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Semelhante a RTD Use Cases Summary
Semelhante a RTD Use Cases Summary (20)
RTD Use Cases Summary
- 1. Юрий Пешков | BI Solutions y [email_address] +7 916 6800224 Oracle Real Time Decisions
- 14. Case Study: Удержание клиентов – электронная коммерция 1. Рекомендации на основе аналитики в процессе работы на сайте 2. Обратная связь 3. Информация передается в Siebel Каждое событие влияет на следующих шаг при работе с клиентом
- 18. Case Study: Привлечение новых клиентов – электронная коммерция RTD Рекомендации Детали
- 21. Case Study: Привлечение клиентов – Контакт центр Лист предложений Информация о клиенте Обратная связь Привлечение клиентов Удержание клиентов Целевое предложение
- 23. Case Study: Удержание клиентов – Контакт центр Обратная связь Информация о клиенте … Лист предложений Детали предложения Отображается в специальном окне
- 38. Oracle Real Time Decisions Логическая архитектура IVR XML / SOAP .NET HTTP Back-End Database Web Call Center Teller / ATM Informant Advisor Java Smart Client Data Mart / Warehouse J2EE Container RTD DB Decision Studio Back-End System JDBC XML / SOAP Java Decision Centre RTD J2EE Decision Server
Notas do Editor
- The internal rate of return (IRR) is a capital budgeting metric used by firms to decide whether they should make investments . It is also called discounted cash flow rate of return (DCFROR) or rate of return (ROR). [1] It is an indicator of the efficiency or quality of an investment, as opposed to net present value (NPV), which indicates value or magnitude. The IRR is the annualized effective compounded return rate which can be earned on the invested capital, i.e., the yield on the investment. Put another way, the internal rate of return for an investment is the discount rate that makes the net present value of the investment's income stream total to zero.
- 1) RTD leverages all existing information to make the optimal recommendations, not only historic purchase data RTD reevaluates recommendations whenever new information becomes available in real-time Such as additional click stream info Or call reason RTD continuously learns how all this information affects customer behavior Detects correlations of each piece of information with customer behavior like interest, purchase etc. Combines these correlations to predict likelihood of acceptance RTD is highly automated and optimized algorithms for performance 2) Recommendations are not only based on likelihood of acceptance but factor in key business metrics RTD accounts for often competing and conflicting business goals Marketing can direct the recommendation system to achieve specific business objectives driving high margin products, lowering inventory, increasing customer loyalty Provides fair alignment with interests of multiple product departments For example, KPIs can be weighted such that the printer and storage groups get their promotions into the mix in an equitable manner
- 3) RTD is designed to support multiple channels Experience shows that customer purchase behavior varies on the web vs. call center Combining data from web and call center is suboptimal, as it waters down the performance / predictive power of the solution RTD offers partitioned models learn channel-specific behavior Siebel RTD uses one set of application metadata (offers, rules, decision logic) to serve multiple channels No need to replicate applications and build siloed solutions for each channel 4) RTD is a platform that provides general-purpose real-time decisioning capabilities Can be used whenever a complex decision is needed in real-time and the system can learn from interactions For example, learn which resolution action plan is best for a new service request or what resource is best suited In sales scenario, help sales people to identify the best sales collateral to send Investment into the future
- 5) RTD features self-learning Traditional models: data prep 80%, validation takes time Siebel RTD is automatic but not a black box, reporting provides insight into inner workings Answer questions of what inputs determine behavior in what way 6) Interactions that RTD learns from should naturally age with time Traditional models need to be retrained completely, leading to periods of poor performance Siebel RTD weighs recent interactions more than older ones sliding weighted time window length are configurable RTD will nonetheless maintain full history of models models for different time periods are comparable allows business users to analyze how customer behavior changes over time
- From an integration perspective, RTD is designed to perform as a good citizen in corporate IT environments 7) RTD runs on industry-standard J2EE application server Metadata is compiled prior to runtime Smart data caching Fast access to decision data, metadata and rules Asynchronous messaging Does not slow down client system when DS is not available Clustering support High performance, high request volumes and failover Support for standards-based integration like SOAP, JDBC etc. 8) Last but not least, Siebel RTD will feature pre-built deep integration with Siebel Marketing, CC and Analytics Pre-packaged web service SOAP integration objects for seamless deployment in a Siebel front office environment Smart client functionality Fall back on default recommendations if DS is not responding with adequate response time One-button export of offer data from Siebel Marketing to DS deployment
- From an integration perspective, RTD is designed to perform as a good citizen in corporate IT environments 7) RTD runs on industry-standard J2EE application server Metadata is compiled prior to runtime Smart data caching Fast access to decision data, metadata and rules Asynchronous messaging Does not slow down client system when DS is not available Clustering support High performance, high request volumes and failover Support for standards-based integration like SOAP, JDBC etc. 8) Last but not least, Siebel RTD will feature pre-built deep integration with Siebel Marketing, CC and Analytics Pre-packaged web service SOAP integration objects for seamless deployment in a Siebel front office environment Smart client functionality Fall back on default recommendations if DS is not responding with adequate response time One-button export of offer data from Siebel Marketing to DS deployment