6 Uses of Python in Digital Marketing (with Examples)

“Does a digital marketer need to learn Python? If yes, how much knowledge should they have?”
“How is Python used in digital marketing?”
“What are good Python courses for marketers?
– Curious marketer

Python improves digital marketing automation and operations. With its features and ability to process data, Python allows businesses to scale. This makes it an excellent tool for professionals today who look to gain a competitive edge in terms of industry skills.

There’s a case to be made for Python in digital marketing. I wrote articles on how coding has become an integral part of the marketing space. Professionals are now curious to know if it’s a good idea to learn it. As marketing automation evolves at a fast pace, so do the skills required. Digital marketers need to level up skills to be employable and be career-proof.

6 Uses of Python in Digital Marketing

As I’m writing this blog post, I learned how to code in Python only five months ago. Python is the easiest to learn compared to other powerful programming languages. Note that I said easiest, not easy. You still need to put in the work. In this article, here are some uses of Python in digital marketing.

Working with APIs

One of the most useful cases of learning Python in digital marketing is APIs. API means Application Programming Interface. In simple terms, APIs allow different software to connect with each other. All software or any tool has an API.

Check any of your favorite software tools. If their website has an API documentation page found on the footer navigation, they have an open API to use.

APIs speed up the work of digital marketers and automates many workflows. Examples of APIs are anything in the marketing automation space. Whether getting social media posts or doing text analysis, there’s an API for it.

Learning Python helped me understand API documentation, and get data from them. An example is a stock market API or an SEO API. Also, here’s an article on Keywords Everywhere API.

Web Scraping

Another often scenario for Python in digital marketing is web scraping. In the case of digital marketing and SEO, it’s used for uncovering text data.

For my projects, I scrape web pages. I want to get the word count or text content from pages to do stuff with them afterward. One of them is to create my own free word counter tool to see the word count of any website content. I see a general view of how long the web pages are since I think they matter for analysis.

Example of Python code:

def get_content(url_argument):
    page_source = requests.get(url_argument).text
    strainer = SoupStrainer('p')
    soup = BeautifulSoup(page_source, 'lxml', parse_only=strainer)
    paragraph_list = [element.text for element in soup.find_all(strainer)]
    content = " ".join(paragraph_list)
    return content

Scraping the content allows me to do advanced stuff like Natural Language Processing. I scrape the content to do entity recognition and uncover semantic SEO. Eventually, I’d like to do topic modeling if I get to the more advanced stuff.

Text Analysis

Text analysis is quite popular in the Python and data science communities. By virtue, it trickles down to digital marketing as well. Anything related to copy or content is a variation of text analysis at some point. These simple projects lead to more advanced stuff like machine learning.

I built a custom Google search results page that has sentiment analysis output. I want to experiment with easy-to-use models to identify the morale or tone of the content. I want to build something fast without having to dive deep into statistics.

Another use case is in social media marketing or customer feedback. Build an algorithm to create your own sentiment analysis algorithms. Uncover the opinions of people about your brand. Or cluster emails and see the patterns through text clustering and topic modeling.

Data Analysis in Marketing Analytics

Digital marketing is packed with data mining especially in marketing campaigns. At one point, it needs to be visualized. Python has amazing libraries and it’s the perfect fit to solve these problems. With Python, you can access marketing APIs such as Google Analytics 4.

The pandas library is a godsend when doing data analysis. A library like matplotlib is a savior for data visualization. On top of that, using Jupyter notebook makes data mining through large datasets super fast.

You might be asking, why not use Looker Studio or spreadsheets? Well, sometimes data is overwhelming and unwieldy that it takes too long to load. Have you experienced a spreadsheet with 50,000+ rows? It’s not fun to filter, clean, sort, delete or add data points.

Technical SEO

Python has a place in digital marketing, particularly in SEO (Search Engine Optimization). SEO is the discipline I come from, and learning Python expanded my toolbox and repertoire. Technical SEO is where I get inspiration to build Python projects, and there are a lot of projects to work on.

SEO Pythonistas is an amazing resource for this. There’s growing popularity within SEO professionals who want to dive into Python and expand their expertise.

Python libraries like advertools and EcommerceTools specialize in technical SEO. Both open-source libraries have features like crawling XML sitemaps and robots.txt files. The beauty of Python is the ocean of libraries for many use cases.

Build Internal Tools

Bringing it all together, you build internal tools. The most amazing feeling is when you create something you thought of. You are the artist of a painting you want to make. Nothing like building, creating and making something to fruition.

Who knows, you might start a software company from internal apps in the future. Marketing Technology expert Yaniss Illous built a tool in Python as a side project. Streamlit is a web Python framework to build apps completely in Python. This is an opportunity to create any tools that help your digital marketing tasks.

Python Crash Course

The prospect of learning Python can seem daunting for many who do not have a technical background. With a bit of dedication, and going through small steps, it is possible to learn the language bit by bit. With patience and practice, Python can become a powerful tool in one’s repertoire.

Variables

A variable is a data value that is stored in memory. When a variable is declared or assigned, the computer will remember the value, which can be used for anything during programming. Declared variables include texts, numbers, True or False, or other data types. There are several different data types available in Python, such as numbers, lists, and dictionaries, each of which has its own features.

By assigning data types, Python can understand the values supplied, ensuring that the program can process the information according to the desired output.

Example:

x = "Lupage Digital"
y = 515
company = "Google"

print(company)

Output:

Google

Functions

A function is described as a verb in programming and lets the programmer instruct the computer to “do something”. A function is called upon with the desired parameters to return a result. Functions are highly useful in programming as they can be used to perform a wide range of tasks, such as performing calculations, validating user input, or retrieving data.

For Python, the keyword in defining the function is def. Functions can be reused throughout a program, allowing the code to be more efficient and maintainable. As such, a good understanding of functions is essential to becoming a programmer.

Example:

def multiply(x, y):
	result = x * y
	print(result)

multiply(11, 31)

Output:

Example:

def greetings(name):
	print("Good morning, " + name + "!")
    
greetings("Spider-Man")

Output:

Good morning, Spider-Man!

Lists

A list is a powerful data type that stores multiple values in a list as a single variable. Lists are the most commonly used programming type given their versatility due to their ability to store values that can be rearranged for specific uses.

A list in Python uses brackets [ ] to present itself. It’s one of the built-in data types that are used to store collections of data. The other ones are called tuples, sets, and dictionaries.

Example:

names = ["Bill", "Elon", "Jeff", "Warren"]
numbers = [412, 45, 368, 1257]
superheroes = ["Superman", "Daredevil", "Spider-Man", "Batman"]

print(superheroes)

Output:

["Superman", "Daredevil", "Spider-Man", "Batman"]

Dictionaries

Dictionaries are used to store “key-value” pairing. A dictionary is a collection of data that is ordered, changeable, and does not allow duplicates. It’s an efficient and reliable tool for storing and accessing data. In short, it’s a more advanced and more powerful data type than lists. Dictionaries can be stored inside lists—a list of dictionaries.

I think dictionaries are important because most of the public APIs are using this data type. I personally learned Python with the end goal of understanding dictionaries so I can build apps on my own.

Dictionaries are represented with braces or curly brackets { }. The items (the left side of the key-value pair) in the dictionary can be accessed by using the associated key name.

Example:

superheroes = [{
"name":"Batman",
"secret identity": "Bruce Wayne",
"year":1939,
"city": "Gotham City",
"marriage": False
},{
"name":"Daredevil",
"secret identity": "Matt Murdock",
"year":1964,
"city": "Hell's Kitchen",
"marriage": True
}]

print(superheroes[1]["secret identity"])

Output:

Matt Murdock

If Statements

If statement in Python is essentially conditional statements. It’s used to determine if a code should be executed or not under certain conditions. It’s similar when humans speak “if…, then…” or “this happens if…”. Furthermore, the main purpose of an if statement is to evaluate if the conditional statement is True. After which, it will run the code. Otherwise, the computer will do something else.

This is where your mathematics class or logic comes in handy. If statements can be used in many scenarios, allowing for a wide range of functions to be implemented into Python programming.

If statements are executed using the if keyword. For more advanced conditions, if statements are written along with elif or else statements depending on the conditions and uses.

Example:

x = 330
y = 2000

if x < y:
	print("x is less than y")
else:
	print("x is greater than y")

Output:

x is less than y

Example:

x = 312
y = 200

if x < y:
	print("x is less than y")
else:
	print("x is greater than y")

Output:

x is greater than y

For Loops

The For loop is a powerful tool in Python for iterating over a sequence. For loops go through a list, a dictionary, numbers, or texts and do something with them. You create a for loop by first defining the iterable object you’d like to loop through, and then the actions you’d like to perform on each item. From its name, loops can be confusing to people. Some might say it’s loopy 🤣. Simply put, it’s saying “for each one…, do this.”

For loops are executed by using the for keyword. For loops are sometimes paired with if statements and thus making your program more powerful.

Example:

superheroes = ["Superman", "Daredevil", "Spider-Man", "Batman"]

for x in superheroes:
	print(x)

Output:

Superman
Daredevil
Spider-Man
Batman

Example:

superheroes = ["Superman", "Daredevil", "Spider-Man", "Batman"]

for x in superheroes:
	print("We meet again, " + x + "!")

Output:

We meet again, Superman!
We meet again, Daredevil!
We meet again, Spider-Man!
We meet again, Batman!

Libraries

A real-life library is a collection of books. A Python library is a collection of code. Libraries in Python are essentially pre-packaged code that is readily available for use. It helps programmers that instead of creating custom code or functions from the ground up, they should just import libraries to pick out ready-made methods or functions.

Libraries allow programmers to speed up their programming to a desired output. Libraries help you extend Python capabilities. Pretend you’re Iron Man and you’re extending your capabilities beyond what you currently have. Libraries vary from data visualization modules to data science modules to machine learning. A library I used is PolyFuzz, which I used when I was building my own URL redirect mapping tool.

Libraries are imported by using the import keyword

import streamlit
import pandas
import request

Resources to Learn Python

There are countless Python guides, tutorials, and courses on the Internet. Be careful not to go down a rabbit hole on which one to pick since they are overwhelming. Don’t be a victim of analysis paralysis or go into tutorial hell. In the end, it’s more important to apply them.

FreeCodeCamp

FreeCodeCamp is one of the best resources out there to learn Python. They have certifications called Data Analysis with Python and Scientific Computing with Python. On top of that, they also have a huge catalog of Python tutorials on YouTube. They have no shortage of lessons to help you build your side projects.

YouTube

Another great resource is YouTube. Many crash courses are uploaded (and still counting) to help get your feet on Python. YouTube has an overabundant of tutorials and it’s up to you which plays to your learning style.

Skillshare

Skillshare contains amazing classes as well. The platform not only has courses on Python but also has complementary classes like web design or data science. What’s great about Skillshare is they have specialized courses like building a website in Shopify or WordPress. A great way to practice what you learned.

Udemy

The courses in Udemy are affordable ranging from $10 to $15 and many of them are on Python. Courses are more structured and some have supplementary materials. Users get a glimpse of the curriculum and they watch video previews on what to expect from them.

I bought Boris Paskhaver’s course Learn to Code with Python. It’s simple to follow, beginner-friendly, and has exercises to practice. This 50-hour course takes its time to explain concepts. So far, I’m happy with my purchase and have never regretted it.

Watch the previews to see the instructor’s teaching style. You don’t want to pay for a course that’s not helpful. I’ve been in the position where I bought courses and regretted that I didn’t get any value from them.

CXL

The CXL team has a technical marketing course and lives up to its name. It contains topics on machine learning, coding, and cloud technology. Check the course and resources of CXL if you’re serious about diving into Python.

DataCamp

DataCamp is also great for learning Python. It has a free sign-up. The prices are steep to some because they do monthly subscriptions. Check out the different curriculum and see if they are worth it.

DataCamp uses an interactive learning environment where the system gives the lecture in chunks. Then the student enters the Python code on the web browser. They serve as interactive challenges.

I don’t prefer this type of learning because it’s too rigid. I prefer the open sandbox approach where I need to set up my coding environment on my computer. Then I start coding like a real programmer.

Conclusion: Python is Another Tool in Digital Marketing

You might ask: Why learn Python if there are tools that do automation for you? Well, you extend the functionality of those tools to your custom needs. Granted that it’s not needed to force yourself to learn Python. It’s another tool to have in your back pocket, just in case. You don’t need to be a data scientist.

To some, Python is the language of choice for many digital marketers. It’s easier to build tools and automation to do repetitive tasks.