Fundamentals of Data Science that Every Marketer should Needs to Know

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Data Science

In the past few decades, internet services have drastically changed and improved the lives of millions of people. According to Statista, there are more than 21 billion devices connected to the internet constantly generating more than 2.5 terabytes of data every single day. This data acts as a gold mine for enterprises as it tells a lot about the current market trends and customer behavior. This disruption has also changed the way companies use their products and services. 

Technologies like Data Science have changed the ways enterprises look at that data and use it for their benefit. It enabled them to analyze the complex datasets and use them to create marketing campaigns, advertisements and plan future strategies. Therefore, it’s imperative that you know Data Science concepts to make effective business strategies and marketing campaigns. 

As a Digital Marketing professional, understanding the market trends and customer behavior is important as it gives you information about the potential customers. Check out the Best Data Science Online Course that is available in the market. It can teach you how to perform data analytics and discover valuable information. However, make sure these courses include these fundamentals concepts of Data Science:

Data Transformation

You may think that if it’s so important for digital marketers to learn data science, why don’t the companies just hire data scientists and get the job done? Well, it’s not that simple. Apart from the technical skills, the candidate should know how to operate in an organization and do the research based on its market goals. Also, he must have knowledge of marketing concepts like Search engine optimization(SEO), Google Ads, social media marketing, email marketing, and more. 

A person with the knowledge of these concepts can easily understand what the company requires and mold the data accordingly. For example, the majority of data generated by social media websites is unstructured and some of it might not be useful at all. By filtering out irrelevant information and null values, you reduce the time required to analyze the data and improve the final results. 

Data Segmentation

Data Segmentation is an integral part of data analytics and businesses spend a lot of time doing it. Here, large datasets are divided into smaller groups of tables or files, which gives users the details about an individual user, webpage, or social network. In this way, companies can leverage their advertisements and make every dollar productive. However, just doing the segmentation doesn’t mean your job is done. 

Other processes are involved in order to produce the desired results and productive workflow. Segmentation allows you to separate and group your customers based on their characteristics, choices, and apply them to create efficient marketing strategies. Demographic, geographic, psychographic, and value segmentation are some of the examples of segmentation done by marketers on the regular basis. 

Integration of data within Organizations 

Any Data Scientist spends a lot of their time gathering data from different sources. After that, he accumulates it with the help of a data analysis software like Tableau. The reason for this is that not all datasets are the same. Companies have a number of sources that store the data in various forms such as text or CSV files, Excel spreadsheets, database tables, etc, stored on both on-premise and cloud servers. So, it’s inherent that you know how to connect different datasets and use the information to increase your business value. 

Predictive Analytics

Predictive analytics use machine learning models and statistical algorithms to analyze the historical data and predict the likelihood of future events based on it. With predictive analytics, you can understand the market trends, demand of the product, its performance, lead scoring, and identify potential customers. With predictive analytics, you easily find the right audience and run targeted advertisements and make the most out of the dollar spent on the advertisement by the organization. Some of the popular models of predictive analytics include collaborative filtering, clustering, regression graphs, and search-based models. 

Data Visualisation

Last but not the least, Data Visualization is an essential part of data analytics when it comes to representation. It makes the processed data look nicer and more understandable. Even a person who’s not related to digital marketing can understand the results you’ve drawn. For this, you need to learn programming languages like Python. It has libraries dedicated to data analytics, visualization, and scientific calculations. 

Also, visualizing the data will help you represent the outcome of your market research in front of the stakeholders, managers, and other collaborators in an understandable manner. This will eventually lead the decision-makers to make data-driven decisions and improve their market value. 

Conclusion

Hope you learned how different concepts of data science are used to make efficient decisions and advertisements in digital marketing. In business, Data Science is all about analyzing the raw data and producing valuable insights from it. These insights are then used by different departments like marketing for their marketing campaigns and ads. Therefore, being able to implement data science concepts in real-world applications would surely give you an advantage in the market. 

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