Data is extremely valuable for any business. Data is often referred to as the “new oil†and rightly so. Marketing can benefit from large amounts of data on users and potential customers. Have you ever searched for a product and then you get bombarded with ads on YouTube and social media platforms? That is data science for marketing at work.
With data on demographics, purchase patterns, reviews, and social media usage, marketing teams can use data science to make sense of this data and uncover useful insights to run better marketing campaigns, construct an accurate buyer persona, etc.
There is a lot that data science can do and in this blog, we will delve into how companies and marketers can use data science for marketing.
What is Data Science?
Data Science is a field of study that combines machine learning, statistical mathematics, and computer science to extract actionable insights and information from huge data sets that can be unstructured or structured.
Data science involves a lot of analytical and statistical methods that are used to extract meaningful data. This is incredibly powerful as it lets businesses make informed decisions based on the raw data collected.
There are a lot of applications for data science in the world and the one we are discussing here is for marketing.
Use of Data Science For Marketing
Marketing involves a lot of data. Who are the customers purchasing the product? What do they like? What are their preferences? Which location are they from? There is a lot of data that can be leveraged by marketing teams to either improve their products or make informed decisions for marketing activities.
Data science for marketing can open a whole new avenue and its applications can range from customer acquisition to retention and even to drive growth for the company. Marketers can use data science to understand and uncover patterns in customer behavior, and preferences and even segment the customers based on various parameters and make the marketing even more efficient (and effective).
For example, Netflix uses data science to recommend movies and shows that the user might like based on watching history. This leads to increased engagement with the service and improved customer retention.
We will see a lot more examples as we explore how data science is used for marketing in the next section.
Also read: Data Analyst vs. Data Scientist: Main Difference
10 Ways to Use Data Science for Marketing
As mentioned earlier, data science for marketing can do a lot of things, and some of them are explained here.
1. Marketing Budget Optimization
For any marketing campaign, there is a budget. Often, the allocated marketing budget is stretched too thin or used inefficiently. This is a very common problem with many marketing campaigns and this is where data science can help marketers.
Data science can be used to allocate and optimize marketing budgets by analyzing historical or past data to understand and estimate the potential Return on Investment (ROI) of different channels and campaigns.
Data science techniques like regression analysis and machine learning models can be leveraged by marketing teams to predict which campaigns will yield the best results and maximum returns.
To demonstrate how this would work, let’s take a small example. A marketing campaign to sell luxury decor is confused about which platform will be the best for their next line of products. They can use data science to analyze data from previous campaigns and discover that social media ads generate a higher engagement compared to running ads on print or TV. Based on this insight, the marketing team can confidently allocate more money to social media ads.
2. Recommendation Engines
The era of physical media copies is over and most of the entertainment is “streamed†online. From games to movies and TV shows, everything is on streaming platforms. Recommendation engines are a core part of such services.
Companies like Netflix, Disney Plus, and even gaming platforms like Steam make use of powerful recommendation engines to suggest content that might interest the user. These platforms are home to thousands of movies and TV shows and a user can’t know about every bit of content on it.
Source: Research Gate
This is where data science comes to the rescue. By analyzing past watching behavior and genre preferences, models can predict what content the user might be interested in. With an overwhelming amount of content, companies like Netflix can recommend the next show or movie to keep the user engaged.
These engines use techniques like collaborative filtering and content-based filtering to offer personalized recommendations, reducing the chance of users leaving the platform.
3. Customer Segmentation and Clustering
Segmentation is a process of dividing a wide customer base into specific groups of customers based on common denominators like demographics, preferences, behavior, psychographics, etc. The data to segment the users can come from various sources like social media, analytics, websites, surveys, and transactional data.
When used with machine learning models, this data can be used to accurately segment the users based on predetermined criteria. This is a powerful tool in the hands of marketers as it allows for a personalized marketing strategy and campaigns targeted toward specific groups of customers or potential customers.
Source: Medium
Clustering is another method that does the same thing as customer segmentation. However, the difference lies in the criteria. In customer segmentation, customers are grouped based on predetermined criteria, while in clustering, the grouping is done without predetermined criteria.
Clustering is a good example of unsupervised learning. Here, the algorithm is trained to identify patterns that might not be very obvious to humans. The goal of clustering is to segment and group customers based on certain shared characteristics. This may lead to some insights or grouping that might not be very obvious otherwise. Moreover, machine learning models employed can learn from the data and over time produce even better results.
4. Predictive Analytics
Predictive analytics is a powerful technique that uses patterns found in historical data to make predictions about future events and outcomes. But, how does this help in marketing? In the context of marketing, this means predicting the potential for customer churn or predicting the likelihood of a customer’s purchase.
Data science for marketing can be extremely powerful if marketers can anticipate the actions of users. For example, an e-commerce platform might use predictive analytics to predict if a customer is at risk of churning and target them with personalized offers and discounts to retain the customer.
5. Sentiment Analysis
Sentiment analysis analyzes the text data from sources like social media posts, voice data, reviews, and customer feedback that reflects public attitudes and emotions toward a brand or product.
Through natural language processing (NLP), it is possible to analyze the unstructured data and identify the patterns in customer sentiment towards a product. NLP helps quantify the sentiment in these texts and enables marketers to measure real-time perception of their brand. Such data can be very beneficial in determining what type of marketing to use, managing public relations, and improving customer satisfaction.
6. Market Basket Analysis
If you have often used food delivery or grocery apps, you will see that based on an item purchase, you will be recommended another product that you often buy together. Or, you are recommended a product that you “need “ to buy.
This is market basket analysis at work. This is a data science technique that is used to identify relationships between products that are often bought together. This is a powerful tool for shopping apps and these sets of products are called “itemsetsâ€.
Source: Deeta Analytics
Let’s take an example. You are preparing a burger at home. You log into an app and add burger buns to the cart. As soon as you add burger buns, you will see mustard sauce, butter, pickles, etc., recommended to you. Based on your first purchase, it anticipates that you are picking up items for making a burger, and with the help of market basket analysis, it will recommend items that are often purchased together.
You can use this information to better understand your customers and optimize product offerings based on purchase patterns. You can also use them to run personalized campaigns or to encourage buying multiple items as a bundle with discounted prices (a usual tactic used by shopping apps).
7. Lead Targeting and Advanced Lead Scoring
Targeting and scoring of leads are key ingredients of any customer acquisition strategy. Marketers can use data science to dissect demographic information, online behavior, and past interactions that allow them to score leads as a function of their probability of conversion.
When you know which customers are more likely to convert, it is easier (and cheaper) to acquire them. This process can be accelerated by machine learning models that learn from the newer data and build useful lead scores with increased accuracy. This is how companies like HubSpot use such techniques to prioritize leads and tailor their outreach which results in much higher conversion rates.
8. Customer Churn Prediction
Customer churn is when a customer or a user stops doing business with you. Customer churn is especially a problem for businesses that are service-based or subscription-based. Subscription businesses usually have a very hard time predicting and preventing customer churn.
This is where data science uses customer churn prediction to stop customers from leaving the service. When data like usage patterns, reviews, and complaints are used, models can accurately predict whether a customer is considering leaving the service.
Source: Graphite Notes
Let’s take an example. You are subscribed to a telecom service and you are not happy with their service. You try to connect to customer service but there is a poor response and your issue doesn’t get resolved. You are now considering switching to another telecom service.
When used right, based on the complaints and declining usage, the models can flag you as a customer likely to churn. This data can be used by the company to run targeted campaigns to retain the customer with offers or solve the issue faced by the customer.
9. Collecting and Managing Marketing Data
All the techniques that we talked about so far have one thing in common. Data. A lot of it. Without huge datasets, it is impossible to employ data science for marketing. For any company looking to use data science for marketing, it is important to have effective data collection and management.
With data science tools, marketers can combine all the data sources like social media, web analytics, CRM systems, customer testimonials, complaints, etc. into a single synchronized database.
Techniques like data cleaning, transformation, and integration ensure that the data is well organized, structured, and ready to be used for analysis or fed to machine learning models. The more data you have, the better the results will be, and having good data is the basis of using data science for marketing.
10. Product Improvement or Development
While product development may not be a subset of marketing. However, a new product or improvement to a product has a lot of input from marketing teams. A new product can leverage data science to create a better product or improve on it.
You can understand what your customers want by analyzing available customer data. Data like reviews can be incredibly valuable when you need to gain insight into what improvements your customers want.
With data science, you can develop the right product that satisfies your customers. By using techniques like clustering and customer segmentation, you can find out what customers are more likely to buy and at what price.
Based on different buyer personas, you can develop variants of a product that might appeal to different customer groups. When you have data that can be analyzed to study your customers better, you can create a better product.
Real-Life Marketing Data Science Examples
Data science for marketing can be incredibly useful and powerful to better position your service and products that cater to your customers. Here are some real-life examples of companies using data science for marketing.
Amazon
Amazon is a prime example (you see what I did there?) of a company that has successfully used data science techniques in its overall marketing strategy. Amazon uses multiple data science techniques to better market its products and its platform.
Amazon uses predictive analysis to understand the demand for a product and manage its inventory to make sure that popular products are always available. Amazon also uses a recommendation engine to suggest products that a customer is likely to buy. It makes use of a recommendation engine and market basket analysis to recommend products based on interest, browsing, and purchase history.
Netflix
Netflix is the best example of the use of recommendation engines and customer churn prediction. Being a subscription business, Netflix has to retain its customers as long as possible. With so much content offered on its platform, a user almost never knows what they want to watch.
This is where Netflix uses powerful recommendation engines to extensively personalize the user experience. The first time you sign up for the service, you will see that Netflix asks you to choose at least 3 things that might interest you. Based on your choices, it populates the feed with content you might like.
Over time, as you watch more content, the models get better at predicting what might interest you next. Combined with churn prediction, this is a really good way for Netflix to keep you hooked to the service.
Spotify
You might have heard of “Spotify Wrapped†every year. Every Spotify user gets a yearly recap of the music they have listened to, the genre, and many other insights. This was a very successful marketing campaign when it first launched and it utilized data science to offer a highly personalized yearly recap of their musical journey.
Source: Teen Vogue
Spotify uses data science to make personalized playlists and music suggestions to their subscribers. Through machine learning, it examines a user’s listening patterns and determines what songs would the user be most likely to listen to. This personalized experience keeps the users on the app engaged and constantly discovering new music, which ultimately keeps them engaged in the platform.
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FAQs: Data Science For Marketing
1. Is it Possible to Use Data Science for Marketing?
Yes, data science can be leveraged in marketing to get a much better understanding of the market, and existing customer base, attract the right leads, and much more. Data science for marketing can be powerful when used correctly.
2. In the Context of Data Science, How do you Solve Customer Segmentation for Marketing?
Data science allows organizations to examine consumer behaviors, and demographics and provide segmentation of the customers in a more predictive manner by analyzing the patterns and grouping them based on specific parameters.
3. How Can One Optimize Marketing Budgets With Data Science
Data science for marketing helps businesses by allocating resources efficiently based on past performance and predictive models. Using these insights, marketers can get higher ROI from ad spending with better campaign outcomes.
4. How do Companies Use Data Science for Customer Churn Prediction?
Customer churn prediction can predict when the customer is likely to stop doing business based on their behavior in advance. Based on this insight, the marketing team can proactively try to keep the user engaged with personalized offers.
5. Can Data Science Improve Personalization in Marketing?
Indeed. Marketing powered by data science can result in highly personalized marketing campaigns. For example, studying customer data from purchase history, preferences, and reviews can give a better understanding of the user. This insight can be used to run marketing campaigns that are highly targeted and personalized.
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