Understand the relationship between Data Science and marketing and learn how to create more strategic actions – WAU

Data Science is fundamental for companies to work with data at a high level and generate insights, obtaining perspectives for the future. It is part of a strategic activity and focused on results and development. Its impacts on marketing range from supporting action planning to forecasting possible market movements.

The era of digital transformation, the historical context of the technology in which we currently live, has made data the center of work for many companies.

Therefore, Data Science has become an important field of work for any company that makes use of software, systems and various platforms.

Capturing, structuring and analyzing this data is part of a fundamental task, allowing companies to work strategically.

For all of this to be possible, Data Science practices and work must be properly disseminated in each business, for the best use of information.

In this post, we will show in detail what Data Science is and what is its current impact on companies! Understand better what it is and how is the routine of the professional in this field.

What is Data Science?

Anyone who works in marketing must have heard the term Data Science quite often. It is very current and, consequently, of great relevance to the company’s positioning strategies.

Decision-making increasingly depends on the collection and study of information that the company generates in its activities.

Data Science is nothing more than the set of techniques, theories, analyzes, observation parameters, algorithms and principles that support working with data.

The main purpose of this field of work is to use this volume of information to extract analyzes and observe specific behaviors.

Data Science’s work is more than a statistical analysis of what is happening at the moment. Through its tools, this field is able to point out possible future movements.

Therefore, with the analysis of current results, it is possible to predict behaviors, trends and events.

Data Science working bases

The basis of this work are resources such as the algorithms that support data science. When dealing with administered data, they are able to learn more about the behavior of the information.

It is what makes it possible to understand the behavior of the data and to have a perspective of what this will generate in facts for the company in the future.

This revolves around continuity work, which is precisely what allows Data Science to be really relevant and to lead the business on a strategic path.

Analysis from different perspectives

With its work tools and technologies, Data Science can analyze data from different perspectives. This makes the work even more fruitful, giving the company the ability to understand not only the current moment, but also what awaits it in the short, medium and long term.

The decision making made possible by Data Science is divided into 3 types of main perspectives: the predictive causative analysis, the prescriptive analysis and the machine learning made possible by the algorithms.

Predictive causative analysis

Predictive causative analysis enables formulation of a model to prevent possible events in the future. A given event is identified as this possibility, thanks to the knowledge that this type of analysis offers.

A good example is the payment options that you have enabled your customers to use. Through this starting point, this analysis allows to predict whether, in the future, these means of payment may generate some type of default. The predictive analysis model helps to have a future vision in this direction.

Prescriptive analysis

In addition to capturing the future perspective, this is a type of analysis focused on also offer the best solutions in face of what is observed. The captured data goes through the readings of algorithms that help to arrive at improvement or optimization insights. Thus, everything can be optimized.

Machine learning

Machine learning, or Machine Learning, is a scientific field focused on allowing systems to learn from data entry. The more they are exposed to information, the better they handle the activities to which they are dedicated. All of this automatically, without supervision!

In practice, machine learning is also a way of capturing insights and perspectives regarding behavior in relation to data.

The machines are able to detect specific trends and deliver these observations to data scientists, who then support decision making.

What is the relationship between Data Science and Big Data?

Big Data is a field of science in which the focus is on large volumes of data, those that exist on a large scale, unstructured and difficult to capture.

The intention is, precisely, that there are no limitations when it comes to accessing this information, regardless of the range of sources in which they are.

A company has data being generated at all times in its operations. The point is that this information is worth gold, since it shows in detail the results of the organization’s activities.

In addition, data on the customer’s relationship with the company also helps to understand preferences and consumption habits.

Big Data has the function of not impose any limitations on the capture of this information, regardless of where it comes from and whether it is mixed and “messy”.

Therefore, this field offers a very interesting support, being one of the pillars of Data Science and helping to work with more data.

Big Data as a fundamental part of Data Science

It is impossible to talk about Data Sciente without going through Big Data, simply because there are no concrete effects working with a low volume of data.

Data Science needs to have access to the largest amount of information possible to then bring deeper analysis and insights, with strategic returns.

Without Big Data, this is not possible. With that in mind, it is what will take all relevant data to the stage of analysis and perspective observations that Data Science takes care of.

Capturing and structuring a high volume of information is part of the fundamental work for Data Science to deliver strategic results.

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What is the impact of Data Science on marketing?

Data Sciente proposes its study of data for the benefit of all areas of the company and, among them, marketing manages to reap very advantageous strategic results.

Insights and perceptions help to better prepare actions and deal with public preferences and consumption habits. Thus, segmentation and relationship become more accurate and with better results.

Next, better understand where Data Science delivers marketing gains and how companies can take advantage of the results of this advanced work with information.

Support annual marketing planning

Marketing planning is one of the most important steps at the beginning of a period. The company needs to have its goals precisely defined, and the results and data from its recent activities certainly have an impact on that.

Data Science helps to understand what is necessary for the company to define how its marketing will work in favor of business development throughout the year.

Planning will only be effective in the face of the latest results. A current analysis, that is, business statistics, is what will provide the support to reach different perspectives.

Thus, Data Science will allow us to understand what the company should expect in the coming months in all its fields. This refers to the market and consumption, that is, it has a direct relationship with marketing, helping to establish safe goals.

From these goals, planning will have more specific actions focused on what the company intends to develop, improve or avoid according to the Data Science perspective.

Analyze consumption habits and propose strategic positioning

One of the pillars of data analysis in relation to results is to bring what the public of the brand is consuming. This does not mean only what he buys, but his habits in that position.

The means of payment, the average ticket and other practices represent a lot for the company. All of this can be perceived by Data Science, which, in addition to bringing information, is capable of realizing perspectives of change or evolution of this behavior in the future.

Because of this, the practice brings a very positive gain with regard to strategically positioning the company before its consumer. This means adjust business activities and practices so that he is able to attend to this behavior and habits of the public.

The trend is for the business to increasingly deliver what the consumer demands, whether in terms of products or services and services, for example.

Develop more accurate marketing campaigns

The marketing campaigns are varied and make it possible to position the company in the market, communicate with the public, publicize its products and be present.

Both in the offline and online context, targeted campaigns generate the necessary impact for the company to show the market that it is ready to occupy its space in it.

This has an exponential gain if Data Science can bring information that helps to build quality actions and campaigns, which are those capable of delivering value, that is, capture the audience’s attention, taking the message clearly, with the right language and appropriate tone.

All of these details depend on a deeper understanding of the consumer’s profile, exactly the work that Data Science makes possible with its study of the data generated between interactions and consumption.

In practice, Data Science studies the information that the public generates on social networks and in the business relationship with the company to generate positioning insights. From this, the actions are even more accurate, that is, capable of generating the real impact on the brand’s public.

Predict possible market movements

One of the most striking features of the digital age is the dynamism in which business forms and consumption habits change.

The market is subject to major changes at any time and, from one hour to the next, profound structural changes take place. Amid this, comes out ahead that company that is able to predict these trends – and Data Science is the tool for that.

As you saw in this content, among the different perspectives of analysis, the predictive ones are the strongest in this sense.

They go beyond a momentary observation based on statistics, which allows us to understand how the analyzed data represent possible trends and changes in behavior in the medium or long term.

This gain is extremely competitive and allows the company to adapt to new scenarios. Strategically, predicting market movements means get ready quickly, both in relation to the operational structure and positioning. The result is a more agile adaptation, avoiding being left behind and losing space in your segment.

Who is the data scientist and how do you become one?

The data scientist is an indispensable professional for Data Science to be implemented and really work in the long run.

He is a key player and has the knowledge, tools and expertise necessary to analyze information, automate readings of algorithms and transform data into insights, perspectives and paths for strategic changes in the company.

To deal with information and turn it into important content, it is necessary that this professional is multidisciplinary, with extensive knowledge in fields of the most varied.

This allows him to understand the problems and demands of the company, how to arrive at the best solutions and extract these proposals from the data he analyzes.

To better understand this information, the data scientist also needs to have deep knowledge of programming language, mathematics, statistics and fields of technology and IT.

So he can program the algorithms and set up mechanisms that can capture strategic returns from the data you analyze in your work.

Data Science is very present in large companies, but this is not always a widely discussed subject. However, companies that want to make better use of their information and turn it into strategic direction cannot give up this field of work.

By the way, using customer data in Data Science practice requires some care. Privacy and sharing of private information must be respected. Check out what the General Data Protection Law proposes regarding this corporate responsibility!