How to Use Python to Improve Market Performance

Did you know you can use Python, a data analysis tool, to improve market performance? Well, you can. Python works by helping to optimize your marketing decisions by providing the data necessary to drive insights to improve decision-making. That’s why marketing is more data-driven than ever! Marketers embrace data collection and analysis to improve market performance by providing data that allows them to personalize the customer experience, evaluate the success of campaigns, present findings to decision-makers, and automate analytic processes to reduce the effort necessary to gain insights. However, dealing with such a huge amount of data that you must analyze on a daily basis is a tough row to hoe.

Using Python, you can not only evaluate tons of data but automate the process to ease the strain on market resources and bring the best data analysis results to the table. Marketers can either hire Python programmers to achieve their goals or learn Python themselves to use it on a daily basis.

To understand where and when we can benefit from Python to improve market performance, first, let us discuss the challenges marketers face.

Challenges marketers face

Digital Marketing is at its peak and marketers face various challenges to keep up with their competitors. Let us have a look at some of the significant challenges they are facing:

• Advertising costs are very high for traditional advertising. For instance, a Super Bowl ad cost around $7 million last week, forcing several large advertisers like Disney and Netflix to share the cost for a single ad rather than produce one strictly marketing their brand. And, it’s not just Super Bowl ads, as you can see below.
• Marketing methods like content marketing are effective but attracting attention is increasingly difficult as more companies embrace the strategy. By understanding which content marketing strategies deliver better results, you can optimize your performance.
• With the splintering of users across multiple channels, marketers must use targeted efforts aimed at users across more than one channel. But it is hard to manage all these channels while maintaining coordination on all the platforms.
• Even after collecting tons of data from your marketing efforts, the massive challenge of analyzing this data to derive insights that improve market performance remains.
• Considering all these challenges, ROI (Return On Investment) is slow, as shown below. Even more concerning is data showing nearly half of the surveyed marketers don’t know the ROI of their digital marketing efforts. Hence, they don’t know how to make the best decisions to improve market performance.

  

Using data to overcome these challenges?

Along with technical implementations, planning and analysis play a major role in efforts to improve market performance. So, before investing your money, it is important to research the market well! It is important to examine competitors, target audiences, demands, and other external factors rather than assuming or making guesses. You need to have an effective marketing strategy to make use of all the data available for planning.

All this planning is not possible, without the use of technology and, when it comes to data analysis, there is no other better technology than Python. Later, we’ll discuss why Python is best for helping you improve market performance.

What is Python?

Python is a programming language that’s fuels Microsoft Excel. It became popular because of the easy-to-understand syntax that makes it the first choice for beginners. In the last few years, it became popular for machine learning and artificial intelligence but it’s a mainstay of other forms of data analysis that aids in better decision-making. Python is also the best platform for automation. It is cheaper to learn Python, which is an open-source platform than to use readymade data analytics tools in the market. Because it’s open-source, new libraries appear all the time to reduce your coding needs, more about these below.

Benefits of using python to improve market performance

Python has a lot of important benefits to help improve market performance. Let’s discuss them one by one:

1. Indefinite libraries

Marketers can benefit greatly from the numerous data analytics-related libraries supported by the Python programming language. These libraries extend the utility of the basic Python syntax and improve flexibility for specific needs. You can use tools like NumPy, StatsModel, Pandas, and SciPy. These libraries are hubs of large-scale libraries marketers find useful for data mining, data visualization, data analysis, cleaning, processing, conversion, summarizing data, and more. Nowadays, if you don’t fuel your marketing strategy with data insights, improving market performance is uncertain.

2. Improves data mining efficiency

Python helps marketers perform data mining by automating processes. Conventional data mining methods use excel sheet processing, however, this program suffers from certain limitations. For example, it is challenging to process an excel sheet with roughly 100 MB of data at a faster rate and with better performance. However, Python code may complete the task in a matter of seconds. By the same token, processing data from different Excel sheets isn’t easy while Python makes this as easy as designating the database and location of the data you wish to analyze.

3. Improves SEO

We all know that one of the essential elements for the success of your marketing effort is search engine optimization (SEO). A higher rank ins search improves the visibility of your website and business when your content matches user queries. More visits to your website, especially from targeted users, correlates with higher conversion rates. Python quickly identifies many issues that lower your rank, including duplicate content, 404 errors, robot text files, meta tags, descriptions, navigation maps, and many more to help you improve market performance by improving rank.

4. Campaign monitoring

Monitoring and course correction are one of the most difficult parts of digital marketing operations. Knowing which decisions helped improve market performance highlights where future efforts should go. Using Python, you can easily monitor and compare ads, clicks, effectiveness, conversion rates, checkouts, and other parameters that improve market performance. Python can help fix the flaws in the campaign component to assist marketers in narrowing the focus of their campaigns to targeted segments. Using the APIs of social websites, good Python code can track ads on Facebook, YouTube, Google, and other websites in real-time.

Python for marketing use cases

Here are some use cases that Python offers for Marketers:

1. A/B testing

A/B testing is one marketing technique for evaluating the performance of various iterations of an app, website, or ads. With Python, it is easy to compare two or more content options to determine which option achieves your goals. For instance, you can compare the conversation rates of different CTAs (calls to action) or email subject lines to determine which converts at a higher rate. Python produces meaningful statistics that explain why one of the options is superior to the other.

Python is the most ideal tool for A/B testing and discovering the significant difference between different approaches without having to wait as long as with the conventional approach. Python helps you choose the best in class for you to offer what customers seek.

2. Data visualisation

Data visualizations are critical for aiding analysis and developing insights because it is difficult to connect the dots and understand the outcome of your efforts from tables of data. Python provides a dedicated library called Seaborn that helps you create eye-catching graphs like the one below showing communication links between social media users. Armed with this data, you can identify linchpins capable of reaching your target market more effectively. Using Python, it takes just one line of code to create such beautiful and easy-to-understand graphs.

Data cleaning and other forms of pre-processing your data needs for accurate analysis are also easier using Python.

3. Customer segmentation

Today, customers and prospects want a personalized experience that meets their needs. In order to offer such an experience, marketers must understand consumers’ interests, behaviors, demographics, geographic locations, and lifestyles so they can customize messages, suggestions, and overall experience to segment a broad market into one or more target markets that offer opportunities for conversion.

The secret to knowing your customers and customizing marketing efforts for them is effective customer segmentation (dividing the market) and positioning (customizing messaging to appeal to a single target market). Building on customer data, Python provides access to the most advanced clustering methods to help identify different segments within a population. By using different types of machine learning approaches with Python, you can easily implement this process to improve market performance by increasing customer satisfaction with offers designed specifically for them. For instance, Netflix’s use of a suggestion engine to recommend movies based on your individual viewing habits improves your satisfaction with the brand by reducing time wasted looking for a movie to watch.

4. Customer feedback

Customers provide feedback on different platforms about the products they use. Much of this data is unstructured, consisting of words rather than numbers and many analytic tools struggle to analyze this type of data. Smaller businesses might manually process this data, as it doesn’t represent a huge volume. However, for large businesses, it is challenging to manually examine all the evaluations posted on various websites and social media channels. Python automation can do wonders to improve this process through the collection of data in one place and using NLP to aid in the analysis.

Natural language processing (NLP) involves analyzing words to provide insightful data that marketers can use to address a variety of concerns, including the ones listed below.

• What aspects of your product do buyers appreciate or dislike?
• Are your customers emotionally invested in your product?
• Has the public’s opinion of your company evolved over time?

Marketers find it tough to analyze this information manually. Fortunately, you can automate customer feedback analysis using a variety of open-source tools and pre-trained models that are readily available online.

Python for automation

For marketers, data mining, price monitoring, indexation, inventory management, and other activities require automation as these are frequently repeated activities as part of normal operations. Python is powerful enough to help you with automating your analysis.

Let’s have a look at a few useful tips on how to automate marketing using Python:

1. Data collection:

Usually, marketers gather data from multiple channels and sources to analyze later. So, Python can help with data collection by automating the whole process and generating a file the marketer can later feed into another program for analysis. Here are some examples:

• It can automate the indexation process and trace changes in ranking
• Collects data when pricing changes in the competitor’s products
• Collects survey data
• Collects email responses from customers
• Data collection on ongoing trends

2. Data formatting

Once the data collection process is completed, you must format the data to sync the entire data processing requirement. Python can help with data formatting by:

• Matching text string functions
• Matching number functions
• Noting down location, time, data source, and other data attributes
• Encrypting PDF file
• Formatting functions like watermarks for PDFs and web ads

3. File operations

Marketers manually edit or remove files to match up the criteria like data strings, timestamps, or any changes in the file. However, with Python, you can easily automate any operations on files.

• Reading file attributes and properties
• Tracking modification in files by comparing them with timestamps
• Custom code to match your workflow
• Automated form filling, renaming files, and sheet formatting

4. Data mining

Data mining plays a major role in all types of marketing given the vast amount of data available today. Although components might vary in different companies. Python can automate the data mining processes providing major relief to marketers.

• Customizing code for data mining and other related tasks to discover useful information
• Creating shortcodes for repetitive tasks rather than doing them manually
• Automating tasks for creating information summaries
• Collecting the new trends in user behaviors

5. Error correction

Last but not least, you can use Python to auto-correct typos and errors. Any software you use for data mining only accepts certain fields and criteria. Python codes detect errors to improve efficiency and save you valuable time.

5 Ways python programming can improve market performance

Marketers can learn Python and upscale their portfolios. As we stated earlier, Python is easy to learn and with its many libraries, you can focus on learning those you find most valuable from a marketing perspective.

1. APIs

Application Programming Interfaces (APIs) are one of the most useful use cases of Python that marketers can learn. APIs are a part of the software that helps your software connect with others. APIs can help you with text analysis, data analysis, data collection, and whatnot. The data that you manually collect, APIs can do that for you. Some websites have open APIs for use. It is usually found in the footer navigation of a website. If you know Python, you can understand those docs and get the data from there.

2. Web scraping

As a marketer, you can relate to the need for you to analyze or basically scan the whole website of your competitors. For instance, you might scrape a competitor’s site for pricing or other public-facing information. You may also want to scrape other information regarding pages, keywords, images, etc. Public data such as that from government sources is another place where scraping data can help. For instance, when I worked for the Small Business Association, we routinely scraped data from government RFPs (requests for proposals), identified clients that might want to develop a proposal, and sent them the RFP.

If you learn Python you can create your own piece of code that scans websites and scrapes information from the public aspects of the website. It saves you time and delivers accurate results. Once you know how to do it, you can use this for multiple websites and analyze the data.

3. Text analysis

Reading trends is important for marketers because they want to create content that matches these trends whether that involves trending keywords, topics, or hashtags. Similarly, analyzing content that ranks well gives you insights into multiple factors like the tone of the content, topic modeling, etc. Or maybe you want to analyze the tone of comments and reviews about your brand from your customers to track sentiment about your brand over time. Text analysis us NLP helps analyze what your customers feel about your brand and what you can do to make it even better. If you learn Python programming, text analysis is much easier.

4. Data visualization

Digital marketing is loaded with data mining intensively. But you need visualization power to make sense of all the data you gathered. Python can help you solve this problem with its amazing libraries. Python frameworks like Jupyter can make data mining with large data easy and fast. Someone handling a spreadsheet with thousands of rows knows it’s definitely no fun to work with, let alone develop insights from these huge tables without visualization. You might miss out on important information that would actually help your business by overlooking it on a table. For instance, the graphic below is easy to derive insights from, such as electronics offers a huge opportunity for new products on a global basis. A monstrous table showing the same information takes much more effort to evaluate.

5. SEO

Python plays a vital role in Search Engine Optimization. You can build code based on your technical SEO knowledge if you know Python. A lot of SEO professionals are diving into Python for this reason.

Resources to learn python

There are a lot of online platforms and resources like Youtube, Udemy, FreeCodeCamp, etc. that can help you learn Python and make the best use of your SEO knowledge. Here are some platforms from where you can learn more about Python:

1. Youtube

Youtube is the best platform to start learning to code. It is loaded with a lot of informative tutorials. If you are from a non-engineering background and have second thoughts on whether you would be able to understand and learn to code, Youtube is the first ladder toward success. However, it doesn’t allow you to practice, which is an integral part of learning any language. But, heck, it’s free.

2. Udemy

Udemy is the most affordable platform for learning Python, with courses ranging from $10 to $15. It offers a number of beginner-level courses for Python. You can enroll for the ones that best fit your budget and needs. These courses have learning materials, tests, and certification at the end of the course. You can watch the preview of the class first to see whether you get along with the teaching style of the instructor.

3. FreeCodeCamp

This resource is one of the most effective educational platforms to learn Python. They have certification courses for Python. Many of their tutorials are available on YouTube so you have a lot of learning material available.

4. W3Schools

W3Schools offers free training as well as paid training resulting in certification across a wide range of programming languages including Python. The site includes training on multiple libraries such as NumPy and allows users to try out the syntax to reinforce learning. The platform also offers quizzes for self-testing.

5. Other platforms

You can also check out CXL, DataCamp, LinkedIn, etc. for Python courses and tutorials. They have really good and informative videos that help you understand the language.

Wrapping Up

A marketer’s job includes a lot of tedious tasks like data filling, collection, analysis, and more. For multiple products with huge audience bases, it is difficult to analyze all this data. But it is inevitable to bifurcate those data to use it for future reference. This is when Python can help you the most. Be it automation, ML, AI, data analysis, data formatting, or any other action on data, Python is the best in the game. Its indefinite libraries and simple coding syntax make it easy for developers to turn thoughts into reality.

There are two things you can do to make the best use of Python. Either learn Python by yourself or hire a Python programmer. We already shared with you what you should learn and how you can learn. But if you want instant results for your business it is best to hire an experienced developer.  They can help you with all the modifications and tools that will help your marketing team each and every bit. Their experience in these fields can help your business achieve new heights.

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