siebel analytics in ibm: building a sense & respond solution

IBM Business Consulting Services

© Copyright IBM Corporation 2003© Copyright IBM Corporation 2004

Siebel Analytics in IBM:

Building a Sense and

Respond Solution

Paige Poore

Project Executive, IBM


© Copyright IBM Corporation 2004Siebel User Week 2004: Siebel Analytics in IBM, Building a Sense and Respond Solution | Paige Poore, IBM2

Agenda

CRM in IBM, today

How Siebel Analytics might fit in IBM

Our evaluation process

Piloting the program

Closing remarks



CRM in IBM today

Nearly 60,000 IBMersSeveral thousand Business Partner firms

ibm.com Contact Centers

Service and Support

Business Partners

Field Sales

Marketing

2000 2001 2002 2003 2004

Common global application, using Siebel

eBusiness Applications, is now enabled in IBM

for most customer touch points

4 Siebel instances worldwide

Globally consistent customer focus

Integrating and enabling core business processes

Sales execution metrics drive improvement

Worldwide customer Db in global application

Strengthening global reporting across businesses



The journey toward Siebel Analytics started with the move to

eBusiness and several challenges around reporting

Multiple Siebel instances

– Multiple, independent shadow reporting approaches

– No integrated reporting solution within our Siebel

environment

Limits to Enterprise Information Warehouse (EIW)

– Contains subset of Siebel data; Not an operational

management data source

– Need for non-Siebel data

– Ongoing operational challenges with Siebel data

extracts, EIW conversions and feeds

Ad hoc reporting is difficult and requires DBA-type

skills to use multiple tools

– Actuate, Brio

– Shadow Database



ABC Client team

builds account plan,

generates opportunities

Much more than just reporting; a sense and respond approach

drives actions to individuals and delivers daily value to sales

reps, providing insight linked to needed actions

S&R Dashboard

& Alerts

XYZ Client team

builds account plan,

generates opportunities.

Process Execution Indicators

Sensing Agent #1

• # of account plans

• Leads generated from

account plans

• Commitment

management

Solution

Proposal

and

Contract

Creation

Sensing Agent #2

• # of leads—metrics

tied to leads rejected,

accepted, turned into

opportunities

Sensing Agent #3

• Pipeline quality

Sensing Agent #4

• Win/loss ratio

Opportunit

y

Manageme

nt

Lead

Manageme

nt

Relationshi

p

Manageme

nt



Phase 1

Oct ‘03

Phase 2

Nov ‘03

Phase 3

Dec ‘03

All parties

agreed that a

production

pilot was the

next step

Our investigation into Siebel Analytics occurred in three phases

Challenge Siebel

Analytics team

Develop analytics

prototype based on

real-life problems

Technical

Assessment

In-depth

architecture

inspection

Scalability

Assessment

High-level systems

and expertise sizing

Interview other SA

customers

Challenges Potential benefits

Information for decision-making managed

manually

Consistent view of business across roles

enables timely, personalized actions

Redundant activities take time away from

customers and revenue generation

Seamless integration into Siebel workspace

speeds access to information

Inaccurate data views prevent early warning

and rapid resolution of issues

Rapid response through early detection;

dashboard changes independent of Siebel

releases

Duplicate investments in multiple reporting

approaches

More than reporting; turns data into

actionable insight; enables cross-sell and

up-sell … and so much more



Pipeline

Analysis

Triangulated

Forecasting

Effectiveness

& Quality

Up-sell /

Cross-sell

Lead

Conversion

Competitive

Analysis

Sales

Analytics

Service

Analytics

Marketing

Analytics

Partner

Analytics

Product, Pricing,

Order Analytics

Churn

Propensity

Customer

Satisfaction

Resolution

Rates

Service Rep

Analysis

Cost

Analysis

Service

Trends

Campaign

Scorecard

Response

Rates

Lifetime

Value

Product

Propensity

Market

Analysis

Loyalty and

Attrition

Channel Lead

Conversion

Pipeline/

Forecasting

Partner Sales

Performance

Partner Portal

Analytics

Partner Service

Trends

Partner

Campaign ROI

Product Sales

Trends

Product

Mix

Pricing

Analysis

Product

Bundling

Avg Selling

Price

Product

Profitability

Usage

Effectiveness

Account

Quality

Contact

Quality

Account

Coverage

Historical

Trends

Usage Trends

in acct/contact

New, Update,

Delete, Outdated

Just a fraction of the total product suite is included in the scope

of the evaluation pilot

Goal: Demonstrate value of the program, development, maintenance, performance, and total

cost of ownership (TCO)

Participants: EMEA field sellers with all business units providing input and most

participating. Roles include sellers and managers, plus sales operations, report writers, and

support personnel.



The production pilot is a practical application of the program

with end users on a production system over an evaluation

period of several weeks

This pilot project must demonstrate that

Siebel Analytics:

– Can easily replace existing reporting

mechanisms

– Is a low-cost alternative to existing reporting

mechanisms

– Allows flexible end-user control

– Can offload current reporting and query

workloads that tax the production system

– Enhances user productivity

– Scales to the same degree as the Siebel

implementation in IBM

– Can adopt IBM's Siebel security

implementation easily

This pilot is testing the following "to-be

process" aspects:

– Use a sense and respond approach to

inspect various pieces of data that have

been input into the Siebel system in IBM

– Notify the user and/or manager of data

quality exceptions and out-of-bounds

conditions, prompting their action

– Deliver a base set of business metrics that

are useful to sales reps and managers, and

that provide insight into their business



The pilot was designed to span 12 weeks of development and 8 weeks

of production, with two user feedback surveys

Discover, Develop, Deploy (weeks)Production Pilot

(months)

1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4

Discovery

Design,

Configure,

Test

Deployment

Readiness

and

Execution

Monitor and

Evaluate



The discovery period lasted three weeks and focused on defining the

business needs, value, and scope

Targeted greatest value-add to 4 job roles:

– Salesperson, Sales manager, Geography data custodian, Sales operations leader

Capabilities of most interest included:

– Sense and respond alerting

– Marketplace indicators

– Progress monitoring: what’s working, what isn’t

Weeks

1 2 3

Discovery

Needs Summary: Focus technical

scoping on high-value items

13

87

0

5

10

15

High Value Medium Value Out of Scope

Approval Rating Q: "Is this report of

use/interest for your business unit?"

70%

21%

9%

7.5%-1.00%

>50%

< or = 50%

Focus areas with biggest value items included:

– Face time improvements

– Data quality and reporting problems very high

interest in all OOTB reports



Our approach to the production pilot used a rapid implementation

method, including iterative development

Weeks

1 2 3 4 5 6 7 8 9 10 11

Discovery

Design,

Configure,

Test

Original scope Final Scope

Sales

Analy

tics

Out

of the

Box

24 reports max

Up to 4 dashboards with up to 6 reports per

dashboard

60 reports

15 dashboads with up to 4 reports per

page

Custo

mizat

ion

Foundation to be OTB dashboards pre-configured

by Siebel. Minimal customization

Custom requests provided as reports via

answers = 14 reports

1 new star and

10 data exception rules

36 CRM custom extension columns

Plus 100+ change requests

Curre

nt

Scop

e

24 reports 60 reports plus 100+ change requests



Deployment readiness phase integrated the prototype into

EMEA and leveraged Siebel training expertise

Weeks Months

1 2 3 4 5 6 7 8 9 10 11 12 1 2

Discovery

Design,

Configure,

Test

Deployment

Readiness

and

Execution

Performance challenges: additional time required for dashboard and extract-transform-load tuning

Same registration process as existing implementation ensured identical visibility and security

User training approach:

Two-hour dashboard training via phone

Six-hour hands-on lab for Answers

Siebel instructors provided substantial value



Pilot architecture diagram



This is where we are in the pilot, and initial feedback is positive!

Discover, Develop, Deploy (weeks)Production Pilot

(months)

1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4

Discovery

Design,

Configure,

Test

Deployment

Readiness

and

Execution

Monitor and

Evaluate

User support during production is critical

Web site for bulletin board posting and emails; eliminates need for push emails

Unique pilot ID for push emails

Q&A sessions are opportunities to ask experts questions and encourage peer interaction

One-on-one calls to significant % of participants to provide targeted support



We’ve learned a lot and overcome several challenges during this

investigation

Key lessons:

– Use best practice, pre-configured

dashboards with limited customization

– Use Siebel OLTP data only

– Use discovery phase to validate specific

users for deployment and best dashboards

for them

– Take small steps in the deploy; a few seats

at a time, validate, repeat

– Close IBM and Siebel teaming helps ensure

success

Key challenges:

– Remote locations of development and user

teams (Atlanta and EMEA, respectively)

– Expansion of pilot beyond original team

scope

– EMEA data privacy

– Hardware availability

– Performance tuning



Closing remarks

Siebel is a great partner to

work with

Prototyping is a valuable

part of the decision-

making process

We are just

scratching the

surface!

siebel analytics in ibm: building a sense & respond solution

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