As you work within Digital Marketing, you are more likely to hear more and more terms such as “Artificial Intelligence”, “Programming Languages” and even “Python” among your group. You might be wondering: “What is meaning the meaning of those terms?” or “Why should I care about Python? I already have my other digital marketing analytics tools for my tasks!”. Well, I am here to explain three main points to you:
- What is Python?
- How it can be implemented in Digital Marketing Analytics?
- How can you start learning?
Let’s not lose further time and learn more about the world of programming!
What is Python?
According to the official definition: “Python is an interpreted, object-oriented, high-level programming language with dynamic semantics.” (python.org, 2022). In more simple terms, Python is an open-source programming language, available on all operating systems and platforms. It can be used for:
- Data Analysis & Vizualisation
- Artificial Intelligence and Machine Learning
- Program Application
- Many Others!
The main advantage of Python compared to other programming languages is its simplicity, syntax, and versatility. It is one of the simplest programming languages to learn for a person without prior coding experience and is efficient in digital marketing analysis!
How it can be implemented in Digital Marketing Analytics?
Now that you understood the main concepts, here are a few examples of how can implement Python with your digital marketing analytic work.
Data Collection & Cleaning
The concept of data cleaning is the removal of values in a dataset that is duplicated, incomplete, or incorrect. It is normal that certain data-collecting tools used for digital marketing have limitations and can lead to data mistakes. With Python, you can easily fix those mistakes and even create an automated process for this. Additionally, you can directly collect your information from a different digital marketing analytics software or tool to Python by using an Application Programming Interface called API.
After collecting and cleaning your dataset, you can use this dataset to do any type of visualization that you wish in your digital marketing analytics. The main advantage of using Python is that you can use long sets of data, add all types of modifications and extract any parts more fluidly.
When segmenting your customers through their behavior by analyzing the graphs and data manually for digital marketing analytics. You can implement an AI in Python based on clustering techniques that will regroup different groups of customers based on their similar characteristics. Simplifying the overall digital marketing analysis!
The given examples are just the tip of the iceberg of things that can be done in digital marketing analytics using Python. It can be used in many other things such as content optimization, sentimental analysis through social media data collection, search engine optimization, and much more!
How can you start learning Python?
Of course, if you wish to learn to use Python to its fullest and fully specialize in this field, it is highly recommended to undergo go an official university program in order to become a data scientist. Although no worries, it is common for people working in other fields such as finance, digital marketing analytics, management, and others to learn Python online in a quick manner! They are a lot of Python courses online, ranging from free to expensive that can teach you how to use the programming language in digital marketing analytics. The main advice would be to learn the basics from one of the following sources :
Usually, it requires around 1 to 7 months to learn the basics of pythons!
Koidan, K. (2019) Why use python in marketing?, LearnPython.com. LearnPython.com. Available at: https://learnpython.com/blog/why-use-python-in-marketing/ (Accessed: November 15, 2022).
Reyes, F.A. (2022) 6 uses of python in Digital Marketing (with examples), Lupage Digital. Available at: https://www.lupagedigital.com/blog/python-digital-marketing/ (Accessed: November 15, 2022).
What is python? executive summary (no date) Python.org. Available at: https://www.python.org/doc/essays/blurb/ (Accessed: November 15, 2022).