What You Could Do with Python in Marketing

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The marketing strategy of any successful business often involves processing huge amounts of data on a regular basis. For example, digital marketers should do the following repetitive tasks:

  • gather marketing campaign statistics,
  • generate reports,
  • analyze customer behavior on a website,
  • measure conversion rates,
  • manage marketing copy,
  • post to social media,
  • run A/B tests, etc.

These types of marketing tasks can take lots of time if done manually. However, marketing automation can help you reduce this time from days to minutes.

How to Automate Marketing Strategy

Data processing tasks can be automated with machine learning and data science. Python programming language is the best choice for such activities. Moreover, it is one of the world’s easiest languages to learn for beginners. There are, at least, two ways to tap into marketing automation with Python:

Now we will share more details on marketing strategy automation with Python.

Python for Marketing Use Cases

Here are some of the digital marketing tasks that you can automate with Python.

RFM Analysis

RFM (recency, frequency, monetary value) is a method of selecting the most significant customers. The method is based on how recently and how often the customers make purchases as well as how much they spend on purchases. Data analyst Ankur Tomar explains how to process each of the three criteria by using Python. In a nutshell, the process is as follows:

  • Recency. Fetch the purchase dates from a customer data sample that is being analyzed. After that, assign a value from 1 to 5 to each date, with the greatest value assigned to the most recent date, e.g. 2 months ago. The output can show you which customers haven’t visited the store for a long time and help you figure out what to do in order to regain them.
  • Frequency. Select the invoice numbers from a data sample that is being analyzed. After that, tag each invoice number with a value, where the greatest value (5) is assigned to the most frequent number. In such a way you will calculate the number of unique purchases per each customer.
  • Monetary value. Take the amount of total price per each customer, and then again tag prices from the lowest to the highest ones with the values from 1 to 5, accordingly.

A/B Testing

A/B testing involves comparing the different versions of a website or app to find out which version performs best. To run A/B tests with the Python script, you will typically need the following values:

  • baseline conversion rate,
  • desired conversion rate,
  • the number of website visitors in a control group (A),
  • the number of website visitors in a test group (B).

Data scientist Nguyen Ngo shares his knowledge and coding skills on how to automate A/B testing by using Python. In his example, he shows the output data as a table, indicating how many visitors from the two groups, A and B, were converted to customers. Nguyen Ngo also uses the pivot table function to summarize the results, determine the conversion rates, and see the difference in conversion rates between the two groups. If the difference is close to the desired conversion rate, it means that the A/B test was completed successfully.

Email Marketing Automation

Obviously, email marketing involves sending tons of emails. These can be the sequences of customer onboarding or retention emails, which are especially important when leading a prospect through the marketing funnel.

Marketers typically use tools such as MailChimp to automate their campaigns, but it’s also possible to do that with Python scripts. Python’s built-in CSV module allows you to process large amounts of data. You will need to create a CSV file with email addresses as well as HTML and/or TXT files containing the email body. If you are sending personalized emails to each customer, you can use an email template with customizable variables. After that, just incorporate these two files in a Python script like this one described here.

Market Basket Analysis

Online retailers use the market basket analysis to identify items that are typically bought together, such as milk and eggs. There can be millions of transactions and item combinations within those transactions, so it would be very hard to process all of them without marketing automation. Therefore, you can use the association rule mining algorithms to identify associations between the purchased items. Data scientist Susan Li gives more details on that. Basically, you need to do the following:

  1. Extract the time of purchase and the number of purchased items per customer.
  2. Use the ddply() function to transform the data into transactions and get all of the items that are purchased together in one row.
  3. Dive deeper into the association rules and determine how often people buy these items together.

Python’s Mlxtend library with an implementation of the Apriori algorithm can help you carry out the analysis and interpret the results.

Coding Skills + Marketing Skills = Perfect Combination

Having both the coding and marketing skills can greatly streamline the business due to the following reasons:

  • Automated routine tasks. Instead of spending lots of time and effort on repetitive tasks, marketers can focus on more important activities while Python scripts will be doing the tedious work for them.
  • The simplicity of Python. Python is one of the world’s simplest and most readable languages, which is perfect for beginners. Though it will not magically turn a beginner into a pro, it can at least help beginners automate their daily routine tasks.
  • A large community of Python developers. GitHub is full of ready-made, open source Python scripts that can simplify the life of any marketer.

The Bottom Line

The daily routine of a marketing manager involves lots of tedious tasks that can be easily automated with Python. This simple programming language can be used for various marketing cases, such as RFM analysis, A/B testing, bulk emails, market basket analysis, and much more. Marketing specialists with coding skills can contribute to the success of a business because they no longer spend their time on repetitive actions and focus on more important tasks instead.

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