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.

Kameleoon natively collects over 45 targeting criteria that can be used to create personalization campaigns. But there is one particularly powerful feature in Kameleoon that enables you to go even further, by activating custom data. What is custom data - and how can you use it to drive results and revenues?

1 What is a custom data point?

To configure personalized experiments, whether manual or predictive, you have to rely on precise visitor data and segment your target according to specific criteria. Kameleoon thus collects three 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, Kameleoon 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 by Kameleoon, 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 will go into more detail around targeting and analysis.

2 Refining targeting of your personalization campaigns

Thanks to custom data, you can target your visitors using highly specific criteria and therefore provide them with an even more relevant experience. Here are few examples of how you can efficiently use your custom data within Kameleoon.

Deliver a promo code to reactivate buyers

Thanks to the data stored in your CRM, you can see how many purchases a visitor has made, and when they last bought from you. You can therefore identify those people that have bought from you, but not for a while, and by creating a custom data point in Kameleoon, use this information to create your digital strategy. For example, you could look to reactivate customers by contacting them a set length of time after their first purchase and offering them a special promo code. So, thanks to this custom data point, you can optimize your promotional campaign by directly addressing the right targets, and without lowering your margins.

Maximize flash sales by targeting visitors interested in a certain product category

You can also use custom data to optimize the success of your flash sales by segmenting visitors according to the type of products viewed during their last visits.

Clearly during a flash sale you want to push the products to those visitors who are most likely to be interested. If your Google Tag Manager (GTM) data layer contains the product category viewed, simply create a custom data point with:

• a scope by "visitor”;
• a “List” type;
• the "Google Tag Manager” acquisition method;
• and specify the name of the variable containing the Product category (for example, here it is product_category)

Kameleoon automatically stores 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 Kameleoon segment using this custom data point, stipulating the value of the “Product category” to target.

3 Identifying new business opportunities via your reports

You can also more accurately analyze the results of your personalizations or A/B tests by using custom data to segment them. On the results page of an experiment, you can filter or break down results according to a custom data point associated with the experiment.

For example, if you want to study the impact of a specific promotional campaign on turnover, the custom data will enable you to identify which visitors actually used the promo code when paying.

On the results page of the campaign, by looking at the impact of the campaign on the "Transaction" goal linked to a purchase being made, you can break down the results according to this custom data point. This enables you to determine the proportion of buyers on the site who used the promo code and to confirm its relevance and its real benefit for the business.

In future promotional campaigns, you can then use this same custom data point to target visitors who have already made a purchase using the discount coupon. These visitors will be recognized automatically thanks to the previously created custom data point. Doing this can prove more profitable than targeting all visitors.

Another example: using a custom data point, it’s easy to know whether or not a visitor is part of your loyalty programme. Breaking down the results of a promotional campaign based on this custom data point enables you to analyze the behavior of “loyal” visitors. Depending on the advantages the loyalty programme offers, their behavior could be different to that of other visitors during promotions. You can therefore identify new campaign opportunities by analyzing the results obtained from these loyal visitors.

4 Other uses for custom data

Segmentation in Kameleoon Insights

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

Our 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.