Voice of the Market, Tom Anderson

Why Now: Difference in Technology ,[object Object],Machine more accurate than human Human more accurate than machine Past Present Level 1 2 3 4 5 Key function Word Count (including inflected forms) Grouping of synonyms Word Association Grouping of related terms Detecting Positive/ negative sentiment Meaning in context Implication Output example Bed=2 Room=5 Wine=6 Great= (fantastic, excellent, wonderful) Dirty=(filthy, smelly, dirty) Furniture=(chair, table, couch) Food=(bread, shrimp Furniture+<positive> Food+<negative> People talking about their dining experience People talk about how the dining experience relate to their overall vacation experience Accuracy

Advantage Machine Coding Human Coding Diagram Copyright © Anderson Analytics, LLC iterative Difficulty in modifying code book Inter-coder reliability issues Similar surveys can be coded easily Text data

AA Text Mining vs Qualitative Qualitative Identified Concepts Text Mining Identified Concepts Universe of text data in a study Extreme Outliers Qualitative analysis only accounts for a small sample of the available data set. Concept proportionality, importance and relevance can get distorted. Extreme outliers might be overlooked. Text mining accounts for most of the data. Extraction of concepts and categorization of data are more accurate. Extreme outliners can be identified.

Validation Through Triangulation Data Mining/Visualization Neural Nets, Factoring, Clustering, Logistic Regression… I. Quantitative Triangulated Validation III. Qualitative II. Psychological Text Mining (non a priori) Random Sample (a priori) Review/Confirmation Psychological Measures Review/Confirmation Verbatim Concepts and Themes

An Unexplored Opportunity: Listening to the “The Voice of a Million Customers” Copyright 2005, SPSS Inc. ,[object Object],[object Object],[object Object],[object Object],“ Good…” “ Service…” “… Bad…” “… Bathroom…” “ Bed…” “ Not Clean…” “… Reservation” “… Not Working” “ Disappointed…” “… Management” “… Check-In” “… Charge” “ Excellent…” “ Loud…” “ Not Acceptable…” “… Not Friendly”

Listening to the “The Voice of a Million Customers” “… Check-In” “ Good…” “ Not Clean…” “… Not Working” “ Disappointed…” “ Excellent…” “ Loud…” “… Not Friendly” “… Management” “… Charge” *For Example Only/Concepts Disguised

About Your Customers ,[object Object],[object Object],[object Object]

Value to Starwood Hotels and Hospitality Industry ,[object Object],Rebecca Gillan VP, Global Market Research and Guest Satisfaction Starwood Hotels and Resorts Worldwide, Inc.

LinkedIn ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Text Mine (Sample & Predict)

Most Used Terms in User Headlines ,[object Object],[object Object],(and their monetary value) Income Purchase Power Title Rank Mean Rank Mean vp 1 $190,000 3 $200,250 advertising 2 $187,500 4 $175,000 contractor 3 $150,000 5 $154,375 chief__officers 4 $145,455 1 $252,262 partner 5 $126,429 25 $54,500 executive 6 $121,094 15 $99,444 owner 7 $118,625 21 $73,698 sales 8 $118,000 24 $57,759 marketing 9 $116,667 12 $105,375 consultant 10 $116,486 29 $40,227 director 11 $115,330 6 $137,712 financial 12 $113,636 14 $99,900 senior 13 $111,116 17 $89,515 operations 14 $103,125 18 $88,750 technology 15 $99,286 8 $127,500 manager 16 $99,042 11 $108,601 computer 17 $97,500 34 $13,750 engineer 18 $92,857 27 $49,528 software 19 $91,912 32 $28,646 services 20 $88,226 23 $58,882 information 21 $87,500 2 $212,500 associates 22 $87,083 16 $95,429 human resources 23 $85,833 22 $61,042 analyst 24 $83,594 20 $81,447 development 25 $83,462 33 $15,735 professional 26 $78,421 26 $53,301 assistant 27 $77,344 9 $116,406 account 28 $77,206 28 $42,105 program 29 $70,833 7 $128,056 medical 30 $66,667 13 $104,444 attorney 31 $66,250 30 $36,250 real_estate 32 $65,625 19 $84,722 designer 33 $65,625 31 $30,417 health 34 $63,824 10 $114,211 teacher 35 $45,833 36 $1,667 student 36 $30,441 35 $3,977

[object Object],[object Object],[object Object],Visualizing Social Networks On LinkedIn 1 st Level Connection Original Seed

Visualizing Social Networks On LinkedIn Seed A (22 Connections) Connection C Seed B (213 Connections) 1 st Level Connection Original Seed

*Variables NOT used in clustering LinkedIn Segments – Important Variables (Neural Net) LI Interests/Purpose Work Situation* Purchase Behavior* Use of LinkedIn

The Future - SNS Source: Anderson Analytics April 2009

Thank You! + 1-888-891-3115 email: Inquiries@andersonanalytics.com twitter: @TomHCAnderson blog: www.tomhcanderson.com

Voice of the Market, Tom Anderson

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