4 Ways Python Can Boost Your Marketing Activities

In this article, we are going to see why and how you can use Python for marketing.

Most people probably think Python is only for programmers, that this is a skill reserved for a small group. This is not true; Python can be useful to everyone.

In this article, I'll tell you why you should start using Python for marketing – even if you've had nothing to do with coding before.

Let’s Talk About Python

You’ve surely heard about Python, but let’s have a brief recap before we move on to how it can make marketing activities faster and easier.

Python is a general-purpose programming language, which means you can use it to build applications of different types: websites, software, games, automation scripts, software tests, and machine learning models, among others. Python is present in almost all industries: tech, financial, automotive, entertainment, and education companies all use Python daily. Check Python’s official wiki and you’ll see that Google, Yahoo, IBM, and NASA use Python!

Although Python has become trendy in the last 10 years (partially thanks to the expansion of the Artificial Intelligence field) it’s been around since the 90s! More precisely, the language was developed in the late 80s by Guido Van Rossum, a Dutch programmer; its first version was released in 1991. It was then used as a scripting language; nowadays, developers use it for almost everything.

Are you thinking you’d like to try learning Python? You’ll be glad to know that LearnPython.com’s interactive course Python Basics: Part 1 is (at the time of writing) totally free! This course will give you a solid foundation for your Python developer journey. And if you like it, you’ll probably also love Python Basics: Part 2 and Python Basics: Part 3. These three courses make up our Python Basics track.

According to Stack Overflow’s 2022 survey, Python is one of the favorite languages of people learning to code, with almost 59% of all newcomers using it. But why is it so popular?

Python is a perfect fit for beginners. It has a clear and understandable syntax and is considered an easy-to-learn and easy-to-write programming language. Look at this piece of code:

# The following code checks if x is even or odd and returns the result
def isEvenOrOdd(x):
    if (x % 2 == 0):
        return "Even"
    else:
        return "Odd"
 
# Calls the function with a parameter and prints the result
print(isEvenOrOdd(5)) 

# Shows: Even

print(isEvenOrOdd(8)) 

# Shows: Odd

Very simple, isn’t it? There are no extra parentheses or brackets; the mandatory indentation makes it understandable. However, the above code could be better:

# Checks if x is even or odd and returns the result
def isEvenOrOdd(x):
    return "Even" if (x % 2 == 0) else "Odd"
 
# Calls the function with parameter and print the result
print(isEvenOrOdd(5)) # Shows: Even
print(isEvenOrOdd(8)) # Shows: Odd

Even simpler! Python is well-known for its one-liners (complex operations coded in a single line). If you want to learn more about it, I recommend 10 Native Python One-Liners That Will Blow Your Mind.

But Python’s syntax is only the tip of the iceberg; it has an enthusiastic and friendly community and a huge number of great libraries available on the official repository. If you want to dive into details, check out Why Is Python So Popular?.

Python for Marketing: A Game Changer

If you are part of the Marketing world, you may wonder How can Python help me? Well, Python can embrace a lot of different marketing tasks; let’s see some examples.

1.   Data Analysis and Visualization

Python has many libraries that can help you analyze and visualize data. With Python, you can quickly and easily manipulate large data sets, extract insights, and create visualizations to help you make better marketing decisions.

Imagine that you need to build a dashboard showing conversion rates, social media engagement, and advertising performance. You can do it easily with the right Python library.

The most famous library for large data sets is undoubtedly NumPy. NumPy is a fundamental library for scientific computing with Python. It provides support for big, multi-dimensional arrays and matrices and has a large collection of mathematical functions to operate on these arrays. Another popular one is pandas, a library designed for data manipulation and analysis. It provides powerful data structures for working with tabular data, including dataframes and series.

Regarding data visualization, the most popular Python library is Matplotlib. This is a powerful and versatile library for creating static, animated, or interactive visualizations in Python. It provides a wide range of 2D and 3D plots, including scatter plots, line plots, bar charts, and histograms. Want to know more libraries for data visualization with Python? Then don’t miss our article on The Top 5 Python Libraries for Data Visualization.

2.   Automation

Python can help you automate many marketing tasks, such as sending emails, verifying email validity through APIs, scheduling social media posts, and generating reports. By automating these tasks, you can save time and focus on more strategic marketing activities.

Let’s imagine a very practical example. You are in charge of your company’s marketing department, and you decide to run a remarketing campaign to re-engage customers who haven't bought a product in the last six months. You could easily create a Python script that matches the desired customers and sends them an email with a special discount. The script could be run once a day as a cron job (i.e. a scheduled task).

You could also create a script to send an email to customers whose membership is about to expire. Imagine doing all those tasks manually; you would waste your time! Automating them allows you to concentrate on more essential activities.

3.   Web Scraping

Python can be used to scrape (i.e. collect) data from websites, such as competitor pricing information, customer reviews, and industry trends. This data can then be analyzed and used to inform your marketing strategies.

Picture the following scenario: Your boss wants you to elaborate on a detailed analysis of your competitors’ pricing strategies for the quarter. Obviously, you don’t have access to the competitors' databases, so you need to check the products one by one on their websites. Taking into account that each competitor may have thousands of products in their catalog, the task would be titanic to do manually! But you could easily automate it by scraping your competitors’ websites and storing the products’ information and prices in a database.

Beautiful Soup is a popular library for web scraping in Python. It allows you to parse HTML and XML documents and extract the data you need. You can also work with the trendy Scrapy, a powerful and flexible Python library for building web spiders that can crawl websites, extract data, and store it in a structured format. Scrapy is particularly useful for scraping large or complex websites. I would also recommend my favorite web scraping Python library: Pyppeteer, an unofficial Python version of Puppeteer, the node.js Google library for web scraping.

4.   Machine Learning

Python's machine learning libraries can help you predict customer behavior, identify patterns, and segment your audience. With machine learning, you can create more personalized marketing campaigns that are tailored to your customer's needs and preferences.

Imagine that your company has launched a new product that is apparently successful, but you want to know what the customers really think about it. Since you don’t want to use an old-fashion survey that nobody cares about, you decide to use another powerful weapon: social media comments. You can access all the comments through the social media platform’s API (Facebook or other) and build a machine learning model that will analyze customer sentiment. You can later use this information to identify areas for improvement and develop more effective marketing and customer service strategies.

Two popular Python libraries for machine learning are scikit-learn and TensorFlow. The scikit-learn library provides a range of algorithms for classification, regression, clustering, and dimensionality reduction and tools for data preprocessing, model selection, and evaluation. TensorFlow provides a range of tools for building and training neural networks, as well as data preprocessing, model selection, and evaluation.

Interested in data science? The track Python for Data Science will give you the keys to start working with data.

Become a Python-Powered Marketing Expert!

You already know marketing. Are you ready to make your marketing more efficient with Python? You are in the right place! LearnPython.com will help you to start your Python journey!

Our courses are 100% interactive, which means you learn a new concept and you immediately apply it by solving an exercise. This is what an exercise looks like:

The explanation and exercise instructions are on the left part of the screen. There’s a real code editor on the right part of the screen so you can run your Python code and see if it works. You don’t need to install anything on your computer; you just need an internet connection and a browser!

So don’t hesitate. Start with our free Python Basics: Part 1 course and find out if using Python for your marketing activities is right for you!