Segmentation of Chinese consumer preference for wine extrinsic attributes based on stratification and weighted clustering algorithm
Contact us if these videos are not relevant.
Contact us if these videos are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new method to segment Chinese wine consumers based on their preferences for extrinsic attributes. It identifies six consumer types across two layers, aiding targeted marketing strategies.
Area Of Science
- Consumer Behavior
- Marketing Science
- Data Mining
Background
- Chinese consumers often prioritize extrinsic wine attributes over sensory ones due to unfamiliarity.
- Understanding consumer preferences for extrinsic attributes is vital for the wine industry.
Purpose Of The Study
- To develop and validate a novel consumer segmentation method for the Chinese wine market.
- To identify distinct consumer segments based on customer value and attribute preferences.
Main Methods
- A stratified and weighted clustering algorithm was employed, utilizing data from an online survey (N=3179).
- The study adapted the Recency, Frequency, Monetary (RFM) model into an Amount, Frequency, Recency (AFR) model for customer value segmentation.
- Factor analysis was used to assign weights to clustering indicators, mitigating issues of multicollinearity and unequal weighting.
Main Results
- The proposed method successfully segmented wine consumers into two layers (potential and mature) and six distinct categories.
- The potential layer includes product-oriented, amorphous, and cheap-fine types.
- The mature layer comprises rational, reputation-price, and random types.
Conclusions
- The developed segmentation method is simple, efficient, and precise, offering actionable insights for wine producers and distributors.
- This research provides a valuable framework for targeted production and marketing decisions in the wine sector.
- The methodology can be applied to consumer segmentation in broader food and beverage markets.
Contact us if these videos are not relevant.
Contact us if these videos are not relevant.
Related Concept Videos
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
This text is adapted from OpenStax, Psychology. OpenStax...
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...