Personalization has evolved from segmentation based on terms of demographics like age, gender, and location to incorporate behavioral  attributes like purchase habits and changing preferences.  Behavioral segmentation allows your brand to create rich profiles of your customers, and deliver delightful experiences that are relevant and personalized. As personalization becomes more of a necessity as opposed to a nice to have, it’s important to go beyond just demographic data that paints a more simple and one dimensional picture of your customer and provide recommendations and experiences that are useful, timely and personal. 

Why is behavioral data important?

Consumers have evolved and the consumer journey both online and offline is so much more complex than just a few years ago. Demographic data only provides a snippet into who your consumer is and what they are looking for at any given moment. Collecting data that includes helps you understand changing preferences, purchase behaviors and the context around will help you deliver personalized experiences more effectively and improve conversions and engagement. 

82% of customers feel more positive about a brand after engaging with personalized content. Behavioral segmentation is a great starting point for a really robust personalization strategy. It allows you to understand your consumer better and provide recommendations that resonate with them at intent rich moments. While segmentation is a great first step of any personalization journey, any type of segmentation will have its limitations at scale. So it’s important to use it as a tool to understand consumers and see patterns, but not rely on it too heavily for a personalization strategy. One way to be mindful about this is to understand the different types of behaviors and the opportunity they present for personalization. 

The 4 Types of Behavioral Data

Usage and Purchase Behavior: 

Understanding usage and purchase behavior can help you define the key moments in the customer journey that are opportunities for conversion. The purchase behavior of a consumer along with other data can provide insights into what their preferences are and what they are looking for. 

For example, your purchase data indicates that Taylor purchases a different hair color every 2-3 months. This indicates that they like to experiment and are looking for something different at every purchase. As a beauty brand it’s important to understand and encourage this! You could recommend seasonal trending products every 2 months so that Taylor is inspired and makes a purchase. 

Time-Based and Occasion: 

Like in the previous example, time and occasion based data helps you understand when consumers buy instead of just what they’re buying. This type of data might include information around how frequently a product is purchased by a consumer, or patterns of  purchase behaviors around certain holidays, seasons, and events.

For example, John buys a case of wine every 3 weeks, and whiskey on special occasions like New Year’s. As an online alcohol retailer, you should be recommending only wine with perhaps some special promotions for the more expensive bottles every 3 weeks. You could also recommend a bundle of wine and whiskey around the holidays, which would really make his purchase easier and more seamless. 

Benefit-Driven:

This type of behavioral data allows you to understand what needs are your consumers trying to satisfy with your product. This is key for personalization and making sure your brand stays relevant with customers.  Understanding the answer to this question is the key to marketing to your consumers successfully.

For example, through your website data you notice that customers are searching for sweat-wicking materials like dry-fit, but don’t really end up buying anything. This would be a great opportunity to offer a discount on dry-fit products in your store to encourage them to make a purchase. Personalized offers like this, especially with enticing pricing, can show customers you understand their needs and encourage them to choose your brand. 

Customer Loyalty: 

The final type of behavioral data is based on customer loyalty and repeat purchases. It’s obviously important to understand whether or not a customer is a one-time buyer or a loyal advocate. But going beyond that, you can personalize your offerings to fit the habits of your loyal consumers. Let’s say Audrey buys feminine products from your brand every month. After six months in your loyalty program, she has earned enough rewards points to redeem a free item that fits her regimen. Audrey won’t see a need to look elsewhere when you have anticipated her needs and rewarded her loyalty.

How to Use Behavioral Data for Personalization

Acting on behavioral data can take your customer experiences to the next level  in terms of personalization. Here’s how to get started in a few steps:

  1. Collect and tag data appropriately. Tagging your consumer data as it comes in will save you hours of work as you try to derive insights from it, and set you up for scaling personalization. Make sure you have a system in place to identify the different types of behavioral data (historical, social engagement, product/app usage, etc.) and tie them toindividual consumers or segments to determine whether or not they are ready to convert. If your marketing team is too small to deal with copious amounts of consumer data, consider investing in a machine learning tool or vendor to assist you with this step.
  2. Start with simple use cases for personalization. Personalization for every consumer doesn’t happen overnight. It begins with a hypothesis that is easy to test and iterate upon. As a company that sells flowers, you could begin personalization with occasion-based behavior data, by promoting your products around certain national holidays. If that works well, you can use first-party data to highlight birthdays, anniversaries, and other milestones. Figuring out what works best is a process, but investing time and resources into personalization can help you increase profits in the long run.
  3. Leverage AI to help scale personalization. Behavioral data is a deep well of knowledge; many brands find themselves with too much data for their marketing teams to work with. When it comes to personalization, however, there is no such thing as too much data. If your team lacks the bandwidth to personalize meaningfully for every consumer, consider investing in an AI solution that can scale personalization for you. AI can help your team find patterns that the human eye might miss and maximize ROI by acting on data insights in real time.

Behavioral data is a powerful tool for creating personalized marketing experiences. In a landscape where personalization is now an expectation, not a perk, it is necessary to use a consumer’s past behavior to forecast future decisions and present them with the right messaging at exactly the right time.