IBM Sales Process Transformation with OBI Analytics

IBM Sales & Distribution – Client Process Transformation
© Copyright IBM Corporation 2008
OBI Analytics in IBM:
Building a Global Sense &
Respond Solution
Best Practices & Lessons Learned
Paige Poore
Program Executive, IBM

IBM Sales & Distribution Client Process Transformation
© Copyright IBM Corporation 2008Oracle Open World 2008: OBI Analytics in IBM, Building a Sense and Respond Solution | Paige Poore, IBM2
Agenda
 CRM in IBM
 How OBI EE fits
 Our Evaluation & Piloting process
 Our Global Implementation
 Lessons Learned - or I wish we’d known then, what we know now
 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 2002 2004 2006 2008
Common global application, using Analytics, is
now enabled in IBM for most customer touch
points
 4 CRM instances worldwide
 Globally consistent customer
focus
 Integrating and enabling core
business processes
 Sales execution metrics drive
improvement

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
ABC Client team
Dashboard
& Alerts
XYZ Client team
Process Execution Indicators
• # of account plans
• Leads generated from
account plans
• Commitment
management
Solution
Proposal
and
Contract
Creation
• # of leads—metrics
tied to leads rejected,
accepted, turned into
opportunities
• Pipeline quality
Win/loss ratio
Opportunity
Manageme
nt
Lead
Manageme
nt
Relationshi
p
Manageme
nt
Dashboard
& Alerts

Agenda
 CRM in IBM, today
 How OBIEE Analytics fits
 Our evaluation & piloting process
 Our Global implementation
 Lessons Learned – or I wish we’d known then what we know now
 Closing remarks

The CRM Vision – It’s a Journey
In the beginning, there was chaos
CHAOS
800 CRM applications
40+ CMRs
No common data definitions
No common processes

STANDARDS
Common Process,
Common Data & Data Model,
Common Tool,
Common Management System
Chaos was transformed to order by defining standards
CHAOS

ANALYTICS
Proactive alerts and analysis
Integrated into common tool
Auto-generating sensors and alerts
Early market insight
Defined by role
IBM needs actionable information
delivered as close to the client as possible
CHAOS
STANDARDS

IBM is a living system with tremendous market insight.
Linking business sensors and alerts for action optimizes the business
CHAOS STANDARDS
AUTONOMIC SALES
MANAGEMENT

Agenda
 CRM in IBM today
 How Oracle BI fits in IBM
 Our evaluation & piloting process
 Our Global implementation
 Lessons Learned – or, I wish we knew then what we know now
 Closing remarks

Best Practices –
Our initial investigation occurred in three phases
Prototype
Challenge
Develop analytics
prototype based on
real-life problems
Technical
Assessment
In-depth
architecture
inspection
Scalability
Assessment
High-level systems
and expertise sizing
Interview other OBI
customers
The next step:
Move forward with production Pilot

Best Practices - The production pilot was a practical
application of the program with end users on a production
system over an evaluation period of several weeks
The pilot had to demonstrate
the following functional capability:
 Enhanced user productivity
 Allowed flexible end-user control
 Could offload current reporting and query
workloads that tax the production system
 Could easily adopt IBM's security
implementation
 Scaled to the same degree as the CRM
implementation in IBM
and…
the following "to-be process" aspects:
 Use a sense and respond approach to
inspect data that had been input into the
CRM system in IBM
 Deliver a base set of business metrics
that are useful to sales reps and managers,
and that provide insight into their business
 Notify the user and/or manager
of data quality exceptions
and out-of-bounds conditions,
prompting their action
The next step:
Expand to a phased internal implementation

Best Practices – Expansion to a Global, World-wide Model
 Worldwide model – phased approach
– A single worldwide infrastructure
– Initially deployed in Europe, other 3 followed
– Initial highly motivated business units
– Action-oriented governance model to allow
for rapid decision-making, implementation
 Target audience
– Sellers and immediate managers
– Nearly 60,000 IBMers
– Managing customers and several thousand Business Partner firms
– 4 Geographies, WW integration of data
Some Specific Examples

Agenda
 CRM in IBM, today
 How Analytics fits in IBM
 Our evaluation & Piloting process
 Our implementation
 Lessons Learned (or, I wish we’d known then what we know now)
 Closing remarks

 Upfront considerations
– Real time capability. Everyone asks for it,
but do they really need it (is there a cost factor)?
In fact, do they really even want it?
Lesson learned: Some BI is more valuable
if it's near real time
– Do you have to start with the latest level of
CRM?
IBM integrated BI with a lower-level
CRM system.
(IBM is upgrading from CRM v7.5 to CRM 8.0)
– Big Bang vs. Just get it out
Caution: avoid analysis paralysis and over design
Oracle Open World 2008: OBI Analytics in IBM, Building a Sense and Respond Solution | Paige Poore, IBM21
Food for thought

 Best practices for design / development / deployment
– Best design practices. Do not start with a blank piece of paper!
(Or, How can you help the business figure out what they really want and set them up
for success?
A: Use templates; provide limited choices; work in small teams; appoint designated decision
makers. Keep it simple and stick to the plan. Don’t try to build the Taj Mahal
– Best development practices - What is the best way to get it out there and
generate value quickly?
A: Prototype approaches; team on business
and development; get iterative feedback (Agile)
– How do you accelerate adoption? If you try to
please everyone, no one will be happy
A: Pick your niche for each
rollout. There will be surprising lessons on
sources of your best input; it's not always who
you think...
Potholes to avoid
Or "I wish we knew then what we know now!"

 Best practices for design / development / deployment
– How much education/training do you need to provide?
A: Education, training, and increasing usage - tips and techniques, a tough lesson for
us to learn. Don't make the same mistakes we did! It doesn't matter if you're giving
them the best thing in the world on a silver platter - if you need education and training
for your users, you didn't do it right!
– What is your secret weapon for moving quickly and to be successful?
A: High Performing resources – experienced –
expertise... Makes all the difference in how
successful you will be. Do not start with
‘we’ll train as we go resources’. You need
the expertise of a small team that has strong
skills in database management, BI, and GUI
interface design (UCD). And don’t release
them after the first triumph – this is very hard
to recapture, and hard to fix if you let it go.
The right expertise & skills retention
Potholes to avoid
Or "I wish we'd known then what we know now"

BACKUP

The journey toward Analytics started with the move to CRM and
several early challenges in IBM around consolidated reporting
 Multiple Approaches
– Multiple, independent shadow reporting approaches
– No integrated reporting solution within our CRM
environment
 Limits to Enterprise Information Warehouse (EIW)
– Contains subset of CRM data; Not an operational
management data source
– Ongoing operational challenges with CRM data
extracts, EIW conversions and feeds
 Ad hoc reporting is difficult and requires DBA-
type skills to use multiple tools
– Actuate, Brio
– Shadow Database

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
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 Out of the box reports

Our approach to the production pilot used a rapid implementation
method, including iterative development, over a six week period
Sales Analytics out of the box capability and ease-of-use allowed
implementation of more function than originally scoped
plus rapid turnaround by small development team
Original scope Final scope
Sales Analytics
Out of the Box
 24 reports: up to 4 dashboards
with up to 6 reports each
 60 reports: 15 dashboards with up to 4 reports
each
Customization  Out-of-the-box dashboards pre-
configured by Oracle
 Minimal customization
 14 reports via Answers to address custom requests
 1 new data star
 10 data exception rules
 36 CRM custom extension columns
 100+ change requests
Summary 24 reports 60 reports plus 100+ change requests

Feedback from the pilot was positive!
Feedback – Dashboard users
 Ease of use, navigation and graphical
display; links on charts was positive.
 Ability to summarize data and quickly drill-
down to understand what was driving the
pipeline.
 Data quality reports with alerts. Overcomes
key problems on data privacy … reporting
consistency.
 Different overviews and direct connections
to opportunity for change.
 Significant to user productivity
Feedback - Answers users
 Easy to build reports.
 Users don't need extensive knowledge of
the data model to design reports.
 Data quality checking good.
 Strong in-person filtering dimension.
 Easier reporting in terms of table structure
and speed of creation.
 Filtering to help identify the key actions.
 Answers requires more training than
dashboard use.

Pilot Results – Lessons Learned
 Key lessons:
– Use best practice, pre-configured
dashboards with limited customization
– Use discovery phase to validate specific
users for deployment and best dashboards
for them
– Iterative development is critical
– Take small steps in the deploy; a few seats
at a time, validate, repeat
– Close teaming
helps ensure success
 Key challenges overcome:
– Remote locations of development and user
teams (Atlanta and EMEA, respectively)
– Pilot audience and scope significantly
expanded
– Compliance with European data privacy
requirements
– Performance tuning

Sense & Respond is a turning point
CHAOS STANDARDS
AUTONOMIC SALES
MANAGEMENT

OPTIMIZATION
Dissect key processes
Measure time and effectiveness
Embed KPIs into management system
Track KPIs, Learn, Improve
Queries, regression analysis, control limits optimize speed and effectiveness,
delivering early insight to market dynamics, process breakdowns
CHAOS STANDARDS

AUTONOMIC SALES MGMT
KPIs kick off actions
Self-correcting system
Optimal resourcing
Fact-based confidence levels
Speed bumps eliminated
Automate actionsreduce parts orders to manage backloghire more
Business Partners to shift channel mixlower prices to target competition
CHAOS STANDARDS

IBM Sales Process Transformation with OBI Analytics

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

Destaque

Destaque (12)

Semelhante a IBM Sales Process Transformation with OBI Analytics

Semelhante a IBM Sales Process Transformation with OBI Analytics (20)

Último

Último (20)

IBM Sales Process Transformation with OBI Analytics