Applying Neuroscience to Marketing: A Data Model for Consumer Insights
Description
This neuroscience-inspired data model aims to simulate and analyze consumer interactions with advertisements by focusing on four key metrics: Attention Span, Emotional Engagement, Memory Recall, and Purchase Decision Likelihood. Attention Span: Captures the duration and intensity of a consumer's focus on the ad, represented as a score from 0 to 100. This metric indicates the initial engagement level of the audience. Emotional Engagement: Reflects the depth of emotional connection the consumer feels in response to the ad, scored from 0 to 100. Emotional engagement is crucial for creating lasting impressions and motivating further action. Memory Recall: Measures the likelihood that a consumer will remember the ad content after viewing it, scored from 0 to 100. A high memory recall score suggests that the advertisement is memorable and reinforces brand awareness. Purchase Decision Likelihood: Represents the probability of a consumer making a purchase after viewing the ad. Calculated as a function of attention span, emotional engagement, and memory recall, this metric provides a holistic view of how likely the ad will lead to a purchase decision. Each observation in the dataset corresponds to a unique consumer's interaction with the ad. The Python code generates a synthetic dataset of 1,000 consumers, analyzing the impact of each metric on purchase decision likelihood through visualization. The correlations and patterns identified can guide marketing strategies, helping prioritize aspects of advertisements that improve consumer engagement and drive conversions.
Files
Steps to reproduce
import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt from scipy.stats import linregress np.random.seed(42) data_marketing_neuro = pd.DataFrame({ 'attention_span': np.random.normal(70, 15, n), # Attention span out of 100 'emotional_engagement': np.random.normal(65, 20, n), # Emotional engagement score out of 100 'memory_recall': np.random.normal(60, 10, n), # Memory recall score out of 100 }) data_marketing_neuro['purchase_decision_likelihood'] = ( 0.3 * data_marketing_neuro['attention_span'] + 0.4 * data_marketing_neuro['emotional_engagement'] + 0.3 * data_marketing_neuro['memory_recall'] + np.random.normal(0, 5, n) ) / 100 # Normalize to get values between 0 and 1 data_marketing_neuro['purchase_decision_likelihood'] = data_marketing_neuro['purchase_decision_likelihood'].clip(0, 1) print(data_marketing_neuro.head()) # Visualizing the data relationships plt.figure(figsize=(10, 8)) sns.heatmap(data_marketing_neuro.corr(), annot=True, cmap="YlGnBu", fmt=".2f") plt.title("Correlation Heatmap of Neuroscience Marketing Metrics") plt.show() plt.figure(figsize=(8, 6)) sns.regplot(x='attention_span', y='purchase_decision_likelihood', data=data_marketing_neuro, color="blue", line_kws={"color": "red"}) plt.title("Attention Span vs. Purchase Decision Likelihood") plt.xlabel("Attention Span") plt.ylabel("Purchase Decision Likelihood") plt.show() plt.figure(figsize=(8, 6)) sns.regplot(x='emotional_engagement', y='purchase_decision_likelihood', data=data_marketing_neuro, color="green", line_kws={"color": "red"}) plt.title("Emotional Engagement vs. Purchase Decision Likelihood") plt.xlabel("Emotional Engagement") plt.ylabel("Purchase Decision Likelihood") plt.show() plt.figure(figsize=(8, 6)) sns.regplot(x='memory_recall', y='purchase_decision_likelihood', data=data_marketing_neuro, color="purple", line_kws={"color": "red"}) plt.title("Memory Recall vs. Purchase Decision Likelihood") plt.xlabel("Memory Recall") plt.ylabel("Purchase Decision Likelihood") plt.show() # Statistical Analysis - Linear Regression for each metric metrics = ['attention_span', 'emotional_engagement', 'memory_recall'] results = {} for metric in metrics: slope, intercept, r_value, p_value, std_err = linregress(data_marketing_neuro[metric], data_marketing_neuro['purchase_decision_likelihood']) results[metric] = { 'slope': slope, 'intercept': intercept, 'r_value': r_value, 'p_value': p_value, 'std_err': std_err } # Displaying the statistical analysis results for metric, stats in results.items(): print(f"\n{metric.title()} vs Purchase Decision Likelihood:") print(f" Slope: {stats['slope']:.4f}") print(f" Intercept: {stats['intercept']:.4f}") print(f" R-squared: {stats['r_value']**2:.4f}") print(f" P-value: {stats['p_value']:.4f}") print(f" Standard Error: {stats['std_err']:.4f}")