Hello! We recently conducted a study to explore the effectiveness of featuring celebrities in advertisements. Our study compared the Click-Through Rates (CTR) of Celeb-featured Ads vs. Product Only Ads, and the results were overwhelmingly positive. We found that the CTR of Celeb-featured Ads was significantly higher than that of Product Only Ads, with a p-value of 0.009.
This indicates that featuring celebrities in ads can be a valuable strategy for marketers looking to increase consumer engagement with their brand. By incorporating celebrity endorsements into their ads, marketers can potentially increase the likelihood that consumers will click on their ads and engage with their brand.
Our study provides valuable insights into the effectiveness of celebrity endorsements in advertising and highlights the potential benefits that can be gained from using this strategy. Therefore, if you are a marketer looking to enhance your advertising game, featuring a celebrity in your ads could be a great strategy to consider.
Thank you for your interest in our study!
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
# read in the dataset
df = data
# create a new column called "ad_type" that indicates whether each ad is a celebrity featured ad or a product-only ad
df['ad_type'] = df['Name'].apply(lambda x: 'celebrity featured' if x[-1].isdigit() or x[-1] == 'B' or x[-1] == 'C' else 'product-only')
# separate the CTR values for each group
celebrity_ctr = df.loc[df['ad_type'] == 'celebrity featured', 'CTR']
product_ctr = df.loc[df['ad_type'] == 'product-only', 'CTR']
# perform the t-test
t_stat, p_value = stats.ttest_ind(celebrity_ctr, product_ctr)
# set up the figure and axis objects
fig, ax = plt.subplots(figsize=(8, 6))
# create the kernel density plots for each group
sns.kdeplot(data=celebrity_ctr, color='hotpink', label='Celebrity Featured Ads', alpha=0.5, ax=ax, fill=True)
sns.kdeplot(data=product_ctr, color='deepskyblue', label='Product-only Ads', alpha=0.5, ax=ax, fill=True)
# show the mean for each group
ax.axvline(celebrity_ctr.mean(), color='hotpink', linestyle='--', label='Celebrity Featured Ads Mean')
ax.axvline(product_ctr.mean(), color='deepskyblue', linestyle='--', label='Product-only Ads Mean')
# set the axis labels and title
ax.set_xlabel('CTR')
ax.set_ylabel('Density')
ax.set_title('Distribution of CTR by Ad Type\n t-statistic: {:.2f}, p-value: {:.5f}'.format(t_stat, p_value))
# show the legend
ax.legend()
# display the plot
plt.show()
Python
복사
