Awareness Levels and Marketing Reach Correlation Heatmap

Published: 30 October 2024| Version 1 | DOI: 10.17632/3tcvzfd754.1
Contributor:
Sunil Maria Benedict

Description

A Pandas DataFrame named data_awareness is created with five variables: awareness_local: Simulated using a normal distribution with a mean of 70 and a standard deviation of 10. This variable represents local brand awareness levels. awareness_regional: Generated from a normal distribution with a mean of 50 and a standard deviation of 12, indicating regional awareness levels. awareness_national: Created with a mean of 30 and a standard deviation of 15, reflecting national brand awareness. marketing_reach_online: Simulated from a normal distribution with a mean of 5000 and a standard deviation of 1000, representing online marketing reach. marketing_reach_offline: Generated with a mean of 3000 and a standard deviation of 800, indicating offline marketing reach.

Files

Steps to reproduce

import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt # Simulating data np.random.seed(42) data_awareness = pd.DataFrame({ 'awareness_local': np.random.normal(70, 10, 100), 'awareness_regional': np.random.normal(50, 12, 100), 'awareness_national': np.random.normal(30, 15, 100), 'marketing_reach_online': np.random.normal(5000, 1000, 100), 'marketing_reach_offline': np.random.normal(3000, 800, 100) }) # Plotting heatmap for awareness and marketing reach plt.figure(figsize=(10, 6)) sns.heatmap(data_awareness.corr(), annot=True, cmap="YlGnBu") plt.title("Awareness Levels and Marketing Reach Correlation Heatmap") plt.show()

Institutions

United International Business Schools

Categories

Economics, Marketing, Brand Management, Brand Equity, Brand Image

Licence