Data has never been at the same time so accessible and indispensable to run a business these times. More and more sources – from social networks to online databases – are providing them on an unprecedented scale. Data scientists process and translate data, enabling companies to use new information to develop their marketing strategies. Let’s read about 11 examples of using data science in marketing

What is data science? 

Data science for marketing – 11 real-life examples: What is data science? 

Data science is an interdisciplinary area combining statistics, business, and programming knowledge. Data science deals with the study of big data using modern tools. It aims to look at raw data holistically and accurately.

This field is the work of a data scientist who collects, processes and analyses data sets, trying to find solutions to analytically complex problems. For this purpose, they use machine learning algorithms which are based on the way the human brain works. However, these algorithms can analyse various data types (e.g. textual, numerical, pictorial) even more efficiently than humans do. 

Data science is closely related to machine learning. You can read more about the differences between these two areas in this article: Data science vs machine learning.

Approximately 2.5 million terabytes of data are generated every day

Online information consumption has increased significantly over the past decade. It is estimated that over 6 billion devices are connected to the Internet. Approximately 2.5 million terabytes of data are generated every day, and this number is constantly growing. This data, properly processed, can be a source of imperative business information. 

Data science in digital marketing 

Data science for marketing – 11 real-life examples: Data science in digital marketing 

While the benefits of data science can be relevant to almost any industry, they are crucial and widely used in the digital marketing sector. How does it work? People leave information about themselves no matter what they are doing – browsing websites, communicating with friends, shopping in an online store, or posting on social media. The information extracted from this data can support a variety of marketing purposes. 

At the same time, consumers are demanding increasingly personalised content, and digital marketing faces the need to deliver targeted, informed campaigns to them. Data science offers insights into how to do just that. So let’s take a look at specific ways to use it in the marketing sector.

11 practical ways to implement data science in marketing: 

1. Data segmentation and customer profiling 

Data science for marketing – 11 real-life examples: 1. Data segmentation and customer profiling 

Data segmentation can be defined as grouping customers based on different characteristics, which becomes more difficult as the number of variables increases. Data scientists use a clustering method to organise customer data into various segments. It is crucial for digital marketing as it vastly depends on interaction with customers. 

By analysing factors such as search history and consumers’ buying habits, you can determine what influences their purchasing intentions. Without understanding who our audience is and what their needs are, it is impossible to create effective campaigns. Audience data can be divided into several categories: 

  • demographic (e.g. age, gender, income, education); 
  • geographic (e.g. country, language, region, climate, city/village); 
  • psychographic (e.g. personality, desires, values, opinions); 
  • behavioural (e.g. shopping habits, search history, life events).
Consumers data segmentation – 4 categories:
1. Demographic: age / gender / income / education
2. Geographic: country / language / region / climate / city
3. Psychographic: personality / desires / values / opinions
4. Behavioural: shopping habits / search history / life events

The data obtained this way can also be used to create the so-called customer persona, a prototype of a model customer. The persona is given specific characteristics, such as name, age, occupation, interests or habits and their preferred sites and media channels. Visualising a potential customer as a particular person helps create an accurate message and select appropriate communication channels.

2. Lead targeting 

Data science for marketing – 11 real-life examples: 2. Lead targeting 

Each successful strategy requires many high-quality leads (in marketing, this is a person potentially interested in purchasing a given product). Initially, an anonymous internet user becomes a lead, e.g., subscribing to a newsletter. 

To generate a sufficient number of leads, it is worth taking advantage of data science to know what content your audience values. Behavioral habit analysis is an excellent tool for just that. It allows you to recognise specific keywords related to a given product or service among the search history, pages visited and information shared by users. 

By collecting behaviour patterns in this way, you can predict that a given user is interested in a product and display his advertisement.

3. Advanced lead scoring 

Data science for marketing – 11 real-life examples: 3. Advanced lead scoring 

Not every lead that a marketer acquires turns into a customer. At this point, it is worth using a predictive lead scoring system. It is based on an algorithm that calculates the conversion probability and segments the potential customers’ list. The list of our sales leads can be divided into three categories: 

  • cold lead – a person who cannot yet be identified as a potential customer, but may become one in the future; 
  • warm lead – a potential customer who is interested in the offer, but at the moment is unwilling or unable to complete the transaction; 
  • hot lead – a potential customer who is determined to finalise the transaction at the moment and take advantage of our offer. 
Lead scoring – 3 types of leads:
1. Cold lead – a person who can't be identified as a potential customer yet but may become one in the future
2. Warm lead – a potential customer who is interested in our offer but can't or doesn't want to complete the transaction at the moment
3. Hot lead – a potential customer who is determined to use our offer and finalise the transaction at the moment

Lead scoring helps you recognise and focus on leads that are more likely to show interest in the brand and eventually become customers. For examples, this method enables to: 

  • identify potential customers who are ready to buy a product or service but need additional motivation; 
  • send promotional codes to customers with the highest likelihood of conversion; 
  • resign from communication with users who are unlikely to take advantage of the offer. 

4. Channel optimisation 

Data science for marketing – 11 real-life examples: 4. Channel optimisation 

Companies’ most basic information about their customers is age, location and gender. They allow us to define who our recipients are. However, this is only the first stage. Information on the sources of customer acquisition is equally important. For example, the analysis of marketing channels allows you to determine their behaviour and preferences. Thanks to that, we know which channels our current or potential clients use, and therefore which are worth including in the marketing strategy. 

Data science can then be used to determine which of the channels are driving the most customer acquisition. Using predictive analytics (which we cover later in this article), a data scientist compares growth across various channels and predicts which one will generate the most profit. A marketer can use this knowledge to determine, for example: 

  • what channels to communicate with your clients; 
  • what types of content to create within them; 
  • what time it is best to publish them. 

5. Content marketing 

Data science for marketing – 11 real-life examples: 5. Content marketing 

Developing an effective content marketing strategy to get new leads can seem complicated. However, what our customers draw from is almost impossible without data analysis. This is where data science comes in. 

Understanding the quality of the content – in the most effective and least time-consuming way – is supported by serial testing, which allows you to explore various details, such as the choice of words or the appearance of the creation.

And predictive analytics techniques can then predict the effectiveness of these choices across all channels. It ensures that fully optimised content will be presented to the right people at the right time. 

6. Predicting consumer behaviour 

Data science for marketing – 11 real-life examples: 6. Predicting consumer behaviour 

Thanks to the use of data science techniques in marketing, it is also possible to predict what may happen in the future in specific situations important for a given company. Predictive analytics is the answer here, which, based on the analysis of historical data, allows to forecast future consumer behaviour. 

It is an advanced type of data analytics that uses data mining techniques, statistical analysis and time-series modeling. This method helps, among others in: 

  • forecasting consumer behaviour; 
  • predicting future trends; 
  • identifying potential threats and opportunities for the company. 

You can read more about predictive analytics in this article on our blog: Predictive Analytics.

7. Influencing consumer behaviour 

Data science for marketing – 11 real-life examples: 7. Influencing consumer behaviour 

Data science techniques in marketing can be broadly divided into three groups: 

  • descriptive – analysis of past events;
  • predictive – predicting future events; 
  • prescriptive – predicting which marketing activities will make the desired event happen in the future. 

The predictive analysis mentioned in the previous point belongs to the second group. In this paragraph we are describing another data science method – uplift modeling – that belong to prescriptive techniques.

Uplift modeling is a technique that provides marketers with knowledge not only about the future actions of consumers but also about what marketing methods to use to encourage them to take the desired action. It is, therefore, a step forward that allows to influence the purchasing decisions of consumers with even greater effectiveness.

8. Product recommendations 

Data science for marketing – 11 real-life examples: 8. Product recommendations 

Another possibility of data science in marketing is recommending products that users may be interested in buying. The main goal here is to increase the purchase conversion and the value of the customer’s cart. One of the most effective predictive analytics techniques used here are recommendation engines. Several types of recommendation engines can be used, such as: 

  • collaborative-filtering – suggests products to customers based on the purchases of other customers who have similar interests or purchasing habits; 
  • content-based filtering – uses information about the products purchased by a given user (e.g. their category, price or appearance) and recommends other products with similar characteristics; 
  • complementary filtering – based on complementarity analyses the probability of buying several products simultaneously; it consists of offering the customer complementary products to the one they are buying, based on the purchase history of other users. 
Product recommendations – 3 types of recommendation engines:
1. Collaborative-filtering – suggesting products to a customer based on purchases of other customers with similar purchasing habits;
2. Content-based filtering – recommending products with similar characteristics to the products already purchased by the client;
3. Complementary filtering – offering products that are complementary to the product the customer is buying at the moment.

9. Real-time communication 

Data science for marketing – 11 real-life examples: 9. Real-time communication 

Real-time data research enables you to see customer behaviour in real-time and provide them with information that can help them make a product purchase decision. It allows you to improve the user experience by reacting in real-time to any problems or doubts that may appear on the purchasing path. By properly analysing the data, you can determine the right time and channel of communication with a given user. 

Positive customer experiences then increase their loyalty, which is cheaper and more long-lasting than acquiring new leads. Data science solutions enable, for example, to: 

  • predict how the client might react in a specific situation; 
  • choose the best offer for the client depending on his interaction with a given website/product; 
  • identify what the problem is if the customer doesn’t come back. 

10. Sentiment analysis 

Data science for marketing – 11 real-life examples: 10. Sentiment analysis 

Sentiment analysis allows marketers to determine what the community thinks about a brand, product or service. Data science supports this method by using natural language processing, text analysis and computational linguistics. Sentiment analysis gives an idea of ​​what the customer/public thinks about a given brand or its products. This method: 

  • enables the collection of data that helps to empathise with the customer, on a large scale; 
  • allows you to monitor customer reactions to the information received; 
  • provides means to get feedback on how the customer engages with the campaign; 
  • enables an overall analysis of the words in the text and their association with specific moods. 

In marketing, the latter is most often done through polarisation, contextual meaning words are assigned a positive, negative or neutral value. Finally, you measure the analysis result and get feedback on how people reacted to the ad in question. This tool can be applied to e-mail correspondence, Google reviews, and even phone conversations (recorded with the caller’s consent, e.g. by call centers), using speech-to-text conversion. 

11. Marketing budget optimisation 

Data science for marketing – 11 real-life examples: 11. Marketing budget optimisation 

The last application of data science in marketing that we want to mention is marketing budget optimisation. Generally speaking, it is making sure that the money in the planned budget is spent in a useful, profitable way. This goal is closely related to all of those mentioned above. 

It is worth remembering that using data science methods, you can build a spending model with the budget spread over locations, channels and campaigns, considering their profitability. Using this knowledge can make it easier to decide whom, where and when to sell certain products or services. 

Data science for marketing – summary 

An effective marketing strategy needs to be planned way ahead but be flexible to stay ahead of the competition. By using data science, you can understand your customers’ buying habits in a much deeper way than it has ever been possible. That is why it is worth including data science opportunities in your marketing activities.

Do you want your organisation to operate in a more conscious and targeted manner using the possibilities offered by data science? Thanks to the tools provided by WEBSENSA, we will help you create the right solution tailored to your industry, company size and business goals. Contact our specialists! We’ll be happy to talk about your needs!