Exploring Data-Driven Marketing Strategies

Data analysis gives businesses valuable tools to manage multiple marketing campaigns across platforms. With access to the right data, marketers can evaluate how current strategies are performing and develop new approaches, such as personalized marketing campaigns, to help them engage with more potential customers.

Data-driven marketing may also give businesses a competitive advantage. A recent survey indicated that only 53% of marketing decisions are based on data.¹ However, data allows marketing teams to identify target audiences for each campaign, determine the most successful products, and identify the best way to connect the audience to these products. This article explores various data-driven marketing strategies and how professionals can implement them.

To develop a successful data-driven marketing strategy, start with a clear set of goals and define the metrics you plan on tracking. Once these are established, it's essential to keep all data in one location to track it over time.

Predictive analytics in marketing involves using previous consumer behavior to predict how customers will act in the future.² Marketers can gain more robust insights into their marketing campaigns and customers by pulling high-quality data from multiple sources. There is usually overlap, but each report offers different insights into the campaign. Together, these data points create a clearer picture of marketing successes and those initiatives that need further development. In addition, data visualization in marketing allows marketers to analyze an overview of data to develop effective communication and outreach strategies.

There are multiple data analytics tools and platforms available to help marketing professionals analyze each facet of a digital marketing strategy, from the website to content marketing.³ By putting this information together, businesses become able to connect with their customers more effectively.⁴ For example, marketers may use analytics to see that most customers engage through Instagram. They can use this data to create targeted campaigns that resonate with their core customer base and run paid campaigns on Instagram.

Better data lets a business segment its customers more effectively. Data-driven consumer segmentation offers a more robust, detailed understanding of customers' needs—an understanding that marketers can use to develop strategies that appeal to specific groups (segments) of people. Effective segmentation can strengthen customer relationships, enhance customer loyalty, and boost sales.⁵

There are many ways to segment customers. Common methods include grouping people based on their demographics such as age, gender, or income.⁶ Some businesses leverage customer journey mapping and group customers based on whether they are at the beginning or the end of the decision-making process.

Digital marketing has transformed the way in which brands interact with customers. Instead of pushing people to buy goods and services, as traditional marketing efforts may have done, digital marketing fosters relationships, driving customers to a brand organically. In fact, the vast majority of customers now expect personalized interactions.⁷

Data analytics and visualization tools give businesses the information they need to personalize marketing messages based on what their customers want to know. Using customer feedback and data-driven insights, they can add keywords that address the primary reasons why a person would want their product or service.

Personalization is also crucial for creating dynamic content—that is, content constructed for a customer in real time. Using website builders, chatbots, and other digital marketing tools, marketers can create personalized messaging content on demand, based on a customer's behavior.⁸ With dynamic content, a business can easily respond to customer queries and even change the way its website looks to separate customers who are looking for disparate things.

It's easier to personalize and develop effective marketing campaigns using predictive analytics and customer behavior analysis. By analyzing past campaigns, information about the customer experience and other consumer data, marketers can determine which messages are more likely to resonate with customers.

Customer behavior analytics helps businesses understand their best customers and what drives loyalty. In addition, predictive analytics offers a better picture of supply and demand. It also allows a business to develop targeted messaging for customers who may be getting ready to leave.⁹ Marketers use these data insights to create effective campaigns to, for example, increase demand during lulls or reenergize customers who may be looking to do business elsewhere.

With so many digital platforms, strong customer engagement can be time-consuming. However, engagement makes people more likely to shop with a business. Companies that don’t engage with customers who are not familiar with their brand have 37% fewer buyers.¹⁰

Marketing automation tools make it easy to engage these customers without spending a lot of time drafting content and sending email messages or posting on social media. Customer relationship management and data management software automate content such as, for example, emails directed toward shoppers who left items in an online shopping cart without completing a purchase.

Evaluating previous marketing campaigns for engagement levels and conversion rates can help a business identify which messages are most likely to resonate with its target audience in the future, and that helps increase conversions.¹¹ Optimizing messages involves creating two slightly different versions of any given message and testing them with small control groups. This method, known as A/B testing, gives a business insights into which keywords generate more traffic.

These and other data analytics offer valuable guidance for tailoring each successive campaign.

Data-driven content marketing involves using data to segment the audience and create buyer personas, or fictionalized versions of a company’s ideal customer, based on real customer data. Analytical insights into high-performing content can also help a business’s marketing team brainstorm ideas for related content and related keywords. The knowledge of which content is not performing well, based on historical data, spurs marketers to find the reasons for low performance so they can create more effective marketing campaigns in the future.

Attribution modeling provides insights into which digital channels are driving the most sales.¹² With this information, a business can allocate its marketing spend to its best digital channels or develop new strategies for underperforming channels.

Data-driven marketing insights can differ based on the preferred method of attribution modeling. The most common models are: ¹³

• First-touch attribution, in which all credit for a conversion is assigned to the method in which a customer first learned about a company, such as social media or a television ad
• Last-touch attribution, in which all credit goes to the last customer touchpoint, such as the company’s website or blog
• Multi-touch attribution, which splits credit between multiple touchpoints¹²

Collecting personal data is a key component of data-driven marketing, but privacy is a big concern for today's customers.¹⁴ It's such an important issue that many governments worldwide have stepped in, passing regulations such as the European Union's General Data Protection Regulation.¹⁵ To stay compliant, organizations must protect customer data and make it clear they are doing so. Customers must be able to opt out of receiving text and email communication.

Additionally, businesses are now using privacy tools such as cookie consent banners on their websites, which enable end-users to decide whether they will allow the sites to collect their personal data.

Like digital marketing itself, data-driven marketing continues to evolve. Some emerging trends in the business include artificial intelligence (AI) and machine learning. Many customer data platforms use AI-powered marketing and machine learning to generate predictive analytics or create marketing dashboards.¹⁶

As personalized marketing becomes the norm, some companies are experimenting with hyper-personalization, or personalized content based on specific preferences and behaviors.¹⁷ For example, a business selling running shoes may draft different messages for casual runners and for marathon runners. A related trend is real-time marketing, or engaging customers instantly based on how they are interacting with your brand.

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