If you’re not building personalized offers and experiences for your customers, you risk losing them. Hyper-personalization is the new holy grail in customer relationships—but it’s only possible when you move beyond broad audience segments and drill down to individuals. 

How can you serve the right message to the right person at the right time? Leading enterprise brands are already succeeding at this, with audience segmentation based on the actions of a single customer. Through working with and observing these brands, we’ve seen a few “best practices” show up time and again: 

• Making analytics accessible to marketers (and the entire organization)
• Getting more granular with audience data 
• Using predictive intelligence to improve customer journey analysis

Read on to learn more about these strategies to improve audience intelligence and deliver more meaningful experiences.

3 best practices for better audience segmentation

Audience segmentation drives marketing personalization by breaking a large customer base into smaller, more manageable groups. By observing these small groups (built around common characteristics) marketers can better understand their audience. And by better understanding the audience, you can deliver more relevant and targeted experiences. 

Segmenting audiences is not only useful for marketing and sales teams, though. Different parts of your business will have varying needs for audience data. But customer journey analysis can benefit your entire organization, be it revealing new business opportunities or ways to spend smarter. 

Here are 3 best practices for better audience segmentation:

1. 1. Make analytics more accessible for marketers

Improving audience segmentation and hyper-personalization (especially at scale) means teams need to move fast. But they can’t, if they have to wait on data scientists or IT to write custom SQL queries. 

Now, thanks to no-code applications, marketers can gather and analyze data without needing outside help. Sometimes called data democratization or self-service analytics, the aim is to make digital information (such as real-time audience behavioral data) easily accessible to the average user. 

Data democratization removes bottlenecks and puts marketing, sales, ops, HR, and all other teams in charge of their data. For example, Bleacher Report adopted self-service analytics to help increase time to insights and personalize content in real-time.

As a result, teams could get immediate answers to their questions about which content users engaged with during certain time intervals. They used the knowledge to build better user cohorts, test new product features, and go from putting out fires to spotting opportunities. 

1. 2. Get more granular with audience data 

Imagine you’re a media brand that works with influencers. What do you think is going to impress them more—a customized video to celebrate them reaching 500K followers, or the generic 15% discount code everyone and their mom got? 

It’s the personal details that make the biggest impact. Demographic data is a start, but today’s consumers expect more. According to research from Salesforce, 66% of consumers expect companies to understand their unique needs. And 52% expect offers to always be personalized. 

That’s why you need to go beyond cookie-cutter data points to explore unique user behaviors, preferences, and attitudes. How much more deeply could you understand and connect with your customers if you could answer questions like: 

• What type of content, offer, or user action leads to the most conversions?
• What type of in-app actions lead to the most loyal users?

Used car buying platform Carvana collected data points like purchase date, location, cultural touchpoints, and vehicle specs. Then, they combined that data with AI to generate over 1.3 million personalized videos about “the day you met your new ride”. 

That ability to collect deep customer behavioral data and explore it in real-time drives both improved CX and increased productivity. The videos enhanced the buying experience and helped buyers connect with their car, the Carvana community, and the brand. 

1. 3. Use predictive models to anticipate the customer’s next move

If only we could know exactly what the customer will do next, and when they’ll do it. Now, we can, with predictive modeling. Predictive analysis analyzes historical and real-time customer data to predict outcomes or trends that might occur down the road. 

By combining techniques like data modeling, data mining, statistics, retention analysis, artificial intelligence, and machine learning, predictive analysis takes the guesswork out of knowing how your customers will act. For example, you can identify high-value customers and customers who are at risk of churning.  

When you search for something on Google, predictive analytics influences the results you see, taking into account factors like your location and previous searches. The New York Times also uses predictive analytics to tailor content for their readers. Based on the articles you read, they can suggest others that you're likely to be interested in, much like the recommended products feature on Amazon.

Remember that audience segmentation is just the first step in the journey toward giving your customers the best possible experience. 

You also need to ensure you can act on your insights at the right time, as part of a holistic customer journey. Choose a solution that not only helps you create granular segments, but also integrates with your other tools and channels. This way, you'll be able to create highly targeted and personalized experiences for your customers, leading to better results and happier customers.

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