This technology helps create personalized ads with the greatest granularity when you have plenty of assets that can be fed through online APIs, RSS Feeds, spreadsheets, or other sources. The platform learns and then applies the learning to recommend ads in real-time, using predictive logic and real-time queries of the assets together in the ads. Personalized ad containers are approved by the clients so that they can maintain aesthetic control.
Once you’ve engaged your customer, the next step is to be smart about re-engaging them. Use the knowledge of what you know about your consumer (their past behaviors), and reach out to them (using retargeting methods), but don’t forget to take their future intent (using predictive targeting) into account.
Our platform uses the logic of cognitive science to re-engage customers by blending existing known retargeting methods with our proprietary targeting algorithms.
Big Data is the engine that is fueling personalized advertising. EngageClick bridges offline and online consumer data by associating it with an individual, and then creates personalized communication streams across different devices.
We create multiple ad styles to assemble different brands assets and get them approved. Then, our platform continues to learn. In this approach, the platform also builds ads programmatically by assembling and personalizing the brand assets together to engage consumers even better.
The EngageClick platform extends beyond basic development of relevant content – it actually delivers relevant engagement for advertisers to reach consumers with persuasive calls-to-action. This consumer-centric approach increases engagement by scientifically determining what ad elements will make the best fit for each consumer, based on their personal preferences, current situation, and physical location.
The learning process starts by being better informed about your target audience. First, our technology looks at several data points that we receive, such as: our first-party data (location, device, platform, time of the day, day of the week, browsers, websites, etc.); your first-party data (optional); and any third party-data (optional). Second, our Artificial Intelligence-based, machine-learning algorithms identify unique patterns of consumer engagement behaviors and group them together to form logical persona. The same person might engage differently with ads at different times, different days, different locations, in different devices, and in any different situation, making the possible behaviors of the total target audience astronomical. So, it’s important to look through more data points and find patterns to predict the best outcomes. This is how we blend big data, machine learning, cognitive science, and other statistical algorithms in order to learn about the consumers and their engagement behaviors.
When we know consumers better, it’s important to reach them in a personal way. We do this in three ways. First, we use existing creatives that are pre-approved to go. We mark each creative with the most important elements in it to learn more about what consumers like, such as descriptions of the image, typography, text, colors, calls-to-action, or any other multimedia assets. In this case, our machine-learning algorithms build knowledge over time to predict which creative should be presented to a consumer in real-time to engage better. Each creative gets to the right home where it is welcomed by consumers.