Why Use Python in Marketing?

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Python can help you get more insights from your data, make better-informed data-driven decisions, automate many routine activities, and increase the ROI from your marketing campaigns. Interested? Then let’s see how exactly Python can boost your marketing efforts.

How Can Your Marketing Activities Benefit from Python?

As a marketer, you probably face a number of challenges:

  • Ads are getting more expensive.
  • Previously profitable marketing channels, like content marketing, are becoming crowded.
  • You need omni-channel presence, but it’s hard to manage and coordinate.
  • You have tons of data coming from your online advertising campaigns, but you don’t know how to get valuable insights from them without investing funds in expensive automation tools.
  • You’re experiencing, or have experienced, a talent bottleneck.
  • Sometimes, it’s even hard to calculate your returns on investment (ROI) due to the attribution problem and other challenges.

Can you solve at least some of these problems with Python automation? The answer is Yes.

Sure, Python is not a magic pill; it’s just another programming language. But because of its simplicity and understandability, it can benefit even those with minimal coding experience.

Python is a tool that can drive automation across all of your marketing activities:

  • Use it to create valuable and professional-looking visualizations and enhance your marketing analysis.
  • Use Python to streamline your data collection process from multiple channels.
  • Perform your data analytics more efficiently with Python.
  • Automate customer segmentation, customer feedback analysis, A/B testing and other marketing activities. No need to search for scarce marketing professionals!
  • Increase the accuracy of your ROI calculations by adopting comprehensive attribution models that will help you understand which channels bring you the most customers.
  • Let sophisticated machine learning models choose which ad should be displayed to which customer and at what time, and then enjoy the increased ROI.

Do You Need to Learn Python Yourself?

If you want to enjoy all the benefits of Python automation for marketing activities, you have several options:

  • Hire a data scientist or Python developer to support digital marketers.
  • Invest in ready-made data analytics tools.
  • Learn Python coding skills yourself.

Considering how expensive data scientists, software developers, and sophisticated data analytics tools are, the last option is often the most accessible and preferred. But can you really learn Python yourself without any software background?

Of course! If you have basic computer experience and know how to perform data analytics with spreadsheet programs like Excel, and you’re motivated and disciplined, then learning Python shouldn’t take too much of your time and effort.

Python is a simple and easy-to-learn programming language because of its clear syntax and legibility. It’s perfect for beginners without any coding experience.

However, considering the vast number of learning resources available online, you just need to be careful with choosing courses that will be the best fit for your particular needs. After all, you don’t need all the knowledge and skills of software engineers—you just want to be able to automate your marketing activities in the nearest future.

Now let’s see some use cases for Python in marketing.

Python for Marketing Use Cases

Reporting

Do you get tons of data from multiple sources for which you need to produce periodic reports? Python is an efficient tool for data pre-processing, analytics, and data visualization. You’ll need to write the code only once to create your first report. Afterwards, you can just run the code on a new dataset, and you’ll get the report within minutes.

Data Visualization

Marketers use visualizations of all types to support reporting and marketing analysis. Unfortunately, it usually takes quite a lot of time to create valuable and professional-looking plots. For your convenience, Python has a specialized library named seaborn that creates appealing, state-of-the-art plots with just one line of code. You’ll just need to pre-process your data first—but again, this process is straightforward with Python. For example, you can create Python heat maps for marketing campaigns in just a few lines of code.

Content Optimization

A/B testing is a popular marketing tool for comparing several versions of a website, app, or ad to determine which one performs best. For example, if you have two groups of customers with one being exposed to ad A and another group to ad B, you can compare these two ad groups’ conversion rates to find the winner. Of course, the difference should be statistically significant for you to conclude that one of the ads is indeed better. Python is a perfect tool for streamlining A/B testing and defining the statistical significance of the resulting difference.

With Python, you can go even further with content optimization. A/B testing is quite a good technique, but it inevitably includes a period of “regret,” when you’re not using the best option for part of your customers and thus lose some revenue. In contrast, multi-armed contextual bandits mitigate opportunity loss through dynamic optimization. With this technique, you don’t need to wait until the end of the test to define the best option since bandit tests explore and exploit different options simultaneously and gradually move to the better one. This advanced technique can be also implemented with Python. But of course, it will require a little bit more coding experience.

Customer Segmentation

Delivering personalized experience to customers is a must for marketers these days. But before you can personalize your messaging and experience, you need to understand your customers’ behaviors, preferences, and habits. Proper customer segmentation is a key to understanding your customers and tailoring marketing campaigns accordingly. Python gives you access to the most sophisticated clustering techniques. A number of machine learning techniques that are easily implemented with Python will help you classify your customers by features that really matter, increasing your revenue and improving your customers experience.

Customer Feedback Analysis

Customers use multiple channels to leave feedback about the products they use. Large companies in particular struggle to manually analyze all the reviews left on different websites and social media platforms. This is a perfect opportunity for automation.

With natural language processing (NLP), you can automate the processing of customer feedback and get some valuable insights to answer questions such as the following, among many others:

  • What are the things that customers like/dislike about our product?
  • Do customers develop an emotional attachment to our product?
  • How does the perception of our brand change with time?

Text processing and classification is not a trivial task for beginners. Fortunately, there are many open-source libraries and pre-trained models available online that will help you automate customer feedback analysis for your company.

Supercharge Your Marketing Automation with Python

Python is a great tool for automating your marketing activities and can simplify many of your daily tasks. Of course, it’s important to remember that Python is just a tool—it’s by no means a replacement for your domain expertise. Success comes from the proper combination of your professional marketing skills and coding skills.

Need to improve your Python coding skills? Be sure to check out some of our courses . Specifically, you may find Introduction to Python for Data Science or the Python Basics series (Part 1, Part 2, Part 3) useful.

In those courses, you’ll learn basic Python skills that prepare you for both programming and data science. The courses touch on many key concepts and include hundreds of interactive exercises for practicing newly acquired skills. They’ll help you develop the necessary coding skills so you can start automating your marketing activities right away.

Kateryna Koidan

Kateryna is a data science writer from Kyiv, Ukraine. She worked for BNP Paribas, the leading European banking group, as an internal auditor for more than 6 years. More recently, she decided to pursue only the favorite part of her job—data analysis. Now she is continuing her self-education with deep-learning courses, enjoys coding for data analysis and visualization projects, and writes on the topics of data science and artificial intelligence. Kateryna is also a proud mother of two lovely toddlers, who make her life full of fun.

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