Today’s marketing is very different from what it used to be a few years back. Nowadays, marketing is a mixture of domain knowledge, psychology, experiments, and quality content, at the least. Businesses are spending heavily on marketing, and this is due to the cut-throat competition. With such high competition, everyone needs to have their own USP to sell their stuff and grow the business, including using Python, and that’s where marketing helps right
Digital marketing has evolved a lot in recent years, and with the widespread adoption of the Internet and such digital tools, it is only going to increase. Digital marketing has helped businesses grow many folds by increasing their brand awareness, reach, and target audience. As 78% of customers research a product online before purchasing it, digital marketing is a must to help you sell the products by providing relevant information to your leads.
If you are a marketer, you already know how hard it is to market effectively and be successful in all your campaigns. But what if you could use technology for this? Well, many digital marketers are using different technologies in their marketing efforts already. You can do this, too, by using Python. Just like all the other domains, Python has a bright future here too. Going forward, we will understand why Python is used everywhere, and we will also explore a few use cases of Python in digital marketing.
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You’ve heard this many times, and yes, it is true. Python is one of the easiest languages to learn. The code looks quite similar to a plain algorithm which is language-independent. It is so easy that anyone can learn basic Python in a week or two and then go on to specializations that suit their needs.
Being the easiest and most popular language, Python has tons of self-help materials in the form of articles, books, tutorials, videos, podcasts, etc. You can even learn by reading through the official documentation, and you’ll not feel anything hard. Plus, there are online compilers where you can run your code without the need to install Python locally.
Imagine anything you want to do, and there’s a library written in Python that will help you achieve your goals. Python has a large number of open-source and closed-source libraries that you can use to get your work done.
Python libraries get regular updates, and they are pretty stable so you can use them in any kind of application. Moreover, there are so many libraries that you often get to decide which library to use for your particular task. All these open-source libraries can be installed using the Pip command, and once you have the library, there’s no stopping.
Python has libraries that can help you create data warehouses, analyze large amounts of data, create machine learning models, develop deep learning applications, build web applications, automate stuff, and many other things. It is one of the fullest languages, and you can do anything that you like by using a suitable library from the repositories.
Moreover, all libraries have excellent documentation and community support. So even if you are using anything new, you’ll have enough things to get acclimated to the code.
Python has so many features and applications that there is something in it for everyone. Due to this, it is used everywhere. Whether you are looking to build web applications or machine learning models, the language has enough features to accommodate both.
It is used heavily in the areas of DevOps, Machine Learning, Analytics, AI, scripting, automation, etc. No matter when and why you start using Python, you’ll always prefer it over other languages once you are proficient with this.
Python is cross-platform, which makes it accessible to everyone. Whether you have a Linux distribution, windows, or macOS, you’ll have a version of Python that you can install and run locally.
It supports all OS and browsers so that it is easy for you to develop and deploy applications. Moreover, Python apps can easily be deployed on different cloud service providers and server types.
Having known about why everyone uses Python, you’d be excited to find out its use in digital marketing. So let’s get started without any further delays.
One of the essential things in digital marketing is campaigning. It is nothing without a campaign. Campaigns are plans and actions that are taken in order to achieve some marketing target.
Companies run campaigns for different motives like raising more awareness, promoting their products, widening their target audience, and many other reasons. Running large campaigns is a challenging task for every marketer, and that’s a place where Python can help you.
With Python, you can create automation for your campaigns and create triggers that take your campaign forward without your interruption. Having campaign automation in place can help you run effective campaigns.
Whether you are already using marketing software or not, including Python will do wonders. Almost all significant applications have APIs that you can access and manipulate through Python.
With API access, you can write code that triggers certain events, and that will help you automate lots of things in your campaign. You can create custom automation scripts for each of your campaigns, and they’ll run without any issues, costs as Python is completely free.
Analytics & Business Intelligence
Running better campaigns is the key to growing your business. The best digital marketing experts keep on learning from their mistakes, and they analyze a lot of data every day.
Marketing campaigns generate tons of data in the form of clicks, redirects, link accesses, views, etc. All this data plays a crucial role in understanding the effectiveness of a marketing campaign. To create better campaigns every time, businesses analyze their past campaign performance and draw insights from them.
Analytics and business intelligence are places where Python is quite dominant. Python has data analytics libraries like Pandas, NumPy, matplotlib, etc., that help you wrangle, analyze, and visualize datasets.
Moreover, Python is also used extensively in creating dashboards and reports to present to high-level managers. The dashboards created by Python are highly customizable, and you can do all sorts of analytics at different levels and present your findings in a great way.
Marketing datasets hold important information inside them, and analyzing them becomes crucial. With prescriptive analytics and business intelligence, marketers can find patterns in the audience and reach data that can be important to predict sales and popularity of their products.
Your website may not have all the details you need, and you’ll often have to rely on external data sources. While some data might be readily available in formats that you can push into data stores, some data is available inside paragraphs of websites. In such cases, getting the data is challenging, but it is possible with Python. Web scraping and parsing are Python’s most popular applications since it makes crawling any page relatively simple.
Python has many different automation frameworks that help you crawl websites. You can inspect elements of a webpage and find out IDs and classes where your data is stored, and then you can parse the page to find out data.
As many websites have JavaScript today, the data you need will likely be coming from some other source. In such cases, you can use the selenium library to control your web browser and automate the loading of pages and fetch your required data from a dynamic website.
Marketers need to get more data about their audience to create better campaigns and understand needs. In such cases, web scraping is an excellent source to find out details and conversations about your brand so that you can quickly understand what is happening in the world.
As humans, we can understand everything we can come across. But we cannot read thousands of tweets and conversations about a brand on different channels. Even if you have some sort of superpower to read everything on a single channel, you cannot do it across all channels.
By now, you have the answer. Python can do this too. Text analysis in Python is quite a common application, and it helps you understand the sentiment of a piece of text. Text analysis involves natural language processing and data mining to find out the hidden meaning behind each text.
There can be tons of messages and posts that mention your brand’s name, and not each of them is meaningful. There can be support chats and messages, and it’s important to segment them. This helps you to understand what kind of support people seek in your products or marketing campaigns and how you can improve. Moreover, having messages separate can help you know the effect of your marketing campaigns.
Machine learning is a thing that has a place everywhere. There is always some space for improvement, and many machine-learning models help you get better.
With machine learning, you can predict the success of your next marketing campaign based on previous campaign data. Moreover, you can even segment and cluster the audience better to have a well-defined target group. There are tons of different things that you can do with machine learning. You can create a predictive model that will predict how users will react to a certain campaign even before you publish anything. These types of predictions help you decide what is good for the business and what might hinder growth.
The more you experiment with machine learning in digital marketing, the better you get. Machine learning can help you find trending topics even before they become the hype so you can create killer strategies beforehand and implement them just as the hype sets in.
All these machine-learning tasks are possible with your Python knowledge, so make sure to learn different machine-learning libraries to create such immensely helpful things.
Whether you knew Python before or not, I am sure that if you are stuck here, you’ll undoubtedly start learning Python today. So take this as a motivation and expand your skills as a digital marketing expert. It will help you make better marketing campaigns and also get better returns on each campaign. Even if you are looking to hire Python developer, make sure the developer has an idea of these benefits and uses. Moreover, everything you need is free, and if you make some great things, you’ll save tons of money on subscriptions across different tools too.
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