BAQMaR - Conference Evening


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  • This document has the following objectives: be a conversation starter during talks with existing as well as new clients provide the opportunity to ask many different different questions to clients so that we understand their context better give a clear picture what InSites Consulting is all about, covering all relevant aspects of our business (business units, spearheads, research, consulting, ...) add credibility to our beliefs and company (via client references, the books, global reach, ...) spread the word on InSites Consulting in a consistent way across our offices and sales people act as the basis for future discussions on specific methods or solutions, going more in-depth support our positioning and brand (as it is shared on Slideshare) Please only distribute in pdf format!
  • Sales and Operations Planning involves aligning operations to follow the actual demand patterns of individual products. Many customers see SAS ‘Demand-Driven Forecasting’ playing a strategic role by quantifying demand and calculating the influence which sales and marketing have on shaping the demand. Sanoma is an international publisher responsible for the distribution of some of the most popular magazines in Belgium. They realize that they often only have one chance to sell a particular title to a particular customer in any given week. In distributing their titles among the different points of sale, they need to strike a delicate balance between minimizing excess safety stock and maximizing sales and title visibility. SAS helps Sanoma make forecasts for each magazine at each point of sale, taking into consideration seasonal trends and marketing actions (a CD with the Humo, a travel voucher with the Flair). This leads to a much more accurate prediction, which feeds into an optimized distribution plan for the whole of Belgium. As a result, more magazines are sold during the week, while less are returned at the the end of the same week. Copyright © 2010, SAS Institute Inc. All rights reserved.
  • Colruyt is one of the largest retailers in Belgium. They wanted to maximize their investment in customer analytics by not only analyzing which customers were most likely to respond to which offer, but when is the right moment to offer which coupon, to which customer. Through the SAS solution, Colruyt not only enjoys a higher utilization rate on less coupons distributed, but also provides a better service to their customers: allowing them to find the relevant coupons in a quicker and personalized way. Come listen to their presentation, directly following this one to hear more. Copyright © 2009, SAS Institute Inc. All rights reserved.
  • 28/03/11
  • 28/03/11
  • BAQMaR - Conference Evening

    1. 1. Annual Conference 2010 WIFI ibahn_conference CODE: 01A3D9
    2. 3. On speed!
    3. 4. #bqmr #mrx
    4. 5. It’s time!
    5. 11. 1
    6. 12. 2
    7. 13. 3
    8. 14. 4
    9. 15. 5
    10. 18. Annual Conference 2010 WIFI ibahn_conference CODE: 01A3D9
    11. 20. Rijn Vogelaar Introducing: The Superpromoter
    12. 21. SORRY
    13. 23. Features of a Superpomoter Enthusiasm Sharing Etnhusiasm Influence
    14. 24. Superpromoters are relevant <ul><li>… cause growth in revenue & reputation </li></ul><ul><li>… are motivators & show you your strenghts </li></ul><ul><li>… they are ideal co-creaters </li></ul><ul><li>cost savings in R&D and marketing </li></ul>
    15. 25. Features of an antipromoter <ul><li>the antipromoter … </li></ul><ul><li>is outspokenly negative </li></ul><ul><li>shares negativity </li></ul><ul><li>has influence </li></ul>the antipromoter is not your average complaining customer!
    16. 26. the ultimate battle familiar with him unfamiliar with him
    17. 27. Superpromoterblindness
    18. 28. Why are we blind? <ul><li>1. Focus on improvement </li></ul><ul><li>2. Enthusiastic customers are loyal anyway </li></ul><ul><li>3. Focus on new customers </li></ul><ul><li>4. Enthusiasme is naïve </li></ul>
    19. 29. Scared of customers classical conditioning: customer = a problem
    20. 30. recommend talk copy
    21. 32. my son Loek (3y.)
    22. 35. What are the steps to take? <ul><ul><li>Define & find </li></ul></ul><ul><ul><li>Listen & understand </li></ul></ul><ul><ul><li>Assist </li></ul></ul>
    23. 37. Brand C C C C C C I I I C C C C C C
    24. 38. Social Media Analyser (SMA)
    25. 39. Luisteren naar de Amstel superpromoter
    26. 40. De superpromoters van ...
    27. 41. Superpromoter activatie Amstel
    28. 42. Case: Listen to Superpromoters Jillz case: Interviewing Buzzers Group 1: women superpromoters Group 2: men superpromoters
    29. 43. Shut your eyes
    30. 44. Flow of enthusiasm
    31. 45. Positive energy motivates
    32. 46. Knowing what the audience likes
    33. 47. Co-creation between band & public
    34. 48. more info: <ul><li> </li></ul><ul><li>Blauw Research </li></ul><ul><li> </li></ul><ul><li>Tel: +3110 4000900 </li></ul><ul><li>Email: </li></ul>@rijn (on Twitter) english edition out: december 2010
    35. 50. Python Predictions
    36. 51. was part of everybody’s toolkit? What if ? Analytics RE!SET Analytics BAQMaR RE!SET █ December 16, 2010 █ Ghent
    37. 52. Would Adam have eaten the apple ? RE!SET Would Santa Claus need letters to know which presents to bring the kids? Would BP then have run into problems? Would we then all receive so much irrelevant information? NO! NO! NO! NO ! NO! NO! NO! NO! NO! NO ! NO! NO! NO! NO! BAQMaR RE!SET █ December 16, 2010 █ Ghent
    38. 53. from the start! RE!SET ANALYTICS BAQMaR RE!SET █ December 16, 2010 █ Ghent
    39. 54. VisionsLive
    40. 55. Why run qualitative research online? “ 83% of online users, ages 18 – 54 use social media online. How do we understand what consumers think without using the channel they use most to communicate?” (Knowledge Networks 2009) ‏
    41. 56. Don’t throw anything away! Re!Set Long-term discussions Video Concept testing Make your research more convenient for your respondents Co-Creation Adaptive/interactive discussions Iterative concept development Bigger, deeper insights In-depth interviews Low-incidence groups Simple Pre/Post tasking Mini-Communities Live online focus groups Diaries with probing/QA User Journeys International Research easy as pie! Ad Testing Usability studies Forum discussions New Product Development
    42. 57. Re!Set your boundaries Why not engage people in an environment that they feel most comfortable in – their home or office?
    43. 58. What does a non-traditional approach yield? <ul><ul><li>As an example, a Next-Gen Bulletin Board Focus Group (BBFG) is a tool that you can build structured online discussions, user diaries, vlogs, forums and blogs that have a ‘social’ feel. </li></ul></ul><ul><ul><ul><li>Run pre tasks online around focus groups, and don’t have people cramming in their homework five minutes before a session! </li></ul></ul></ul><ul><ul><ul><li>When you need that extra time for probing respondents after a group – bring it online </li></ul></ul></ul><ul><ul><ul><li>When you need honest, in-depth considered responses from respondents, without interference from other respondents. </li></ul></ul></ul><ul><ul><ul><li>When you need to track a respondents activities over several days or weeks </li></ul></ul></ul><ul><ul><ul><li>When you need to get respondents to post up and paint a picture of their own life experiences, in real-time, with videos and pictures they create themselves. </li></ul></ul></ul>
    44. 59. Re!Set Insight Discovery and Insight Sharing <ul><li>Share the experience of discovery and insight - </li></ul><ul><ul><li>Allow stakeholders to watch research unfold - But stay in control. Easier to knowledge within the organisation, get better buy-in from everyone and get new insights long after the fieldwork has ended. </li></ul></ul>
    45. 60. Re!Set Respondent Engagement <ul><li>Get rich and engaged responses </li></ul><ul><ul><li>Let respondents reply or discuss any way they want either by replying by text , annotating a whiteboard or image, recording a webcam entry, uploading their own images, arranging a collage/moodboard and even uploading videos… </li></ul></ul>
    46. 61. Re!Set and Empower your researchers Let them go wild!
    47. 62. Re!Set and empower Your respondents Let them express themselves the way they NEED to!
    48. 63. Use visual representations to illustrate and ask questions the way you want to! Re!Set your respondent engagement
    49. 64. Use visuals for deeper responses Collages and mood boards keep respondents engaged!
    50. 65. The time to Re!Set is NOW… “ Online Focus Groups - 40% growth in 3 years - 76% of UK and US consumers have Internet connectivity” (Research May 2009, Esomar 2009)‏ Get an edge now for future growth The online environment is now a part of almost everyone's ‘natural environment’ - so make sure your research is relevant!
    51. 66. Brands Re!Setting as we speak Website usability Marcomms Testing Concept Testing Ad Testing Co-Creation Online Collaboration *courtesy of Insites NV - using online focus groups Focus Groups
    52. 67. UK/EU - Ph. +44 (0)8453374484 26 York St London W1U 6PZ United Kingdom
    53. 68. Business Insight
    54. 69. conference 2010 Frank Vanden Berghen
    55. 70. High performance Predictive Analytics For EveryBody People usually see predictive analytics as something « inaccessible » and very expensive. We make Predictive Analytics as easy to use as simple OLAP reporting (and cheaper)! Forget other technique (OLAP/Segmentation) to analyze your data! Go straight to the best technique with garantueed ROI: Predictive Analytics!
    56. 71. 1-click modelling & Extreme ROI 68000 € (the first week) (not using TIMi) KDD2009: (orange competition) 1% difference in the lift ≈ 250.000 € difference in ROI (This is a pessimistic estimation)
    57. 72. 1-click modelling Best accuracy = Best ROI World-level predictive datamining competitions:
    58. 73. 1-click modelling on any database My experience: To create a predictive model: Classical Tool: From 10 to 50 computing hours TIMi: Less than 5 minutes. KDNugget poll: What was the largest database or dataset you data-mined?
    59. 74. 1-click modelling on any database « French Telecom » internal benchmark: 148 columns Using the «  In-database scoring engine » of TIMi: 60 millions rows scored in a few minutes (compatible with teradata, sqlserver, mysql, oracle, etc.) Dataset Size Modelling: Scoring: (Millions rows) Computing Time Computing Time 0.5 1.8 minutes 5 sec. 1 3.5 minutes 10 sec. 3 10.8 minutes 30 sec. 5 18.5 minutes 50 sec. 10 19.9 minutes 100 sec.
    60. 75. <ul><li>With standard tools: </li></ul><ul><li>You need to thoroughly « clean » the data. </li></ul><ul><li>99% of the time: you need to create a specialized dataset for each different model that you are building. </li></ul><ul><li>With TIMi: </li></ul><ul><li>No need to clean the data (the rule «  Garbage IN = Garbage OUT » does not apply anymore) </li></ul><ul><li>You can create ONE (very large) Repository to build ALL your models, because: </li></ul><ul><ul><li>TIMi is unlimited in the number of columns. </li></ul></ul><ul><ul><li>The accuracy of TIMi does not degrade with the number of columns. </li></ul></ul><ul><ul><li>TIMi can model very small targets (less than 0.003% of the dataset is ok.) </li></ul></ul><ul><li>CONCLUSION: No more Lengthy Data Preparation BUT TIMi requires a different way of working: . You should federate your data in a single very large Dataset (i.e. 50.000 columns is Ok for Anatella and TIMi). </li></ul>Datamining = Lengthy Data Preparation?
    61. 76. <ul><li>: not true anymore! </li></ul><ul><li>99% of the time: </li></ul><ul><ul><li>TIMi delivers predictive models with around 10 to 15 variables. </li></ul></ul><ul><ul><li>These models have higher accuracy than any model built using any « old » predictive tools. </li></ul></ul><ul><li>TIMi delivers clear Excel and Word reports that explain how these 10 to 15 variables contributes to the prediction. </li></ul><ul><li>Non-trained Business-Uers can read & understand the Excel and Word reports easily. </li></ul>Accuracy = Incomprehensible model ? AUSDM2009 datamining competition: devoted to the study of « ensemble learning »: « Team UniQ » (1st place): a few hundreds of models combined as one « ensemble learner »: AUC= 69.72%. 1 model created with TIMi: AUC= 69.24%.
    62. 77. TIMi open new doors in predictive Busines-Intelligence <ul><li>To Summarize, with the « TIMi suite »: </li></ul><ul><li>Very limited data preparation required (No data cleaning & One dataset for everything): </li></ul><ul><li>Predictive Datamining for Everybody. </li></ul><ul><li>Any Business-User can use TIMi to create 95% of the standard predictive models: </li></ul><ul><li>Extreme Accuracy ( top winners at KDD cups ), and thus Extreme ROI in a few mouse-clicks: </li></ul><ul><li>Accuracy and easily comprehensible models, at the same time: </li></ul><ul><li>100% automated. </li></ul><ul><li>«  Near-Real-Time  » datamining: (Computing time is divided by 100 to 1000 compared to other solutions: Instead of several days, we have a few minutes computing-time). </li></ul><ul><li>Unlimited data size: (for both the ETL tool: Anatella & the Predictive tool: TIMi ) </li></ul><ul><li>«  in-database  » scoring. </li></ul><ul><li>Bargain price ( th of SAS/SPSS price for 4 time more licenses) </li></ul>
    63. 78. <ul><li>Don’t take our word for it! </li></ul><ul><li>World-Level, vendor-Neutral KDD cups demonstrates the superiority of TIMi. </li></ul><ul><li>Test for free the complete « TIMi suite » yourself: Send us an e-mail: </li></ul><ul><li> [email_address] </li></ul><ul><li>The « TIMi suite » is free for educational purposes (marketing school) . </li></ul>TIMi open new doors in predictive Busines-Intelligence
    64. 79. BrainJuicer
    65. 80. BAQMaR – RE!SET What works in market research and what needs change! Month Yr
    66. 81. The Catch-22 of Market Research January 2010 But bridging the gap between the two has always been a challenge… The market research industry has rested on two approaches to gathering intelligent insights: quantitative & qualitative.
    67. 82. The Best of Both Worlds! January 2010 BrainJuicer ® has pioneered a true hybrid quali-quant methodology that measures emotion and connects the rich ‘why’s’ of qualitative diagnostics with the robust ‘what’s’ of quantitative metrics. BrainJuicer © 2006 Contempt Surprise Anger Disgust Happiness Sadness Fear Neutral
    68. 83. Bridging the Gap Provides Insights January 2010 Capturing and utilizing both qualitative and quantitative data allows researchers to tap insights that could be lost in statistical analysis or limited by the scope and reach of focus groups. I want to re!set quali-quant research!
    69. 84. Month Yr Sep 10 Carola Verschoor Managing Director, BrainJuicer Netherlands [email_address] +31 (6) 484 332 01
    70. 85. 4C
    71. 86. RE!SET... ??
    72. 87. <ul><li>VALUE OF DATA </li></ul><ul><li>POWER OF TECHNOLOGY </li></ul><ul><li>BUSINESS INTEREST & BELIEF </li></ul><ul><li>NEW SECTORS, NEW APPLICATIONS </li></ul><ul><li>MORE ANALYST, MORE RESEARCHERS, MORE BOOKS.... </li></ul>KEEP THE INTEREST ALIVE
    73. 88. <ul><li>DON’T INVESTIGATE EVERYTHING </li></ul><ul><li>KEEP RELEVANT DATA </li></ul>FOCUS ON RELEVANCE
    75. 90. Drobots
    76. 91. Drobots Reintroducing Statistics into Research What?
    77. 92. Re!Setting Reseach Reporting <ul><li>Classic Survey Research Reporting: </li></ul><ul><li>Problem? </li></ul><ul><li>Solution? </li></ul><ul><ul><li>Drobots! </li></ul></ul>What?
    78. 93. Re!Setting Reseach Reporting <ul><li>Drobots! </li></ul><ul><li>Result? </li></ul>What?
    79. 94. The Survey Analyser <ul><li>6 parts </li></ul>The Software Platform Concept Level Facet Level Question Level Data Selection Survey Information Sample Results
    80. 95. The Survey Analyser The Software Platform
    81. 96. The Survey Analyser The Software Platform
    82. 97. The Survey Analyser The Software Platform
    83. 98. The Survey Analyser The Software Platform
    84. 99. The Survey Analyser The Software Platform
    85. 100. The Survey Analyser The Software Platform
    86. 101. Extra Services <ul><li>Extra Services we perform for our Customers </li></ul><ul><li>Consulting </li></ul><ul><li>Customization </li></ul><ul><li>IT development </li></ul><ul><li>Broad & Continuous research (Barometers, Research Platforms, Evolutions: e.g. Annual Survey Comparison) </li></ul>What can we do for you?
    87. 102. <ul><li>Thank you for your attention! </li></ul>Thanks!
    88. 103. D&B
    91. 106. InSites Consulting
    92. 107. Ready for the revolution?
    93. 108. 1978: Bill Gates starts the software revolution.
    94. 109. 1997: 4 young guys start the research revolution. InSites Consulting, 1997
    95. 110. 2010: and are even more passionate about it today.
    96. 111. 100 people across 5 offices joined the revolution.
    98. 113. SAS
    99. 114. Make Data Serve Your Business
    100. 115. More Data = More Mess
    101. 116. More Data = More Potential
    102. 117. More Data = More Analysis
    103. 118. Smarter Filters FORECAST OPTIMIZE PREDICT
    104. 119. Smarter Plans
    105. 120. Smarter Actions
    106. 122. Profacts
    107. 123. SHOULD WE PUSH RES!ET ? 28/03/11
    108. 124. OF COURSE WE SHOULD! 28/03/11
    110. 127. OUR RES!ET WORKED!
    111. 128. OUR RES!ET WORKED! 230K 730K 880K 1,6M
    112. 129. Can you resist the temptation?
    113. 130. Askia
    114. 131. bytes* *= 1 zettabyte
    115. 134. /maartenbossuyt /pollepel /askiasoftware Thank you
    116. 135. RedesignMe
    117. 136. Maxim Schram CEO RedesignMe 16 december 2010 BaQMaR Conference
    118. 137. 24% of 302 large companies have some form of online community either in pilot or fully operational. “ ” - Forrester Research. May 2010
    119. 138. An additional 31% of them are planning to launch some form of community in the next 12 months. “ ” - Forrester Research. May 2010
    120. 139. RedesignMe Ideations Customizable Co-creation platforms “ Every customer deserves a community.” TM
    121. 140. In less than 5 years, every respectable company will have their own online community of customers. “ ” - Maxim Schram, Dec 2010
    122. 141. ...this requires massive adaptation from research companies! “ ”
    123. 142. <ul><li>Our vision... </li></ul><ul><li>There will be less interest for panels; </li></ul><ul><li>There will be less interest for qualitative research; </li></ul>
    124. 143. <ul><li>Instead... </li></ul><ul><li>People will love to talk about with brands. </li></ul><ul><li>MRs will help interpret company owned community data. </li></ul><ul><li>MRs will focus on consultancy & implementation of results. </li></ul>
    125. 144. The new Market Researcher is a Community Manager and Interpretation Artist . “ ”
    126. 146. Annual Conference 2010 WIFI ibahn_conference CODE: 01A3D9
    127. 148. Increasing Marketing Relevance through Personalized Targeting Geert Verstraeten December 16, 2010 █ Ghent
    128. 149. Overtoom International <ul><li>Business-to-Business distance selling company (Market leader) </li></ul><ul><li>Penetration rate in Belgium: </li></ul><ul><li>7% (  850.000 companies) </li></ul><ul><li>Database customers: </li></ul><ul><li>85.000 companies / 240.000 contacts </li></ul><ul><li>Database products : </li></ul><ul><li>40.000 references </li></ul>
    129. 150. Overtoom International <ul><li>Marketing Channels </li></ul>Yearly Catalogue: Office Supplies Yearly Catalogue: Warehouse Supplies Monthly Leaflet: Promotional Brochure
    130. 151. Overtoom International <ul><li>Marketing Channels </li></ul>Company Website
    131. 152. <ul><li>Challenges </li></ul>Overtoom International Reaching the right Customer By offering the right Product(s) Through the most appropriate Marketing Channel
    132. 153. Python Predictions <ul><li>Core business: Customer Intelligence </li></ul>
    133. 154. Python Predictions <ul><li>Core business: Customer Intelligence </li></ul><ul><li>Based in Brussels </li></ul><ul><li>Since 2006 </li></ul><ul><li>Team </li></ul><ul><li>Customers: </li></ul>
    134. 155. Customer Intelligence Benefits Marketing Accountability Marketing Relevance
    135. 156. <ul><li>How it all started… </li></ul>Personalized Targeting Through the most appropriate Marketing Channel By offering the right Product(s) Reaching the right Customer
    136. 157. <ul><li>Increase targeting efficiency of current marketing actions to existing clients </li></ul><ul><ul><li>Yearly catalogues </li></ul></ul><ul><ul><li>Monthly leaflets </li></ul></ul><ul><li>Increase response and turnover </li></ul>Reaching the right customer <ul><li>Segmentation Predictive Model </li></ul>
    137. 158. <ul><li>Segmentation is exploratory </li></ul><ul><li>Prediction is discriminatory </li></ul>Reaching the right customer Prediction Segmentation Prediction
    138. 159. Reaching the right customer <ul><li>Data </li></ul><ul><ul><li>4 years of historical data (3 MIO observations) </li></ul></ul><ul><ul><li>850 variables </li></ul></ul><ul><li>Model Building and Assessment </li></ul><ul><ul><li>Data partitioning (train, selection, validation) </li></ul></ul><ul><ul><ul><li>Objective assessment </li></ul></ul></ul><ul><ul><li>Split models for each action type </li></ul></ul><ul><ul><li>Logit modeling </li></ul></ul><ul><ul><li>Variable selection </li></ul></ul><ul><ul><ul><li>Reduce complexity </li></ul></ul></ul><ul><ul><ul><li>Avoid overfitting </li></ul></ul></ul><ul><li>Software </li></ul>
    139. 160. Reaching the right customer <ul><li>Turnover during field test </li></ul>
    140. 161. Reaching the right customer <ul><li>Turnover during field test </li></ul><ul><ul><li>Short term </li></ul></ul><ul><ul><ul><li>Reduction target size: -10% </li></ul></ul></ul><ul><ul><ul><li>Turnover: +28% </li></ul></ul></ul><ul><ul><li>Long term </li></ul></ul><ul><ul><ul><li>Reduction target size: -10% </li></ul></ul></ul><ul><ul><ul><li>Turnover : +10% (average) </li></ul></ul></ul>
    141. 162. <ul><li>The plot thickens… </li></ul>Personalized Targeting Through the most appropriate Marketing Channel By offering the right Product(s) Reaching the right Customer
    142. 163. Customized Offers Well known examples: Google
    143. 164. Customized Offers Well known examples: Amazon
    144. 165. Customized Offers Motivation: the paradox of choice 6 jams 24 jams 40% stops 60% stops 30% purchased 3% purchased S. Iyengar & M. Lepper, When Choice is Demotivating: Can One Desire Too Much of a Good Thing? Journal of Personality and Social Psychology, 2000, Vol. 79, No. 6, 995-1006 Source
    145. 166. Customized Offers Motivation: Overtoom facts All categories are purchased to a certain degree Most customers purchase in a limited number of categories
    146. 167. Customized Offers Solutions Market Basket Analysis Response Modeling Similarity Modeling
    147. 168. <ul><li>Method </li></ul>Customized Offers Response Models Product Model Customer X Best offer A A A C B B B C C C <ul><li>Company ‘O’ has 3 products </li></ul><ul><li>3 propensity-to-buy models are built </li></ul><ul><li>Customer X is scored on each of these models </li></ul><ul><li>The product with the highest probability-to-buy/expected return </li></ul><ul><li>is offered to the customer </li></ul>
    148. 169. <ul><li>Goal: 400 propensity models </li></ul>Customized Offers Response Models Product Taxonomy 40.000 Art. 400 Cat. Models
    149. 170. <ul><li>Goal: 400 propensity models </li></ul>Customized Offers Response Models <ul><li>Data: </li></ul><ul><ul><li>4 year customer information  1296 inputs </li></ul></ul><ul><ul><li>1 year purchase data to define targets </li></ul></ul>2007 2003-2006
    150. 171. <ul><li>Results: </li></ul><ul><ul><li>288 useful models (72%) </li></ul></ul>Customized Offers Response Models
    151. 172. Customized Offers Initial format (April 2009)
    152. 173. Customized Offers Extended Format
    153. 174. <ul><li>Method </li></ul>Customized Offers Similarity Model Customer X Customers Products Best offer 1 A C 2 X B 3 C <ul><li>We compare any customer with all other customers </li></ul><ul><li>Company ‘O’ has 3 customers </li></ul><ul><li>Company ‘O’ has 3 products </li></ul><ul><li>Based on the purchases of the most similar customers, we offer the best possible suggestion to each customer </li></ul><ul><li>Customers have bought products </li></ul>
    154. 175. <ul><li>Method </li></ul>Customized Offers Similarity Model Customer X Customers Products Best offer 1 A C 2 X B 3 C <ul><li>Advantages </li></ul><ul><li>Client-based vs product-based </li></ul><ul><li>1 model, simple data structure </li></ul><ul><li>Inclusion of all products, categories </li></ul><ul><li>Development time </li></ul><ul><li>Comparison with existing models possible </li></ul><ul><ul><li>Performance </li></ul></ul><ul><ul><li>Variety </li></ul></ul>
    155. 176. Results <ul><li>Evaluation: </li></ul><ul><ul><li>Conversion rate </li></ul></ul><ul><ul><li> Percentage of buyers who purchased the specific offer </li></ul></ul><ul><ul><li>Success Rate </li></ul></ul><ul><ul><li> Percentage of buyers who purchased at least 1 of the offers </li></ul></ul><ul><ul><li>Variety index </li></ul></ul><ul><ul><li>Indicator of the global variety of the offers across all customers </li></ul></ul>
    156. 177. <ul><li>Summary – 10 recommendations </li></ul>Results - development Success Rate Variety Index
    157. 178. Results - development +14.6% +8.6% <ul><li>Summary </li></ul>Similarity Modeling Response Modeling Most Popular Product
    158. 179. <ul><li>Conversion rate based on rank of the offer: </li></ul><ul><ul><li>Extended format (14 customized offers) </li></ul></ul>Results - infield 300 % more relevant
    159. 180. Validation Comparison of 5 Response Models with 1 Similarity Model +28% +25% -50%
    160. 181. <ul><li>Stakeholders </li></ul>Implementation Purchasing Inventory Management Marketing Management Digital Printing Partner Communication Partner Customer Intelligence Partners General Management
    161. 182. <ul><li>The future… </li></ul>Personalized Targeting Through the most appropriate Marketing Channel Reaching the right Customer By offering the right Product(s)
    162. 183. <ul><li>SAS Success Story (coming soon) </li></ul><ul><li>Visit our websites: </li></ul><ul><li>Contact information: </li></ul> [email_address]
    163. 184. Annual Conference 2010 WIFI ibahn_conference CODE: 01A3D9
    164. 186. Annual Conference 2010 WIFI ibahn_conference CODE: 01A3D9
    165. 187. SMART Award 2010
    166. 188. Winner:
    167. 189. Winner: Vera Pringels
    168. 190. #bqmr #mrx
    169. 196. Thanks, Partners!
    170. 198. Annual Conference 2010