IBM Peer Exchange - Oracle

1. Data Strategy for Oracle Digital
1. What datasets?
2. How to blend them?
3. How to deliver actionable insight?
4. How to deliver execution and transformation
whitespace
installed
1
Any bright ideas?

2. Executive Summary
2
Problem Statement : Why Do We Need Data?
How to Deliver Insight
Agile Analytics Approach Lead Conversion and Speed Seller Artefacts and Enablement
How to Execute
Implementing Data Driven Business Transformation
What Type of Data Should We Consider How to Blend and Integrate
Providers and Use Cases

3. What do we need the data for?
What do the sales community want to be able to achieve?
◦ Show me the quickest way to achieve my sales target with optimized territory management!
◦ Give me sufficient insight to allow me prioritize, prepare and engage my client set effectively!
What are the questions we want to answer with the data we looking for?
3
Am I talking to the right
PERSON at the right
ORGANISATION?
Who
do I call next?
What do we KNOW about
client and what’s the right
TOPIC to lead with?
What
do I talk about?
Where do I get TIME TO
CALL and when is best to
ENGAGE?
When
do I call?
How do I provide my
customer with RIGHT
INFO the RIGHT WAY
How
should I engage?
Which is BEST SALES
RESOURCE to use for
client set?
Which
resource do I use?
1 2 3 4 5
focus area focus area focus area
long term strategic objective

4. What Types of Data Should We Consider
4
Get me in the door!
Help me close the deal!
Profile Data
Engagement Data
Low
High
Simple
Complex
Impact
What can we see about
the client from an external
perspective?
What can we tell about
client behaviour from
direct observation?
Fit Intent
Does client fit the typical ‘need’ profile? Is there intention or buying signals?
Why?
Who? What?
• Web Scraping
• Strategic Plans
• Firmographic
• Market Segmentation
• Oracle install
• Competitive install
• Propensity Modelling
• Applications
• Community Listening
• News-As-A-Service
• Social Listening
• Interaction Data

5. Data Providers and sources….BYO!
5
Fit Intent
Profile Data
Engagement Data
Interaction Data
Oracle Install
Get me in the door!
Help me close the deal!
Why?
Who? What?

6. How to model and create insight – use cases
Data is the fuel – you still need ‘models’ as the engine to drive insight creation….
6
Start with what’s
in front of you
‘New’ Client
Profiles
Digital Alert
Systems
3
4
5
Proxies for
Client Need
1
Expert vs Data
Models
2
• If you’re selling bananas – knowing who has lunchboxes will do!
• Circumvent lack of data by codifying expertise models to start the process.
• Asking the 99% you know but don’t buy will tell you tons about the market.
• Try profiling first time buyers to discern patterns about next gen clients
• Integrate data streams into digital toolsets…e.g. HootSuite

7. How to Blend
7
COMMON KEY
Pursue datasets that
adhere to a common key
architecture – e.g. Dun &
Bradstreet #
FUZZY MATCH
Use natural language
processing or machine
learning to match likely
entities.
MANUAL MATCH
Use SME business analysts
to spend short bursts on
‘interpretive matching’ of
anomalies or low scores.
GAMIFY
Engage end user seller
community in ‘Captcha’
like approach to help find
matches.
Adopt a triage approach which ‘washes’ datasets through various stages of matching to maximise integration

8. Inside Out : Where we think clients are….
Outside In : Where clients actually are…
How to Deliver – an agile end-to-end process
8
integrate &
simplify
observe &
adjust
aggregate &
analyse
marketplace
targeting
engage &
codify
1 3 4 5 6
All things considered,
who is best customer to
engage next and why?
What is the data trail
captured by each client
engagement?
What are the best lead
indicators of client
need?
Where are the clusters
of best opportunity
found
What is the data-driven
reason of call to aid
seller insight?
Who and where is our
target market for inbound
market spend?
identify &
prioritise
2
new ‘intent’ data
structured
Un-structured
internal external
The
99%
market product

9. How to Deliver – improved targeting
9
observe &
adjust
observe &
adjust
observe &
adjust
“inside-out” model
“outside-in” model
2%
10%
Time
Lead
Conversion
Lead Conversion
1
1% 2% 6%
1
Year
6
Mths
2
Mths
lead conversion rate
time to achieve target
Speed to Pipeline
2

10. How to Deliver – actionable seller insights
10
VIZUALIZATION
Create Next Best Customer, Next Best
Solution and Client 360 Views that
provoke questions and build a narrative.
Threshold for usage is an obvious to
interpret Reason of Call.
GUIDANCE
Use expert scoring and models to
help sellers know which Reason of
Call to Leverage. Insight overload
impedes results so you need to
help guide call strategy.
ENABLEMENT
Are sellers comfortable to handle
the ‘consultative’ questions raised
and value propositions required?
How does data help align
territories and skills to markets?
1 3
2

11. How to Execute data-driven transformation
11
Stakeholder Development
1
Technical Development
2
Business Transformation
3
Impact and Results
4
• Is there strategic coherence?
• What are the causes vs symptoms of performance?
• Is there urgency and buy-in?
• Is there an agile development environment?
• Where is the low-hanging technical fruit?
• Is analytics function really embedded in business?
• Can we draw straight line to ledger results?
• Can we measure opportunity cost of non-performance?
• Can we quantify the benefit impacts below the line?
• Does business need to adapt to leverage capability?
• Are there transformation agents on sales side?
• What is the urgency to drive the adoption?

12. Inside Out : Where we think clients are….
Outside In : Where clients actually are…
How to Deliver – an agile end-to-end process
12
integrate &
simplify
observe &
adjust
aggregate &
analyse
marketplace
targeting
engage &
codify
1 3 4 5 6
All things considered,
who is best customer to
engage next and why?
What is the data trail
captured by each client
engagement?
What are the best lead
indicators of client
need?
Where are the clusters
of best opportunity
found
What is the data-driven
reason of call to aid
seller insight?
Who and where is our
target market for inbound
market spend?
identify &
prioritise
2
new ‘intent’ data
structured
Un-structured
internal external
The
99%
market product