The digital marketing present marketing tactics are highly subjective by deeper data analytics based on the rich data, AI, and inventive marketing concepts.

I strongly believe without using the technologies like Python, Java or PHP for marketing automation and data analysis then only we can accomplish the marketing return on investment in faster way.

Several organizations are hiring Python developers to support their digital marketers, but that solution is not feasible due to high-cost in the fiercely competitive marketplace.

Nonetheless, knowledge or little hands on experience of two or three languages would help greatly for an online specialist’s even at a learner level everyday job and, therefore, huge gain over your struggle.

What is Python?

Python is an interpreted, high-level, general-purpose programming language.

Python is created by Guido van Rossum and first released in 1991.

It uses English keywords frequently where as other languages use punctuation, and it has fewer syntactical constructions than other languages.

Reasons why should choose to learn Python:

Below are the various reasons to choose Python

Open source Simple and Easy to learn Both Object and Procedure Oriented Platform Independent Portable Dynamically typed and garbage-collected Supports multiple programming paradigms Extensible Embedded Extensive Library

How do we get benefit of Python in Digital Marketing?

With the use of the Python language, all the tedious manual tasks can be automated without giving a second thought.

The online retailers use the market range analysis to identify the transactions and item combination within those transactions according to the customer behavior.

It also does the regency, frequency and monetary value analysis which are essential to identify the customer choice and interests. Based on the customer search and behavior, the marketers analyze how recently and how often the customers make purchases as well as how much they spend on purchases.

For marketers best analyzed data is more important in order stand first in competitive world of digital marketing

Different steps to get the analyzed data

A process of data collection, processing, cleansing, and analysis are standard for big data analytics.

Data collection

Before data can be analyzed, it first needs to be collected.

Below are three types of data that are typically collected during this first step.

Structured data

Unstructured data

Semi-structured data

Data processing

After data has been collected and stored, it is now ready to be processed and sorted through for usage.

This has led to the rise of real-time processing, but it’s not the only method for processing big data.

Batch processing

Stream processing

Data cleansing

Data cleansing, although necessary, can be one of the most time-consuming processes when it comes to big data analytics.

Data analysis

With your data collected, stored, processed, and cleansed for quality, it’s finally ready to be analyzed.

Below are four of the different types of big data analytics:

Descriptive analysis

Diagnostic analysis

Predictive analysis

Prescriptive analysis

Data mining is plays vital role to perform quality analysis it greatly useful for the marketers

Data mining

Data mining also referred to as “knowledge discovery in databases,” sits at the intersection of machine learning, AI, and statistics. Data mining is a scientific and mathematical approach to interpreting data, and it provides valuable insight to businesses.

AUTOMATION OF VARIOUS PROCESSES USING PYTHON

Python has very rich and very large number of data analytics libraries in order to automate the analyzed data.

Automate SEO indexation through a python code that can trace the changes in ranking

Try to automate the price changes of the competitor products with a Python code

Marketers gather survey data, email, SMS responses, chat chains, other commercial data files and top marketing trend information and automate the collected data using python.

The main activities of repetitive formatting tasks include:

Make use of text string matching functions, number matching functions, marking/tagging data source, location, time, and other attributes of data and automate repetitive data formatting

Formatting functions for PDFs and web ads like splitting, watermarking and other such function are available to automate.

Custom-made error checking should be automated any kinds of typo or other errors in the data that is mandatory for your organization should be automated to improve efficiency and save valuable time.

Massive operations on the files like copying, editing or removing the files based on certain criteria such as timestamp, data strings, changes in files and other conditions should be automated through Python codes. This will improve the efficiency of data processing.

Reading the file properties and its attributes

Tracking of modifications made to the files in comparison with the timestamps

Always develop the custom code, the way you work and based on your own marketing skills

Automate the filling out of forms, naming renaming files, and formatting sheets

The data mining process plays a pivotal role in all types of marketing in the marketplace. The data mining components may vary from company to company. It is always a good idea to automate the major functions related to the processing of big data.

Conclusion:

After discussion of many technical and business aspects of Python and digital marketing and other crucial aspects of data and data processing, we come to conclude that:

Simple coding skills are very important in the present digital marketing field

Automation of data collection, processing, mining, and repetitive tasks that is time-consuming and less productive which can be easily automated by using the Python programming language.

Python leads all other languages in data analytics and digital marketing

Python can be used for various marketing cases like A/B testing, automating bulk emails, market basket analysis and much more.

Python will open a door to both analytics and coding (skill which you already have). With your skills, you can become a growth hacker.