Oracle Real-Time DecisionSyaifuddin Ismail

Agenda• What is RTD• RTD Use Cases• Who are Their Customers • How RTD works• RTD and Big Data

What is RTD

• The least talked about product in Oracle BI stack• Acquired from Sigma Dynamics in 2006• Initially embedded to Siebel CRM as part of its real-time marketing

• Predictive Analytic that recommends the ‘best action’ and optimises the decision processes using LEARN – ACT – LEARN cycle

• It continuously learns from data feed (raw, BI or Big Data), historical interaction to the system, business goals, feedbacks to improve decision making

• Personalised decision at the point of interaction and in real-time decision!• Business rules and goals can tested using multiple scenarios to ensure expected

outcomes before deployment• API libraries to interface with external application using many common Web

language (Java, .NET, Web Service)

A closed-loop recommendation engine

License is not included in OBIEE

Use Cases

• Cross Selling (selling products or services to existing customers)– Credit card offers to bank customers– Home insurance offers to car insurance customers

• Personalised products and services recommendation– Similar to Amazon’s Item and Featured recommendations

• Personalised Content– Display content selection based on customer profiles and previous customers

selections

• Fraud Detection and Avoidance– Reduced exposure using transaction patterns to detect suspicious transactions

• Customer Retention– Offering on a discounted products to a loyal customer

• Process Optimisations

How RTD can benefit our clients

A few months to go-live: a month to design, another to deploy and a third to get into production

Who are Their Customers

Prominent Customers

• Betfair (http://www.computing.co.uk/ctg/news/2144534/betfair-boosts-revenue-oracle-analytics)

– Products recommendation (what their customers like to bet based on 300 items of customer data)

• Dell (RTD featured customer)– Personalised products and services

• BT – Products and Services recommendation, product campaigns– BT Managed Fraud Reduction (product collaboration with Oracle) – developed in 2008

• Allstate Insurance (recruiting RTD developers recently)

– Products and Services recommendations

• Codelco (2012)

– Process optimisation (production variation analysis)

Industry

e-Commerce, Telecommunication, Financial Services, Insurance, online Gambling, High Tech, Retail, Mining.

• Choices/Assets– Possible process outcome: offers, messages, contents,

treatments

• Entity– Attributes used as input to decisions: customer, account

and interaction data

• Models & Rules– Rules that support scorings and decisions– Predictive models that support decisions

• Performance Goals/KPI– Business objectives by which the value of decision is

measured

• Decisions– Executes models & rules then applies performance

goal to get the best Choices

• Recommendations– Decision presented

How RTD worksConceptual

• Choices can dynamically created from external application/data

• Business Rules can be tested using multiple scenario

• Business users can use web UI to update rules

• Decisions from RTD are sent to requested external applications

• API in Java, PHP, .NET and Web Service

How RTD worksIntegration with external applications

• Decision Engine can be designed for highly scalable environment

• Learning Engine can be deployed separately from Decision

• Both can cope with large volume of data

• Learning engine learn from data fed by batch processing

• Decision Engine gets the input data from low latency environment

RTD and Big DataHow they interact