SAP - GERÇEK ZAMANDAN DOĞRU ZAMANA - SAP Forum 2013

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Gerçek zamanlı analizlerle müşterinizin hareketlerini öngörün, müşterinizden daha talep gelmeden isteklerine cevap verin. Düşünce hızında tekliflerin nasıl oluşturulduğunu bu sunumda görebilirsiniz

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  • 1. Engage Your Customers Like Never Before Real Time Decisioning in Moments of Truth
  • 2. © 2013 SAP AG. All rights reserved. 2 SAP Real-Time Offer Management is a self-learning recommendation engine that enables organizations to conduct effective customer interactions RTOM recommends contextual optimal products and next best actions in real-time that are likely to be accepted and provide the desired business goals Real-Time Decisioning Engine Offer Management Environment Self Learning and Analytics Recommend optimal offers Create & manage offers portfolio Learn and adapt Measure and provide insights RTOM Process SAP Real-Time Offer Management (RTOM)
  • 3. © 2013 SAP AG. All rights reserved. 3 SAP Real-Time Offer Management (RTOM) Real Time Decisioning in Moments of Truth Enabling effective interactions across customer interaction channels by recommending the Right Offer  Products and Next Best Actions  Optimizing customer needs and business goals To The Right Customer  Personalized contextual offers  Self learning to maximize acceptance rates At The Right Time  In real time according to the interaction context  Across all interaction channels Real-Time Decisioning Engine Offer Management Environment Self Learning and Analytics Recommend optimal offers Create & manage offers portfolio Learn and adapt Measure and provide insights RTOM Process
  • 4. © 2013 SAP AG. All rights reserved. 4 Why Clients Use RTOM? Typical pains and solution value Typical Pains RTOM Value Need to increase customer wallet share and proftability; need to reduce service costs o Turns every contact into cross/up sell opportunity while maintaining productivity o Enhances customer’s experience in self-service channels with personalized and relevant offers Complex offering and service interactions; Desire to boost adoption of self service channels o Empowers customer facing personnel with Next Best Actions o Streamlines self-service channels Challenging customer loyalty o Assesses existing and new risks in real-time and provides personalized retention offers to increase customer’s lifetime value Need to frequently respond to competition offers with short TTM o Business users’ tool that enables low cost testing of marketing ideas and short TTM for new offers launch
  • 5. © 2013 SAP AG. All rights reserved. 5 Measurable Benefits in Real Cases Short time-to-market for new offers introduction o 3-5 hours from marketing idea to offer deployment o 1-3 days from offer deployment to offer insights Rapid ROI by maximizing revenue opportunities o Typical 20-40% acceptance rates o 10+ times better response than outbound marketing o 60%+ increase in call center offering success 52% 57% 60% 48% 50% 52% 54% 56% 58% 60% 62% Jan Feb Mar Booked Accounts vs. control group
  • 6. © 2013 SAP AG. All rights reserved. 6 Solution Strategy  End-to-end offer management solution: from offer design to offer analytics. From integration tools to and runtime monitoring tools  Central offer management for closed loop relationship: multi-channel but channel- neutral; integrated with outbound tools such as Campaign Management, to maximize impact and provide a coherent customer experience  Self-learning user-friendly solution: to enable short time to market and liberate business users from day-to-day analysis, so they can focus on enriching the offers portfolio  Unique hybrid recommendation technology: combining business rules and self-learning to enable business goals consideration and recommendation reasoning  Scalability: unlimited linear scalability via multi-engine architecture to any concurrent interactions volumes while committing to less than half-second response time  Openness: APIs and included Integration Tools enable quick time to value in any environment  Immediate value for SAP customers: Native out-of-the-box integration with Campaign Management, CRM Masterdata, Product catalog, Interaction Center, ERP Sales Orders, BW, and more (including industry solutions)
  • 7. © 2013 SAP AG. All rights reserved. 7 What is in the Box?  Self learning multichannel real time recommendation engine with full APIs  Offer design environment and simulation tools  Integration and configuration tools free of programming  Monitoring tools to monitor and control the real time environment  Business Analytics - BW infocubes, reports and xCelsius dashboards  Connectors and native integration with SAP CRM Masterdata, Product catalog, Interaction Center and Marketing Campaign Management  Industry Solutions for SAP for Utilities and SAP for Communications Real-Time Decisioning Engine Offer Management Environment Self Learning and Analytics Recommend optimal offers Create & manage offers portfolio Learn and adapt Measure and provide insights RTOM Process
  • 8. © SAP 2010 / Page 8 Optimal Recommendation  Cross/up sell offers  Retention offers  Marketing Messages  Next Best Action … Real Time Offer Management in Action Make the right offer at the right time Offers Information  Offer / Message  Target Audience  Goal / Priority  Channels & Context RTOM Real-time customer information  Real time customer profile and transactions  Previous customer’s responses Real-time contextual information  Customer ID, Channel, LoS, …  Type of transaction, volume,… Capture and learn from response
  • 9. © 2013 SAP AG. All rights reserved. 9 6. Feedback Other Data Sources Data Warehouse 5. Recommendations Real Time Recommendation Engine Customer Interaction Channels CRM Master Data 4 2. Events Real Time Offer Management Architecture Landscape and Flow Offer Creation (manual or automatic) 7. Experience Extract 1 Product Catalog and Promotion Systems Real time data retrieval # Flow Step 1 Offers are designed and/or uploaded to the engine 2 Interaction application event triggers RTOM and sends information to the engine 3 RTOM retrieves more data from data sources (optional) 4 The engine detects the optimal offers 5 Recommended offers are provided to the application 6 Offers response is fed-back for learning, re-offer policy and analytics 7 Experience is extracted and exported for Analytics ExperienceOffers RTOM Analytics Applications Toolkit (Manage, Integ., Admin) Automatic offers creation and upload
  • 10. © 2013 SAP AG. All rights reserved. 10 Example - RTOM in the Interaction Center “What and why” for agent support Integration with product catalog and downstream processes RTOM Recommendations
  • 11. © 2013 SAP AG. All rights reserved. 11 Real-Time Offer Design in Marketing UI <Real Time Offer > Campaign Type
  • 12. © 2013 SAP AG. All rights reserved. 14 Mobile Marketing Initiative
  • 13. © 2013 SAP AG. All rights reserved. 15 RTOM Applications Toolkit Integration Manager, Monitoring & Business Tools Integration Manager provides System Integrators guided procedures GUI for expanding RTOM integration to new data sources and for configuring the engine reaction to session events Monitoring tools enable IT professionals to control and manage RTOM deployment in runtime Business Tools enable business professionals to design and simulate RTOM offers
  • 14. © 2013 SAP AG. All rights reserved. 16 Offers and Next Best Actions Main parameters and example Targeting Profiles / related Campaigns / lifetime events Personalized Suitability per profile/campaign Hypothesis for real-time learning (optional) Session events / context Eligibility / Policy Prerequisites Validity Time Frame Re-offer policy Offer Items Description Links to Products and Activities Business Priority Business Goals Quad-core performance plus 1GB of discrete graphics equals … Customer Eligibility: Does not have open complaints. Did not wait on line more than 3 Minutes. Agent Eligibility: Part of the Sales and Service team 1.Expressed interest in a new laptop with similar properties (script) 2.Has a PC with similar properties that went/is going out of warranty 3.Was targeted by e-mail offer but never contacted 4.Has the product in his Internet shopping cart Service ticket was successfully saved HP Pavillon dv6t Quad Edition 90 Days See next slides
  • 15. © 2013 SAP AG. All rights reserved. 17 All Customers and Potentials Eligibility Does not have open complaints. Did not wait on line more than 3 Minutes. Profiles and self learning of Predictors RTOM will assign each eligible offer a predictor according to the customer’s matching profile Has the product in his Internet shopping cart Has a PC with similar properties than went / is going out of warranty Was targeted by e-mail offer but never contacted Expressed interest in a new laptop with similar properties (script) Predictor 4 Predictor 1 Max (P1,P2) Predictor 3 Predictor 2 8 other potential predictors for behavior hypothesis Owns HP PC Male MVC
  • 16. © 2013 SAP AG. All rights reserved. 18 Optimization and prioritization Optimization and prioritization RTOM Recommendation Technology Offers Arbitration and Optimization Eligibility Targeting Previously offered Validity Offer 1 Offer 2 Offer N Arbitration phase Select the relevant offers based on: subject of the call, agent skills, eligibility criteria and more Optimization phase Optimal recommendation based on propensity scores, value to the organization and goals Adaptation phase Real time self learning to adapt propensity scores and discover response profiles Arbitration Optimization Adaptation Feedback for self learning Recommend optimal offers Create & manage offers portfolio Learn and adapt Measure and provide insights RTOM Process
  • 17. © 2013 SAP AG. All rights reserved. 19 Selection of the optimal offers Combination of propensity to buy and business priorities o The system maintains predictor per offer per target profile (and hypothesis) per channel. o The predictor will be updated by self learning based on the feedback. o All valid and applicable offers are added to queue based on their Mark (Score) Mark = Offer’s highest validated profile Predictor x Priority o Priority is optional and can be a category (e.g. High, Medium, Low) or some value that we want to maximize (e.g. margin, revenue, lifetime,…) o Business users can enforce offers to be recommended, regardless of their Mark by setting them to <Must Show> (e.g. we want to promote something this week on every relevant interaction) Show by Priority (ordered by Mark) Must Show (ordered by Mark) Show by Default (ordered by Mark) Max No. of Offers to be Recommended(e.g. 5) Note: <Must Show> will govern <Max No. Offers>. E.g. if Max No of offers is 5 and 7 valid offers are Must Show then 7 offers will be recommended Ranked Recommended Offers
  • 18. © 2013 SAP AG. All rights reserved. 20 Real-time modeling by profile hypothesis learning Profiling Hypothesis Predictors on Jul 20Owns HP PC MVC Male   NA 61.1%   NA 42.5% NA NA  15.8% 10%  Business users can provide response profile hypothesis regarding customer characteristics that may impact acceptance ratios  Self learning of the actual responses validates and fine-tunes these hypothesis Pavillon dv6t Quad Edition; Profile: Showed interest by script MVC & Owns HP PC Owns HP PC & not MVCMale
  • 19. Eligibility Laptop P4 High Perf. & 15”-17” P2 Was targeted by e- mail campaign P3 Has a similar PC out of warranty Eligibility Desktop P1 High Perf. & Kids room P2 Was targeted by e- mail campaign P3 Has a similar PC out of warranty Example dv6t Quad Edition 0.1 0.2 0.4 Eligibility Check HPE-560z  (desktop) Dv6t Quad  Dv6t Ent.  Profiles check and ranking Predictor x Priority Dv6t Quad P4: 0.3 * 100 = Dv6t Quad P3: 0.6 * 100 = 60 Dv6t Entr. P4: 0.4 * 200 = 80 Priority = Margin = $100 Ranked Recommendations for John ----------------------- # 1 Dv6t Entr. # 2 Dv6t Quad Offers ranked by predictor x priority Priority = Margin = $100 Priority = Margin = $200 John Smith Asks for a high- performance laptop with a screen of 15”-17” X HPE-560z Desktop 0.3 0.4 0.6 Predictor    dv6t Entertainment Eligibility Laptop P4 High Perf. & 15”-17” P2 Was targeted by e- mail campaign P5 Has item in shopping cart    0.4 0.3 0.6 Predictor Predictor Maximal predictor per offer
  • 20. © 2013 SAP AG. All rights reserved. 22 Business Content Customer transactions and Predictors evolution  RTOM out-of-the box Analytics and Dashboards are built from 2 predefined delta views on RTOM Internal experience database 1. Customer interactions – the offers made and their response 2. Predictors evolution – the trends of offers predictors along time  This information is loaded into predefined multi- dimensional cubes. Offer = iPhone Package ; Profile = Approaching EOC ; Classification = Media Fans Gender = Male Gender = MaleContract Type = Postpaid
  • 21. © 2013 SAP AG. All rights reserved. 23 Recommend optimal offers Create & manage offers portfolio Learn and adapt Measure and provide insights RTOM Process RTOM Analytics Controlling and Improving Offer Management Offer performance analytics analyzes the performance of offers along with customer profiles, and interaction events over time Customer analytics analyzes the response profiles of the various offers Channel analytics provides insights about offers performance and profitability in different channels Agent performance analytics analyzes the use and success of offering by different agents, as well as the impact on productivity over time
  • 22. © 2013 SAP AG. All rights reserved. 24 Summary – RTOM Value Proposition (in CRM)  Boosts cross/up sell and increases revenues  Enhances loyalty with relevant personalized offers  Enables short time to market for new offers launch  Improves agents’ productivity and self-service channels utilization  Recognizes the right offer at right time to the right customer  Automatically learns from response and optimizes offering strategy  Provides business insights for control and improvement  Quick ROI and low TCO solution in the hands of business users Typical business benefits …achieved through unique real time decisioning © SAP 2010 / Page 24
  • 23. Thank you Contact information: John Heald Head of 360 Customer UKI +44 7966 975203
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