Data science encompasses scientific approaches associated with computer science, statistics tools, and machine learning to receive invaluable data from huge arrays of information.

What is data science with examples? The information received through marketing data science gives companies solid grounds for making smart decisions in promoting their goods and creating models that would help forecast demand. Examples of data science spheres are finance, marketing, telecommunications, education, healthcare, human resources, transportation, etc.

Companies hire marketers or cooperate with agencies that develop strategies to reach clients and grow income. Marketers work with huge datasets to make those strategies the most efficient for companies. How do marketers use data to develop product strategies? Here is a brief list of what they do (we’ll discuss these points in more detail further):
• analyze information about clients;
• detect patterns;
• group potential clients;
• forecast client's preferences;
• do social networks research;
• analyze competitors;
• detect the need for improvement.

Based on the specifics of a marketers’ work and given the amount of client information they gather and process for building their campaigns, data science methods are vital for efficient work in this field. How do marketers use data to develop promotional strategies? They utilize data science tools in every stage of marketing campaign development. These are optimization of channels, analysis of social media, product development, segmentation of clients, composing a buyer persona, etc. Let’s take a closer look at what methods marketers use in these and other cases.

By scrutinizing client information, scientists identify the details clients would like to add. This information helps adjust the existing or develop an entirely new product.

A thorough investigation of a buyer persona helps identify what other products people would buy and at what price they would do that. So marketers give the companies information about products clients prefer to buy and additional items that would catch their eye.

How do marketers use data to develop product strategies? An essential part of developing product strategies is an investigation of different data sources (channels). Marketers use diverse channels that help connect their products with buyers. These include emailing, advertising through social networks, and other ways. It explains why people receive recommendations and advertisements that perfectly match their tastes and life philosophy on social networks.

Data science is critical for efficient work with potential clients on social media. By employing natural language processing, marketers evaluate interactions on social networks, uncover behavioral patterns, and gauge their sentiments. These findings are further used in developing strategies. The information received from this research helps identify drawbacks in published content and fix them.

Data science largely simplifies the work of a marketer, lowering expenses for companies, while providing much more efficient results. The purpose of a marketing data strategy is actually to make sure that every dollar the company spends on marketing will bring maximum profits.

When a company has a clear understanding of who its clients are and what goods they need, it avoids expenses on inefficient campaigns. Thus, companies receive a ready marketing data-based strategy that involves every detail - from understanding who is the target client to how one’s preferences may change depending on season or trends.

For a more fruitful analysis of consumers and awareness of their preferences, marketers divide them into groups. This segmentation enables the creation of a personalized approach geared towards achieving higher conversion rates. For that purpose, marketers choose the main criteria (psychographic, geographic, etc.) and analyze information across those predefined criteria. Machine learning helps discover patterns and common things among consumers and group them accordingly.

Examining past client behavior provides marketers with valuable comprehension of future actions. This analysis not only aids in preventing customer churn but also allows for an assessment of Customer Lifetime Value (CLV). By delving into historical data, companies can grasp which goods to market and to whom.

That’s another case of how marketers use data to develop product strategies. Here marketers apply segmentation and predictive research. These approaches help group consumers by their tastes and detect the features of those most likely to become paying customers. With a clear idea of a buyer persona, marketers create efficient strategies. 

By practicing a personalized approach, companies manage to deliver goods and services that align with the needs of clients. To that end, potential consumers need to be divided into groups. After that, marketers can communicate with them in a more personalized manner, boosting sales.

Cross-selling is the practice of suggesting additional goods to clients to complement their initially chosen products. It aims to stimulate clients to purchase more goods. Upselling implies tempting clients to pick a more costly or upgraded version of the item they originally intended to buy. It often includes highlighting additional features to explain the higher cost. Data science in marketing facilitates detecting these additional opportunities and making combinations of items that would grow sales.

Customer outflow is a widespread phenomenon, signifying the person’s intention to stop buying a product. With data science and marketing analytics, professional marketers uncover this intention and do their best to avoid it. For instance, they may reach a client and fix one’s concerns, propose discounts, etc. This way, companies retain the loyalty of their clients. To avoid client outflow, marketers apply predictive modeling and study sentiments.

Often, when buying online, customers share their emotions, impressions, or expectations on social networks. Customer-written comments or reviews gathered in a big set of information are the subjects of marketers’ study. 

For comprehensive research, marketers use the NLP tools, allowing for identifying patterns in user reactions. This approach helps understand if consumers are satisfied with the bought product and if not, how it should be modified.

Whether it is an internet provider, a streaming service, a cosmetic shop, or an auto parts store, customer support should be at the highest level. Unsatisfied client’s application may cause customer churn. To maintain client loyalty at a high level, businesses should provide customer assistance. 

When the client base is big, it may be a problem to satisfy every single application timely. To automate this process, companies provide such tools as chatbots that clients can address whenever they have a problem. To make this service as quality and personalized as possible, marketers research sentiments and forecast clients' behavior using machine learning. It allows training chatbots to recognize and answer clients' applications automatically without compromising on quality.

The most daunting (yet efficient) task in a marketer’s work is reaching the right client at the right time. Handling the vast array of data can be both costly and labor-intensive. Yet, using data science and machine learning accelerates this process for marketers. Through research on client behavior, language patterns, and segmentation, marketers can swiftly assess the potential cost of each client and elaborate lead-targeting strategies.

Basket analysis helps identify items that tend to be obtained together. For instance, someone who buys shampoo will also grab a hair mask or nourishing oil, or so. So these products are placed near each other to help customers easily access them. 

However, things are not always so obvious - some entirely different products can be bought together, remaining undiscovered by buyers. An example is the “beer and diapers” case study, claiming that visitors of a Midwest grocery chain often buy beer and diapers on Friday evenings.

This type of research demands comprehending clients’ habits and psychology to uncover hidden correlations that go undiscovered by people.

 So what is the overall impact of data on marketers and their companies? Having explored how data science is applied in marketing, there are no doubts left: it is crucial for this field.

Here are the proving facts about the necessity of data science for marketing:

• Stop wasting resources on inefficient marketing
• Reaching only valuable clients
• Prolonging the CLVQuick improvements based on client feedback
• Prediction of consumer needs and building products keeping them in mind
• Improving advertising
• Growing conversion.

How do marketers use data to identify goals? It helps align real consumer requirements with the goals of the business (or manufacturer). By connecting the company’s goals with data-driven objectives, marketers make weighted strategic decisions. It helps companies reach better returns and keep competitive advantages.

In the past, when organizations did not have access to extensive data, marketing specialists would personally handle many of the tasks mentioned above. They assessed client sentiments and did research by sending surveys. Targeted advertising relied on their intuitive understanding of client behavior. However, in our digital era, where organizations accumulate and store a lot of information, a data-based marketing approach has become the norm and even a necessity.

The reasons why data science should be integrated into a marketer’s work:

1. Data-based decisions. Marketers make decisions based on real information instead of guesswork. It helps make accurate targeting, improve communication, and allocate resources smarter.

2. Improved client understanding. Through data analysis, marketers receive an awareness of the behavior, pains, and tastes of customers.

3. Advanced targeting. Data science enables precise audience segmentation. It ensures that advertisements will be directed to the right group of people.

4. Improved campaigns. How do marketers use data to evaluate results? Techniques, such as A/B testing, help modify marketing campaigns by detecting which approaches and elements are the most efficient. 

5. Increased ROI. By lowering expenses and utilizing strategies with proven success, data scientists significantly increase profit for the business.

6. Predictive analytics. Specialists can foresee future trends, client behavior, and demand. These predictions help marketers timely modify their approaches.

7. Competitive advantage. In a modern data-driven world, marketers who use data science win in a competition, because they quickly react to market changes and promptly change their campaigns.

Becoming a professional marketing data scientist requires a lot of work and effort. This job combines in-depth knowledge of marketing basics with the addition of programming and statistics skills.

So, first and foremost, you should receive an education in marketing. Then study programming languages and machine learning. As with any job, you first need to practice and understand how it works. For that purpose, search for some entry-level job positions and do your best to handle all the tools of a marketer.

Don’t forget that data science and marketing are constantly evolving. As time passes, new techniques and instruments arise, so you should not miss out on the chance to learn them. To keep pace, you should always learn new emerging tools, approaches, and methodologies, and practice a lot. Otherwise, in just a year, your knowledge and skills will become outdated. It works similarly to programming where specialists need to learn and improve skills year by year. However, the result is worth that.

Let’s consider illustrative data science examples:

Netflix
When you first enter Netflix, you are suggested to view popular and recent films that most subscribers enjoyed and praised. Once you click your first reaction on the film, Netflix will offer you an adjusted range of movies, collected from those matching the genre and cast you have first liked. Netflix’s personalized approach is so efficient due to an in-depth study of view history and other subscribers’ actions (likes or dislikes given to specific movies).

Facebook
Facebook utilizes a multi-level form of data science. On the one hand, it possesses the instruments to provide its users with matching information and news feeds in compliance with their likes. Also, Facebook provides those tools for users who represent companies and want to promote their products and reach their audience.

Google
Google simplifies analytical processes for its users, facilitating returns of its business-owning users’ investments. Companies that use Google to promote their products do not hire an in-house data scientist, relying solely on Google’s services and tools. 

Airbnb
From the very beginning, Airbnb implemented marketing with data science and it brought fruit. Over time, as the business expanded, its marketing approach has become multi-faceted. Now data scientists are working at every level of the company, assessing every arising opportunity and solving all emerging problems along the way.

Wrapping up, data science has become an essential tool for marketers who aim to keep track of the rapidly evolving world. It allows marketers to build their strategies relying on data analysis, personalize their approaches, reduce marketing costs for companies, increase ROI, and foresee clients' needs.

We dare say that data scientists bridge product producers with each client by comprehending their pains, ultimately, satisfying them in the best way.

As the array of data to be handled is growing and information is digitized, previous approaches to analysis based on sending surveys or intuition, no longer work. Here comes the need for massive data analysis a human can't cope with alone. It becomes possible with AI and other instruments mentioned in this article. They are an essential part of data science, which is the reason why they should be implemented in every marketer’s work if one aims to stay ahead of competitors.