Using Marketing Analytics with Python had always been a hot topic since the advent of Data Science and Artificial Intelligence in Python. Researchers in Marketing Analytics have always been looking about the programmatic way of solving their problems and Python had equipped them with the most advanced tools, which we are going to discuss today.
Marketing Analytics with Python has become one of the most popular programming languages for data analysis because of its simplicity, efficiency, and versatility. If you are a marketer who wants to harness the power of data-driven marketing, Python is an essential skill to master. In this tutorial, we will go through how you can use marketing analytics with python.
Marketing Analytics with Python in Pandas
Pandas is a library in Python for data manipulation and analysis. It provides easy-to-use data structures and data analysis tools, making it a valuable tool for marketers who want to analyze large datasets. Here’s an example of how you can use Pandas to analyze a dataset containing information about website visitors:
import pandas as pd
data = pd.read_csv('visitor_data.csv')
print(data.head()) # prints the first 5 rows of the dataset
# calculate the average time on site
average_time = data['time_on_site'].mean()
# calculate the percentage of visitors who made a purchase
num_purchases = data['purchase'].sum()
total_visitors = len(data)
purchase_percent = (num_purchases / total_visitors) * 100
print('Average time on site:', average_time)
print('Purchase percentage:', purchase_percent)
Marketing Analytics Data Visualization with Matplotlib
Matplotlib is another library in Python that is used for data visualization. It allows users to create charts, graphs, and other visual representations of data. Here’s an example of how you can use Matplotlib to create a pie chart that shows the percentage of visitors who made a purchase:
import matplotlib.pyplot as plt
labels = ['Purchasers', 'Non-purchasers']
sizes = [purchase_percent, 100 - purchase_percent]
colors = ['#1f77b4', '#ff7f0e']
explode = (0.1, 0) # explode the first slice
fig1, ax1 = plt.subplots()
ax1.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90)
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle
plt.show()
Marketing Analytics Data Cleaning with Regular Expressions
Regular expressions are powerful patterns of characters used for searching and manipulating text. In marketing analytics, regular expressions can be useful for cleaning up messy data. Here’s an example of how you can use regular expressions to clean up a list of email addresses:
import re
email_list = ['example@gmail.com', 'john@doe.com', 'jane@test.', 'com@abc.com']
# remove any email addresses that don't end with ".com"
cleaned_list = [email for email in email_list if re.search('.com$', email)]
print(cleaned_list)
Marketing Analytics with Python is a powerful tool for marketers who want to leverage data-driven marketing. By using libraries such as Pandas and Matplotlib, marketers can easily analyze and visualize large datasets, while regular expressions can be used to clean up messy data. With a basic understanding of Marketing Analytics with Python, marketers can gain valuable insights that will help them make better decisions and improve their marketing campaigns.
You may also be interested in many of our blogposts wherein we have talked about Customer Lifetime Value prediction using Python and also Building a Recommendation Engine using Python. These two tools i.e. Customer Lifetime Value prediction and Recommendation Engine is also most used tools in Marketing.
Want to learn more about Python, checkout the Python Official Documentation for detail.