Data Science for Marketing: Top 12 Examples

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10th Jun, 2022
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15th Mar, 2022
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Data Science for Marketing: Top 12 Examples

Data Science is present in all fields. There is not a single industry or sector in modern times which can benefit from Data Science. The significance of data Science combines domain knowledge from programming, maths, and statistics to provide insights and make sense of data. When we consider why data science is becoming more essential, the answer is that the value of data is skyrocketing. Data science is in great demand since it explains how digital data is reshaping organisations and assisting them in making more informed and essential decisions. As a result, digital data is everywhere for anyone wishing to work as data scientists.  

A data scientist's work often entails processing massive volumes of data and then evaluating it using data-driven approaches. They bridge the business gaps by delivering the data to the information technology leadership teams and identifying the patterns and trends through visualisations once they have made sense of it. Data scientists employ Machine Learning and AI, as well as their programming skills in Java, Python, SQL, Big Data Hadoop, and data mining. They must have excellent communication skills in order to properly communicate their data-finding ideas to the organisation. 

A great way to get started with Data Science is this practical data science with python course. Learn Python, analyse and visualise data with Pandas, Matplotlib and Scikit-learn, create robust predictive models with advanced statistics and have industry exposure.  

Data scientists are a new breed of professionals that are in high demand right now. This word was coined a few years ago by data leads to LinkedIn and Facebook corporations. And now, we've seen a massive surge of data scientist geeks working in a variety of industries. This need arose as a result of an unexpected requirement for brains who could wrangle data and assist in the discovery of new information, eventually empowering corporations to make data-driven choices. Undertaking a few data science courses will help you in understanding the field better. There are many online learning programs and choosing the right one might be difficult. Knowledgehut has some of the best online courses out there. We have listed the top data science courses in India and abroad for our learners. 

Data Science in Marketing

It may appear that incorporating marketing data science into a digital campaign is a difficult task. Gaining deeper insights from your data may be easier than you think, with a rising number of organisations incorporating machine learning and AI into existing marketing. 

If done properly, implementing Data Science for Marketing could yield great results.  

Data Science in Marketing

Even if a company or individual has a fantastic product or service, getting clients would be tough without adequate marketing and advertising. The notion of market research isn't new. Market researchers, on the other hand, are today flooded with data as compared to data collected in the early twentieth century. We can now collect an exponential quantity of information on customer preferences and other trends that may affect marketing efforts via Twitter, Facebook, Google, and other social media platforms. To learn about Data Science in marketing, you can try a marketing data science course 

As the marketing process evolves and changes with time, Data Science and Marketing will get more related and Data Professionals will work with Marketing teams to work on a data-driven marketing strategy.  

Data Science Workflow:

Data Science Workflow

The phases (or steps) of a data science project are defined by a data science workflow. A well-defined data science process is beneficial in that it serves as a straightforward reminder to all data science team members of the tasks that must be completed in order to complete a data science project.  

  1. First, we have problem-framing and understanding the goals. The ability to precisely articulate the problem is more of an art than a science, yet it's a necessary first step in every Data Science effort. It's quite simple to waste a lot of time doing a lot of things without making much progress toward generating company value if you don't have a compass that everyone on your team follows. 
  2. There are a lot of applications of Data Science in Sales and Marketing. To achieve a business goal, the problem must be understood.  
  3. Next the data is prepared. Clean and prepared data is very important, hence this step can take a lot of time. 
  4.  An important step of data science is analysis, which entails building, executing, and refining computer programmes to evaluate and gain insights from the data prepared in the preceding phase. Data scientists commonly switch between the analysis and reflection phases: the analysis phase focuses on programming, while the reflection phase involves thinking about and sharing about the results of analyses.  
  5. Disseminating results in the form of a data science product or written reports such as internal memos, PowerPoint presentations, business/policy white papers, or university research publications is the last phase of data science. 

The data gathered through marketing activities is massive, enormous, gargantuan, and every other possible adjective for massive. While marketers are experts in strategy development, optimization, and execution, data scientists are experts in managing, manipulating, and extracting useful insights from data. There are numerous uses of Data Science in Sales and Marketing.  

These evaluations address client intent, approach, and purchasing behaviour, as well as everything in between. An in-depth understanding will provide you with all you need, from awareness to income.  

Practical Ways in which Data Science can be used in Marketing:

There are many ways in which Data Science can be used in Marketing, let us have a look at some of the most prominent ways.   

1. Consumer Profiling 

Thousands of consumers are looking for the same item, yet none of them are identical. A marketer must be familiar with his customers' profiles and the features that distinguish each one. Data science can assist leverage each prospect's interest and like based on these attributes and give optimum outcomes as a consequence. Understanding a customer is very important, and consumer profiling helps in that.   

2. Channel Optimisation

Choosing the correct channel for marketing is very important. The major insights that businesses have gathered about their clients throughout the years have mostly been their age, location, and gender. This information provides firms and marketers a very limited understanding of who their clients are and what they desire. 

Data science, in the form of techniques like affinity (or market basket) analysis, may provide a far more precise picture of the type of customer a company wants to attract and where they should be targeted. A complete examination of the customer's social media involvement may be used to make connections that will construct a specific tale or pathway. 

3. Customer Segmentation

Customers are all unique people. As a result, a one-size-fits-all strategy is ineffective. In this situation, customer segmentation comes to the marketers' rescue. Marketers may slice data and group customers with the use of statistical analysis. Customer segmentation is the process of categorising consumers into groups based on the overlap of specific criteria in their attributes.  

Customers can be divided into groups depending on factors such as their geographic region, previous purchase history, and how they visited your website. Specific machine learning algorithms may be used by data scientists to assess the potential worth of each ideal client group as well as which items are most likely to appeal to them. This information may then be used to guide your content strategy, channel optimization, and advanced lead targeting. 

Customer Segmentation

4. Marketing Budget Optimisation

The overarching goal of data science is to guarantee that every dollar of your company's marketing budget is used wisely and profitably. Your company may save money on marketing methods that aren't working by optimising who you promote certain items to and when you market them.  

5. Content strategy

An important part of marketing is content. Technically speaking, marketing is entirely dependent on content. Creating an efficient content marketing plan to attract new leads might feel like a shot in the dark at times. This is where data science comes into play. Though testing is still required to really assess the quality of your material, approaches such as serial testing allow you to do it in the most efficient and time-efficient manner possible, employing an unsupervised machine learning algorithm.  

6. Lead Scoring

Converting a lead into a customer is the most difficult stage in marketing. It is all about how skillfully you guide them down the funnel. Customers' paths through the sales funnel are populated with a variety of possibilities, alternatives, and choices. Lead scoring is used to identify prospective buyers who will progress through the funnel and decide to use your product or service. What is the ruse? 

Lead scoring assigns a value to each lead and ranks the prospect accordingly. Each lead's value may be designated differently, although they are frequently referred to as hot, warm, or cold. Lead scoring entails gathering information on customers' demographics, reactivity, purchase history, preferences, web page views, visits, likes, shares, and even the types of emails they frequently respond to.  

7. Interaction Analytics

There are advantages to leveraging data gathered over time to drive marketing decisions, but delays in acquiring this data might put organisations at a disadvantage. 

Real-time analytics allow organisations to track and evaluate consumer activity in real time, offering relevant, actionable data at a vital time for customer conversion. Real-time analytics also enable speedier reaction times when your target market fluctuates, saving you money and wasted marketing in the long run. 

8. Recommendation Systems

A strong predictive analytics system serves as the foundation for recommendation systems. Everytime we go to Amazon or other e-commerce sites, we see recommendation systems. When you shop online, a recommendation system steers you to the most likely to be purchased goods. Users are frequently irritated by alternatives and want aid in finding what they are searching for, therefore recommender systems are an essential component of our digital world. Customers will be pleased as a consequence, and earnings will rise as a result. Recommender systems are analogous to salesmen who know exactly what you want based on your past and current preferences. 

Recommendation systems are extremely important in various businesses since they may create a considerable amount of revenue when they are efficient or serve as a method to drastically differentiate oneself from competition. 

9. Market Basket Analysis

Market basket analysis refers to data science approaches that use unsupervised learning to understand purchasing patterns and reveal co-occurrence correlations between purchases. The use of these strategies allows for the prediction of future buying decisions. 

Furthermore, market basket analysis may greatly increase the effectiveness of the marketing message. Aside from the style of marketing message, whether it is a direct offer, an email, social media, a phone call, or a newsletter, you may provide the next best product fit for a certain consumer. 

10. Sentiment Analysis

Sentiment analysis (also known as opinion mining) is a natural language processing (NLP) approach for determining whether input is positive, negative, or neutral. Sentiment analysis on textual data is frequently used to assist organisations in monitoring brand and product sentiment in consumer feedback and understanding customer demands.

Data science methods may be used by marketers to perform sentiment analysis. This implies they'll be able to learn more about their customers' views, opinions, and attitudes. They may also track how clients respond to marketing initiatives and how engaged they are with their company. 

11. Understand Customer Loyalty

Customer loyalty and retention is important for any company or business. Customers that are loyal to a company assist it to survive. They are less expensive than acquiring new customers. Marketers may use data science to better their marketing to existing consumers and so increase their loyalty. 

As customers engage with your website or product, data science and machine learning models may help businesses discover the next best action or offer for each consumer, as well as how a customer could behave in a certain situation and what the problem is if a customer does not return. 

12. Planning Email Campaigns

Data science may be used to send customers relevant emails that are tailored to their requirements. Customers may find value in these targeted advertisements, open them, read them, click through, engage with them, and ultimately make a purchase. 

Marketers may employ data science to gain access to relevant groupings of data gathered from a range of sources, including website analytics, email marketing providers, and e-commerce platforms. This information may then be used by digital marketers to predict what customers like, how they shop, and when they're most likely to make a purchase in the future. 

Examples of How Some Leading Brands Incorporated Data Science Into Their Marketing Mix : 

Let us look at how some of the big brands have used Data Science to improve their growth and reach to more users.  

Netflix Recommendation System:

The attention span of someone searching for something to watch on a Friday evening is very low. If you're a Netflix user, you've probably noticed that the service favours certain specific genres, such as Romantic Dramas with left-handed protagonists. How did Netflix come up with such specific genres for its more than 100 million subscribers? What happens when the artwork on Netflix changes? Machine learning, artificial intelligence, and behind-the-scenes innovation are used to predict what would make a viewer choose a certain show to watch. Netflix uses machine learning and data analytics to customise your viewing experience depending on your viewing history.  

Read more about it here: Research Netflix

Data Driven Ads By Coca Cola: 

Coca-Cola is the largest beverage corporation in the world, with over 500 soft drink brands distributed in over 180 countries. Coca-Cola generates a significant quantity of data across its value chain, including sourcing, production, distribution, sales, and consumer feedback, due to the magnitude of its operations. The corporation has adopted Big Data to drive its corporate strategic decisions throughout the years.  

Coca Cola has millions of followers on social media and various other ways by which they can gather data on customers. Coca-Cola is known to have invested heavily in artificial intelligence (AI) research and development to guarantee that it is extracting every ounce of insight possible from the data it collects. 

The information collected provides insight into who is drinking their products, where their consumers are, and what conditions cause them to comment about their brand. When images of its items, or those of rivals, are posted to the internet, the corporation employs AI-driven image recognition technology to detect them and uses algorithms to decide the best way to display ADs to them. According to the firm, ADs targeted in this way have a four times higher probability of being clicked on than those targeted in other ways. 

Read More here: Forbes

EasyJet Marketing Campaign

EasyJet began their 20th anniversary celebrations with a data-driven campaign. The company generated personalised tales based on each customer's travel history. Customers' information, such as when they first travelled with the airlines, was used to make predictions about where they would fly next. Personalised emails were at the heart of the campaign, with material based on 28 important data points and other requirements. As a consequence, this campaign's open rates were 100 percent higher than their usual newsletters, with a 25 percent higher click-through rate. 

Read more about it: Campaignlive

Spotify Wrapped:

We use music streaming platforms throughout the year and submit particular information about your music likes, podcast preferences, and time spent on the app. 

Spotify, a forerunner in well-developed and forceful approaches, provides key data about consumers' activity with them. This frequently encourages users to post the results on social media, allowing all of their followers to participate in a number of ways. 

Since 2016, the company has used this data to launch its "Wrapped" campaign, which aims to uncover some of the most odd listening behaviours. This resonates with the target audience — people from various demographics with a wide range of items on their to-do lists. This brand strategy paid out for the company, resulting in millions of devoted customers. 

Benefits of Using Data Science in Marketing:

A marketer often collects information on clients after a campaign has been launched in order to assess its success. This method, however, has been overturned with Data science. 

Rather than examining the efficacy of prior marketing strategies, data science aids in the collection of real-time data based on current industry trends and customer purchasing patterns. The real-time information gathered may be used to improve your present and future marketing campaigns.  

To Market with Data

Using a data science oriented marketing strategy can transform the methods of marketing. Data science may be used to examine the data you have on your social media platforms and website. This information may provide you with a wealth of information on your target audience, including when, where, and how they interact with your business. You need a wide range of skills to execute these tasks with precision. The roles and responsibilities of a data scientist are very diverse. One needs many technical skills to enter the field of Data Science. The Knowledgehut practical data science with python course is a great way to know about the practical aspects of Data Science in marketing.  

With the skills you learn in this course, you'll be able to design and conduct your marketing campaign based on your company's needs, client behaviour, and the data you've gathered, resulting in increased sales.

Frequently Asked Questions (FAQs)

1. Do Marketers Need to Know Data Science?

Marketers must know about Data Science in current times. Using data to plan marketing will be a better decision.  

  • Usind data, helps marketers make informed decisions. 
  • Real life factors can be incorporated into maketing. 
  • Marketers need not rely on just insticts to plan marketing strategies. 
  • Marketers can understand, which channels of marketing are generating traffic. 

2. Can a Marketer Become a Data Scientist?

Yes, any one can get started in Data Science by learning online. Check top data science courses in India by Knowledgehut.  

3. How Can I Use Data Science in Digital Marketing?

Data Science is a great tool for digital marketers. A large amount of data analysed by Data Science methods is crucial for detecting your audience's behaviour and interests, which can then be used to improve your marketing initiatives.  

Profile

Prateek Majumder

Author

Prateek Majumder is an engineering graduate from IEM Kolkata. His expertise is in Data Analytics, Python programming, Data Science, and Content Creation. He also likes blogging and is an active Kaggle contributor and part of many student communities. In his free time, he likes to watch Science Fiction movies and his favorite Sci-Fi franchise is Star Wars.