The "last mile" of any decisioning or personalization initiative is very difficult for solutions to deliver upon. With the complexity of digital across all channels, organizations don’t even realize how few of their offers or experiential changes even make it to the consumer due to poor coding, data time outs, and other challenges common in the market today. In addition, even if the content was delivered, there is no real reporting on the visibility of those changes.
Decisioning engines rely on machine learning models to determine which messages work best for which customers. Data used to train models look for whether content was loaded, plus whether a customer clicked on that content. If a customer doesn’t click, machine learning will assume the message didn’t work, but what if the message wasn’t even visible?
This problem with assumed visibility has far wider impacts beyond machine decisioning:
Visibility Detection was added to Celebrus CDP in 2020 and has been further enhanced with the release of Visibility Detection 2.0 to provide protection from bias in the machine learning models as mentioned above.
Visibility Detection 2.0 generates the most accurate insights into how visitors interact with targeted content, including:
The use cases deployed today with customers span across the entire organization, but here are some of the most common benefits of this feature:
models for real time decisioning and outbound marketing, leading to increased sales
or Multi Touch Attribution Models improved to drive better return on marketing spend
reporting will accurately reflect the relative performance of different pieces of content/messages
around whether customers have reviewed T’s & C’s or other regulatory content
We use necessary cookies to make our site work and analytics cookies to help us improve it. We will not collect any personal identifiable information unless you enable us to do so. For more detailed information about the cookies we use, see our cookies policy & settings page.