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Engage Your Customers Like Never Before
Real Time Decisioning in Moments of Truth
© 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)
© 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
© 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
© 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
© 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)
© 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
© 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
© 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
Experience Offers
RTOM
Analytics
Applications Toolkit
(Manage, Integ., Admin)
Automatic offers
creation and
upload
© 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
© 2013 SAP AG. All rights reserved. 11
Real-Time Offer Design in Marketing UI
<Real Time Offer > Campaign Type
© 2013 SAP AG. All rights reserved. 14
Mobile Marketing Initiative
© 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
© 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
© 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
© 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
© 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
© 2013 SAP AG. All rights reserved. 20
Real-time modeling by profile hypothesis learning
Profiling Hypothesis
Predictors
on Jul 20 Owns 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 MVC Male
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
© 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 = Male Contract Type = Postpaid
© 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
© 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
Thank you
Contact information:
John Heald
Head of 360 Customer UKI
+44 7966 975203