Consumer Engagement Behavior Segmentation

We know that all consumers behave differently, and that they engage with ads differently. No single ad can engage everyone because different consumers like various types of messages, interactivity, imagery, typography, colors, and sizes. And they prefer to engage differently at different times, on different devices, from different locations.

In such a complex consumer engagement world, advertisers who fail to effectively engage consumer preferences are wasting their digital advertising budgets. It’s critical to focus on engagement technologies, and the more important issues involve learning more about consumers and understanding their engagement preferences.

EngageClick uses state-of-the-art artificial intelligence technologies to incorporate multiple consumer data attributes. This is how we discover the underlying patterns of consumer engagement behaviors. Our machine-learning classification technology then builds up categories of distinct audiences who engage in similar patterns, known as consumer personas.


Once the consumer personas are identified, we develop the ad elements that can best engage each persona type. Our predictive models make real-time selections of ads, or they create personalized ads in real-time, using preferred ad elements. These ads are delivered to consumers to create a higher likelihood of engagement, and then the results are cycled back through our machine-learning models to constantly improve the results.

Content Personalization - We add personalized content within ads, supported in text, display, mobile, video, and smart TV formats.

Interactivity Personalization - We also add personalized interactive features on the ads, supported in display, mobile, video, and smart TV formats.


Engagement is necessary, but for most approaches it’s like saying hello to a stranger. If you want to build a real relationship, you need to continue the conversation in a more meaningful way. In our world, this process is called re-engagement.

Our re-engagement technology uses a retargeting methodology that not only understands the context and content of the ad, but also the consumer’s preferred engagement behaviors.


Retargeting is based on understanding past interactions of consumers, but it’s even more important to understand their future intent. Marketers can learn meaningful insights based on knowledge about past consumer interactions. However, this knowledge only leads to strong engagement by applying deeper predictive analysis to anticipate future intent.

Standard retargeting is built on a cookie and rule-based model. Predictive retargeting goes one step further by using advanced machine-learning models that make better predictions about future intent. And this is why EngageClick’s platform delivers better results!


Dynamic creative can do wonders for an ad campaign, but first it must grab the consumer’s attention and provide personalized information.

We use cognitive science to create ads with personalized and dynamic content. We dynamically blend creative content together in interesting new ways that outperform any other dynamic creative treatments. To remove the guesswork that’s typically used in the ad tech marketplace, we rely on real data in real-time. Our machine-learning algorithms continuously crunch various data sources to update these dynamic creatives, before converting them and personalizing the consumer experience.


Since digital ads are being sent to interactive screens, it is important to make the advertising experience more interactive as well, which is what consumers increasingly expect. Not all interactive ads involve cool gaming features, but they should provide some format for two-way communications.

Our platform provides interactive features on all types of mobile, display, and video ads to make interactive marketing a reality.


In today’s consumer world, everyone starts the morning on one device before progressing through several digital touch-points throughout the day. It’s important to understand the impact of advertising on all of these digital touch-points.

Our machine-learning system integrates numerous data points such as IP address, time, location, past behaviors and ad preferences to understand how engagement impacts consumer decisions across various devices and screen sizes.

Our proprietary algorithms go one step further by not only identifying consumer personas across several screens, but also understanding how those engagements impact the goals for marketers. The best part is that our algorithms don’t require any previous data, because they can dynamically develop these insights during the campaign.