EPQ & HMM Research | Consumer Behavior Analysis

The Effect of Emotions on Consumer Purchasing Behaviour

Grade A* (Distinction) Stochastic Analysis Hybrid HMM-LR Model Predictive Modeling

This dissertation quantifies the "sensory marketing" phenomenon by decoding the temporal dynamics of consumer sentiment. By integrating Hidden Markov Models (HMM) to capture latent state transitions and Linear Regression for quantitative validation, the project provides a robust framework for predicting purchase intent within high-involvement market segments.

Technical Approach Key Findings

Latent State Transition Dynamics

HMM State Transition Logic

*Mapping non-observable emotional hidden states (categorized via Plutchik’s Wheel) to categorical purchase outcomes through a Markovian process.*

Hybrid Modeling Architecture

Quantitative Regression Analysis

Impact Weighting: Determines exactly how much an increase in specific emotions, like Surprise, affects the final purchase probability.
Sample Size Validation: Utilizes power analysis for model validation to ensure statistical significance of regression coefficients.
Segmental Sensitivity: Evaluates how consumers with differing income levels respond to various marketing stimuli.

Hidden Markov Model (HMM)

Transition Probabilities: Measures the likelihood of moving between emotional states.
Emission Matrix: Bridges latent emotional states to observable data points.
EM Algorithm: References Expectation-Maximization to refine model parameters.

HMM Diagram Linear Model

Empirical Research Insights

High-Conversion Catalysts

Transitions into Happy and Expectancy states demonstrate the strongest statistical correlation.

Negative State Mitigation

Curiosity is a critical mediator that can neutralize the deterrent effects of Anger.

Temporal Trajectory

The sequence of emotions is a more robust predictor of behavior than static measurements.

Impulse State Triggers

Unexpected rewards disrupt logical defense mechanisms, triggering impulse states.

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