RESPONDING TO THE CUSTOMER EXPERIENCE:

The intelligent value chain that evolves will have many facets, but it will remain
focused on customer satisfaction. The architecture that makes such a value chain
possible is described in Figure 8.6. It progresses from the back-office systems,
necessary to meet the needs of the customers, to the customer touch points so
critical to the provision of value-added services. In between, a customer intelligence
hub is at work, using BPM and providing the profile, rules management,
events and treatments, and the quality data needed to enhance the ASCM/CRM
systems.
New definitions are then brought to the benefits and values being delivered
to the most strategic customers. Differentiated (often customized) answers to
members of a particular segment�s business problems are part of the delivery.
Points of view are specific to each market segment. Solutions are comprised of
a mix of tools, competencies, and offerings matched to actual needs. Specific
solutions are packaged and delivered with a defined and quantifiable business
value � measured across the entire value chain, and for the individual partners,
using the economic value added tools described. The customer intelligence
system at work synthesizes data consolidation and analytics so that a single
view of the customer emerges, as well as individual customer analytics, which
are used in profiling, evaluation, and modeling for success. A single up-to-date,
integrated view of the customer relationship is constantly maintained, along
with robust customer insights to tailor the correct treatment to the right customer
at the right time.
There are three dimensions to customer intelligence, with specific features
and advantages:
1. Customer information integration
Integration and rationalization of disparate customer data, to provide
a persistent cross-channel data store to serve as a focal point for analytic
processing and as a clearinghouse for multiple disparate
touch points
Establishment of relationships in the data to support analysis at the
customer, prospect, household, and segment levels
Development of an operations format for use of customer knowledge
through all customer interaction points
Development of event-based or delta-based sensing mechanisms to
identify changes in front-end CRM systems, such as customer behavior
or profile
Transfer of information on event or delta to the hub-based repository
for integration and consolidation
Utilization of enterprise application integration or low-latency tools
to move data from front-end systems to operational data storage
2. Customer insights: segmentation and modeling
Ability to analyze cleansed and consolidated customer data to develop
descriptive and/or predictive models
Understanding of the economic or lifetime value of each individual
customer
Customer segmentation based on value, demographics, and behavioral
information
Quantification of each customer�s responsiveness to marketing and
other stimuli
Identification of the appropriate treatment or offer for each customer,
and delivery of this insight to front-end application
Mining of vast amounts of data to identify hidden customer insights
Capture and codification of analytical best practices in a business
rules engine, to create intelligent recommendations in a near realtime
environment
3. Customer insights: operationalization
Ability to offer insights at the point of contact
Products and services matched to individual customers
Rules-driven customer interactions
Differentiated service treatments for valuable customers