Hands on Consumer Driven Product Optimization, Chicago April 10-12 2019

Designed to give you the knowledge, practice and tools to analyse and interpret consumer test data and to optimise the sensory properties of food, beverages and personal care products.  Gain confidence in analysis of your data using XLSTAT, which method to use when and which outputs to present, using our exercises with written solutions and interpretations.

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

Preis

Daten

Beginn:

Ende:

Sprache

Englisch

Ort

Chicago United States

Profil des Schulungsleiters


Anne Hasted

Anne ist Seniorberater bei Qi Statistics Ltd, einer im Vereinigten Königreich basierten Beratungsunternehmen mit einem vollständigen Portfolio an statistischen Dienstleistungen mittels Schulung, Datenanalyse, Projektberatung und Softwareentwicklung. Sie ist eine bewanderte Statistikerin mit über 25 Jahren und Beratungserfahrung bei einer großen Zahl an Unternehmen. Sie veranstaltet weltweit Schulungen und ist anerkannt für „benutzerfreundliche“ Lehrmethoden und Veranstaltungen.

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