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 IBMers
Several 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
8
7
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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