How to design personalized digital experiences using custom data

When you launch a personalization campaign to optimize your visitors’ digital experience, you aim to achieve the best possible return on investment (ROI).

The quality of your visitor targeting is crucial to ensuring this: the more refined and accurate the targeting is, the more effective your campaign will be.

Our partner Kameleoon provides users natively with a collection of over 45 targeting criteria that can be used to create personalization campaigns. But there are other powerful features that enable you to go even further, by activating custom data. What is custom data — and how can you use it to drive results and revenues? Read all about it in this guest post.

To configure personalized experiments, whether manual or predictive, you have to rely on precise visitor data and segment your target according to specific criteria. There are 3 types of “hot” data, directly linked to the current website visit:

• Behavioral data (such as visit frequency or browsing journey)
• Contextual data (including day, weather, and geolocation)
• Technical data (for example, device, browser, or source of traffic)

At the same time, you can integrate complementary custom data from external sources, such as a data layer, DMP, CRM, or personalized web services. Such data can include the age or gender of the visitor, the amount in their last cart, or even their membership of a loyalty program. In general, this data depends very much on the specific needs of your company. For example, if you manage a peer-to-peer marketplace, you could have a custom data point that identifies whether a visitor is mainly a “buyer” or a “seller.”

Once uploaded to the platform and cross-referenced with the data collected, custom data provides you with even more precise information. This enables you to segment targets based on your specific business requirements and to analyze the results of your experimentation in-depth. To help customers get started in this article we’ll go into more detail around targeting and analysis.

Tools like Kameleoon automatically store all of the product categories viewed by a visitor in a custom data point in a list. Then all you need to do is create a segment using this custom data point, stipulating the value of the “Product category” to target.

Segmentation

Custom data points are objects that are shared by all our modules. In Kameleoon Insights, you can break down the data of a segment according to the custom data.

Use a custom data point as the cross-device reconciliation key

In the case of cross-device experiments, a custom data point can be used as the reconciliation key to display a similar variation of a test to the same signed-in visitor and to do so regardless of what device they are on. It can also be used to reconcile the results of the same signed-in visitor in the reporting tool.

Guide machine learning

The Kameleoon Predict AI-driven personalization engine relies on custom data to predict visitors’ purchasing intention. You can even tell us which custom data seems the most valuable for you so we can feed it into our machine learning algorithms.

Custom data is therefore essential to create customized experiences with the Kameleoon platform, at every stage of your experimentation process. It enables you to perfect your personalizations and provide your visitors with flawless experiences every time.