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Businesses aren’t easy to run – especially if you’re doing it all manually. The good news is that software has made it much more efficient, saving a lot of time and money. Software can automate various tasks that would otherwise require a budget, and allow your team to work more efficiently together. Paper processes that previously used to be routine in businesses are almost completely eliminated thanks to software advancements.
In particular, Python has various uses in marketing strategy. Python is well suited for areas such as data analytics and AI, which are a key part of marketing. Python’s accessibility and ease of use also make it a great choice for businesses to use for improving their marketing strategy. This is combined with a number of reasons to choose Python as well. Other advantages of Python include the reduced cost compared to other data analytic tools. Additionally, Python has an extensive number of libraries that can be used.
Let’s take a closer look at specific applications of Python in marketing:
Task Automation:
Automation is one of the more appealing uses of Python in marketing, and for good reason. Various marketing tasks can be automated with Python to save time and money.
One example of this includes web scraping. Web scraping gives you the ability to extract data from multiple web pages and save it. This would be a highly time-consuming task to perform manually. If you have a web application, it’s important to monitor how well your content is doing. SEO analysis and lead targeting can be monitored with Python, providing you with information on possible improvements.
Another example is API integration. APIs are a very useful tool in marketing, as they can provide your Python application with additional features. Python allows you to connect to APIs with ease, and extract information in large quantities. Tasks such as social media posts can be automated via the usage of APIs, making it something you should certainly consider using.
Campaign Analysis:
A marketing campaign is a broad term that encompasses various forms of marketing. This can include content management, social media management, email, SEO optimisation, promotions and others. Campaign analysis is an issue for many businesses, as powerful software is required. Without campaign analysis, it’s difficult to determine what aspect of your marketing campaign you need to improve.
Python can make campaign analysis much easier, and provides various packages to help with your campaign analysis. Examples include NumPy, Matplotlib, SciPy, Sklearn, and Pandas. All of the aforementioned packages contain tools that can be used for data analysis, and campaign analysis as a result. In particular, Pandas can be used for data manipulation whilst Matplotlib can be used for data visualisation.
Business Analytics:
There are three main ways in which Python can be used for business analytics: descriptive, predictive and prescriptive analytics.
Descriptive analytics looks at trends and patterns for existing data. Having information on trends allows you to make informed decisions on any needed changes to your marketing strategy. Libraries such as panda and Matplotlib become especially important for this type of analytics. Pandas allows for a numerical approach in summarising the data, whilst Matplotlib provides for a more visual summary with data visualisation.
Predictive analytics uses previously known data to make predictions about the future. This is done through machine learning, which can be integrated into Python. Machine learning algorithms commonly used for this purpose include k-means clustering, Naïve Bayes and decision trees. Having access to future results can be a massive advantage for your business, as it will allow you to make accurate decisions.
Prescriptive analytics gives detailed information on future outcomes, and gives suggestions on what should be done. This is the last phase of business analytics, and provides the most advanced usage of the predicted data. It goes into detail about potential outcomes that would occur if a certain decision were made.
Data Visualisation:
Data visualisation is a widely used strategy in data analytics. There are multiple libraries included in Python that are great for data visualisation. The most commonly used library for data visualisation is Matplotlib, which also happens to be one of the oldest. Python data visualisation libraries as well. Different plot types that can be made with Python include line plots, side-by-side bar charts, stacked bar charts, pie charts and histograms.
Data visualisation is important for a number of reasons. It allows you to find trends that are occurring in your data over time. Moreover, using data visualisation allows you to see what is working and what isn’t working. Correlations in relationships can be noticed with data visualisation, which can then be used to make informed decisions. Ultimately, data visualisation is a powerful tool for analysing how well your marketing is working. It’s also used to make important decisions that lead to increased conversion rates and revenue.
Customer Segmentation:
A crucial aspect of marketing is understanding your audience. If you don’t know what your audience wants, it’s hard to make good decisions that will pay off. That’s where customer segmentation comes in, a means of understanding your audience. Python supports customer segmentation via machine learning.
Specifically, the unsupervised learning technique called k-means clustering is very useful for this purpose. This technique allows you to group your customers, and gear your marketing toward each group.
Conclusion:
Marketing is a key aspect that any business has to carry out, but it often poses a massive challenge. With the usage of python, time and money can be saved in various ways.
Marketing tasks such as web scraping and API interaction can be automated. Analysis of marketing campaigns can also be carried out using Python, which is essential to understand how effective your marketing campaigns are. In addition, Python provides the tools and libraries needed for the three stages of business analytics. Data visualisation is made much more convenient through the usage of libraries such as Matplotlib and Seaborn. And finally, carrying out customer segmentation using Python will give you the ability to understand your customers better.
Don’t hesitate to reach out to the Raw Jam team if you have any questions!
Written and researched by Saleem Maroof