3 Best Practices for Turning Customer Data Into Marketing Insights

The evolution of the digital world has given us many things, and an explosion of data growth is just one of them. Every day, we produce more than 2.5 quintillion bytes of data. With the number of internet users growing year over year, that figure will only continue to climb exponentially.

The explosion of data growth has also created a new kind of consumer. To participate in modern techno-society, consumers must become technologically proficient. There are many benefits to this—including click-of-a-button access to services, products, and entertainment—and the output is a digital footprint that serves businesses with consumer behavior data.

As a result, organizations have collectively realized how important consumer data is to maintain competitive advantages. Data tells us a lot about people. We use it to find out consumers’ wants, needs, likes, dislikes, and values.

But with the sheer scope of data being generated, how can you best use consumer data for marketing to create a consumer experience that’s relevant and useful?

What Types of Consumer Data Are Most Valuable for Marketing?

With the growth of consumer data, business decision makers need ways of sifting through the data and finding the insights that will be helpful for marketing to consumers.

That’s why the practice of defining your business’s marketing goals has never been more critical. With well-defined goals, you can determine what types of consumer data points you actually need. An understanding of your audience is also important. If you don’t know who exactly you’re trying to reach with your messaging—their preferences, behaviors, and habits—you won’t be able to speak to them meaningfully.

First-party data will be extremely helpful in giving you an understanding of the people who are likely to interact with your business. It provides insights into your current consumers’ purchasing behaviors, demographics, and pain points. In addition, data about users’ interactions with your social channels will show how audiences engage with your brand online.

With these types of consumer data points, you can begin to create personalized, highly relevant experiences on your site and offer visitors a message or product that excites them. In turn, this can lead to increased sales and revenue.

How Can You Best Use Consumer Data for More Effective Marketing?

There is so much consumer data out there that it can be tough to decide the first actions to take to create digital consumer experiences that are highly relevant. To begin, follow these best practices for using consumer data for your business’s marketing:

1. Collect and organize your data effectively. It’s not enough to simply have a lot of consumer data. You also need to collect, tag, and manage it systematically. That way, you can more easily uncover the actionable insights contained in your data and make better decisions regarding the consumer experience. To do this, it’s essential to build a foundation of data science within your organization.

This should be a holistic, end-to-end process, rather than focusing on a single use case when implementing data science. AI and other predictive tools make this process simple, allowing you to collect and analyze insights much more quickly than when done manually.

Done correctly, a foundation of data science—where you collect, tag, and analyze data using digital tools such as AI—can not only help you create better, more relevant consumer experiences, but also solve many other business problems throughout your organization.

2. Use data-driven insights to optimize the consumer journey. When you begin collecting and tagging your data systematically, you can then figure out how best to optimize the consumer journey with that data. However, your use cases should go beyond delivering targeted banner ads or product recommendations.

Instead of waiting for the data to tell you something meaningful, you should already have an idea of what you’re looking for and why you’re looking for it. Improper data collection and tagging make it extremely difficult to identify use cases for personalization that can be measured. As an organization, it’s imperative that you are looking within and asking the right questions. For example, what specific data would you need to enable dynamic content?

To build personalization tactics that make consumer experiences more relevant, you first have to understand how to use your data to move consumers through their specific journeys. You might even find gaps in your funnel where personalization is missing. That’s OK. Those gaps allow you to create personalized experiences that help your brand build deeper connections with consumers. In doing so, you show consumers that you understand and care about their needs and preferences.

3. Use technology to deliver dynamic, personalized experiences. Once you’re working from a solid foundation of data science and optimizing the consumer journey, you can begin to create a consumer experience that’s relevant and useful in a scalable way using your data and leveraging technology.

For example, rather than just showing a consumer a general “You Might Also Like” carousel with product recommendations, you can enable your whole website to adapt to the consumer’s changing behaviors and preferences in real time. This could mean that every single consumer who goes to your site could see different combinations of layouts and content uniquely relevant to their needs at that specific moment in time.

You can also use location and contextual data to send marketing messages with localized content across marketing channels. These kinds of personalized, data-driven actions make consumers feel seen and heard and are way more likely to hit home than generic, one-size-fits-all messages.

Data is a crucial part of creating digital consumer experiences that adapt to changes in consumer preferences. Start by building a solid foundation of data-driven practices in your team, and you’ll be able to keep growing your digital experience in a way that actually benefits and engages your target consumers.

Diane Keng is the CEO and cofounder of Breinify, an AI and predictive personalization engine. A noted software innovator who frequently speaks on the intersection of AI, personal data, privacy, and the future of smarter products, Keng is on Forbes’ 30 Under 30 for enterprise technology and has been featured in The Wall Street Journal, HuffPost, TechCrunch, OZY, and Inc. Breinify works with retailers and consumer packaged goods brands to enable data science in marketing campaigns that secure 51 percent year-over-year online sales, 20 times the click rate, and six times the reaction rate.