discover emerging issues? Can we identify any systemic issues?

4 Objectives Automate the analysis of Call Center and Warranty Data
Determine issues of interest for improving customer satisfaction and reducing costs What issues are customers complaining about the most? What issues are the most costly? Can we discover emerging issues? Can we identify any systemic issues?

5 Objectives Automate the analysis of Call Center and Warranty Data
Determine issues of interest for improving customer satisfaction and reducing costs What issues are customers complaining about the most? What issues are the most costly? monetary cost downtime labor hours Can we discover emerging issues? Can we identify any systemic issues?

6 Objectives Automate the analysis of Call Center and Warranty Data
Determine issues of interest for improving customer satisfaction and reducing costs What issues are customers complaining about the most? What issues are the most costly? Can we discover emerging issues? Can we identify any systemic issues?

7 Objectives Automate the analysis of Call Center and Warranty Data
Determine issues of interest for improving customer satisfaction and reducing costs What issues are customers complaining the most? What issues are the most costly? Can we discover emerging issues? Can we identify any systemic issues?

8 Objectives Automate the analysis of Call Center and Warranty Data
Determine issues of interest for improving customer satisfaction and reducing costs What issues are customers complaining the most? What issues are the most costly? Can we discover emerging issues? Can we identify any systemic issues? product line customer region

9 Challenges to Overcome
Non-homogeneous data sources Unstructured, free-form text Domain-specific terminology Inconsistent naming Irrelevant information Call Center Data Warranty Parts Data Warranty Labor Data

10 Challenges to Overcome
Non-homogeneous data sources Unstructured, free-form text Domain-specific terminology Inconsistent naming Irrelevant information “Machine showing oil over temperature. After machine verification was noticed that in this machine has two auto dump valve, see bellow.”

11 Challenges to Overcome
Non-homogeneous data sources Unstructured, free-form text Domain-specific terminology Inconsistent naming Irrelevant information Coolpix Hopper Moveable Platen

12 Challenges to Overcome
Non-homogeneous data sources Unstructured, free-form text Domain-specific terminology Inconsistent naming Irrelevant information oring = o-ring = o ring = Oring = ORING

13 Challenges to Overcome
Non-homogeneous data sources Unstructured, free-form text Domain-specific terminology Inconsistent naming Irrelevant information Call Center, Warranty Parts, Warranty Labor © 2011 Megaputer Intelligence Inc.

14 Data Loading & Integration
Methodology Data Loading & Integration Call Center Warranty Parts Warranty Labor Data Cleansing Fix spelling errors Filter irrelevant information Rename for consistency Text Analysis Parts Extraction Issues Extraction

15 Analysis Flowchart

16 Data Loading & Integration
Methodology Data Loading & Integration Call Center Warranty Parts Warranty Labor Data Cleansing Fix spelling errors Filter irrelevant information Rename for consistency Text Analysis Parts Extraction Issues Extraction

17 Data Loading

18 Data Loading Summary statistics enable easy exploration of attributes.

19 Data Loading Column information can be summarized. Here, we quickly see the distribution of Product Line.

20 P7 is the product line with the most calls.
Data Loading P7 is the product line with the most calls.

21 Join disparate data sources using Join Node.
Data Loading Join disparate data sources using Join Node.

22 Data Loading & Integration
Methodology Data Loading & Integration Call Center Warranty Parts Warranty Labor Data Cleansing Fix spelling errors Filter irrelevant information Rename for consistency Text Analysis Parts Extraction Issues Extraction

23 Spell Check

24 Dictionary Manager Existing dictionaries can be edited and custom dictionaries can be imported for domain-specific analysis.

25 Data Cleansing

26 Data Loading & Integration
Methodology Data Loading & Integration Call Center Warranty Parts Warranty Labor Data Cleansing Fix spelling errors Filter irrelevant information Rename for consistency Text Analysis Parts Extraction Issues Extraction

27 Analyst-driven Analysis
Methodology Data-driven Analysis Analyst-driven Analysis

28 Analyst-driven Analysis
Methodology Data-driven Analysis Analyst-driven Analysis

29 Keyword Extraction

30 Keyword Extraction

31 Keyword Extraction

32 Keyword Taxonomy

33 Keyword Taxonomy

34 Keyword Extraction

35 Keyword Extraction

36 Keyword Extraction

37 Keyword Extraction

38 Keyword Extraction

39 Keyword Extraction

40 Keyword Extraction

41 Keyword Taxonomy

42 Keyword Taxonomy

43 Analyst-driven Analysis
Methodology Data-driven Analysis Analyst-driven Analysis

44 Custom taxonomies categorize problematic
Analyst-Driven Taxonomy Outline Custom taxonomies categorize problematic parts and issues in call center notes

45 Expressions query the data to assign records to categories
Analyst-Driven Taxonomy Outline Expressions query the data to assign records to categories PROJECT!  Nouns Tax: mold sensor  phrase(3, mold, sensor) Robot > Other  keywords  robot vacuum  Verbs & Adj. Tax: damage, break, crack  Broken (Generalize)

46 Issues Taxonomy Outline

47 Issues Taxonomy Outline Leakage Category Sub-Category Verbatim
Oil Leakage "Extruder gearbox leaking hydraulic oil. Oil leak at about 1 gallon in an hour. Customer will call region to request service tech.” Water Leakage “Repair mold dehumidifer water leakage at piping. Water leak from Auxualiy Coolpik blower water hose.” Created by analyst. Access results of data-driven directly using, e.g., Keyword function, or search for patterns using other PDL functions  proximity constraints, direct vs. negated, semantic relationships

48 Most Complained About Issues

49 Issues with Most Downtime

50 Issues with Longest Resolution Time

51 Issues with Highest Warranty Costs

52 Most Problematic Issues
Failure Replacement Broken Damage Oil Leakage

53 Relationships Between Issues

54 Outline Parts Taxonomy
Created by analyst. Access results of data-driven directly using, e.g., Keyword function, or search for patterns using other PDL functions  proximity constraints, direct vs. negated, semantic relationships

55 Outline Parts Taxonomy Hose Category Sub-Category Verbatim Water Hose
"Water hoses worn out. replaced hoses” Air Hose “coolpik air blow hose get damaged by scratching with conveyor.” Created by analyst. Access results of data-driven directly using, e.g., Keyword function, or search for patterns using other PDL functions  proximity constraints, direct vs. negated, semantic relationships

56 Most Complained About Parts

57 Parts with Most Downtime

58 Parts with Longest Resolution Time

59 Parts with Highest Warranty Costs

60 Most Problematic Parts
Kit Injection Piston Check Valve Tooling Rod

61 Relationships Between Parts & Issues

62 Dimensional Analysis: Parts & Issues
Project!

63 Treemap: Parts & Issues
Project!

64 Emerging Issues over Time

65 Emerging Problematic Parts over Time

66 Emerging Issues over Product Age
Project!

67 Systematic Issues by Product Line
P7 is the most frequent product line among escalated calls, with almost 50%.

68 Systematic Issues by Region

69 Systematic Issues by Customer

70 Objective and uniform analysis Reduce analysis time
Conclusion Automated analysis Objective and uniform analysis Reduce analysis time Reduce need for staff Early detection of costly issues Reduce costs

71 Contacting Megaputer Questions?


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