The attention span of individuals and organizations is reducing and the time is compressing. There are only brief windows that can be devoted to any given transaction. Expectations around situational knowledge, updated in real-time, are compounding. Customers assume that applications know who they are and that their transaction can be completed as quickly as possible whatever their location. If that doesn’t happen, they will abandon the transaction and quickly move on to another provider.
Financial institutions must do more in ever shorter time frames. Access to a real-time view of positions enables organizations to manage risk more closely, in terms of their aggregate position. For banks and financial institutions, it means knowing the content a customer has seen and the environment where they encountered it. Continually adaptive models use the answers to select active customers and this recommend options that address what they are searching for. The customer’s digital experience is composed of multiple linked processes. Operating at hyperscale enables an organization to deliver exponentially more data than traditional databases. This makes models more accurate and, by extension, more valuable. The use of more data enables the process to be completed in less time. By building models based on hyperscale capability, 10 milliseconds are saved. As more data points are added to customer profiles as they scale up to include more data overall, it’s important not to experience performance degradation. These time benefits will remain. This is the real-time data innovation dividend that’s driving up the customer experience.
