How to Use Data Science in Marketing

The field of data science is growing as businesses seek ways to create value from the huge amounts of data they can access. From industry to industry, data science can give businesses or organizations insight and help predict their business outlooks and goals with informed decision-making. 

One field benefiting from the insights provided by data science strategies is marketing. In this article, you can learn how data impacts modern marketing campaigns and how to use these strategies.

What is data science in marketing?

Data science in marketing is the process of using and analyzing data to improve outcomes and make informed decisions. This information is beneficial in the marketing industry, as understanding data can enable you to tailor your marketing strategies to find the ideal price point for a product, maximize the return on investment of your campaigns, study customer behavior, and more.

Although data science in marketing has its benefits, several challenges also exist, making it important to have a skilled professional who knows how to handle all this data correctly. Common challenges include ensuring the use of high-quality data for analysis, interpreting the results correctly, and properly scaling systems that can handle massive amounts of data.

Types of data used for marketing purposes

The data you use for data analytics processes comes from several different sources, making it critical to have the correct infrastructure in place to handle multiple data systems and the knowledge to properly utilize the data gained. 

Here are some sources of marketing data:

• Social media data to learn about how effective your campaigns are and how consumers are speaking about your products 

• Website monitoring and tracking, where you can gain insights like how often website visitors make a purchase, whether customers are leaving items in their shopping cart, or how much it costs to acquire customers

• Customer surveys to learn more about how customers feel about your product and identify opportunities for improvement

• Digital and paid advertisement tracking to see where your ads are most successful. 

• Hypothesis testing through A/B tests where you can directly compare the effectiveness of different ads

• Transaction data from sales to identify how your products are performing with certain demographics 

• Customer retention through loyalty programs, where you can learn about the behavior of loyal customers and what gets them coming back

Examples of data science in marketing

Data science in marketing allows you to develop and implement several valuable strategies. Here are some examples of specific use cases for data in marketing.

1. Creating targeted marketing campaigns

To create a cost-efficient and effective marketing campaign, it’s important to understand your target audience so you can direct your efforts toward them. With the help of data, you can create customer profiles to ensure the people seeing your products will most likely make purchases.

2. Understanding consumers

Analyzing data allows you to develop accurate predictions about how consumers will behave and, therefore, interact with your product in the future. You can use data to measure how consumers respond to your current marketing efforts and factor that information into better strategies.

3. Identifying new opportunities

With marketing analytics, you can find out how trends are emerging and changing within the market to help you capitalize on trends before it's too late. Data provides insight into what products and services are growing so you can match your offerings with what consumers actually want.

Who uses data science in marketing?

The field of data science contains several positions, with marketing data scientist being just one of them. Data is a prominent factor when marketing professionals make campaign decisions. Although the value of data is widely recognized, having a clear strategy is critical for deriving value from the data, creating a need for talented individuals who know how to leverage analytical tools. Explore some of these positions in digital marketing analytics and strategic development. 

1. Marketing analyst

Average annual US salary (Glassdoor): $75,554 [1]

Job outlook (projected growth from 2022 to 2032): 13 percent [2]

Requirements: Marketing analysts are generally expected to have a bachelor’s degree in an area such as marketing, statistics, or business administration.

As a marketing analyst, you may help strategize how to collect and analyze data from different sources. With that data, you can then measure market trends, develop insights about consumers, and create reports and visualizations to present your findings.

2. Digital marketing specialist

Average annual US salary (Glassdoor): $60,127 [3]

Job outlook (projected growth from 2022 to 2032): 6 percent [4]

Requirements: The education requirements for digital marketing specialists include a bachelor’s degree in business, marketing, digital media, English, or communications.

As a digital marketing specialist, you may research marketing trends and your competition to best develop a plan to differentiate your brand. You may also focus on details of a marketing campaign, such as ad placement and determining what media streams generate the best results.

3. Strategic marketing analyst

Average annual US salary (Glassdoor): $88,616 [5]

Job outlook (projected growth from 2022 to 2032): 13 percent [2]

Requirements: Most strategic marketing analyst positions require at least a bachelor’s degree. Relevant bachelor’s degrees include business, marketing, social sciences, or communications.

As a strategic marketing analyst, you can focus largely on collecting and analyzing data that you can use to influence marketing strategies. This includes examining different market areas, such as your competitors, optimal product pricing, and more.

How to use data science in marketing

If you’re ready to implement data science in your marketing campaigns, follow these steps to learn how.

Step 1: Identify objectives.

Your data-driven marketing approach ultimately begins with a clearly defined goal in mind. This objective could be wanting to properly leverage social media or growing brand awareness within a specific demographic.

Step 2: Collect data.

After establishing your objective, you can focus on collecting the data you may use for your analysis. While you may have access to internal data, depending on what you’re trying to learn, you may need third-party data to collect more information on your target audience.

Step 3: Analyze data.

Before beginning any analysis, ensure your data is properly prepared. This means cleaning your data to remove any duplicates and addressing missing values. Then, you can perform your analysis by selecting from potential methods, such as decision trees, linear regression, A/B testing, and other predictive analysis techniques.

Step 4: Make marketing recommendations. 

With the insights provided from your analysis, you can start learning from the data, whether the goal was to measure the overall success of your campaign or gain a better understanding of your target audience. You can create reports and presentations using visualization tools, interpret your results to clearly communicate the findings, and establish a plan moving forward.

Step 5: Repeat.

With your findings, you can observe what’s working in your marketing campaigns and what isn’t, allowing you to continuously adjust and improve your strategies as you repeat this process.

Learn more with Coursera.

On Coursera, you can find highly-rated courses to learn more about data science and implementing data into your marketing strategies. The Data Science Specialization course from Johns Hopkins University can teach you how to gather and prepare data, perform analysis, and more.

Meta’s Marketing Analytics Foundations course is another option for learning about implementing data strategies specifically for marketing purposes, allowing you to make informed campaign decisions.