An Approach for External Preference Mapping Improvement by Denoising Consumer Rating Data

Ibtihel Rebhi,Dhafer Malouche

doi:10.14569/ijacsa.2017.081266

Abstract

In this study, denoising data was advocated in sensory analysis field to remove the existing noise in consumer rating data before processing to External Preference Mapping (EPM). This technique is a data visualization used to understand consumers sensory profiles by relating their preferences towards products to external information about sensory characteristics of the perceived products. The output is a perceptual map which visualizes the optimal products and aspects that maximize consumers likings. Hence, EPM is considered as a decision tool to support the development or improvement of products and respond to market requirements. In fact, the stability of the map is affected by the high variability of judgments that make consumer rating data very noisy. This may lead to mismatch between products features and consumers’ preferences then distorted results and wrong decisions. To remove the existing noise, the use of some filtering methods is proposed. Regularized Principal Component Analysis (RPCA) and Stein’s Unbiased Risk Estimate (SURE), based respectively on hard and soft thresholding rules, were applied to consumer rating data to separate the signal to noise and maintain only useful information about the given liking scores. As a way to compare the EPM obtained from each strategy, a sampling process was conducted to randomly select samples from noisy and cleaned data, then perform their corresponding EPM. The stability of the obtained maps was evaluated using an indicator that computes and compares distances between them before and after denoising. The effectiveness of this methodology was evaluated by a simulation study and a potential application was shown on real dataset. Results show that recorded distances after denoising are lower than those before in almost cases for both RPCA and SURE. However, RPCA outperforms SURE. The corresponding map is made more stable where level lines are seen smoothed and products are better located on liking zones. Hence, noise removal reduces variability in data and brings closer preferences which improves the quality of the visualized map.

Highlights

In marketing research, listening to the voice of consumers has become a fundamental strategy to make good decisions about the development or improvement of products
Under the fixed effect model of Principal Component Analysis (PCA) [14], Y data is generated as a fixed structure of low rank that corresponds to signal, corrupted by noise
The way to generate consumer rating data was inspired from data structure given in model 1

Summary

Introduction

In marketing research, listening to the voice of consumers has become a fundamental strategy to make good decisions about the development or improvement of products. The method is to conduct a survey on a sample of consumers asking them to evaluate products by rating their liking This data is known to be called hedonic data or consumer rating data. The 9point-hedonic-scale defined by David Peryam and colleagues [1] is often used: the consumers rate products according to a score ranging from 1 to 9 such that 1 indicates that the consumer extremely dislikes the product and 9 indicates that he extremely likes it. This hedonic scale was used for rating various products such as household products, personal care products, cosmetics, etc. In case of food products, descriptive data can be collected from a set of measures of physico-chemical components through successive analyses in chemiometrics laboratories

Methods

Results

Conclusion