2. YOU MUST FUNDAMENTALLY
CHANGE THE DATA ANALYTICS PROCESS
TO ACHIEVE UNPRECEDENTED RESULTS
THE PROCESS OF ANALYZING DATA
IS INEFFICIENT AND CAN BE FUNDAMENTALLY OVERHAULED
3. ACCENTURE: SERVING THE NON-STOP CUSTOMER
Source: Accenture, October 2012
ACCENTURE’S NON-STOP CUSTOMER EXPERIENCE MODEL REDEFINES
CUSTOMER INTERACTION
• Digital technologies are driving this
change
• Customers no longer enter a channel
but are continuously in the channel
• Transactional, dynamic, experimental
• UNPREDICTABLE
Analytics is Getting Increasingly More Complex
4. TODAY’S ANALYTICS PROCESS (PRE OPTIER)
ENTERPRISE
APPLICATION
DATA
DATA
TOOLS (ETL)
ENHANCED
DATA SETS
FINANCE
MARKETING
Mobile
App Database
E-Commerce
App Database
Call Center
App Database
Retail Branch
App Database
DATA
WAREHOUSE
MODELING
& BI STEP
OPS
Expensive, time-consuming, inefficient
5. WHY IS THIS TRADITIONAL ANALYTICS PROCESS FLAWED?
IT COSTS SO MUCH TIME & MONEY
WE OFTEN JUST GIVE UP
Problem Time To Fix
Data not in enhanced data set 5 Days
Data not in data warehouse 1 – 3 Months
Data not in application database 3 – 12 Months3
2
1
6. THE DATA ANALYTICS PROCESS IS RIPE
FOR A FUNDAMENTAL OVERHAUL
BECAUSE OF THE WAY APPLICATIONS STORE DATA
7. TOMORROW’S ANALYTICS PROCESS (WITH OPTIER)
ENTERPRISE
APPLICATION
DATA
Mobile
App Database
E-Commerce
App Database
Call Center
App Database
Retail Branch
App Database
CASSANDRA
DATABASE
ENHANCED
DATA SETS
FINANCE
MARKETING
OPS
OpTier
Scalable, intuitive, responsive
8. TOMORROW’S ANALYTICS PROCESS WITH OPTIER
CREATES MASSIVE VALUE
Problem Time To Fix
Data not in enhanced data set 5 Days
Data not in data warehouse 1 – 3 Months
Data not in application database 3 – 12 Months3
2
1 ____
1 Hour
______
1 - 2 Days
_______
1 - 2 Days
9. WHY IS THIS IMPORTANT?
1 IT can supports routine changes in business requests quickly.
2 Analytics that address complex issues like “non-stop” customer
completed in days or weeks not years.
3 Saves huge amounts of money.
The traditional demand funnel - that describes a linear and generally predictable customer path to buying - has lost its relevance. It’s too slow, too static and too generic to be used as the foundation for marketing, sales and service strategies.Now, while buyers still go through the same stages of awareness, consideration, evaluation, purchase and use, they no longer enter a channel but, instead, are continuously in the channel.Nonstop customers today are frequently re-evaluating their decisions, and the alternatives.
Data stored in the application data bases lack the context and uniformity to enable analytics to quickly make use of it.Data is the bricks and context is the cement that holds the data together. Context is defined as the relationship between different data elements that answers who, what, where, when and why?
The traditional demand funnel - that describes a linear and generally predictable customer path to buying - has lost its relevance. It’s too slow, too static and too generic to be used as the foundation for marketing, sales and service strategies.Now, while buyers still go through the same stages of awareness, consideration, evaluation, purchase and use, they no longer enter a channel but, instead, are continuously in the channel.Nonstop customers today are frequently re-evaluating their decisions, and the alternatives.
The traditional demand funnel - that describes a linear and generally predictable customer path to buying - has lost its relevance. It’s too slow, too static and too generic to be used as the foundation for marketing, sales and service strategies.Now, while buyers still go through the same stages of awareness, consideration, evaluation, purchase and use, they no longer enter a channel but, instead, are continuously in the channel.Nonstop customers today are frequently re-evaluating their decisions, and the alternatives.