The Influence of Data Science on Marketing Strategies

Data science in marketing involves collecting, processing, and analyzing customer data and market trends. It helps businesses understand consumer demographics, purchasing behaviors, and preferences so they can develop tailored, more effective marketing strategies.^{1, 2}

According to the multinational strategy and management consulting firm McKinsey, an organization that embraces data-driven marketing is 23 times more likely to acquire new customers and nine times more likely to retain them than competitors who don’t rely on business analytics for marketing.³ Read on to learn how data science in marketing enhances targeting and the impact it has on marketing strategies.

One crucial role of data science in marketing is to help an organization know specific traits of its target audience. To achieve that, marketing professionals usually develop buyer personas—representations of a company’s ideal customers—based on real customer information, such as:^{4, 5}

• Demographic data: Age, gender, location, income, and education level (gathered from the organization’s customer relationship management systems)
• Consumer behavior: Browsing patterns and website engagement (from website analytics)
• Transactional records: Purchase history, payment methods, and the amount that an ideal buyer typically spends on each transaction (from sales tracking systems, such as point-of-sale solutions)

Other data sources for creating buyer personas include customer responses to online surveys, focus groups, and interviews with target customers.⁴ With detailed buyer profiles, marketers can craft offers and messages that their core audience will find particularly appealing and compelling.⁵

After building several buyer personas, an organization can use the profiles to divide its existing customer base into segments. The goal is to put customers with shared characteristics, preferences, and interests into the same group.⁴ Marketing teams can then create targeted, personalized marketing campaigns tailored to the needs of each group. This is important because, as McKinsey has reported, 71% of consumers expect every interaction they have with a company to be personalized.⁶

Traditional marketing segmentation methods, however, rely on human data analysis, which is typically slow. In addition, old data science techniques usually group customers into broad categories, without considering subtle similarities or differences in consumer behavioral data sets.⁷

Modern data science in marketing uses technology, such as artificial intelligence (AI) and machine learning (ML) to automate audience segmentation. ML algorithms can dig deeper into customer data, identify hidden patterns or correlations that would otherwise go unnoticed, and quickly create narrowly defined customer segments to enhance personalization. Artificial intelligence in marketing can analyze large volumes of customer data more quickly and accurately than humans do.⁷

Data science in marketing enables businesses to analyze current and historical data to predict future consumer trends and use the actionable insights to optimize marketing campaigns. Nobody can anticipate with complete certainty what will happen, no matter how much customer information marketers may have. But instead of relying on gut feeling or guesswork, firms can more accurately forecast probable changes in customer behavior and preference using AI-powered predictive analytics.⁸

Software solutions, such as Adobe Analytics, Azure Machine Learning, and Oracle Analytics, can reveal patterns in a company’s consumer data to help marketers forecast likely outcomes in their campaigns.⁹ For instance, they can use data science tools to anticipate the likelihood of a prospect converting into a buyer and the number of customers who will stop using the company’s product in a given period.¹⁰

Additionally, the technologies can help them predict the messages that will have the strongest impact on customers, the most suitable times to launch marketing campaigns or send offers, and the best marketing channels to use when reaching out.⁸ This information can be valuable when marketing teams are building any data-driven strategy.

Most customers don’t instantly buy when they discover a brand. They typically check out the company’s product and evaluate alternatives before purchasing. Data science in marketing campaigns enables the development of a customer journey map, which shows how and where prospects interact with an organization from the time they are aware of a brand's existence to when they buy the company’s product.¹¹ As a result, businesses gain data-driven insight into prospects' needs and desires at every stage of the buying process.

The maps also reveal factors that streamline or hinder someone’s purchasing journey. This information can help improve the customer experience, boost conversions in marketing efforts, and increase customer loyalty.¹¹

Using data in marketing enables businesses to make data-driven decisions and run consumer-centered campaigns that enhance customer experience and satisfaction. This, however, raises concerns, such as those surrounding consumer privacy. For instance, using website cookies to track a person’s online activities without that individual’s explicit consent is unethical. Ethical practices include asking for permission before collecting any customer data and being transparent about how the information will be used.¹²

Low-quality information is also a challenge when using data in marketing. For example, inaccurate or incomplete details can lead organizations to make marketing decisions based on misleading insights, which may translate to financial losses and serious reputational damage.¹²

Data science in marketing also faces compliance challenges because data privacy and security are often legal obligations. Depending on industry and location, U.S. companies that use data analytics for marketing decisions may need to comply with the following regulations:

• Health Insurance Portability and Accountability Act (HIPAA): Prevents health organizations and practitioners from disclosing sensitive patient data without permission¹³
• California Consumer Privacy Act: Gives California residents more control over how a business collects, stores and uses their data¹⁴
• Payment Card Industry Data Security Standard (PCI DSS): Requires any organization that collects, transmits, or stores credit card details to have sufficient security measures to prevent card fraud¹⁵

Additionally, organizations that analyze customer data of European residents must follow the requirements of the General Data Protection Regulation (GDPR).¹⁶ While complying with all these rules helps organizations process customer data ethically and lawfully, it can be a regulatory burden when building data-driven marketing strategies.

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