BAQMaR 2007

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  • 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.
  • BAQMaR 2007

    1. 2. Intro – The story of ...
    2. 3. Intro – Time line <ul><li>BAQMaR was born! </li></ul><ul><ul><li>Quantitative & Qualitative Marketing Research </li></ul></ul><ul><ul><li>Young & Dynamic </li></ul></ul><ul><ul><li>Informal </li></ul></ul><ul><ul><li>Open </li></ul></ul><ul><ul><li>Independent </li></ul></ul><ul><ul><li>Not-for-profit </li></ul></ul>JAN MAR 2007
    3. 4. Intro – Time line <ul><li>> 300 subscribers to our newsletter </li></ul><ul><li>> 145 members on LinkedIn </li></ul><ul><li>Monthly > 500 unique visitors </li></ul>www.BAQMaR.be
    4. 5. Intro – Time line <ul><li>> 300 posts in different categories </li></ul><ul><li>> 250 comments </li></ul><ul><li>30 contributors </li></ul>www.BAQMaR.be
    5. 6. Intro – Time line <ul><li>Our ambassadors! </li></ul>
    6. 7. <ul><li>Afterwork Work Drinks </li></ul><ul><ul><li>3/6/9 </li></ul></ul><ul><ul><li>Free drinks powered by our official partners </li></ul></ul>Intro – Time line JAN MAR 22/MAR 28/JUN 27/SEP 2007
    7. 8. Intro – Magic Quadrant Content Fun Online Offline
    8. 9. Intro – Magic Quadrant Content Fun Online Offline ‘ Content’ posts Job offers BAQMan/BAQMadam Conferences Newsletter Q&A
    9. 10. Intro – Magic Quadrant Funny Fridays ‘ Fun’ posts Pictures Content Fun Online Offline ‘ Content’ posts Job offers BAQMan/BAQMadam Conferences Newsletter Q&A
    10. 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
    11. 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
    12. 13. Intro – Time line <ul><li>Conference </li></ul><ul><li>‘ We Are All Analysts’ </li></ul>2007 JAN MAR 22/MAR 28/JUN 27/SEP 06/DEC
    13. 14. Eye Tracking Research Added Value for Traditional Techniques? Ludovic Depoortere, Managing Director Rogil Research Quanti , Quali , Eye-tracking
    14. 15. Agenda <ul><li>Technology & Data </li></ul><ul><li>Eyetracking Research </li></ul><ul><li>1 + 1 = 3 </li></ul><ul><li>After drink topics </li></ul>
    15. 16. Agenda <ul><li>Technology & Data </li></ul><ul><li>Eyetracking Research </li></ul><ul><li>1 + 1 = 3 </li></ul><ul><li>Learnings </li></ul>
    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 ?
    17. 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 … … … …
    18. 19. 550 BILLION Information area & knowledge economy 7,5 Petabyte 1 petabyte = 1000 Terrabyte 1 terrabyte = 1000 gigabyte Number of digital & online available documents
    19. 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!!!!
    20. 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
    21. 22. Agenda <ul><li>Technology & Data </li></ul><ul><li>Eyetracking Research </li></ul><ul><li>1 + 1 = 3 </li></ul><ul><li>Learnings </li></ul>
    22. 23. Eye tracking Research <ul><li>Infrared measurement </li></ul><ul><li>Registration of projection on the Fovea </li></ul><ul><li> EYE FOCUS </li></ul>
    23. 24. Consumer facts <ul><li>How long are people looking at an individual search result in Google ? </li></ul><ul><li>What is the total gaze time at the first Google-results page ? </li></ul><ul><li> </li></ul><ul><li>What is the average number of characters in a Google line ? </li></ul>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 !
    24. 25. Consumer facts <ul><li>Less time to process </li></ul><ul><li>Split second decisions </li></ul><ul><li>Harder to get the attention </li></ul>More Media More Stimuli <ul><li>Importance of an efficient visual communication ! </li></ul><ul><li>Gain insights in how visual communication works </li></ul>
    25. 26. Perception versus sensation <ul><li>Bottom-Up </li></ul><ul><ul><li>Perception = data-driven </li></ul></ul><ul><ul><li>Starts with sensory data – raw input </li></ul></ul><ul><li>Top- Down </li></ul><ul><ul><li>Perception = conceptual driven </li></ul></ul><ul><ul><li>Starts with stored knowledge & expectations </li></ul></ul>Artificial boundary – both theories are applicable. Important is to what extent and in which circumstances are they applicable?
    26. 27. Influence of learning & experience
    27. 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
    28. 29. Agenda <ul><li>Technology & Data </li></ul><ul><li>Eyetracking Research </li></ul><ul><li>1 + 1 = 3 </li></ul><ul><li>Learnings </li></ul>
    29. 30. Print Ads
    30. 31. Eye Tracking data: male or female ?
    31. 32. Would you get this result out a quanti/quali? MEN WOMEN
    32. 33. Is this hot spot from female or male viewers?
    33. 34. Would you get this result out a quanti/quali? MEN WOMEN
    34. 35. After… what we learned out of quanti? <ul><li>Overall liking </li></ul><ul><ul><li>54% (min. 7 on 10) </li></ul></ul><ul><ul><li>Men vs Women </li></ul></ul><ul><ul><li>67% vs 42% (min. 7) </li></ul></ul>
    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% > < > > < < >
    36. 37. <ul><li>The divergence in overall appreciation between men and women is perceptible in different evaluated aspects of the advertisement </li></ul><ul><li>Something is wrong with the credibility , relevance and easiness to understand </li></ul><ul><li>But what is the exact problem ? </li></ul>After… what we learned out of quanti?
    37. 39. Reading Pattern 1 4 2 5 6 3
    38. 40. <ul><li>The elements that tell what After is, are positioned at the end of the reading pattern ( headline , hyperlink and bottom text ) </li></ul><ul><li>This implies that the explanatory information (such as the product definition in the bottom text) of this rather unknown product is overlooked by most of the viewers. </li></ul>Key findings Eye Tracking
    39. 41. Johan, what about the quali ?
    40. 43. Web Research
    41. 44. <ul><ul><li>The Coca Cola corporate website should mainly underline the “health, people & environmental responsibility” of the Coca Cola Company </li></ul></ul>Web Case
    42. 45. Market People Environment & Community Annual report Funding News-headlines
    43. 46. Quanti results <ul><li>What is the key message of this homepage ? </li></ul><ul><li>47% = promo of Coca-Cola beverages </li></ul><ul><li>40% = health-issues & Coca Cola </li></ul><ul><ul><ul><li>Conclusion: </li></ul></ul></ul><ul><ul><ul><li>Acceptable, but is it enough to open the debate with the creative s ? </li></ul></ul></ul>
    44. 47. SCROLL-LINE
    45. 48. 1 3 2 3
    46. 49. Eye Tracking insights Cola Case <ul><li>Menu Bar is a crucial navigation point: is seen by only 50% of visitors. </li></ul><ul><li>First focus point are product images, last focus point is textual information covering the environmental issues </li></ul>
    47. 50. New site based on combined insights: ENVIRONMENT & HEALTH CLEAR MENU STRUCTURE PEOPLE & PRODUCT
    48. 51. Agenda <ul><li>Technology & Data </li></ul><ul><li>Eyetracking Research </li></ul><ul><li>1 + 1 = 3 </li></ul><ul><li>After drink topics </li></ul>
    49. 52. <ul><li>Research 2.0 </li></ul>IT-consultants @ Research Desk New research model: Two-directional We Are All Analysts ! Profile of researcher 2.0 ?
    50. 53. Thanks for your attention! Ludovic Depoortere - Wim Hamaekers l.depoortere@rogil.be - w.hamaekers@rogil.be
    51. 54. Rogil Research
    52. 55. Speed-Company-Dating
    53. 56. Geert MARTENS Managing Consultant
    54. 57. 4C Consulting | Our mission <ul><li>4C Consulting </li></ul><ul><li>helps companies </li></ul><ul><li>win, keep and grow </li></ul><ul><li>customer value </li></ul>
    55. 58. 4C Consulting | Our services Strategic Insight Intelligence Solutions Process Excellence Business requirements definition Package selection & implementation Post-launch care
    56. 59. 4C Consulting | Our clients today
    57. 60. Véronique JOUBERT Recruiter
    58. 61. <ul><li>Reflect on your career. </li></ul><ul><li>And then swing. </li></ul>
    59. 62. <ul><li>Accenture worldwide </li></ul><ul><li>Accenture is a global Management Consulting, Technology Services and Outsourcing company. </li></ul><ul><li>49 countries </li></ul><ul><li>170.000 employees (BE = 1.300 employees) </li></ul><ul><li>Turnover FY 2007: $19.70 billion </li></ul>
    60. 63. 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 <ul><li>Five specialties </li></ul><ul><li>Segmentation & Analytics </li></ul><ul><li>Loyalty management </li></ul><ul><li>Marketing Resource Management </li></ul><ul><li>Campaign Management </li></ul><ul><li>Customer Strategy Development </li></ul>
    61. 64. <ul><li>Wide Job Variety </li></ul><ul><li>Accenture offers the widest range of job opportunities in Belgium & Luxembourg: </li></ul><ul><ul><ul><li>Cross-industry </li></ul></ul></ul><ul><ul><ul><li>Cross-function </li></ul></ul></ul><ul><ul><ul><li>Cross-borders (international) </li></ul></ul></ul><ul><ul><ul><li>Cross-specialization </li></ul></ul></ul><ul><ul><ul><li>(consulting, technology or outsourcing) </li></ul></ul></ul>
    62. 65. <ul><li>Deep career development </li></ul><ul><li>Accenture employees benefit from the deepest level of career development: </li></ul><ul><ul><ul><li>Clear career path </li></ul></ul></ul><ul><ul><ul><li>Extensive training program </li></ul></ul></ul><ul><ul><ul><li>Learn at each project </li></ul></ul></ul><ul><ul><ul><li>Coaching from senior colleagues </li></ul></ul></ul><ul><ul><ul><li>Accenture people become high performers </li></ul></ul></ul>
    63. 66. <ul><li>Leading client projects </li></ul><ul><li>Accenture employees work on major projects for leading clients: </li></ul><ul><ul><ul><li>Most clients are BEL20 ranked </li></ul></ul></ul><ul><ul><ul><li>High-performance businesses </li></ul></ul></ul><ul><ul><ul><li>All industries </li></ul></ul></ul>
    64. 67. <ul><li>Want to know more? </li></ul><ul><li>Meet Accenture ’ s ambassadors at the BAQMaR conference today or apply on … </li></ul><ul><li>www.accenturebelux.com/careers </li></ul>
    65. 68. Tim DUHAMEL CEO
    66. 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
    67. 70. Wouter BUCKINX Partner
    68. 71. Python Predictions <ul><li>Focus on C.I. </li></ul><ul><ul><li>Predicting Individual </li></ul></ul><ul><ul><li>Customer Behavior </li></ul></ul><ul><li>History </li></ul><ul><li>References </li></ul>
    69. 72. Python Predictions <ul><li>BAQMaR Commitment </li></ul><ul><ul><li>First Sponsor </li></ul></ul><ul><ul><li>Integration of MR Domains </li></ul></ul><ul><ul><li>For Knowledge and For Fun </li></ul></ul>
    70. 73. Python Predictions
    71. 74. Wim HAMAEKERS Research Director
    72. 75. ROGIL Full Service, specialised in Sensory Research
    73. 77. FIELD COORDINATOR LABO ASSISTENT SENIOR PROJECT MANAGER JOIN ROGIL NOW !
    74. 78. Lieve GOEDHUYS Academic Program Manager
    75. 79. SAS <ul><li>SAS – The power to know </li></ul><ul><ul><ul><li>Worldwide company – 410 local offices </li></ul></ul></ul><ul><ul><ul><li>42.000 customers in 110 countries </li></ul></ul></ul><ul><ul><ul><li>Stable software growth and R&D investment </li></ul></ul></ul><ul><ul><ul><li>Integrated software offering, adapted to </li></ul></ul></ul><ul><ul><ul><ul><li>Different industries </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Different business pains </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Different user types </li></ul></ul></ul></ul>
    76. 80. SAS
    77. 81. Pierre-Antoine Dejace Account Manager
    78. 82. SOLIDPartners Performance Management Business Intelligence DataWarehouse 1300+ Consultants International Coverage Leader on the market Independent Player Who are we ?
    79. 83. SOLIDPartners Services portfolio
    80. 84. SOLIDPartners <ul><li>Performance Management and BI is our core business and we are growing and investing </li></ul><ul><li>We have significant proven customer experiences </li></ul><ul><li>Majority of our senior consultants has significant experience in Performance Management and BI </li></ul><ul><li>SOLIDPartners supports business and IT processes, meaning that we use to facilitate the bridge between both realities. </li></ul><ul><ul><li>Internal HR policy promotes: </li></ul></ul><ul><ul><ul><li>Knowledge sharing and transfer </li></ul></ul></ul><ul><ul><ul><li>Heavy training program </li></ul></ul></ul><ul><ul><ul><li>>80% of staffing with 5y+ experience </li></ul></ul></ul>Why SOLIDPartners ?
    81. 85. SOLIDPartners <ul><ul><li>Evolution of SOLIDPartners </li></ul></ul><ul><ul><ul><li>Started as DWH company </li></ul></ul></ul><ul><ul><ul><li>Evolved into BI reportings </li></ul></ul></ul><ul><ul><ul><li>Now evolving into analytics & data mining </li></ul></ul></ul><ul><ul><li>~ Evolution we see in the market </li></ul></ul><ul><ul><li>One-stop-shopping </li></ul></ul><ul><ul><li>SOLIDPartners can now offer you a complete BI solution , in a personalized & customized way </li></ul></ul>Why Baqmar ?
    82. 86. Jeroen VAN GODTSENHOVEN Account Manager
    83. 87. SPSS <ul><li>Pure Analytics Player </li></ul><ul><li>40 years experience embedded in our solutions </li></ul><ul><li>We deliver: average pay-back of 10.7 months (nucleus research) </li></ul><ul><li>We leverage existing infrastructure and all available data whether structured or unstructured (text, web, voice, surveys, ...) </li></ul><ul><li>Did you know? we opened up and integrated with R </li></ul>
    84. 88. Break
    85. 89. A hybrid approach to analyzing open-ended questions Annelies Verhaeghe, R&D Consultant InSites Consulting Quanti , Textmining , Research 2.0
    86. 90. The traditional way of postcoding open-ended questions
    87. 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
    88. 92. The issue with open-ended questions <ul><ul><li>Likes: 31% (108) no idea </li></ul></ul><ul><ul><li>Dislikes: 37% (115) no idea </li></ul></ul><ul><li>Panel experience: (Panelsatisfaction study 2006) </li></ul><ul><li>I find open questions annoying: 24% </li></ul><ul><li>I find open questions too time intensive: 32% </li></ul><ul><ul><li>Fit* between 2 experienced coders </li></ul></ul>57%
    89. 93. Two alternatives <ul><li>Text mining </li></ul><ul><li>Web 2.0 approach: online delphi </li></ul>
    90. 94. Text mining (STAFS) Extracting Categorization
    91. 95. Web 2.0 OLD MEDIA LOSES WE ARE THE MEDIA & CONTENT CITIZEN JOURNALISM
    92. 96. Connected Research Learn from the consumer Learn from consumer interactions consumer consumer company Online research 2.0 Traditional research consumer consumer company
    93. 97. Online delphi methodology
    94. 98. Online delphi methodology
    95. 99. Online delphi methodology
    96. 100. Online delphi methodology
    97. 101. Online delphi & web 2.0 Involve your consumer in your analysis Learn from the interactions of consumers
    98. 102. Research questions <ul><li>Do we obtain a similar number of final categories? </li></ul><ul><li>Is there an overlap in content for the categories? </li></ul>
    99. 103. Results Textmining 130 Online delphi 130 Manual postcoding 130
    100. 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
    101. 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
    102. 106. Results Data for dutch likes Manual postcoding Online delphi Textmining
    103. 107. The challengers And there is more....
    104. 108. Online delphi: inspiration effect Initial no idea: N = 62 No idea after inspiration: N=5
    105. 109. Online delphi: panel experience <ul><li>Web 2.0 attitude </li></ul><ul><li>Social control system </li></ul><ul><li>Avoiding chaos </li></ul>
    106. 110. Textmining: Finding connections
    107. 111. Developing a frame work for textmining <ul><li>Categorization by two independent postcoders </li></ul><ul><ul><li>10 categories in common </li></ul></ul><ul><ul><li>Level of categorization </li></ul></ul><ul><ul><ul><li>‘ Attractive packaging’ of the product versus colour of the packaging, can, ... </li></ul></ul></ul><ul><ul><li>Irrelevant text </li></ul></ul>Framework
    108. 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
    109. 113. Conclusions <ul><li>Existing problems with traditional coding </li></ul><ul><ul><li>Time & cost intensive activity </li></ul></ul><ul><ul><li>Loss of data caused by no idea option </li></ul></ul><ul><ul><li>Coding open answers is subjective: On average 57% fit between two experienced coders </li></ul></ul><ul><ul><li>At least 1 on 4 panelmembers does not like open questions </li></ul></ul>
    110. 114. Conclusions <ul><li>Is online delphi a solution? </li></ul><ul><ul><li>Full online delphi solution </li></ul></ul><ul><ul><li>Richer coding: </li></ul></ul><ul><ul><ul><li>The number of core ideas in 1 verbatim is bigger & number of final categories is bigger for online delphi </li></ul></ul></ul><ul><ul><ul><li>More diversity in the content with online delphi (f.i. Looks nice, attractive packaging, can) </li></ul></ul></ul><ul><ul><ul><li>Online delphi also captures emotional information </li></ul></ul></ul><ul><ul><li>inspiration score leads to additional insights </li></ul></ul><ul><ul><ul><li>Unaided versus aided </li></ul></ul></ul><ul><ul><li>Online delphi is response driven </li></ul></ul><ul><ul><ul><li>Manual coding: N = 1 </li></ul></ul></ul><ul><ul><ul><li>Online delphi coding: N= total sample size: consensus approach </li></ul></ul></ul><ul><ul><li>No data loss </li></ul></ul>
    111. 115. Conclusions <ul><li>Is textmining a solution? </li></ul><ul><ul><li>Richer coding </li></ul></ul><ul><ul><ul><li>More words are extracted </li></ul></ul></ul><ul><ul><ul><li>Emotional information is also captured </li></ul></ul></ul><ul><ul><li>More objective process </li></ul></ul><ul><ul><ul><li>Automatic extraction and categorization </li></ul></ul></ul><ul><ul><ul><li>Human finetuning guided by a framework </li></ul></ul></ul><ul><ul><li>More fun for the coder </li></ul></ul>
    112. 116. InSites Consulting
    113. 117. Market Research & Data Mining Pascal Mignolet, International Market Research Manager Sara Lee Coffee & Tea Datamining , Quanti
    114. 118. Myself: <ul><li>This presentation is a “personal story” of my experiences, as a market researcher, with the world of Data Mining </li></ul><ul><li>Career in Market Research: - 1988-1999: INRA (market research agency) - 1999-2004: Mobistar , head of market research - 2004-2006: Orange , Market Research Director, Europe - 2006- … : Sara Lee , International Research director (Coffee & Tea) </li></ul>
    115. 119. Sara Lee Coffee & Tea: <ul><li>3rd Coffee supplier worldwide </li></ul><ul><li>Brands : - Douwe Egberts - Senseo - Moccona - Pickwick - …. </li></ul><ul><li>Active worldwide : - Western & Eastern Europe - Latin America - Australia - Asia </li></ul>
    116. 120. Market Research: <ul><li>Gathering and interpretation of information on markets and consumers through: - questionnaires (90%) - observation (10%) </li></ul><ul><li>Established practice with established players and procedures </li></ul><ul><li>Most information is delivered by research agencies (World players: TNS, Ipsos, Synnovate, Research International, …) </li></ul>
    117. 121. Mission of an internal Market Research Department <ul><li>“ To turn consumer knowledge into a competitive advantage for the company” It’s not just about collecting data, creating intelligence, spreading knowledge, … “ It’s about creating a competitive advantage by knowing, unveiling, understanding, … something about the market or the consumer that the competition does not know yet”. </li></ul>
    118. 122. Market Research and “Data Mining” <ul><li>Overlapping objectives and challenges </li></ul><ul><li>Same deliverables: actionable data, competitive advantage, … </li></ul><ul><li>Often competing for budgets and FTE’s </li></ul>A lot of possible conflicts …
    119. 123. Data Mining ? <ul><li>Data Mining as a “C orporate Function”: 1) “ Data Mining versus “Market Research” </li></ul><ul><li>Data Mining as a “critical attitude towards available data” 2) Data Mining and Market Research 3) Data Mining within Market Research </li></ul>
    120. 124. Market Research versus Data mining 1999
    121. 125. Market Research and Data Mining: a rather uneasy start … <ul><li>MARKET RESEARCH - traditional monopoly on consumer information - integrated (mostly) in Marketing Department - run (mostly) by employee s - boring , redundant - indirect link with business - based on relatively small samples - collected at relatively high costs - reported on “declared” consumer attitudes, intentions and behavior </li></ul><ul><li>DATA MINING - New source of consumer information - proliferated across several departments (marketing, sales, customer service, finance, …) - run (mostly) by external consultants - fashionable , new - direct link with business </li></ul><ul><li>- based on big numbers - collected at relatively low costs - reporting actual consumer behavior </li></ul>But: a similar/identical mission So: Friction and Conflicts
    122. 126. Market Research versus Data Mining <ul><li>Typical challenge for a Market Researcher, anno 1999 </li></ul>*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
    123. 127. Market Research versus Data Mining <ul><li>Most alternative “data owners” had no or little experience with the scientific and holistic interpretation of these new data/information streams. Market Researcher: (basic) knowledge about statistics, psychology, marketing, market dynamics, company, … </li></ul><ul><li>Market Research was/is often the only department that … - distinguishes between cause & consequence - distinguishes between causality & correlation - stimulates hypothesis testing - uses common sense: “Why do you think a consumer would do that?” </li></ul><ul><li>Relatively easy for Market Research to proof its added value </li></ul>
    124. 128. Market Research and Data mining 2001
    125. 129. Market Research and Data Mining: combining the best of both worlds <ul><li>The easiest and most certain way to turn information into a competitive advantage is by combining information sources </li></ul><ul><li>This is a matter of pure statistics and probabilities If 9/10 competitors have information source A and if 8/10 competitors have information source B then only 7/10 can benefit from the integration of both sources </li></ul><ul><li>Big challenge and internal pressure to enrich market research data (“attitudinal”) with internal data (behavior) and perform an analysis on the total dataset. </li></ul>Interesting challenge that turned into a nightmare for Market Research
    126. 130. Market Research and Data Mining <ul><li>Typical challenge for a Market Researcher, anno 2001 </li></ul>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.
    127. 131. Market Research and Data Mining <ul><li>Typical challenge for a Market Researcher, anno 2001 </li></ul>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.
    128. 132. Market Research and Data Mining <ul><li>Typical challenge for a Market Researcher, anno 2001 </li></ul>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
    129. 133. <ul><li>Although not really enjoyable on the very moment, such conflicts were very fruitful for Market Research in the longer term. </li></ul><ul><li>Challenged by Data Mining, Market Research was/is pushed to review its traditional way of looking at consumers, to challenge its models, to change the way research conclusions are translated into recommendations Market Research is pushed outside its comfort zone </li></ul><ul><li>Internal research departments seem to have understood this better than the traditional Research Agencies, still sticking to traditional models and interpretations. </li></ul>Market Research and Data Mining
    130. 134. Data Mining within Market Research 2005
    131. 135. Challenging the Models <ul><li>Most Research Models are oversimplifications of reality and show only the obvious and easy to explain phenomena </li></ul><ul><li>Most research models are based on the same theoretical foundations </li></ul><ul><li>As a result of this, most research models are generating the same type of information, leading to the same type of conclusions. The probability that your competitor won’t see them is nihil </li></ul><ul><li>The real valuable information is hidden behind the standard PowerPoint slides Research agencies are not structured to fully exploit this non-standard information. </li></ul>
    132. 136. Challenging the Models <ul><li>Using internal ressources ( people & data ) to go beyond the research models, to fully exploit the gathered information </li></ul><ul><li>Building our own research models: - our own definitions - our own diagnostics & norms - our own reporting formats </li></ul>
    133. 137. Challenging the Models <ul><li>At Sara Lee, Market Research developed “own models” for: - Evaluating concept potential - Evaluating volume potential of new products - Evaluating Advertising copy - … All based on an intensive rework of data , delivered by traditional research agencies, and combined with internal data </li></ul><ul><li> </li></ul>
    134. 138. Example: NPD potential (= concept screening) What is the volume potential of a new concept / product idea? <ul><li>Research model: concept test = all agencies using almost identical questionnaires (concept appeal / concept uniqueness / concept relevancy / buying intention …..) - We all know that there is a difference between “declared intentions” and “actual behavior” How do we deal with this? </li></ul>
    135. 139. Example: NPD potential (= concept screening) <ul><li>How do we deal with this?: 1) always using the same methodology 2) relating concept test scores to actual post launch/market data 3) adapting “definitions” to match test results with market performance </li></ul>
    136. 140. Example: NPD potential (= concept screening) <ul><li>How do we deal with this?: 1) always using the same methodology 2) relating concept test scores to actual post launch/market data 3) adapting “definitions” to match test results with market performance Ex: Trial potential (% of population that intends to buy the new product) according to </li></ul><ul><li>Sara Lee definition = 5 conditions - concept appeal - concept relevancy - positive price evaluation - priced buying intention - buying speed </li></ul>Only respondents who meet all conditions are considered “trialists” and used as basis for volume potential calculation
    137. 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?
    138. 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
    139. 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%
    140. 144. Conclusions
    141. 145. Datamining & Market Research <ul><li>For me, a very enriching experience </li></ul><ul><li>Not substitutes but complementary </li></ul><ul><li>Pushing each other to become better/more relevant/more solid/more credible/more actionable </li></ul>
    142. 146. THANK YOU!
    143. 147. Sara Lee Coffee & Tea
    144. 148. Closing – Wrap-up <ul><li>Discussion & slides on www.BAQMaR.be </li></ul>
    145. 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
    146. 150. Closing – Thank you <ul><li>Official Partners </li></ul>
    147. 151. Closing – Thank you <ul><li>Conference Partners </li></ul>
    148. 152. Closing – Thank you

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