The Data You Need for Effective Content Individualization

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Data is the most critical component for delivering personalized experiences to customers. The best martech solutions providers have a solid data structure in place (whether that’s third-party data, your brand’s customer data or data of their own) to help you personalize your website, deliver targeted content more efficiently and optimize the entire customer journey with personalized content. However, we believe that in order to deliver the most relevant customer experiences, businesses need to take their personalization efforts a layer deeper by optimizing the experience for each individual customer.  

Delivering individually relevant content experiences requires knowing customers intimately based on all the data they share with you. And there are many ways you can acquire this data, from email databases and transaction data to customer relationship management and BI tools.

At the same time, customer data can be one of the biggest barriers to efficient and scalable individualization. With customer data being gathered and siloed across various systems and tools, it becomes difficult to gain a complete picture of your customers, who they are and what they want. Here we share a closer look at the types of customer data you need for effective individualization and why it’s important.

Data about what customers say—In order to optimize your content based on what an individual wants, you first have to understand their personal preferences. If your website collects user-generated content like ratings and reviews, you have their personal preferences right in front of you. This data is useful to more than just your product development team; it’s invaluable to help you understand what to offer more (or less) of based on individualized customer data.

Data about what customers do—While explicit feedback might seem like a great way to get started with personalization, many companies have found that what customers actually do is a much stronger signal for customer interests or preferences. For example, Netflix has removed its star ratings and reviews and moved to using actual viewing data as their signal for what types of programming a viewer will be interested in.

With the right personalization tools, you can track what customers do on your website, including pages they viewed, links and buttons they clicked and videos they watched. In addition to general traffic data, you can also view how deeply customers engaged with content, like how long they dwell on a page, how deep they scroll through and if they finish watching the product video on your website. Use these insights to understand how customers are engaging with your brand to optimize the content you serve up to them.

Data about the customer’s current context—Delivering the right content at the right time is essential to effective individualization. That’s why it’s important to also have insight on the customer’s situation, meaning where they are both physically and virtually. For example, are they at a desk or on the move? Are they at work or at home? Are they on their computer or mobile device? What pages are they viewing now versus one month ago? These data points make it possible to detect the customer’s current context to narrow down the types of content you present to them at various stages in their journey. What might be perfect for someone in one context might be inappropriate in another.

With these layers of customer data, you have the insights to deliver individualized content experiences. But in order to scale individualization, you must have the tools in place to unify the data across all the data acquisition mediums to build complete customer profiles. Your individualization platform should also be able to react and respond to real-time updates to user behavior, input or context in an automated way (most likely with machine learning). Last, the platform should be able to deliver individualized content in a way that makes sense to the customer. For example, make it clear that you are recommending content or offers based on their preferences or previous transactions with messaging such as, “Because you watched …” or “People who bought this also bought …”

For more information on how you can leverage customer data to deliver individualized content experiences, contact us.