BAQMaR - Conference Evening

Annual Conference 2010 WIFI ibahn_conference CODE: 01A3D9

On speed!

#bqmr #mrx

It’s time!

Rijn Vogelaar Introducing: The Superpromoter

Features of a Superpomoter Enthusiasm Sharing Etnhusiasm Influence

Superpromoters are relevant
… cause growth in revenue & reputation
… are motivators & show you your strenghts
… they are ideal co-creaters
cost savings in R&D and marketing

Features of an antipromoter
the antipromoter …
is outspokenly negative
shares negativity
has influence
the antipromoter is not your average complaining customer!

the ultimate battle familiar with him unfamiliar with him

Superpromoterblindness

Why are we blind?
1. Focus on improvement
2. Enthusiastic customers are loyal anyway
3. Focus on new customers
4. Enthusiasme is naïve

Scared of customers classical conditioning: customer = a problem

recommend talk copy

my son Loek (3y.)

What are the steps to take?
Define & find
Listen & understand
Assist

Brand C C C C C C I I I C C C C C C

Social Media Analyser (SMA)

Luisteren naar de Amstel superpromoter

De superpromoters van ...

Superpromoter activatie Amstel

Case: Listen to Superpromoters Jillz case: Interviewing Buzzers Group 1: women superpromoters Group 2: men superpromoters

Shut your eyes

Flow of enthusiasm

Positive energy motivates

Knowing what the audience likes

Co-creation between band & public

more info:
www.superpromoters.nl
Blauw Research
www.blauw.com
Tel: +3110 4000900
Email: rijn.vogelaar@blauw.com
@rijn (on Twitter) english edition out: december 2010

Python Predictions

was part of everybody’s toolkit? What if ? Analytics RE!SET Analytics BAQMaR RE!SET █ December 16, 2010 █ Ghent

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

from the start! RE!SET ANALYTICS BAQMaR RE!SET █ December 16, 2010 █ Ghent

VisionsLive

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) ‏

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

Re!Set your boundaries Why not engage people in an environment that they feel most comfortable in – their home or office?

What does a non-traditional approach yield?
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.
Run pre tasks online around focus groups, and don’t have people cramming in their homework five minutes before a session!
When you need that extra time for probing respondents after a group – bring it online
When you need honest, in-depth considered responses from respondents, without interference from other respondents.
When you need to track a respondents activities over several days or weeks
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.

Re!Set Insight Discovery and Insight Sharing
Share the experience of discovery and insight -
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.

Re!Set Respondent Engagement
Get rich and engaged responses
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…

Re!Set and Empower your researchers Let them go wild!

Re!Set and empower Your respondents Let them express themselves the way they NEED to!

Use visual representations to illustrate and ask questions the way you want to! Re!Set your respondent engagement

Use visuals for deeper responses Collages and mood boards keep respondents engaged!

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!

Brands Re!Setting as we speak Website usability Marcomms Testing Concept Testing Ad Testing Co-Creation Online Collaboration *courtesy of Insites NV - using Visionslive.com online focus groups Focus Groups

UK/EU - andreiko@visionslive.com Ph. +44 (0)8453374484 26 York St London W1U 6PZ United Kingdom www.visionslive.com

Business Insight

conference 2010 Frank Vanden Berghen

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!

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)

1-click modelling Best accuracy = Best ROI World-level predictive datamining competitions:

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?

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.

With standard tools:
You need to thoroughly « clean » the data.
99% of the time: you need to create a specialized dataset for each different model that you are building.
With TIMi:
No need to clean the data (the rule « Garbage IN = Garbage OUT » does not apply anymore)
You can create ONE (very large) Repository to build ALL your models, because:
TIMi is unlimited in the number of columns.
The accuracy of TIMi does not degrade with the number of columns.
TIMi can model very small targets (less than 0.003% of the dataset is ok.)
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).
Datamining = Lengthy Data Preparation?

: not true anymore!
99% of the time:
TIMi delivers predictive models with around 10 to 15 variables.
These models have higher accuracy than any model built using any « old » predictive tools.
TIMi delivers clear Excel and Word reports that explain how these 10 to 15 variables contributes to the prediction.
Non-trained Business-Uers can read & understand the Excel and Word reports easily.
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%.

TIMi open new doors in predictive Busines-Intelligence
To Summarize, with the « TIMi suite »:
Very limited data preparation required (No data cleaning & One dataset for everything):
Predictive Datamining for Everybody.
Any Business-User can use TIMi to create 95% of the standard predictive models:
Extreme Accuracy ( top winners at KDD cups ), and thus Extreme ROI in a few mouse-clicks:
Accuracy and easily comprehensible models, at the same time:
100% automated.
« 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).
Unlimited data size: (for both the ETL tool: Anatella & the Predictive tool: TIMi )
« in-database » scoring.
Bargain price ( th of SAS/SPSS price for 4 time more licenses)

Don’t take our word for it!
World-Level, vendor-Neutral KDD cups demonstrates the superiority of TIMi.
Test for free the complete « TIMi suite » yourself: Send us an e-mail:
[email_address]
The « TIMi suite » is free for educational purposes (marketing school) .
TIMi open new doors in predictive Busines-Intelligence

BrainJuicer

BAQMaR – RE!SET What works in market research and what needs change! Month Yr

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.

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

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!

Month Yr Sep 10 Carola Verschoor Managing Director, BrainJuicer Netherlands [email_address] +31 (6) 484 332 01

RE!SET... ??

VALUE OF DATA
POWER OF TECHNOLOGY
BUSINESS INTEREST & BELIEF
NEW SECTORS, NEW APPLICATIONS
MORE ANALYST, MORE RESEARCHERS, MORE BOOKS....
KEEP THE INTEREST ALIVE

DON’T INVESTIGATE EVERYTHING
KEEP RELEVANT DATA
FOCUS ON RELEVANCE

WANT TO JOIN US? WWW.4CCONSULTING.COM

Drobots

Drobots Reintroducing Statistics into Research What?

Re!Setting Reseach Reporting
Classic Survey Research Reporting:
Problem?
Solution?
Drobots!
What?

Re!Setting Reseach Reporting
Drobots!
Result?
What?

The Survey Analyser
6 parts
The Software Platform Concept Level Facet Level Question Level Data Selection Survey Information Sample Results

The Survey Analyser The Software Platform

Extra Services
Extra Services we perform for our Customers
Consulting
Customization
IT development
Broad & Continuous research (Barometers, Research Platforms, Evolutions: e.g. Annual Survey Comparison)
What can we do for you?

Thank you for your attention!
Thanks!

DECIDING WITH CONFIDENCE

InSites Consulting

Ready for the revolution?

1978: Bill Gates starts the software revolution.

1997: 4 young guys start the research revolution. InSites Consulting, 1997

2010: and are even more passionate about it today.

100 people across 5 offices joined the revolution.

1) CONNECT WITH THE MULTI-FACETED CONSUMER 2) KEEP AN ONGOING DIALOGUE WITH CONSUMERS 3) BRING THEM INTO YOUR REALITY Taking research forward is serious business.

Make Data Serve Your Business

More Data = More Mess

More Data = More Potential

More Data = More Analysis

Smarter Filters FORECAST OPTIMIZE PREDICT

Smarter Plans

Smarter Actions

Profacts

SHOULD WE PUSH RES!ET ? 28/03/11

OF COURSE WE SHOULD! 28/03/11

ON THE GO EVALUATION GO TO RESTAURANT HAVE A NICE DINNER EVALUATE RESTAURANT How would you evaluate this resto?

OUR RES!ET WORKED!

OUR RES!ET WORKED! 230K 730K 880K 1,6M

Can you resist the temptation?

1.000.000.000.000.000.000.000 bytes* *= 1 zettabyte

twitter.com /maartenbossuyt /pollepel /askiasoftware askia.com Thank you

RedesignMe

Maxim Schram CEO RedesignMe 16 december 2010 BaQMaR Conference

24% of 302 large companies have some form of online community either in pilot or fully operational. “ ” - Forrester Research. May 2010

An additional 31% of them are planning to launch some form of community in the next 12 months. “ ” - Forrester Research. May 2010

RedesignMe Ideations Customizable Co-creation platforms “ Every customer deserves a community.” TM

In less than 5 years, every respectable company will have their own online community of customers. “ ” - Maxim Schram, Dec 2010

...this requires massive adaptation from research companies! “ ”

Our vision...
There will be less interest for panels;
There will be less interest for qualitative research;

Instead...
People will love to talk about with brands.
MRs will help interpret company owned community data.
MRs will focus on consultancy & implementation of results.

The new Market Researcher is a Community Manager and Interpretation Artist . “ ”

Increasing Marketing Relevance through Personalized Targeting Geert Verstraeten December 16, 2010 █ Ghent

Overtoom International
Business-to-Business distance selling company (Market leader)
Penetration rate in Belgium:
7% (  850.000 companies)
Database customers:
85.000 companies / 240.000 contacts
Database products :
40.000 references

Marketing Channels
Yearly Catalogue: Office Supplies Yearly Catalogue: Warehouse Supplies Monthly Leaflet: Promotional Brochure

Marketing Channels
Company Website www.overtoom.be

Challenges
Overtoom International Reaching the right Customer By offering the right Product(s) Through the most appropriate Marketing Channel

Python Predictions
Core business: Customer Intelligence

Python Predictions
Core business: Customer Intelligence
Based in Brussels
Since 2006
Team
Customers:

Customer Intelligence Benefits Marketing Accountability Marketing Relevance

How it all started…
Personalized Targeting Through the most appropriate Marketing Channel By offering the right Product(s) Reaching the right Customer

Increase targeting efficiency of current marketing actions to existing clients
Yearly catalogues
Monthly leaflets
Increase response and turnover
Reaching the right customer
Segmentation Predictive Model

Segmentation is exploratory
Prediction is discriminatory
Reaching the right customer Prediction Segmentation Prediction

Data
4 years of historical data (3 MIO observations)
850 variables
Model Building and Assessment
Data partitioning (train, selection, validation)
Objective assessment
Split models for each action type
Logit modeling
Variable selection
Reduce complexity
Avoid overfitting
Software

Turnover during field test

Turnover during field test
Short term
Reduction target size: -10%
Turnover: +28%
Long term
Reduction target size: -10%
Turnover : +10% (average)

The plot thickens…
Personalized Targeting Through the most appropriate Marketing Channel By offering the right Product(s) Reaching the right Customer

Customized Offers Well known examples: Google

Customized Offers Well known examples: Amazon

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

Customized Offers Motivation: Overtoom facts All categories are purchased to a certain degree Most customers purchase in a limited number of categories

Customized Offers Solutions Market Basket Analysis Response Modeling Similarity Modeling

Method
Customized Offers Response Models Product Model Customer X Best offer A A A C B B B C C C
Company ‘O’ has 3 products
3 propensity-to-buy models are built
Customer X is scored on each of these models
The product with the highest probability-to-buy/expected return
is offered to the customer

Goal: 400 propensity models
Customized Offers Response Models Product Taxonomy 40.000 Art. 400 Cat. Models

Goal: 400 propensity models
Customized Offers Response Models
Data:
4 year customer information  1296 inputs
1 year purchase data to define targets
2007 2003-2006

Results:
288 useful models (72%)
Customized Offers Response Models

Customized Offers Initial format (April 2009)

Customized Offers Extended Format

Method
Customized Offers Similarity Model Customer X Customers Products Best offer 1 A C 2 X B 3 C
We compare any customer with all other customers
Company ‘O’ has 3 customers
Company ‘O’ has 3 products
Based on the purchases of the most similar customers, we offer the best possible suggestion to each customer
Customers have bought products

Method
Customized Offers Similarity Model Customer X Customers Products Best offer 1 A C 2 X B 3 C
Advantages
Client-based vs product-based
1 model, simple data structure
Inclusion of all products, categories
Development time
Comparison with existing models possible
Performance
Variety

Results
Evaluation:
Conversion rate
Percentage of buyers who purchased the specific offer
Success Rate
Percentage of buyers who purchased at least 1 of the offers
Variety index
Indicator of the global variety of the offers across all customers

Summary – 10 recommendations
Results - development Success Rate Variety Index

Results - development +14.6% +8.6%
Summary
Similarity Modeling Response Modeling Most Popular Product

Conversion rate based on rank of the offer:
Extended format (14 customized offers)
Results - infield 300 % more relevant

Validation Comparison of 5 Response Models with 1 Similarity Model +28% +25% -50%

Stakeholders
Implementation Purchasing Inventory Management Marketing Management Digital Printing Partner Communication Partner Customer Intelligence Partners General Management

The future…
Personalized Targeting Through the most appropriate Marketing Channel Reaching the right Customer By offering the right Product(s)

SAS Success Story (coming soon)
Visit our websites:
Contact information:
www.overtoom.be www.pythonpredictions.com [email_address]

SMART Award 2010

Winner:

Winner: Vera Pringels

#bqmr #mrx

Thanks, Partners!

Annual Conference 2010

BAQMaR - Conference Evening

by BAQMaR on Mar 28, 2011

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