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Intro – The story of ...
Intro – Time line ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],JAN MAR 2007
Intro – Time line ,[object Object],[object Object],[object Object],www.BAQMaR.be
Intro – Time line ,[object Object],[object Object],[object Object],www.BAQMaR.be
Intro – Time line ,[object Object]
[object Object],[object Object],[object Object],Intro – Time line JAN MAR 22/MAR 28/JUN 27/SEP 2007
Intro – Magic Quadrant Content Fun Online Offline
Intro – Magic Quadrant Content Fun Online Offline ‘ Content’ posts Job offers BAQMan/BAQMadam Conferences Newsletter Q&A
Intro – Magic Quadrant Funny Fridays ‘ Fun’ posts Pictures Content Fun Online Offline ‘ Content’ posts Job offers BAQMan/BAQMadam Conferences Newsletter Q&A
Intro – Magic Quadrant BAQMaR AWD Partner in the Spotlight Funny Fridays ‘ Fun’ posts Pictures Content Fun Online Offline ‘ Content’ posts Job offers BAQMan/BAQMadam Conferences Newsletter Q&A
Intro – Magic Quadrant BAQMaR AWD Partner in the Spotlight Conference Funny Fridays ‘ Fun’ posts Pictures Content Fun Online Offline ‘ Content’ posts Job offers BAQMan/BAQMadam Conferences Newsletter Q&A
Intro – Time line ,[object Object],[object Object],2007 JAN MAR 22/MAR 28/JUN 27/SEP 06/DEC
Eye Tracking Research  Added Value for Traditional Techniques? Ludovic Depoortere, Managing Director Rogil Research Quanti ,  Quali ,  Eye-tracking
Agenda ,[object Object],[object Object],[object Object],[object Object]
Agenda ,[object Object],[object Object],[object Object],[object Object]
Technology is a gift of God. After the gift of life it is perhaps the greatest of God's gifts. It is the mother of civilizations, of  arts and of sciences.”  (Freeman Dyson) For a list of all the ways technology has failed to improve the quality of life, please press three.  (Alice Kahn) Technology….opportunity or threat? Also for research ?
A growing pool of data….. Source:  Robert van Ossenbruggen - ProCression ??? ??? ??? ??? ??? Textmining Clickstreaming GPS Blogging RFID Eye Tracking Facial coding … .. … .. … .. STB CATI CAWI Mood boards Desk Research Mystery Shopping CAPI Focus Groups FTF interviews … … … …
550 BILLION Information area & knowledge economy 7,5 Petabyte 1 petabyte = 1000 Terrabyte 1 terrabyte = 1000 gigabyte Number of digital & online available documents
Need for techniques to help  us with processing this info Information area & knowledge economy 300.000 KM It would reach the moon / equals 7,5 times perimeter Earth 5,7 Million Years to read it all!!!!
A problem for us, analysts …. TIME AVAILABLE DATA Available Data Analytical Capacity Executive Capacity Knowledge Gap Execution  Gap Source: Gareth Herschel, Research Director, Gartner Inc.,  Gartner Business Intelligence Summit 2005
Agenda ,[object Object],[object Object],[object Object],[object Object]
Eye tracking Research ,[object Object],[object Object],[object Object]
Consumer facts ,[object Object],[object Object],[object Object],[object Object],1,1 second 10,4 second 25 characters You’d better use  the right 25 characters  in your  “ 1 second introduction” to a potential customer !
Consumer facts ,[object Object],[object Object],[object Object],More Media More Stimuli ,[object Object],[object Object]
Perception versus sensation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Artificial boundary – both theories are applicable.  Important is to what extent and in which circumstances are they applicable?
Influence of  learning &  experience
Stimuli specific factors Person specific factors Eye Movement (attention) Recognition (intensity) ATTENTION  MEASUREMENT IMPACT TASK Eye Movement Registration  as an added value TIME INTENSITY TRADITIONAL RESEARCH TECHNIQUES QUALITATIVE QUANTITATIVE OBJECTIVE MEASUREMENT OF BEHAVIOUR EYE MOVEMENT REGISTRATION
Agenda ,[object Object],[object Object],[object Object],[object Object]
Print Ads
Eye Tracking data: male or female ?
Would you get this result out a quanti/quali? MEN WOMEN
Is this hot spot from female or male viewers?
Would you get this result out a quanti/quali? MEN WOMEN
After… what we learned out of quanti? ,[object Object],[object Object],[object Object],[object Object]
After… what we learned out of quanti? n= 131  Not important / Not relevant Doesn’t give new information Unpleasant Incredible Ordinary / Banal Difficult to understand  Doesn’t invite to buy the product Aimed at women Important / relevant Gives new information Pleasant Credible Distinguishing Clear / easy to understand Invites to buy the product Aimed at men Top 2 % Bottom 2 % 27% 33% 46% 34% 23% 55% 28% Benchmark  top 2% > < > > < < >
[object Object],[object Object],[object Object],After… what we learned out of quanti?
 
Reading Pattern 1 4 2 5 6 3
[object Object],[object Object],Key findings Eye Tracking
Johan, what about the quali  ?
 
Web Research
[object Object],Web Case
Market People Environment & Community Annual report Funding News-headlines
Quanti results ,[object Object],[object Object],[object Object],[object Object],[object Object]
SCROLL-LINE
1 3 2 3
Eye Tracking insights Cola Case ,[object Object],[object Object]
New site based on combined insights: ENVIRONMENT  & HEALTH CLEAR MENU STRUCTURE PEOPLE & PRODUCT
Agenda ,[object Object],[object Object],[object Object],[object Object]
[object Object],IT-consultants @ Research Desk New research model: Two-directional  We Are All Analysts !  Profile of researcher 2.0 ?
Thanks for your attention! Ludovic Depoortere  -  Wim Hamaekers l.depoortere@rogil.be - w.hamaekers@rogil.be
Rogil Research
Speed-Company-Dating
Geert MARTENS Managing Consultant
4C Consulting  |  Our mission ,[object Object],[object Object],[object Object],[object Object]
4C Consulting  |  Our services Strategic Insight Intelligence  Solutions Process Excellence Business  requirements  definition Package selection  & implementation Post-launch  care
4C Consulting  |  Our clients today
Véronique JOUBERT Recruiter
[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object]
Marketing  Transformation Sales  Transformation Service Transformation Services include  design & delivery of advanced capabilities sales interaction mgmt. Service offerings that help clients to generate and act on deep customer insights Services include  design & delivery of capabilities service interaction mgmt. CRM Service Line ,[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],[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]
Tim DUHAMEL CEO
Online research Online research Team work Team work Team work INTERNATIONAL PROJECTS INTERNATIONAL PROJECTS Learning Learning Excellence Excellence Online research Online research Team work Team work Team work INTERNATIONAL PROJECTS INTERNATIONAL PROJECTS Learning Learning Excellence Excellence Curious? Quantitative research consultant Qualitative research consultant http://jobs.insites.eu
Wouter BUCKINX Partner
Python Predictions ,[object Object],[object Object],[object Object],[object Object],[object Object]
Python Predictions ,[object Object],[object Object],[object Object],[object Object]
Python Predictions
Wim HAMAEKERS Research Director
ROGIL Full Service, specialised in Sensory Research
 
FIELD COORDINATOR LABO ASSISTENT SENIOR PROJECT MANAGER JOIN ROGIL NOW !
Lieve GOEDHUYS Academic Program Manager
SAS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
SAS
Pierre-Antoine Dejace Account Manager
SOLIDPartners Performance Management Business Intelligence DataWarehouse 1300+ Consultants International Coverage Leader on the market Independent Player Who are we ?
SOLIDPartners Services portfolio
SOLIDPartners ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Why SOLIDPartners ?
SOLIDPartners ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Why Baqmar ?
Jeroen VAN GODTSENHOVEN Account Manager
SPSS ,[object Object],[object Object],[object Object],[object Object],[object Object]
Break
A hybrid approach to analyzing open-ended questions Annelies Verhaeghe, R&D Consultant InSites Consulting Quanti ,  Textmining ,  Research 2.0
The traditional way of postcoding open-ended questions
The traditional way of postcoding open-ended questions = counts/total sample = counts/ # respondents that mentioned a like = counts/ # likes mentioned Likes Counts % of cases % net  sample % total  sample Coffee taste 21 11% 19% 12% Cooled drink 43 23% 38% 24% Attractive packaging 31 16% 28% 17% Easy to store/take away 19 10% 17% 11% Mocca taste 8 4% 7% 4% Softness 1 1% 1% 1% Ready to eat 7 4% 6% 4% Good volume 9 5% 8% 5% Nice colours 11 6% 10% 6% Foam layer 6 3% 5% 3% Douwe Egberts 10 5% 9% 6% Energy drink 5 3% 4% 3% Original 5 3% 4% 3% Good name 4 2% 4% 2% Makes curious 6 3% 5% 3% Relaxing 0 0% 0% 0% Alternative for pep drink 3 2% 3% 2% Total 189 100% Total respondents 179 Mentioned at least one like 112 Did not mention a like 67 Total likes 189
The issue with open-ended questions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],57%
Two alternatives ,[object Object],[object Object]
Text mining (STAFS) Extracting Categorization
Web 2.0 OLD MEDIA LOSES WE ARE THE MEDIA & CONTENT CITIZEN JOURNALISM
Connected Research Learn from the consumer Learn from consumer interactions consumer consumer company Online research 2.0 Traditional research consumer consumer company
Online delphi methodology
Online delphi methodology
Online delphi methodology
Online delphi methodology
Online delphi & web 2.0 Involve your consumer in your analysis Learn from the interactions of consumers
Research questions ,[object Object],[object Object]
Results Textmining 130 Online delphi 130 Manual postcoding 130
Results Manual postcoding Online delphi Textmining Energy  drink coffee  taste cooled  drink Attractive  packaging Easy to  store /  take away Mocca  taste Softness Ready to  eat Good  volume Nice  colours Foam  layer Douwe  Egberts  Innovative Good  name Makes me  curious Relaxing Alternative for  energy  17  different core ideas Average # ideas / person:  1.44 Range # ideas / person: 0-5 # categories per verbatim 228  different core ideas Average # ideas / person:  2.37 Range # ideas / person: 1-5 201  different core ideas Automatic extraction of core ideas product idea not much boost  taste  not classic boost solution option presentation plastic corny asset coffee everywhere
Results 14 Manual postcoding Online delphi Textmining Energy  drink coffee  taste cooled  drink Attractive  packaging Easy to  store /  take away Mocca  taste Softness Ready to  eat Good  volume Nice  colours Foam  layer Douwe  Egberts  Innovative Good  name Makes me  curious Relaxing Alternative for  energy  # categories per verbatim 17 Cooled drink With coffee taste Attractive packaging Tasty Brand Nice colors Take away Energy boost Take away/longer conservation time Looks nice Can  200 ml Good volume serve cool Softness Practical packaging Mocca taste Ideal for the summer Refreshment Innovative idea Security Strong taste New Variant on warm drink Foam layer Longer conservation time Good name Ready to eat Creamy Makes me curious Original Nice  Great idea Alternative for energy drink Easy to store Packaging  Practical  Relaxing drink 25 Automatic extraction of core ideas Cooled drink Tasty With coffee taste Attractive packaging Take away Can  Innovative idea Strong taste Energy boost Variant on warm drink Great idea Take away/longer conservation time Nice colors Mocca taste Good name
Results Data for dutch likes Manual postcoding Online delphi Textmining
The challengers And there is more....
Online delphi: inspiration effect  Initial no idea: N = 62 No idea after inspiration: N=5
Online delphi: panel experience ,[object Object],[object Object],[object Object]
Textmining: Finding connections
Developing a frame work for textmining ,[object Object],[object Object],[object Object],[object Object],[object Object],Framework
Both methodologies: crossing invalid convenience volume energy drink foam layer brand nice colours nice name can cofee taste nice packaging cooled drink appeal summer 18  -24 jaar 25-  34 jaar 35- 44 jaar 45  -54 jaar 55-  64 jaar
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
InSites Consulting
Market Research & Data Mining Pascal Mignolet,  International Market Research Manager Sara Lee Coffee & Tea Datamining ,  Quanti
Myself:  ,[object Object],[object Object]
Sara Lee Coffee & Tea: ,[object Object],[object Object],[object Object]
Market Research: ,[object Object],[object Object],[object Object]
Mission of an internal  Market Research Department ,[object Object]
Market Research and “Data Mining” ,[object Object],[object Object],[object Object],A lot of possible conflicts …
Data Mining ?  ,[object Object],[object Object]
Market Research  versus  Data mining 1999
Market Research and Data Mining: a rather uneasy start …   ,[object Object],[object Object],[object Object],But: a similar/identical mission So: Friction and Conflicts
Market Research versus Data Mining ,[object Object],*Dummy data Service Center Reports* (n= 60.000 calls) Reasons for calling: - Invoice Problem:  45% - Handset Problem: 25% - Network Problem: 14% Conclusion:  Invoice  is  our most serious problem Customer Satisfaction Survey* (n= 600 interviews) Satisfaction ratings - Invoice :  8.5/10 - Handset :  7.5/10 - Network :  7,3/10 Conclusion: Invoice is  certainly not our most serious problem
Market Research versus Data Mining ,[object Object],[object Object],[object Object]
Market Research  and  Data mining 2001
Market Research and Data Mining: combining the best of both worlds ,[object Object],[object Object],[object Object],Interesting challenge that turned into a nightmare for Market Research
Market Research and Data Mining ,[object Object],Satisfaction score given during interview   (10-point scale) Interesting Challenge: Let’s define the critical satisfaction score by comparing satisfaction given in market research with real customer behavior.
Market Research and Data Mining ,[object Object],Satisfaction score given during interview   (10-point scale) Interesting Challenge: Let’s define the critical satisfaction score by comparing satisfaction given in market research with real customer behavior.
Market Research and Data Mining ,[object Object],Satisfaction score given during interview   (10-point scale) Nightmare: No significant difference in average satisfaction score between customers who left and those who stayed  Satisfaction score not predictive for churn/loyalty
[object Object],[object Object],[object Object],Market Research and Data Mining
Data Mining within Market Research 2005
Challenging the Models ,[object Object],[object Object],[object Object],[object Object]
Challenging the Models ,[object Object],[object Object]
Challenging the Models ,[object Object],[object Object]
Example: NPD potential  (= concept screening) What is the volume potential of a new concept / product idea? ,[object Object]
Example: NPD potential  (= concept screening) ,[object Object]
Example: NPD potential  (= concept screening) ,[object Object],[object Object],Only respondents who meet all conditions are considered “trialists” and used as basis for volume potential calculation
Example: Sales modeling  (= enriching Nielsen Retail panel data by ad hoc analysis) 1 2 3 Which variables have a significant influence on sales? How do these variables contribute in terms of value/volume? How can changes in these variables improve results?
Example: Sales modeling  (= enriching Nielsen Retail panel data by ad hoc analysis) Sales 1 2 3 Which variables have a significant influence on sales? How do these variables contribute in terms of value/volume? How can changes in these variables improve results? Input Mass Media Advertising Sponsoring Leaflets Promotion – 1-,2-,3-,4-,5- pack Price (offer/normal) Price lag Distribution New SKUs Competitors Advertising Competitors Pricing Competitors Distribution Seasonality Calendar – X-mas, Easter, Summer etc. Monthly salary payments Temperature Int. Coffee Price/Dollar  Retail Change- & Offer combination Sales in  volume
Output example Decomposition of Merrilds sales 2006 Factor B 22% Factor A 10% Brand Equity 50% Factor D 10,9% Factor E 2,4% Factor F 3,5% Factor C 2%
Conclusions
Datamining & Market Research ,[object Object],[object Object],[object Object]
THANK YOU!
Sara Lee Coffee & Tea
Closing – Wrap-up ,[object Object]
Closing – Magic Quadrant 2008 Funny Fridays ‘ Fun’ posts BAQMaR AWD Partner in the Spotlight BAQMaR conference Workshops Pictures Content Fun Online Offline ‘ Content’ posts Job offers BAQMan/BAQMadam Conferences Newsletter Q&A
Closing – Thank you ,[object Object]
Closing – Thank you ,[object Object]
Closing – Thank you
 

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BAQMaR 2007

  • 1.  
  • 2. Intro – The story of ...
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8. Intro – Magic Quadrant Content Fun Online Offline
  • 9. Intro – Magic Quadrant Content Fun Online Offline ‘ Content’ posts Job offers BAQMan/BAQMadam Conferences Newsletter Q&A
  • 10. Intro – Magic Quadrant Funny Fridays ‘ Fun’ posts Pictures Content Fun Online Offline ‘ Content’ posts Job offers BAQMan/BAQMadam Conferences Newsletter Q&A
  • 11. Intro – Magic Quadrant BAQMaR AWD Partner in the Spotlight Funny Fridays ‘ Fun’ posts Pictures Content Fun Online Offline ‘ Content’ posts Job offers BAQMan/BAQMadam Conferences Newsletter Q&A
  • 12. Intro – Magic Quadrant BAQMaR AWD Partner in the Spotlight Conference Funny Fridays ‘ Fun’ posts Pictures Content Fun Online Offline ‘ Content’ posts Job offers BAQMan/BAQMadam Conferences Newsletter Q&A
  • 13.
  • 14. Eye Tracking Research Added Value for Traditional Techniques? Ludovic Depoortere, Managing Director Rogil Research Quanti , Quali , Eye-tracking
  • 15.
  • 16.
  • 17. Technology is a gift of God. After the gift of life it is perhaps the greatest of God's gifts. It is the mother of civilizations, of arts and of sciences.” (Freeman Dyson) For a list of all the ways technology has failed to improve the quality of life, please press three. (Alice Kahn) Technology….opportunity or threat? Also for research ?
  • 18. A growing pool of data….. Source: Robert van Ossenbruggen - ProCression ??? ??? ??? ??? ??? Textmining Clickstreaming GPS Blogging RFID Eye Tracking Facial coding … .. … .. … .. STB CATI CAWI Mood boards Desk Research Mystery Shopping CAPI Focus Groups FTF interviews … … … …
  • 19. 550 BILLION Information area & knowledge economy 7,5 Petabyte 1 petabyte = 1000 Terrabyte 1 terrabyte = 1000 gigabyte Number of digital & online available documents
  • 20. Need for techniques to help us with processing this info Information area & knowledge economy 300.000 KM It would reach the moon / equals 7,5 times perimeter Earth 5,7 Million Years to read it all!!!!
  • 21. A problem for us, analysts …. TIME AVAILABLE DATA Available Data Analytical Capacity Executive Capacity Knowledge Gap Execution Gap Source: Gareth Herschel, Research Director, Gartner Inc., Gartner Business Intelligence Summit 2005
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27. Influence of learning & experience
  • 28. Stimuli specific factors Person specific factors Eye Movement (attention) Recognition (intensity) ATTENTION MEASUREMENT IMPACT TASK Eye Movement Registration as an added value TIME INTENSITY TRADITIONAL RESEARCH TECHNIQUES QUALITATIVE QUANTITATIVE OBJECTIVE MEASUREMENT OF BEHAVIOUR EYE MOVEMENT REGISTRATION
  • 29.
  • 31. Eye Tracking data: male or female ?
  • 32. Would you get this result out a quanti/quali? MEN WOMEN
  • 33. Is this hot spot from female or male viewers?
  • 34. Would you get this result out a quanti/quali? MEN WOMEN
  • 35.
  • 36. After… what we learned out of quanti? n= 131 Not important / Not relevant Doesn’t give new information Unpleasant Incredible Ordinary / Banal Difficult to understand Doesn’t invite to buy the product Aimed at women Important / relevant Gives new information Pleasant Credible Distinguishing Clear / easy to understand Invites to buy the product Aimed at men Top 2 % Bottom 2 % 27% 33% 46% 34% 23% 55% 28% Benchmark top 2% > < > > < < >
  • 37.
  • 38.  
  • 39. Reading Pattern 1 4 2 5 6 3
  • 40.
  • 41. Johan, what about the quali ?
  • 42.  
  • 44.
  • 45. Market People Environment & Community Annual report Funding News-headlines
  • 46.
  • 48. 1 3 2 3
  • 49.
  • 50. New site based on combined insights: ENVIRONMENT & HEALTH CLEAR MENU STRUCTURE PEOPLE & PRODUCT
  • 51.
  • 52.
  • 53. Thanks for your attention! Ludovic Depoortere - Wim Hamaekers l.depoortere@rogil.be - w.hamaekers@rogil.be
  • 57.
  • 58. 4C Consulting | Our services Strategic Insight Intelligence Solutions Process Excellence Business requirements definition Package selection & implementation Post-launch care
  • 59. 4C Consulting | Our clients today
  • 61.
  • 62.
  • 63.
  • 64.
  • 65.
  • 66.
  • 67.
  • 69. Online research Online research Team work Team work Team work INTERNATIONAL PROJECTS INTERNATIONAL PROJECTS Learning Learning Excellence Excellence Online research Online research Team work Team work Team work INTERNATIONAL PROJECTS INTERNATIONAL PROJECTS Learning Learning Excellence Excellence Curious? Quantitative research consultant Qualitative research consultant http://jobs.insites.eu
  • 71.
  • 72.
  • 75. ROGIL Full Service, specialised in Sensory Research
  • 76.  
  • 77. FIELD COORDINATOR LABO ASSISTENT SENIOR PROJECT MANAGER JOIN ROGIL NOW !
  • 78. Lieve GOEDHUYS Academic Program Manager
  • 79.
  • 80. SAS
  • 82. SOLIDPartners Performance Management Business Intelligence DataWarehouse 1300+ Consultants International Coverage Leader on the market Independent Player Who are we ?
  • 84.
  • 85.
  • 86. Jeroen VAN GODTSENHOVEN Account Manager
  • 87.
  • 88. Break
  • 89. A hybrid approach to analyzing open-ended questions Annelies Verhaeghe, R&D Consultant InSites Consulting Quanti , Textmining , Research 2.0
  • 90. The traditional way of postcoding open-ended questions
  • 91. The traditional way of postcoding open-ended questions = counts/total sample = counts/ # respondents that mentioned a like = counts/ # likes mentioned Likes Counts % of cases % net sample % total sample Coffee taste 21 11% 19% 12% Cooled drink 43 23% 38% 24% Attractive packaging 31 16% 28% 17% Easy to store/take away 19 10% 17% 11% Mocca taste 8 4% 7% 4% Softness 1 1% 1% 1% Ready to eat 7 4% 6% 4% Good volume 9 5% 8% 5% Nice colours 11 6% 10% 6% Foam layer 6 3% 5% 3% Douwe Egberts 10 5% 9% 6% Energy drink 5 3% 4% 3% Original 5 3% 4% 3% Good name 4 2% 4% 2% Makes curious 6 3% 5% 3% Relaxing 0 0% 0% 0% Alternative for pep drink 3 2% 3% 2% Total 189 100% Total respondents 179 Mentioned at least one like 112 Did not mention a like 67 Total likes 189
  • 92.
  • 93.
  • 94. Text mining (STAFS) Extracting Categorization
  • 95. Web 2.0 OLD MEDIA LOSES WE ARE THE MEDIA & CONTENT CITIZEN JOURNALISM
  • 96. Connected Research Learn from the consumer Learn from consumer interactions consumer consumer company Online research 2.0 Traditional research consumer consumer company
  • 101. Online delphi & web 2.0 Involve your consumer in your analysis Learn from the interactions of consumers
  • 102.
  • 103. Results Textmining 130 Online delphi 130 Manual postcoding 130
  • 104. Results Manual postcoding Online delphi Textmining Energy drink coffee taste cooled drink Attractive packaging Easy to store / take away Mocca taste Softness Ready to eat Good volume Nice colours Foam layer Douwe Egberts Innovative Good name Makes me curious Relaxing Alternative for energy 17 different core ideas Average # ideas / person: 1.44 Range # ideas / person: 0-5 # categories per verbatim 228 different core ideas Average # ideas / person: 2.37 Range # ideas / person: 1-5 201 different core ideas Automatic extraction of core ideas product idea not much boost taste not classic boost solution option presentation plastic corny asset coffee everywhere
  • 105. Results 14 Manual postcoding Online delphi Textmining Energy drink coffee taste cooled drink Attractive packaging Easy to store / take away Mocca taste Softness Ready to eat Good volume Nice colours Foam layer Douwe Egberts Innovative Good name Makes me curious Relaxing Alternative for energy # categories per verbatim 17 Cooled drink With coffee taste Attractive packaging Tasty Brand Nice colors Take away Energy boost Take away/longer conservation time Looks nice Can 200 ml Good volume serve cool Softness Practical packaging Mocca taste Ideal for the summer Refreshment Innovative idea Security Strong taste New Variant on warm drink Foam layer Longer conservation time Good name Ready to eat Creamy Makes me curious Original Nice Great idea Alternative for energy drink Easy to store Packaging Practical Relaxing drink 25 Automatic extraction of core ideas Cooled drink Tasty With coffee taste Attractive packaging Take away Can Innovative idea Strong taste Energy boost Variant on warm drink Great idea Take away/longer conservation time Nice colors Mocca taste Good name
  • 106. Results Data for dutch likes Manual postcoding Online delphi Textmining
  • 107. The challengers And there is more....
  • 108. Online delphi: inspiration effect Initial no idea: N = 62 No idea after inspiration: N=5
  • 109.
  • 111.
  • 112. Both methodologies: crossing invalid convenience volume energy drink foam layer brand nice colours nice name can cofee taste nice packaging cooled drink appeal summer 18 -24 jaar 25- 34 jaar 35- 44 jaar 45 -54 jaar 55- 64 jaar
  • 113.
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  • 117. Market Research & Data Mining Pascal Mignolet, International Market Research Manager Sara Lee Coffee & Tea Datamining , Quanti
  • 118.
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  • 120.
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  • 122.
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  • 124. Market Research versus Data mining 1999
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  • 128. Market Research and Data mining 2001
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  • 133.
  • 134. Data Mining within Market Research 2005
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  • 140.
  • 141. Example: Sales modeling (= enriching Nielsen Retail panel data by ad hoc analysis) 1 2 3 Which variables have a significant influence on sales? How do these variables contribute in terms of value/volume? How can changes in these variables improve results?
  • 142. Example: Sales modeling (= enriching Nielsen Retail panel data by ad hoc analysis) Sales 1 2 3 Which variables have a significant influence on sales? How do these variables contribute in terms of value/volume? How can changes in these variables improve results? Input Mass Media Advertising Sponsoring Leaflets Promotion – 1-,2-,3-,4-,5- pack Price (offer/normal) Price lag Distribution New SKUs Competitors Advertising Competitors Pricing Competitors Distribution Seasonality Calendar – X-mas, Easter, Summer etc. Monthly salary payments Temperature Int. Coffee Price/Dollar Retail Change- & Offer combination Sales in volume
  • 143. Output example Decomposition of Merrilds sales 2006 Factor B 22% Factor A 10% Brand Equity 50% Factor D 10,9% Factor E 2,4% Factor F 3,5% Factor C 2%
  • 145.
  • 147. Sara Lee Coffee & Tea
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  • 149. Closing – Magic Quadrant 2008 Funny Fridays ‘ Fun’ posts BAQMaR AWD Partner in the Spotlight BAQMaR conference Workshops Pictures Content Fun Online Offline ‘ Content’ posts Job offers BAQMan/BAQMadam Conferences Newsletter Q&A
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Notes de l'éditeur

  1. Good evening! We are glad to welcome you all at our first BAQMaR conference. It’s a real pleasure to see this broad range of professionals in the audience: both quantitative and qualitative market researchers, customer intelligence professionals and marketeers.