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Data Science: 6 insights to understand the concept in practice

Renan Andrade
Renan Andrade

Aug 9, 2023

Data Science

The concept of Data Science emerged with the advancement of the internet and the different possibilities of connectivity. Since it became easier to access certain services through the network and communicate with people online, for example, a large volume of data has been generated.

Right now, we generate more data than ever before, and the importance of using it consciously is increasingly recognized. Data Science is the scientific approach to this information with the development of analytical models that allow its strategic use.

For companies, the advantages are clear and unquestionable. It is possible to identify new business opportunities, make investments with a higher rate of return and anticipate trends. For all these reasons and more, Data Science is directly linked to a more current and strategic approach to marketing.

Want to better understand this data-driven approach and what its advantages are for educational marketing? Read the post to the end and check out these 6 insights on the subject!

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Data Science Cycle: from theory to practice

It's no use having an impressive volume of data in hand without knowing what it means or what trends it may indicate. That's why Data Science has a cycle that guides the identification of problems and the search for solutions. This circuit is continuous and formed by six essential steps for the process.

The first phase is precisely the understanding of the problem. What is the issue that needs resolution right now? From there, the second stage follows: the collection of data that can provide an answer to the problem that was raised. Not all of them will be relevant, so it is essential to know which ones should actually be consulted in the search for elucidation.

Next, we move on to processing the data that were selected. They are organized in advance so that they can go through the analysis in the next phase. The fourth stage of the cycle is exploration, that is, where the information collected is analyzed, allowing the formulation of some hypotheses.

The fifth phase is the moment to present the results to the rest of the team. After extracting the insights, all professionals involved must have access to what can be inferred through the analysis and, from there, reach the feedback, which is the sixth and final step. This is where the team tries to see if the conclusions drawn really respond to the identified problem. 

Feedback allows investigating whether the analyzed data were sufficient and whether the process could have been done differently. From this, other problems can arise that will start new cycles. The objective, in fact, is for these studies to be carried out on an ongoing basis, with a focus on optimizing the business.

There are different types of data analysis

It is already known that data can be used strategically, but it is essential to know how to extract the necessary information. For this, there are different types of analysis with different objectives, ranging from understanding what happened to defining the next steps.

One of the best known is predictive analytics, a kind of “forecasting the future” based on data from the past. For educational institutions, this is a type of analysis that allows discovering the risk of a student canceling enrollment and what strategies can be taken forward to promote retention, for example.

This forward-looking characteristic of predictive analytics has everything to do with the core focus of Data Science as a whole. Unlike Business Intelligence, it is intended to understand what is to come.

In addition to the predictive one, there is also the prescriptive one, which points out which are the best actions for the moment. It also gives an important direction after the study of the obtained data. In descriptive analysis, the objective is to understand what has already happened. Through this understanding, it is possible to reproduce past effects or outline new goals.

Diagnostic analysis is often confused with descriptive analysis, however, it seeks to understand the reasons that led to certain events. Interpreting the data will not only clarify what happened, but why it happened.

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Data Science is directly linked to smart marketing

It is not difficult to understand why Data Science has gained so much prominence in recent years, a trend that has everything to continue. This data is generated in a digital environment, the same environment in which marketing has concentrated its most profitable strategies.

Data powers this industry, making marketing campaigns much more efficient with smaller budgets. In addition, they can provide crucial information, such as which platforms to bet on to obtain a greater return, how to optimize the segmentation of the database and what type of tests can be performed.

Talking about smart marketing is, without a doubt, also talking about Inbound Marketing. After all, this is a model that is more in line with the current market, which establishes a relationship with customers and potential customers instead of just focusing on sales. 

The correct analysis of the data meets the sales funnel, so it also meets the leads where they are. In this way, it is possible to offer the right information, product and/or service exactly when the potential customer is ready to carry out the transaction.

Remembering that the lead is important for you to know that this contact created interest in one of your services. You can contact them through marketing automation and convert them into a customer using landing pages (digital marketing), offering content (content marketing), or even creating a campaign. 

Data Science provides a greater generation of qualified leads

The data analysis carried out aims to bring more clarification on the behavior patterns of the company's target audience, their desires, desires, pains and the main market trends. With this information properly collected and interpreted, strategies can be targeted more easily.

Through well-suited and segmented campaigns, it is possible to get a greater number of leads. The data also helps to accompany them throughout the purchase journey, maintaining a flow of nutrition appropriate to the stage they are in.

Qualified Lead can result in a higher conversion rate, as the qualification is done at the right time and in the right way. Close communication with the marketing and sales team makes this process even more streamlined.

Efficient data analysis makes it possible to generate more sales opportunities

And speaking of sales, it's easy to come to the conclusion that they increase when analyzes are done for that purpose. Whether through a descriptive analysis or through a prescriptive analysis, for example, it is possible to better understand the scenario and review what needs to be revised.

Competitiveness, whether in the educational market or outside it, is growing, and the advantage is on the side of those who identify the best opportunities and anticipate them. Predictive analytics are excellent allies at this time, showing possibilities and even guiding a possible change in positioning, if applicable.

The team's expertise makes all the difference

Data alone does nothing on its own. It is essential that there is a team able to carry out the necessary analyzes for a particular business. The work of these professionals ranges from problem formulation to resolution, that is, it goes through the entire Data Science cycle mentioned above.

Therefore, when deciding to add Data Science to your institution's marketing strategy, look for someone with experience and knowledge in the subject. This decision can affect the results obtained, undermining or exceeding your expectations. At Mkt4edu, this is a serious matter, so we work with the best resources and professionals.

Learn more about the specific use of Data Science in marketing, how it can generate qualified leads and why your educational institution will benefit from it in our post on the subject!

Check out the reasons to use data science in your marketing strategy!

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