SAP FORUM İSTANBUL 2014Basit Düşün Fark YaratGERÇEK ZAMANLI TEKLİF DÖNEMİ BAŞLADI: SAP RTOMEngin VolkanSAP Danışmanlık
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Contents
The Market Need and Challenges
Detailed Solution Overview
Case Studies and Proof Points
Summary and More Information
NEW MARKETING REALITY:THE DIGITAL MIGRATION
of consumers leverage more than 2 channels56%CROSS CHANNEL INTERACTIONS
spent by people daily on digital media5 hoursSHIFT TO DIGITAL MEDIUM
of CMO’s believe data is the most underutilized asset50%DATA EXPLOSION
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ACROSSMULTIPLE CHANNELSSIMULTANEOUS INTERACTIONS
Social
POS
eCommerce
Web
Mobile
Phone
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ONE THAT REQUIRES CROSS CHANNEL ORCHESTRATION
marketers have no clear cross-channel marketing strategy.
57%
SOURCE – ECONSULTANCY AND EXPERIAN
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Marketing in the Digital AgeThe Value of Recommendations
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REAL TIME MARKETINGReal-time marketing is marketing performed "on-the-fly" to determine an appropriate or optimal approach to a particular customer at a particular time, context and place.
Real time marketing is commonly associated with inbound marketing that seeks the most appropriate next best action and next best offer for a given customer, in contrast to the traditional outbound marketing (or interruption marketing) which aims to acquire appropriate customers for a given 'pre-defined' offer.
The dynamic 'just-in-time' decision making behind a real-time offer aims to exploit a given customer interaction defined by its activity and context
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Marketing in the Digital AgeOutbound vs. Inbound Marketing
Outbound Marketing Inbound Marketing
Create static customer group using segmentation
Define customer eligibility and targeting method
One way communication
Distribute offers and messages via Email, SMS, telemarketing, etc.
Ask customer to take action
Two way dialog
Suggest relevant offers during interaction
Capture and analyze customer response and actions to provide more added value
Customer involved and taking actions
Use reports to analyze results and improve future campaigns / segmentation / offers /…
Improve the offering process using online learning
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Marketing in the Digital AgeAnalyst Perspective – the value of inbound and event based marketing
CampaignEnterprise-initiated,
marketing-driven
“Intrusive”
3% success
Event-driven
Real timeCustomer-initiated,
relationship-driven“Appropriate”40% success
Customer-triggered,
product as service
“Convenient”
20% success
Gartner studies on insight-driven sales and marketing activities show that promotions (cross- and up-sell; retention offers) on customer-initiated interactions, which leverage advanced analytics to make relevant offers, are 40% more successful.
“
Key findings• Analytics-driven marketing
efforts yield higher success rates
• Inbound customer-initiated interactions are a great opportunity to market
• Increased customer satisfaction rates with intelligent offers
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The Real Time Offer Management MarketPains and suggested value
Pain Suggested Value
Need to increase customer wallet share and proftability; need to reduce service costs
o Turns every service interaction into cross/up sell opportunity while maintaining productivity
o Enhances customer’s experience in self-service channels with personalized and relevant offers
Challenging customer loyalty
o Improve cross and up sell to increase customer engagement
o Assesses existing and new risks in real-time and provides personalized retention offers to increase customer’s lifetime value
o Differentiation via event based offers (including location based)
Complex offering and service interactions; Low sales effectiveness
o Empowers customer facing personnel with Next Best Actions
o Streamlines self-service channels
Frequent need to innovate or respond to competition offers with short TTM
o Business users’ tool that enables low cost marketing ideas test and short TTM for new offers launch
11© 2014 SAP AG or an SAP affiliate company. All rights reserved.
Contents
The Market Need and Challenges
Detailed Solution Overview
Case Studies and Proof Points
Summary and More Information
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SAP Real Time Offer Management (RTOM)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 with no coding
< Monitoring tools to monitor and control the real time environment
< Business Analytics – cubes, reports and dashboards
< Connectors and native integration with Interaction Center, Marketing Campaign Management SAP for Utilities, Telco Order Management, and many 3rd party systems
Real Time Recommendation Engine
Offer Management Environment Analytics & self learning
Recommend optimal offers
Create & manage offers portfolio Learn and adapt Measure and provide
insightsRTOM Process
Integration, configuration, simulation and administration tools + full web services API
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BusinessUser
4. Optimal Recommendations n Cross/up sell offersn Retention offersn Next Best Actionsn Marketing Messages …
SAP Real Time Offer Management in ActionMake the right offer at the right time – the flow
1. Offers DefinitionWhat - Offer / Message
Who - Target Audience
Where - Channels
When - Context
SAP RTOM
3. Data Sources Real Time Access and Fetchn Previous customer’s responsesn Optional - historical transactions, more attributes
5. Response capture and online learning
2. Triggering Event (typically by consuming app) n Customer ID, Channel, Page, Profile Information…n Type of activity, volume,…
Consumer
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RTOM Consumption ExamplePrebuilt integration with SAP Interaction Center
“What and why” for agent support
Integration with product catalog and downstream processes
RTOM Recommendations
Watch a video online https://www.youtube.com/watch?v=QL_Op3_N2-8
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RTOM Consumption ExampleCloud for Customer (C4C)
Videohttps://www.youtube.com/watch?v=X0_ycLLdV_4
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RTOM Consumption ExampleBanking Self Service Web Channel
RTOM Offers and Next Best ActionsRTOM Offers and Next Best Actions
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RTOM Consumption ExampleMobile Apps and E-commerce sites
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Watch a video online
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Next Best Offers and Next Best ActionsMain design time parameters
TargetingRecipient profiles / Related Campaigns
Personalized Suitability per profile/campaign
Profiling hypothesis
Triggering events / context
(optional) Downstream and batch activities
Eligibility / PolicyPrerequisites
Validity timeframe
Re-offer policy
Offer ItemsDescription
Links to Products and Activities
Business Priority
Business Goals / Keywords
HELOC offer with no cancelation fee at specific exit points
Customer Eligibility: Resident & Not blocked & Does not already have a line of credit & House owner & above 18
Agent Eligibility: Retail customers agents (offer is not available for Businesses)
1. Recently finished or about to finish paying a mortgage
2. Frequently redraws money from long term savings
3. Customer used a loan simulator on the website
4. Targeted by e-mail campaign but was never contacted
Customer is confirmed, Loan origination, Savings redraw, Mortgage closure
Example: Home Equity Line of Credit Offer
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Example - Real-Time Offer Design in SAP Marketing UISetting Rules (used for eligibility, recipient profiles and computed attributes)
Watch a video online https://www.youtube.com/watch?v=B_04JQyAXbA
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Real-Time Offer Design in Marketing UI (HELOC)Setting rules with BRF+
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- Charles Darwin
It is not the strongest of the species that survives, nor the most intelligent, but the one most responsive to change.
RTOM Recommendation Strategy
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RTOM Recommendation StrategyRTOM learns behavior of target segments in specific context
1.Recently finished or about to finish paying a mortgage
2.Frequently redraws money from long term savings
3.Customer used a loan simulator on the website
4.Targeted by product launch e-mail campaign but was not contacted
Customer is confirmed, Loan origination, Savings redraw, Mortgage closure
Target segments Context
TargetingRecipient profiles / Related Campaigns
Personalized Suitability per profile/campaign
Profiling hypothesis
Triggering events / context
(optional) Downstream and batch activities
Eligibility / PolicyPrerequisites
Validity timeframe
Re-offer policy
Offer ItemsDescription
Links to Products and Activities
Business Priority
Business Goals / Keywords
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All Customers and PotentialsAll Customers and Potentials
RTOM Recommendation StrategyRecipient Profiles and Hypothesis (supportive attributes)
Eligibility: Resident, not blocked, Does not already have a line of credit, House owner, and above 18
Predictor 1
Predictor 2
Predictor 3 Predictor 3
Customer used a loan calculator on the website
Targeted by product launch e-mail campaign but was not contacted
Recently finished or about to finish paying a mortgage
Max (P1, P2)
Frequently redraws money from long term savings
H2H1
H3
Has more than one account
Has No savings account
Paying mortgage
8 other potential predictors from real
time learning of marketing hypothesis
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Optimizationand prioritization
Optimizationand prioritization
RTOM Recommendation StrategySelecting valid offers è Ranking è Response Collection and Modeling è Adaptation
Previously offered
Eligibility TargetingProfiles
Validity
Offer 1Offer 2
Offer N
Arbitration phase Select the relevant offers
based on: subject of the
call, agent skills, eligibility
criteria and more
Optimization phaseOffers are ranked according
to propensity scores, value
to the organization and
goals
Adaptation phaseSelf-learning to adapt
propensity scores and
discover response profiles
Arbitration(policy based)
Optimization(by propensity & value)
Adaptation(response modeling)
Feedback for self learning
Recommend optimal offers
Create & manage offers portfolio Learn and adapt Measure and provide
insightsRTOM Process
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RTOM Analytics4 Main Categories
1. Offer performance analytics – conversion analysis along offers, profiles, and interaction events over time
2. Customer analytics - response profiles and dynamic profiling analysis
3. Channel analytics - insights about the offer performance in different channels as well as channel specific offering profitability.
4. Agent performance analytics - analysis of the use of RTOM and the success of offering by different agents, as well as the impact on productivity over time.
Recommend optimal offers
Create & manage offers portfolio Learn and adapt
Measure and provide insights
RTOM Process
28© 2014 SAP AG or an SAP affiliate company. All rights reserved.
Contents
The Market Need and Challenges
Detailed Solution Overview
Case Studies and Proof Points
Summary and More Information
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 29
Measurable Benefits in Real CasesMore Revenues, Loyalty and Business Agility
Short time-to-market for new offers introduction
o 3 hours from a marketing idea to offer deployment
o Offer insights within a day
o E-Commerce interactions (e.g. Mobile, web)
ü 20% increase in digital coupons’ conversions
ü 15% increase in average basket size
ü 10% increase in purchase of suggested products
Rapid ROI by maximizing revenue opportunities
o Agent assisted interactions (e.g. call center)
ü 20-40% end with accepted offer(s)
ü 60%+ lift in number of cross-sells ;
ü 130+% revenue lift
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Measurable Benefits in Real CasesTop US Bank – launched 35 cross/up sell & retention offers within 1 month
One of the top 10 largest financial services holding companies in the United States
Business challengesn Complex offering – the bank manages a comprehensive offering environment: banking, brokerage,
insurance, investment, mortgage, trust and payment servicesn Wish to create and manage a multitude of cross line of business offers
(e.g. mortgage + insurance) n All product should be available over all channels
CRM strategy & objectivesn We are always here for you – Service available 24 hours a day, 7 days a week; 24-Hour Bankers are
available 24 hours a day, 7 days a week; etc.n We have the product you need and if not we will create itn Immediate and accurate execution guaranteed
Results with SAPn Agents are doing 50-60% better and improving month to monthn 18% positive response to RTOM offers; additional 12% requested follow upn 3 hours to train new agentsn Up and running in call center within 3 monthsn ROI within 6 months
52%
57%
60%
48%
50%
52%
54%
56%
58%
60%
62%
Jan Feb Mar
Booked Accountsvs. control group
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RTOM Proof PointsANZ Bank
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o Several projects in Singapore, China,
Australia and New Zealand
o Integration with Sybase M-Banking
and Online Banking as well as
SAP/legacy front-end and back-end
systems
o Innovative banking and insurance
scenarios, e.g. location based
according to credit card swipe
location, ATM, Branch
First go live within 3 months
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§ Real time recommendation based on current shopping basket contents and past purchases
§ A dozen of quantity, discounting and Mix & Match promotion templates
§ Real time identification of promotion status and guidance towards benefits
RTOM Proof PointsAvon (www.avon.ca) live since April 2013
o “Fully Qualified” – you basket contents qualifies you for benefit X
o “Partially Qualified” – you need to add … to your basket in order to get X
o “Reverse” – you have X in your basket that you could have got as benefit
o “Missed” – this promo is active but you don’t have any of its requirements in your basket
§ Real-time analysis of overlapping offers (e.g. X can be part of several promos) and intelligent prioritization of messages to consumer
§ Proved in large volume scenarios with SAP ERP SD, SAP CRM, Vistex and IBM WebSphere-Commerce
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RTOM Proof PointsLarge mobile operator
o Mobile operator with 30M+ customers
o Key use-cases
Ø Real time plan upgrade offers to customers
approaching plan limits (see on the right)
Ø Automatic retention treatments to valuable
customers suffering from dropped calls
Ø Real time analysis and personalized automatic
reaction to Social network activity (e.g. complaints,
service needs)
o Deployed on the HANA Enterprise Cloud (HEC)
Before RTOM With RTOM
User gets message that
data speed will be
reduced
Plan upgrade is offered in real time when quota is about to exhaust
RTOM can be live within 8 weeks and 6 man month of effort
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Contents
The Market Need and Challenges
Detailed Solution Overview
Case Studies and Proof Points
Summary and More Information
© 2014 SAP AG or an SAP affiliate company. All rights reserved. 35
SAP RTOM Value PropositionSolution Strategy and Key Differentiators
Single end-to-end closed loop real time marketing & offer management solution
Patented recommendation technology – provided recommendation personalized and automatically
learns from response
Business user oriented – minimal Time To Market with no IT support
Inherent Multi-channel functionality for self-service and agent assisted channels
Native analytics and dashboards
Fully configurable and extendable via standard API
Unlimited scale-out with guaranteed sub-second response time
Field experience, best practice and solid roadmap
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For M
ore
Info
rmat
ion Solution description on sap.com
http://www.sap.com/lines-of-business/marketing/real-time-offer-management/index.epx
Whitepapershttp://www.sap.com/crm
Videos, blogs and more on SAP Community Networkhttp://www.sdn.sap.com
Thank [email protected]
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