The digital marketing has become so sophisticated and data-oriented powered by numerous business intelligence tools in the present day marketing field. The modern marketing strategies are highly influenced by deeper data analytics based on the rich data, artificial intelligence, and creative marketing ideas. The main goal of a successful marketing strategy is to achieve the marketing return on investment MROI greater and faster, which is not possible without using the technologies like Python, Java or PHP for marketing automation and data analysis.
Many companies hire Python developers to support their digital marketers, but that solution is not viable due to high-cost in the fiercely competitive marketplace. The best solution to overcome all glitches in the effectiveness of digital marketing strategies is to learn coding skills of Python or other such powerful languages.
Python in Marketing Strategy
A modern marketing strategy consists of numerous components like social media, SEO, paid search, content marketing, ads, video, and others. You need technical expertise to understand the crux of all those components and analyze the data achieved by the virtue of those components. Python and R languages are the most popular languages used in the data analysis field, according to the Digital Vidya information.
To have a deeper insight in the marketing strategy, you should develop your own custom code to analyze the data collected in the digital marketing so that you can find out the fault lines, take the corrective measures, and launch the right campaign. According to the CodeAcademy information, Python is at the top of all other big programming languages like Java, PHP, and others. The enrollment to learn to programme in Python has also increased rapidly during the past few years.
Useful Tips on How to Automate Marketing in Python
The main components for automating the marketing campaigns include data mining, competitor price monitoring, SEO indexation, and such other tasks. Python is a very powerful programming language that can help you out in automating your marketing strategies with short and simple coding.
Let’s have a look at a few very useful tips on how to automate marketing in Python:
#1 Automate Data Collection
The marketers gather data from multiple sources repetitively for processing and analyzing. So, the collection of data should be fully automated to generate a big file of data. The main points in collecting data may include:
- 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
- Gathering survey data, chat chains, and other commercial data files
- Collecting email and SMS responses
- Top marketing trend information gathering
#2 Automate Repetitive Data Formatting
Once the raw data is collected from the multiple sources, you need to format that data in such a way that the entire data looks in sync with the data processing requirements. The main activities of repetitive formatting tasks include:
- Text string matching functions
- Number matching functions
- Marking/tagging data source, location, time, and other attributes of data
- Encrypting PDF file repeatedly
- Formatting functions for PDFs and web ads like splitting, watermarking and other such functions
#3 Automate Customized Error Checking
The software or the Python module that your company uses for data mining should accept the certain criteria and fields. 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.
#4 Automate Massive File Operations
The 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
#5 Automate Data Mining Process
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.
- The customized code should be developed for all data mining related tasks to find out the useful information
- Create a shortcode for the repetitive marketing tasks rather than doing them manually
- Making information summary as an automated task
- Highlighting the new trends of user behaviors
Why Use Python for Marketers?
According to the IEEE Spectrum ranking of the top programming languages in 2018 information, Python sits on the top of the list. The marketers, data scientists, big data engineers, and machine learning developers extensively use Python language in their respective fields.
There are numerous advantages of using the Python programming language in the digital marketing field. Those benefits are listed below:
- It is cheaper to learn Python than using readymade data analytics tools in the market
- It is very simple and easy to learn for even a novice programmer
- It has a large number of libraries for data analysis
- It is an open source programming language without any fee to use
- It is powered by a large community to support
- It is an interpreted language, which does not require compilation
- Its code is cross-platform portable
- It is an object-oriented language with high performance
- It is widely used in the marketing so new marketing-related features are counting
Top 5 Reasons to Use Python for Marketers
Python is extensively used in automating different tasks used for digital marketing campaigns nowadays. The main objective of using Python as an automation code development is to improve the marketing efficiency and effectiveness to create a competitive advantage over the competitors.
Let’s figure out a few important reasons for using the Python for marketing in the modern digital marketing field.
#1 Large Number of Data Analytics Libraries
Python language is powered by numerous data analytics related libraries that are extensively useful for the digital marketing professionals. The examples of such tools include NumPy, Pandas, StatsModel, SciPy and others. These tools are large-scale libraries for data mining, analyzing, converting, cleaning, processing, summarizing, visualizing, and reporting. There are many other libraries that can help you get a deeper perspective on the user data that you as a marketer, are interested in. The present-day digital marketing is useless if it is not properly driven by the meaningful information behind it. That information can efficiently be achieved by using the power of the Python language.
#2 Increased Data Mining Efficiency
By using the Python programming language, the marketers achieve huge efficiency in the data mining process. The traditional data mining processes mostly used excel sheet processing, which has its own limits and performance. For instance, processing an excel sheet of about 100 MB data at a better speed and performance would be difficult.
But, Python code can just do it in a few seconds without sweating at all. Thus, Python increases the efficiency of data mining processes commonly used for getting insight into the marketing campaigns as well as launching the new campaigns.
#3 Improved Search Engine Optimization (SEO)
Search engine optimization or SEO is one of the core components to make your marketing campaign a success. A better ranking index of the website can help improve the visibility of your website and business. A large number of matters related to SEO, such as 404 errors, meta tags, descriptions, robot text file, content duplication, faulty navigation map, and others can easily be detected through a custom Python code for automating SEO process.
Once the SEO faults detected, it is easy to remove them instantly before they could damage the search engine ranking badly. It is very critical to use the best white label SEO rules recommended for a high ranking index, which can be achieved by getting a deeper perspective on the website technical and content related issues in the early stages.
#4 Efficient Use of Big Data
According to the Research and Markets predictions, the global market of big data will grow over 14% CAGR for the next three years from the present value of about $65 billion in 2018. The total volume of big data will cross 44 zettabytes by 2020. To skim the valuable information from this valuable heap of data, Python plays an important role. Developing customized Python codes to combine, process, analyze, and visualize the big data makes the big data so useful for the marketers.
#5 Effective Campaign Monitoring
One of the most critical bottlenecks in making the digital marketing campaigns successful includes the monitoring and course correction of the marketing campaigns. The use of Python custom codes can make the life so easy in monitoring the ads, effectiveness, clicks, checkouts, conversion rate, and other parameters in the real-time.
This monitoring can help the marketers make the campaigns more focused towards the desired segments by correcting the fault lines in the campaign components. A good Python code is able to monitor Facebook, Google, YouTube, and other ads in real time by using the APIs of the social websites.
After having discussed the different technical and commercial aspects of Python and digital marketing, we come to conclude that:
- Programming skills are important in the modern digital marketing field
- YouTube course on learning python
- Python leads all other languages in data analytics and digital marketing
- Top 5 useful tips out of the many include automation of data collection, processing, mining, and repetitive tasks that are time-consuming and less productive.
- Top 5 important reasons for using Python for digital marketing include big data handling, automatic campaign monitoring, data mining efficiency, SEO automation, and powerful libraries of the platform