sap - gerÇek zamandan doĞru zamana - sap forum 2013

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)


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








Process


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


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


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)


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






Learn and adapt

Measure and provide insights

RTOM

Process

© SAP 2010 /

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


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


Example - RTOM in the Interaction Center

“What and why” for agent support

Integration with product catalog and downstream processes

RTOM Recommendations

C:/Users/I044828/Videos/RTOM in the IC PC4U.avi


Real-Time Offer Design in Marketing UI

<Real Time Offer > Campaign Type


Mobile Marketing Initiative

C:/Users/I044828/Videos/Apollo Short.swf

http://www.apollo.sap.com/Apollo.En/Apollo.dashboard/


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


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


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


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





Process


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


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


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”


mail campaign

P5 Has item in shopping

cart

0.4

0.3

0.6

Predictor Predictor

Maximal predictor per offer


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




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

Industries and Customers/Hi-Tech/HP/ZDEMO_HP_IC_RTOM_OFFER_O.swf - Shortcut.lnk

Industries and Customers/Hi-Tech/HP/ZDEMO_HP_IC_RTOM_ICMGN_O.swf - Shortcut.lnk


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 /

Thank you

Contact information:

John Heald

Head of 360 Customer UKI

+44 7966 975203

sap - gerÇek zamandan doĞru zamana - sap forum 2013

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