Hands on Consumer Driven Product Optimization, 3 one-day workshops, NYC (21-23 March 2018)
The session can be attended either at MicroTek Training Facilities, 180 Maiden Lane, New York 10038 or using a virtual training room from your own office.
Hands on Consumer Driven Product Optimization, 3 one-day workshops
Day 1 is designed to take you through the key methods that we use in consumer science and explain how they work and how they are applied to consumer data. Emphasis is given to the practical decision making that is made after each analysis. Participants will apply the methods to real life data using XLSTAT routines and will be guided by the written solutions that are a unique feature of Hal MacFie Training. These solutions give you the possibility to pick up the notes after 6 months or a year and remind yourself how a technique works and then apply it to your own data. Day 2 is a Segmentation Masterclass: This will give attendees a unique perspective: understanding and experience with the three main segmentation algorithms. Practical guidance on selecting the correct algorithm for the task, selecting the optimum cluster set, testing for stability will be given. The key question with clusters is to identify any predominant characteristics of the membership and we will show a variety of approaches for this. Day 3 afternoon Concept optimisation and Product matching. Many concept trials ask multiple questions including sensory about one or more concepts. How can we examine segmentation in the response to a single concept? Which concepts do we select for further development?
Course Outline
Day 1 Exploratory and Analytical methods for Consumer Science
9.00-9.15 Introductions
9.15-10.45 Exploratory- Principal Component Analysis
- Definition, graphical explanation, scaling
- Sample maps, correlation maps, biplots. Interpretation.
- XLSTAT exercises
- Rotating for Interpretability
10.45-11.30 Exploratory- Correspondence Analysis
- Frequency data applications
- Theory and interpretation
- CATA
- XLSTAT analysis
11.30-12.30 Exploratory- Cluster Analysis
- Distance and similarity measures
- Clustering algorithms, dendrogram, defining clusters.
- XLSTAT exercises
12.30 -13.30 Lunch
13.30-14.30 Analytical- Quadratic regression and simple Preference mapping
- Ideal point model, quadratic response, surface response diagrams
- Picking an optimum
- Multi-response problems
- XLSTAT exercise
14.30-15.30 Analytical- Partial Least Squares
- Concept, graphical explanation, cross validation, predictive modelling.
- Liking to sensory, sensory to instrumental.
- Reverse PLS to identify target product.
- XLSTAT exercises
15.45-17.00 Analytical- Path Analysis – Simple and PATHPLSmodelling
- Rotated factor modelling
- XLSTAT exercises
- PATH PLS theory and demonstration
Day 2 Segmentation, Mapping and Product Optimisation
9.00-11.00 Segmentation Masterclass:
- AHC versus Kmeans versus Latent Class Analysis
- Choosing the method. How many clusters?
- PCA versus CVA plotting strategies
- XLSTAT exercises
- Testing the Stability of the solution. How many non-discriminators?
- XLSTAT exercises
11.00-12.00 Demographics, Psychographics and Segments.
- ANOVA approach
- Chi-square, Correspondence and CHAID approach
12.00-13.00 Preference Mapping using the XLSTAT PREFMAP module
- Ideas behind the method
- Different strategies (overall versus segment means or individuals)
- Contour plotting
- Interpretation of output, decision making
- Exercises in XLSTAT
13.00-14.00 Lunch
14.00-15.00 What Sensory Properties will the Desired Product Have?
- Using the preference maps to identify optimal products
- Estimating their sensory properties using reverse regression
- XLSTAT exercise
15.00 -16.00 Using Open ended comments in Preference mapping:
- Role of open ended comments, Quantification
- Alternative Preference Map basis
- XLSTAT exercises
16.00-17.00 Portfolio optimisation and other approaches:
- Selecting products for global markets
- Internal versus External versus Probabilistic models
- A practical approach
- XLSTAT Exercise
Day 3 am What can you do with 3 products or less?
9.00-9.45 JAR Scales and Penalty Analysis
- Analysis of JAR scale data (Friedman v ANOVA)
- Correlation with liking, XLSTAT Penalty Analysis routine
- Interpretation and decision making
- XLSTAT Exercises
9.45-10.45
- Ideal Point Profiling
- Ideal point scoring, Radar Plotting, Analysis, Product Optimisation
- XLSTAT exercise
11.00-12.00
- CATA Data and Kano Impact Analysis
- CATA data, significance testing, Kano approach.
- XLSTAT CATA routine
12.00-13.00 Lunch
Day 3 pm Concept Optimisation and Product matching
13.00-14.30 Concept and single product trials and analyses
- Data set up and theory
- Factor Analysis and Segmentation analysis of concept question batteries and interpretation
- XLSTAT exercises
14.45-15.45 Matching Product to concept
- PLS of Fit to concept on sensory data
- Expectation, Blind and Informed testing, design, conduct and analysis
- Multiple Factor Analysis theory and XLSTAT Exercises
- Context, Virtual reality trials and analysis
16.00-17.00 Conceptualisation theory, Best-Worst scaling and Analysis
- Matching sensory and emotion to concept.
- XLSTAT exercises
17.00 Close
Prix
Dates
Début :
Fin :
Langue
Anglais
Lieu
New York United StatesProfils des formateurs
Anne Hasted
Anne Hasted est consultante senior chez Qi Statistics Ltd, un cabinet de consultants basé au Royaume-Uni qui offre un portefeuille complet de soutien statistique à travers la formation, l'analyse de données, l'accompagnement de projet et le développement de logiciels. Elle est statisticien agréé avec plus de 25 années d'expérience de conseil dans un large éventail d'entreprises. Elle a animé des ateliers de formation dans le monde entier et est reconnu pour fournir des sessions de formation "conviviale".
