In-the-moment personalization is something organizations (end users and vendors alike) talk a lot about, but in reality, very few are able to achieve. There are many reasons for this but in essence organizations need granular data capabilities, automated marketing signals, and a solution that can send millisecond data streams to enterprise decisioning systems to enable genuine live-time personalization.
In practice any system which captures or generates a large volume of data must provide a way of filtering this data to enable real-time use cases. Downstream decisioning systems are simply not designed to handle or process vast amounts of data. These solutions require only the most relevant interaction data (which is focused on the outcomes they were implemented to solve for). To maximize efficiency and effectiveness the upstream application must filter out any irrelevant behaviors.
For example, let's imagine a bank who wants to boost personal loan applications through real-time personalization. They're hosting 1 million customer sessions at any given point in time via their mobile app, but 80% of customers are just checking their balance or doing routine banking tasks. Of the remaining 20% of visitors, half are engaging with the mortgage calculator and the other half have clicked on a banner promoting personal loans. If the bank connects the data from all 1 million sessions to their decisioning application, that solution has to run analytics before decisioning can happen, which would cause increased latencies and defeat their real-time capabilities.
Instead, by instantly recognizing behavioral patterns which signify opportunity and intent the bank can take advantage of live-time personalization. Creating predictive models which instantly score customer behavior ensures only the most relevant customer data is connected to decisioning systems for the creation of personalized content. As the maturity of enterprises (and in particular banks) increases, there are powerful new machine learning features which can be leveraged to reduce time-to-value when deploying real-time decisioning. This pre-configured functionality is called Automated Marketing Signals (AMS).
AMS automates the filtering process described above thanks to out-of-the-box, fully embedded predictive algorithms, which are pre-configured to detect the 50 behavioral signals which are most relevant to the banking industry. These include interest in specific products (loans, mortgages, credit cards, etc.), subscriptions to services, and changes to personal circumstances which could indicate a heightened propensity to buy. The benefit of these pre-configured signals is a massive savings in development and configuration work - not to mention $Millions in resources. These models can also be easily adapted by financial organizations to create new signals as needed - without having to start from scratch.
Organizations often spend years trying to achieve this functionality with various MarTech solutions, at huge expense and without notable success. A customer context solution that's designed to connect highly focused marketing signals to the enterprise decisioning solutions is the only way to deliver true in-the-moment personalization to banking customers. And the added benefit? Freeing your data science teams to focus their resources on other cash generating activities.
