14. Eye Tracking Research Added Value for Traditional Techniques? Ludovic Depoortere, Managing Director Rogil Research Quanti , Quali , Eye-tracking
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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
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
34. Would you get this result out a quanti/quali? MEN WOMEN
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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% > < > > < < >
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
82. SOLIDPartners Performance Management Business Intelligence DataWarehouse 1300+ Consultants International Coverage Leader on the market Independent Player Who are we ?
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
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
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
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
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%
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.