Data-driven marketing is a marketing approach that uses data to inform all aspects of the marketing process, from campaign planning to execution. By collecting and analyzing data about their target audience, businesses can gain insights that can help them create more relevant and effective marketing strategies in an increasingly complex world.
Data-driven marketing is becoming increasingly important as the data available to businesses grows. Previously, many companies relied on gut instinct and intuition to make marketing decisions. However, in today's digital age, companies can (and must) analyze a wealth of data to inform their marketing decisions. Otherwise, they can quickly become eclipsed by the competition.
The process of data-driven marketing
Data-driven marketing is complex and frequently tailored to an organization. However, the process of data-driven marketing generally follows these steps:
Define your goals. What do you want to achieve with your marketing campaigns? Do you want to increase your brand awareness, generate leads, or drive sales?
Identify your target audience. What are your audience's interests, needs, and pain points? Who are you trying to talk to—what is your ideal customer to engage?
Collect data. There are many ways to collect data about your target audience, including website analytics, social media monitoring, and email marketing. As you collect customer data, remember that you must also secure it—especially if you're collecting personally identifiable information (PII) or working with third-party data.
Analyze the data. Once you have collected data, you must analyze it to identify trends and insights. Analysis helps you better understand your target audience and demographics, creating more relevant content marketing.
Create and execute your marketing campaigns. Use the marketing insights from your data analysis to create and execute marketing campaigns that are more likely to resonate with your target audience.
Measure your results. It is essential to measure the results of your marketing campaigns to see what is working and what is not. These results will help you to optimize your campaigns and to get the most out of your marketing budget.
The data-driven marketing process is iterative, with constant data collection used to produce informed decisions. Today's customer journey is constantly shifting, and digital marketing strategies must move too.
Examples of data-driven marketing
You can improve your reach to new customers and potential customers—and improve customer retention—through data-driven marketing. Any time you collect marketing data through a customer relationship management suite or paid ad A/B testing, you can improve your KPIs and marketing ROI.
Here are a few examples of data-driven marketing:
Personalized email marketing: Businesses can use data to segment their email lists and send personalized messages to each segment. For example, a business could segment its email list by customer location and send messages to customers in different parts of the country.
Targeted social media ads: Businesses can use data to target their social media ads to specific audiences. For example, a business could target its ads to people who have visited its website or liked its Facebook page.
Retargeted ads: Businesses can use data to retarget ads to users who have seen their website but have yet to purchase. For example, a business could show ads for products that a person has viewed on its website on other websites that the person visits.
Geofencing: Businesses can use data to create geofences around their physical locations. When someone enters a geofence, ads show for the business. For example, a coffee shop could create a geofence around its location and show ads for its coffee to people who enter the geofence.
Chatbots or live chat: Businesses can interact with customers on their websites or social media pages. Chatbots can answer customer questions, provide support, and even make sales. These chatbots can then produce data sources and points regarding what customers want the most.
Predictive analytics: Businesses can use data to predict customer behavior based on prior customer behavior. Marketers can use these predictive analytics to create marketing campaigns that are more likely to be successful. For example, a business could use data to predict which current customers are most likely to churn and then create a marketing campaign to prevent those customers from leaving.
The importance of data-driven marketing
Data-driven marketing is crucial because it allows businesses to make more informed decisions across an increasingly complex landscape. By collecting and analyzing key metrics, such as conversion rates, companies can gain insights into their target audience that would not be possible to obtain with traditional analysis methods.
A comprehensive strategy leads to the following core benefits of data-driven marketing:
Increased ROI: Data-driven marketing can help businesses to increase their ROI by targeting their marketing messages more effectively.
Improved customer satisfaction: Data-driven marketing can help businesses to improve customer satisfaction by creating more relevant and personalized experiences.
Stronger branding: Data-driven marketing can help businesses to build a stronger brand by creating a more positive and memorable customer experience.
Today’s marketing plans can’t be crafted through intuition alone. To improve customer lifetime value, brand engagement, and reach, marketers need to dig into their data.
The current status of data-driven marketing
Many businesses are increasing their data-driven marketing strategies. While data has always been used in marketing, the sheer volume of data available and actively used in marketing has significantly increased. Two things drive the current state of data-driven marketing:
More data: More data means better insights, but it also means it's harder to glean actionable insights. With the right analysis, businesses can make increasingly sophisticated and accurate decisions. And if they aren’t analyzing their data, their competitors are.
Better tools: Another factor driving the growth of data-driven marketing is the increasing sophistication of data analytics tools. Businesses now have access to powerful tools that can help them analyze data and identify trends—far beyond what a human analyst could accomplish.
Big data and AI/ML are two technologies playing an increasingly important role in data-driven marketing.
Big data refers to large data sets modern businesses collect and store. Artificial intelligence (AI) and machine learning (ML) are two technologies frequently used to analyze these exceptionally large data sets.
For example, businesses can use big data and AI/ML to track customer behavior across multiple channels, such as website visits, social media interactions, and email opens. Once analyzed, this big data might reveal even more complex behavior patterns and customer journeys. You can also use AI and ML to automate tasks, such as data analysis and campaign optimization. Automation can free marketing teams to focus on more creative and strategic tasks.
The future of data-driven marketing with Emotive
Data-driven marketing in the right hands is very powerful. But without the right tools, the data collected and analyzed could be potentially misleading. You can only use data-driven marketing if your data analysis is complete and your data is accurate.
Emotive Attribution is an out-of-the-box product that provides an in-depth analysis across all your marketing channels, giving you key insights into customer behavior and marketing effectiveness. Start your journey in data-driven marketing today with Emotive Attribution.