I have not seen many sessions on SaaS and business applications at OracleOpen World. Yet SaaS is becoming increasingly more important. The number of SaaS applications or at least the number of functions that standard available applications can perform is growing rapidly. The availability to any organization of SaaS functions that will support them with a large portion of their business process is growing. The main challenge of corporate IT departments is going to shift from creating IT facilities to support the business [processes] to enabling SaaS applications to provide that support – by mutually tying together these applications through integration and mash up as well as embedding in authentication, authorization, data warehousing, scanning, printing, enterprise content management and other enterprise IT facilities.
Business Applications not only support many more niche functions and allow fine tuning to an organization’s ways of doing things, they also become much smarter and proactive. Smart Business Applications – apply machine learning to help humans focus on the tasks that require human attention and handle automatically the cases that fall within boundaries of normal action.
Some simple examples:
- Marketing – who to send email to
- Sales – who to focus on
- Customer Service – recommend next step with calling customer
Oracle is permeating AI into business apps (AI Adaptive Apps),
also leveraging its Data as a Service with 3B consumer profiles in DaaS, and records on over $4 Trillion spending.
Oracle offers “a full suite of SaaS offerings” :
(although they clearly do not yet all have ideal mutual integration, similar look & feel and perfect alignment)
During the Keynote by Thomas Kurian at Oracle OpenWorld 2017, an extensive demo was presented of how consumer activity can be tracked and used to reach out and make relevant offerings – as part of the B2C Customer Experience (see https://youtu.be/cef7C2uiDTM?t=47m35s )
For example – web site navigation behavior can be tracked:
and from this, a profile can be composed about this particular user:
By comparing the profile to similar profiles and looking at the purchase behavior of those similar profiles, the AI powered application can predict and recommend purchases by the user with this profile.
Here follow a number of screenshots that indicate the insight in customer interest in products – and the effects of specific, targeted campaigns to push certain products
Information can be retrieved using REST services as well:
Recommendations that have been given to customers can be analyzed for their success. Additionally, the settings that drive these recommendations can be overridden – for example to push stock of a product that has been overstocked or is at of line:
The Supervisory Controls allow humans to override the machine learning based behavior:
Change weight between channels
