SAS USER FORUM Real-Time Marketing exploiting stateof-the-art SAS technologies By Morten Schrøder & Jan Løwe
Introduction to Customer Experience
The Big Vision OMNI-CHANNEL CUSTOMER EXPERIENCE
The Customer Decision Hub CUSTOMER DECISION HUB Dynamic Channel Management Channel Agnostic Decision Logic Central Administration and Orchestration Easy To Use For Business Users Value Driven Marketing Using Optimization Rules
Introduction to SAS Real-Time Decision Management
SAS Real-Time Decision Manager Real-Time Decision Manager (Back End) Application Layer (Front End) Multiple Touchpoints Real-Time Analytics 3 Execute a decision process 2 Request is received via web service 1 Real-Time customer interaction triggers a request Website CRM Data Offline Data 4 Make a decision based on a comprehensive customer view 5 6 Receive decision and Response is received via web service take action while the customer is still engaged Loyalty App Call Center
SAS Real-Time Decision Manager Actions Next Best Action/Offer Real-Time Decisioning Offer arbitration Insights A/B testing Contact & Response history Performance More than 1000 transactions per second with 12 cores Response time range: ~50 ms - ~1-2 s
SAS Real-Time Decision Manager Business user friendly UI Reusable components Testing tools Deployment utilities Integration Models from SAS Model Manager Business rule flows from SAS Business Rules Manager Database & Web service integration Custom code as Custom Nodes
Introduction to demo
The Customer Decision Hub Real-Time Orchestration using the CUSTOMER DECISION HUB Request 1 2 Next-Best-Action (NBA) Real-Time Analytics Optimized Offers (NBA) Optimization & Orchestration Rules Reply 3 Decision Logic
SAS Forum Bank Call Center
RTDM functionalities The basic building blocks of a decision campaign Data Process reading additional data. A gate only allowing specific values to pass through. Splitting the flow into two or more branches. Have common rules in a separate decision campaign. The offer either with pre-defined attributes or dynamically set during run-time
Challenge to focus on Dynamic set of indicators / potential offers Customer A Customer B SAS Real-Time Decision Management enables you to work with data grids.
Challenge to focus on Too many possible offers to evaluate in real-time SAS Real-Time Decision Management enables you to work with sophisticated decision flows.
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Introduction to Event Stream Processing
SAS Event Stream Processing Actions Pattern detection at event stream source Detect and monitor events continuously, taking real-time relevant action or notify relevant parties immediately when relevant Business user friendly UI Dataflow centric modeling Drag & drop visual modeler Performance Millions of events per second throughput Millisecond-microsecond response latency **On standard commodity hardware
PUBLISHING INTERFACE SUBSCRIBING INTERFACE SAS Event Stream Processing ESP STUDIO ESP STREAM VIEWER EVENT STREAM PROCESSING ENGINE Processes data continuously, on the move, in-memory with very high speed and low latency Apply rules and analysis using a dataflow centric ESP model Filtering, aggregation, pattern detection, calculations, correlations, procedural, text mining, thresholding and much more
Customer does transactions during the day Demo Additional Info Start Balance Limit alerts threshold Real Time Decision Engine Customer Profile Legend ESP Publisher Event Stream Processing Event Stream Processing model continuously monitor real-time balance RTDM Adapter Decision Flow Real Time event stream is enhanced with customer information and contacts the customer in real time while updating our offer / event table Offer / Event table SAS Solution Customer Interface SMS to Customer with overdraft message: Hi Jan SMS Service Call Center App
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Introduction to Self-Learner Functionality
Self-Learner Automated, closed-loop process for creating and updating predictive models Uses customer data to predict propensity for offer acceptance. Built using actual contact and response history Re-trained with new or additional observations Score generated in real-time Re-training runs in batch
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Conversion rates - Initial distribution
Conversion rates change over time (new contacts and responses)
Conversion rates - New distribution
Self-Learner which statistical models? Based on standard performance metrics*, the modeling process automatically chooses between: Naïve Bayes model Logistic model Too few training data: Random model with random output *Area Under Curve, Misclassification Rate, and root Average Squared Error
Integrations to CI 360 a teaser
Agent API Hybrid deployment Provide secure external access to events and operations in Customer Intelligence 360 SAS Customer Intelligence 360 Datahub Event Bus Engage Email Web Stand-alone application, or agent, is used to establish a secure WebSocket connection with the use of HTTPS and token-based authentication Agent can be configured to integrate with on premise customer data and RTDM Cloud On Premise Discover Customer Data Mart Upload and download events Agents Event API Websocket SAS RTDM SAS ESP App / SMS Additonal Data Streams Call Center, Apps & other inbound channels Letter Shop / Mail & other outbound channels
Questions & Answers