The Automotive industry is very competitive and winning the hearts and minds of consumers is critical. PMSquare's Consumer Analytics Blueprint for the Automotive industry creates the platform for competitive differentiation within this space and leverages IBM technologies

1. Automotive Industry
Consumer Intelligence Blueprint
powered by
PMSquare
Consumer Analytics to Drive Business Results

2. PMSquare Provides Consulting Services & solutions with
detailed practical experience across all industries
C o n s u lt in g S e r v ic e s Focus Areas:
• C u s t o m e r A n a ly t ic s & R e p o r t in g :
Helping businesses stay focused on their customers
with innovative and effective approaches to Telco & Financial Healthcare Consumer Automotive
marketing, sales and customer service. High Tech Services Products
• F in a n c e & P e r f o r m a n c e
M a n a g e m e n t : Exploring the relationship
between finance mastery and achieving high
performance, and delivers experience and assets that
can help businesses and governments master their Financial Performance Management
finances with comprehensive approaches backed by -Budgeting, Forecasting & Planning
proven assets. -Risk Management
• Ta l e n t & O r g a n i z a t i o n
Business Intelligence
P e r f o r m a n c e : Guiding companies toward
strategic ways of elevating an organization’s -Analytics, Reporting, Scorecards & Dashboards
performance with services in change management, -ETL & Data-warehousing
human capital and organization effectiveness, -Predictive Analytics
• O p e r a t io n s & S u p p ly C h a in Productivity Improvement Solutions
P e r f o r m a n c e : Improving visibility of
operational performance, turning insights into action
-Improve administration & mainteance
that drive performance.

3. Business Value Achieved
A f e w IB M c o g n o s c u s t o m e r s t h a t h a v e t a k e n
o u r a d v ic e :

4. Automotive Consumer Intelligence Blueprint
Business
Priorities Revenue Protection Profitable Growth Replace Organic Demand
Approach Design Consumer Experience & Value
Understand Consumer Propensities Deliver Branded Consumer Experience
Proposition
• Purchase • Brand/model research • Dealership
• Defect • Purchase/financing • Web
• Repurchase • Ownership/OnStar • Contact center
• Recommend • Warranty/Service • Brand advertising
Opportunity Segments Core Enterprise Segments Demographic Segments
Illustrative Discontinued Brands Most Valuable Brand Advocates Geographic
Consumer
Segments
Competitor Defectors Highest Potential Highest Risk Lifestage/Lifestyle
Analytics & Propensity Reporting Marketing Automation Consumer Centric KPIs
Key
Enablers Integrated View of the
Organization Marketing Processes Consumer Research
Consumer

5. three themes emerged as operational priorities
Operational Priorities
Data Foundation Advanced Analytics Marketing Programs
Simplify, integrate and make data Develop and deliver timely, Enable faster, more effective
accessible for efficient and effective relevant, actionable insights to drive responses to changing market
analysis business decisions conditions and consumer needs
What We Heard
“There’s a mentality that more [data] is “We want to be able to see results in “We conduct a number of marketing
better, but not everything is valuable. “ easy formats…including what worked, activities, but we don’t know what
what didn’t and why” drives which behavior.”
“A lot of things already in our DW are
not used” Most of the current [reports and “What intrigues me is knowing what
dashboards] are measuring effort, not levers to pull with fence sitters”
“We need to get analytics and insights results”
quicker…there’s a lot of help needed “We do everything in such big
on the analytic setup.” “We need to be able to understand numbers that we don’t have time [to
the customer in a cohesive and be proactive] because the next wave
“Operationally, analytics are painful.” integrated manner.” is coming.”
“There is too much data; we are lost in “We need to understand how the “We are struggling to get out what we
the noise.” information will be used to drive are getting out.”
decision making”
“It has been nobody’s job to bring all “There is no time to just “step back
this information together. ” “I need help prioritizing and digesting and think”
the volumes of data that already exist”
“The governance issue is the hardest… “We have a lot of capabilities; we just
We have a culture where we say ‘Play “We need…information that will help don’t have the time”
nice, but do it my way’.” drive actions. ”

6. Recommended “Consumer Analytics Strategic Roadmap”
Data Simplification
Data
Foundation Consumer Info Governance 360° View of the Customer
BI Dashboards (in waves)
Advanced
Analytics Make/Model Propensity Propensity for Car Service
Defection Probability Consumer Segmentation
Free Agent Contact Strategies Next Best Product & Service Call Center: Next Best Action
Marketing
Programs Vehicle Service History Optimize Contact Strategies
Regional Web Marketing Consumer Insights Sharing
Near Term - 180 Days Longer Term Capability Building

7. Consumer Intelligence Architecture
Dealer Online Customer
Access CARS
Services Owner Centre
BI Portal Industry Info Web Sites
Service
Integrated Sales, Data Mining, Event Detection & Customer
Analysis & Service, Marketing & Clustering,
Communication
Pattern Recognition Portfolio
Test Cell
Discovery Management Reporting & Analytical Modelling
Management
Next Best Action Optimization
Development
Information Services
Master
Data Data Lifecycle Hierarchy Event
Transport and Collaboration
Information Integrity Management
Services Management Management
Security and Privacy
Information Contact Data Metadata Scheduler Customer
ETL Survivorship
Integration Hygiene Services Agent Recognition
Source Sales Leads, Service, RO, Customer
Data (CDR, CUV) Handraisers CARS Parts Warranty Cust Service Other
Satisfaction
Interactive Data
Campaign Analytic Reporting Quick Count,
Data Repositories Mart Mart
Digital E-Data Store
Mart Mart OLAP Mart (ODS)
Systems and Systems Management
Network & Middleware
Complex Event
Hardware & Software
Infrastructure & Administration Processing

8. Solution Initiatives
Page 8

9. Defection Probability and Prediction
Leverage data and analytical tools to identify orphaned or disenfranchised consumers and drivers of high defection probability
DESCRIPTION:
Defection probability analytic used to Dashboard
determine customer level of risk and prime Element of
drivers for defection based upon existing Customer Profile
consumer data.
Customer
The output of the analytics will drive Pay for Customer Customer
defection-aversion marketing campaigns, Repairs
Data
Satisfaction
Analytics Marketing Initiative
provide customer knowledge dashboard Credit
Drivers
Card
elements, and provide insight into sales Warranty
Dealer
Information
Call
forecast fluctuations. Center Data
Prod/Ops Data
Systems Input
BUSINESS VALUE:
Mitigate risk of defection by allowing for a
pre-emptive, targeted marketing and/or DEPENDENCIES:
incentive response to at-risk, in-market
• Technology/Data: Minimal data integration
consumers based upon consumer Data Analytics Programs
preferences, perceptions, and sensitivities to increase accuracy and completeness of
and prime drivers for defection. This would analytics
• Process: Campaign management process
RESULTS & OUTPUTS:
potentially reduce cost for high-return, • Defection probability by consumer that can
focused campaigns. should incorporate the output of the model
be analyzed by dimensions such as
• People/Organization: N/A
geography, segment, brand, defection
driver, demographic
• Prescriptive analytics on next steps based
180 days 12mo 24 mo 36mo on consumer demographics and behavior
DATA ELEMENTS: • Visual risk spectrum (green, yellow, red)
• Consumer demographic, behavioral, imbedded in the customer profile.
CONSIDERATION: historical, marketing survey data, consumer • Delineators indicate trigger points for
• Marketing needs to incorporate high-risk service interaction, CARS interaction incentive program eligibility.
consumers in targeted campaigns • Analytics database to support probability
• Recommend continuous improvement algorithm calculations
training and update key performance
indicators (KPI’s)
• Recommend posting to IR services

10. Make and Model Propensity Mapping
Application of vehicle preference to key populations for precision targeting
DESCRIPTION:
Predictive algorithm deployed against all
consumers to identify make and model Dashboard
propensity. Element of
Customer Profile
This model can be used solo or in concert Customer
Pay for Customer
with other indicators such as orphans/free Customer
Satisfaction Marketing Initiative
agents brand correlations or defection
Repairs
Data Analytics
Drivers
indicator to trigger an outbound offer for Credit
Card
retention. Call
Warranty
Dealer
Information
Center Data
Prod/Ops Data
BUSINESS VALUE: Systems Input
Increase probability of sales by
allowing for a pre-emptive, targeted marketing
and/or incentive response to in-market
consumers based upon predictive consumer DEPENDENCIES:
preferences, perceptions, and sensitivities and
prime drivers for purchase. This would • Technology/Data: Minimal data integration
potentially reduce cost for high-return, focused to increase accuracy and completeness of Data Analytics Programs
campaigns. analytics
• Process: Campaign management process RESULTS & OUTPUTS:
should incorporate the output of the model • Production algorithm to identify brand and
• People/Organization: Will require cross make propensities
180 days 12mo 24 mo 36mo brand collaboration • Summary of propensities tied to in market
indicators for immediate targeting of those
consumers with interest in corresponding
CONSIDERATION: DATA ELEMENTS: offer
• On some makes/models, 90% of • Scoring of inbound leads to create greater
• Consumer demographic, behavioral,
customers will defect after name plate is intelligence for dealers in prospecting
psychographic data
eliminated • Make and model mapping can be
• Historical, regional, territory sales
• Recommend continuous improvement constrained by channel or demographics
training and update key performance • Consumer-level historical data for smarter target marketing (e.g. Under 35
indicators (KPI’s) • Customer communication preferences market)

11. Free Agent Contact Strategies
Relevant and timely offers and messages for at-risk and orphaned customers
DESCRIPTION:
Construction and execution of treatment At Risk –
plans and strategies to retain free agents and
Contact Filtered
other at-risk owners.
Rules Stream
Performance
Contact
Reporting
BUSINESS VALUE: Universe
At Risk
Focus on the most effective cadence of Scores No Risk
communications to decrease out of pocket Standard
costs and improve total ROI. Stream
DEPENDENCIES:
• Technology/Data: Leverage data from other
Data Analytics Programs
initiatives
180 days 12mo 24 mo 36mo • Process: Develop a contact management
strategy by treatment plan RESULTS & OUTPUTS:
CONSIDERATION: • People/Organization: Will require cross • Periodic identification of at risk owners for
• Leverage data from unstructured target treatment plans
brand collaboration
communications. • Identification of foundational metrics to
• Information feeding the treatment plans and baseline customer interaction
strategies would support proactive contact • Reduction of non-targeted market spend
strategies to acquire customers.
• Tailoring contacts to free agents and DATA ELEMENTS:
orphaned consumers will enhance overall • Consumer demographic, behavioral,
experience and messaging, and reinforce psychographic data
the ‘they know me, they get what I need” • Campaign history across all channels and
perception lines of business:
• Unica campaign management can be used • Campaign history
to provide automation • Card member services
11

12. Consumertrends such as household, consumer value
An integrated view of key performance factors and
Centric BI Dashboards
DESCRIPTION:
Visualization portal providing access to key
prospect and customer metrics and trends at
campaign, corporate, dealer or consumer
segment levels. Offering parameters for
predictive inquiries, and predicated by the
assessment of customer value and
engagement metrics
BUSINESS VALUE:
Ability to analyse underlying performance
drivers (positive/negative) to take advantage of
marketing opportunities and corrective action DEPENDENCIES:
on underperformance. • Technology/Data: Leverage data from other
Centralization, consolidation and prioritization initiatives, centralized dashboard portal. Data Analytics Programs
of dashboard and information there in.
KPIs need to be defined and agreed on
“Subtract as we add”- Assess existing • Process: Adoption of Voluntary analytics to RESULTS & OUTPUTS:
dashboards to remove clutter as we drive decision making • Strategic, Tactical and Operational
add new focused dashboards. • People/Organization: Cultural shift to dashboard that enables scenario
consumer orientation planning / decision making. E.g.
• Current and predictive customer
180 days 12mo 24 mo 36mo value by key customer segments
DATA ELEMENTS: and divisions, displayed in
• Service, Sales and lead data dashboard format, coupled with
CONSIDERATION: • Consumer demographic, behavioral,
• Technology can be scaled to accommodate
customer engagement metrics and
psychographic data trends, and associative activity
additional or complementary analytics and • CSAT Engagement Indices
visuals i.e. media/channel spend • Current and forecasted campaign
• Call Center history/flags response metrics, drillable by
• Dashboards should serve different needs
• Brand health metrics consumer, offer, regional attributes
from strategic to tactical to operational
• Advanced marketing science needs to be
applied for advanced analytics

13. Online Vehicle Service History
Enabling better customer service with more complete vehicle service histories
DESCRIPTION:
Presentation of vehicle service records into
an integrated vehicle service history view on
the online owner centre; also available via
6/8/2008 Upgrade Accessory
web service to customer service centres, and Consumer
dealers. Login to 12/1/2007 Warranty Repair Service
Web Portal 10/1/2005 Oil & Filter Change
BUSINESS VALUE: 12/1/2005 Warranty Repair Service
Increase customer satisfaction and retention by 8/1/2004 Oil & Filter Change
providing better ownership experience. Enable
contact points to better serve consumers and
driving traffic to websites.
DEPENDENCIES:
• Technology/Data: Comprehensive dealer
TIMING: service data
Data Analytics Programs
• Process: Contact point training
• People/Organization: Collaborate with RESULTS & OUTPUTS:
180 days 12mo 24 mo 36mo service channels to develop standard • Standardized interface to populate
interfaces and gain buy-in to maximize integrated service history view into various
speed to market customer service applications and portals
CONSIDERATIONS: • Instructions, technical guidelines, and
• Motivating dealers to share vehicle service terms and conditions for use of integrated
data is a critical first step toward delivering service history data
a consistent branded consumer experience
DATA ELEMENTS:
• Vehicle service records
• It is important to keep the interface simple
and easy to use for service representatives • Owner identification
and consumers
• Potential to provide service history by VIN
on demand to consumers

14. Regional Web Marketing
Enable to identify and act on the regional characteristics of anonymous consumers on its websites
DESCRIPTION:
This effort would allow customer to identify
the country and/or region of users accessing
its websites. P2
R 2
Technology to enable customer to display P3
regional and/or country-specific website
advertising using a rules-based engine and Car.com R1 R3 P4
product mapping for each region / country.
P4
R 4
P4
BUSINESS VALUE: Dynamic ad display based on region/country source of web surfer
Increase leads by providing relevant content on
websites DEPENDENCIES:
• Technology/Data: Rules-based product
and/or service advertisement mapping Data Analytics Programs
based on regional and/or country
identification to enable packet-specific RESULTS & OUTPUTS:
advertisement pops. Dynamic website • Deterministic traceback services to enable
messaging technology region / country identification of website
• Process: browsers
• People/Organization: Legal review for
identification considerations in each country
180 days 12mo 24 mo 36mo
conducts business
CONSIDERATION: DATA ELEMENTS:
• Government entities have upheld the right • Consumer IP address
for individual anonymity on the web • Creative content
• Deterministic traceback software provide
identification at the packet (region or
country) level.

15. Consumer Information Governance
Define consumer metrics and establish governance around consumer information
Information Governance Levels
DESCRIPTION: Sophisticated
14%
Processes and management
42%
Establish governance for customer systems that are strong with some
supporting automation in place
information including definitions, 32%
3X
metrics/KPI’s, usage rules, business
ownership, and where applicable systems of Competitive
Defined processes and management
record. 25% systems that are understood and
adopted by most people
54%
33% Rudimentary
A few basic processes and the
beginnings of a management
system
Lower performers Top performers
(i.e., 4th and 5th quintiles relative (i.e., 1st quintile relative to
to industry peers) Source: Breaking Away with Business Analytics
industry peers)
BUSINESS VALUE: and Optimization: New intelligence meets
enterprise operations at www.ibm.com
/gbs/intelligent-enterprise.
Will promote cross brand collaboration by
propagating single source of truth. Will DEPENDENCIES:
establish common metrics used to run/assess
business performance, speed analysis and • Technology/Data: N/A
• Process: Business participation and
Data Analytics Programs
action/reaction time to events. Reduce cost of
managing data and effective marketing/spend. adoption
• People/Organization: Data stewardship, RESULTS & OUTPUTS:
Change Management Office establishment • Documented list of consumer metrics/KPIs
• Defined governance and ownership model
for consumer data attributes
• Common definitions for all relevant
180 days 12mo 24 mo 36mo consumer attributes
• Establish and document business usage
rules for relevant consumer attributes
CONSIDERATION: DATA ELEMENTS: • Define system of record for relevant
• Requires coordination with MDM initiative. • All consumer attributes
• Must be accomplished with business
involvement

16. Propensity to bring a vehicle in for service to Dealer
Understanding what drives consumers to bring their vehicles in for branded service
DESCRIPTION:
Statistical modeling to identify the most
predictive and actionable variables indicating Dashboard
whether or not a consumer will bring a vehicle Element of
in to a dealer for service/maintenance. Customer Profile
Customer
Pay for Customer
Customer
Repairs
Data
Satisfaction
Analytics Marketing Initiative
BUSINESS VALUE: Credit
Drivers
Increased customer retention through an Card
Warranty
Dealer
increase in service visits to dealers. Allows Call
Center Data
Information
customer to establish relationships with used- Prod/Ops Data
car owners to keep brand top of mind. Systems Input
DEPENDENCIES:
• Technology/Data: Minimal data integration
Data Analytics Programs
TIMING: to increase accuracy and completeness of
analytics
• Process: Campaign management process RESULTS & OUTPUTS:
180 days 12mo 24 mo 36mo should incorporate the output of the model • Development of service propensity-based
• People/Organization: Will require cross customer segments, and assignment of
organization collaboration (e.g. service and vehicle owners to those segments
CONSIDERATIONS: sales) • List of actions that can be taken by and/or
• Several business stakeholders commented dealers to increase the percent of owners
during interviews that they felt this was the who bring their vehicles in for service
single most important gap in actionable
DATA ELEMENTS:
customer intelligence with respect to • Owner purchase history
customer retention • Demographic/psychographic profiles
• High value for customer relationship over • Service data
the long term • Warranty data
• Call Center data

17. Consumer Segmentation
Understanding the unique characteristics among consumers for differentiated treatments
Identify Segments
DESCRIPTION: 1
Production-level algorithms to enable
Identify Insights
consumer clustering based on variables to 3
group customers into logical segments.
Initial models will focus on behavioral
Recommend Treatments
characteristic clustering and value-based
clustering and progress to incorporate 4
Cluster Segments
consumer research attitudes.
2
BUSINESS VALUE:
The customer will deepen its understanding of
its consumers, their behaviours and their value
drivers. This knowledge can then be applied to DEPENDENCIES:
increase its consumer relationships across all • Technology/Data: Minimal data integration
aspects of the business including direct Data Analytics Programs
marketing, sales, service, dealer relations, to increase accuracy and completeness of
advertising and call centre operations. analytics
• Process: Targeted consumer strategies RESULTS & OUTPUTS:
should incorporate the output of the model • Production-level segmentation algorithm
• People/Organization: Cultural • Availability of segments to multiple lines of
transformation from product to consumer business
focused segments • Training and guidelines for use of
180 days 12mo 24 mo 36mo segmentation
DATA ELEMENTS: • Enablement of segmented consumer KPIs
CONSIDERATION: • Consumer demographic, behavioral,
• Business users must determine the psychographic data
appropriate actions to be taken based on • Consumer-level accounting aggregation
each insight including historical data
• Segmentation should be an iterative
process that is recalculated both
periodically and with new data sources

18. 360° View of the Customer
Master Data Management approach to create a comprehensive source of truth for consumer information
DESCRIPTION:
A set of processes and tools that consistently
defines and manages consumer data entities
with the objective of providing processes for
Operational
collecting, aggregating, matching, Customer Multiform Master Data Management Data Systems
consolidating, auditing, persisting and Pay for Customer Customer
Satisfaction
distributing such data throughout the Repairs
Data WH Analytical
customer organisation to ensure consistency Collaborate Operationalize Analyze
Data Systems
Credit CARS
and control. Card Dealer
Warranty Information
Security
Call Metadata
Center Database Multi Channel
Data Governance
Data Systems
BUSINESS VALUE:
Business agility and scalability resulting in
better consumer Intelligence. DEPENDENCIES:
Standardization of infrastructure, data
• Technology/Data: Appropriate customer
architecture and platform resulting in cost Data Analytics Programs
reduction. MDM platform
Increases speed to market for new systems • Process: Business participation and
that use consumer data adoption RESULTS & OUTPUTS:
• People/Organization: Data stewardship, • Provides common/synchronized definition,
Change Management Office establishment value and usage of a consumer attribute
across disparate systems using consumer
data
• Standardized data access, storage, audit,
180 days 12mo 24 mo 36mo traceability and provisioning processes
DATA ELEMENTS: • Consumer data continuously synchronized
• Golden attributes from all systems of record for to provide a single 360° view of the
CONSIDERATION: consumer data consumer
• Organization - Design an organization to • Efficient data integration platform that applies
maintain the data data governance and enterprise business rules to
• Ownership - Identify ownership of data vs. customer master attributes
data management process • Platform to acquire, retain, enhance, provision
customer master attributes to all channels and
downstream systems

19. Consumer Insight Sharing with Dealers
Streamline delivery of this project to improve quality and consistency of consumer experience at branded dealers
DESCRIPTION:
Identify which insights are most relevant to
the dealer experience, and share those
insights with dealers on a regular basis in a
simple and automated way that maximizes
dealer adoption and effectiveness
BUSINESS VALUE:
Increased customer satisfaction and retention
via better customer experience. Increase in
Revenue per Household via selling more
vehicles to existing households. Reduced
selling cost via more effective for lead
management
DEPENDENCIES:
TIMING:
• Technology/Data: Appropriate technical
Data Analytics Programs
platform
180 days 12mo 24 mo 36mo • Process: Business participation and
adoption RESULTS & OUTPUTS:
• People/Organization: Training & change • Standardized interface to populate
CONSIDERATIONS: management for dealers and support customer insights/treatments into various
• Build on the trust and value created via personnel. customer service applications and portals
Integrated Service History (ISH) to recruit • Instructions, technical guidelines, and
partners to participate in delivering a terms and conditions for use of data
consistent branded consumer experience DATA ELEMENTS: • Suggested consumer treatments for top
• Develop interface and implement pilot in opportunity and risk area
• Specific KPIs and dealer relevant data to
first year, with a few dealers begin rolling
drive better consumer experience
out to additional dealers in second year and
beyond
• Dealer cooperation and partnership is
paramount

20. Data Simplification
Re-engineering of data preparation, migration and storage processes
DESCRIPTION:
Analytical Data Mart
Simplification of the overall data management
processes from the source systems to the
consolidated data warehouse and the GM Credit
Card
Dealer
Information ODS
downstream data marts to make it more Everest
Customer
flexible and scalable. Pay for Call Center
Repairs Modeling
Integration
Governance
Security
Infrastructure and Metadata
BUSINESS VALUE:
Decrease the cost to prepare data for
business consumption. Reduce the DEPENDENCIES:
maintenance and storage costs. • Technology/Data: N/A
• Process: N/A Data Analytics Programs
• People/Organization: N/A
RESULTS & OUTPUTS:
• Elimination of the data redundancies and
low value processes
• Data storage and architecture
• Streamlined architecture
180 days 12mo 24 mo 36mo • Business appropriate data model
DATA ELEMENTS: • Improved data quality
• Exhaustive review of all data repositories • Increased traceability
CONSIDERATION: and integration processes
• Requires coordination with MDM initiative.
• No adverse impact to business users

21. Call Centre: Next Best Action
Enable “Smart” decision making at the Call Centre Operator level, real-time, and accurately.
DESCRIPTION:
Customer Support decision engine used by
Call Centre Operator to drive “smart” decision
navigation from the Call Centre, enabled by
Everest
current issue, customer experience, historical Customer
and preference data. Pay for
Repairs
Customer Customer
Satisfaction
“Smart” Pop-up
Data WH
Navigation at Call
CARS
Analytics Ctr. Screen
Credit CARD
Card Dealer
Warranty Information
Call
Center Database
Call Centre DB
BUSINESS VALUE: Feedback Loop
Retain and reactivate customers by properly
addressing pain points with current, relative DEPENDENCIES:
information through CARS. Provide “next
• Technology/Data: Customer segmentation
best decision” direction to call centre Data Analytics Programs
operators to resolve client issue. and analytics. Access to promotional data
• Process: Training and documentation for
call centre RESULTS & OUTPUTS:
• People/Organization: N/A • Next Best Action issue navigation tool and
option recommendations via screen pops at
operator terminal
• Customer information and communication
history on operator screen to facilitate
180 days 12mo 24 mo 36mo communication interaction
DATA ELEMENTS:
• Navigation path feedback loop to CARS DB
• CARS, analytic algorithm and feedback to update “next best decision” algorithm
CONSIDERATION: infrastructure
• Customer service training is imperative • Service repair order data
• Unstructured data capture and analysis for • Voice/Chat/TTL communication enabled
real-time and future analysis
• Operator terminal with screen pop enabled
• Navigation decision path capture for refining
algorithm and updating decision tree

22. Product & Service Add-on propensity
Identify most appropriate product and service extension offers for current owners
DESCRIPTION:
Predictive algorithm deployed against all Dashboard
consumers to identify product and service Element of
add on propensity. Customer Profile
Customer
Customer
This model can be used solo or in concert Pay for Customer
with other indicators such as customer
Repairs
Data
Satisfaction
Analytics Marketing Initiative
segments or make/model propensity to Credit
Drivers
Card
trigger an outbound offer. Warranty
Dealer
Call Information
Center Data
Prod/Ops Data
Systems Input
BUSINESS VALUE:
Increase probability of sales by allowing for a
pre-emptive, targeted marketing and/or DEPENDENCIES:
incentive response to owners based upon
predictive consumer preferences, perceptions, • Technology/Data: Minimal data integration
to increase accuracy and completeness of Data Analytics Programs
and sensitivities and prime drivers for
purchase. This would potentially reduce cost analytics
for high-return, focused campaigns. • Process: Campaign management process RESULTS & OUTPUTS:
should incorporate the output of the model • Production algorithm to identify service and
• People/Organization: Will require cross product propensities
brand collaboration • Summary of propensities tied to in market
indicators for immediate targeting of those
consumers with interest in corresponding
180 days 12mo 24 mo 36mo
DATA ELEMENTS: offer
• Consumer demographic, behavioral, • Identification of consumers with next best
CONSIDERATION: psychographic data product and service fits
• Recommend continuous improvement • Historical, regional, territory sales • Targeted consumer messaging guidelines
training and update key performance • Consumer-level historical data
indicators (KPI’s)
• Ensure insight is adequately communicated • Customer communication preferences
to improve sales and marketing strategy • Current service offering data

23. Optimize Contact Strategies
Relevant and timely offers and messages to maximize consumer interaction and optimize consumer experience
DESCRIPTION:
Construction and execution of treatment At Risk –
plans and strategies to maximize consumer
Contact Filtered
interaction and impact of direct marketing
Rules Stream
Performance
Contact
Reporting
BUSINESS VALUE: Universe
At Risk
Focus on the most effective cadence of Scores No Risk
communications to decrease out of pocket Standard
costs and improve total ROI. Stream
DEPENDENCIES:
• Technology/Data: Leverage data from other
Data Analytics Programs
initiatives
180 days 12mo 24 mo 36mo • Process: Develop a contact management
strategy by treatment plan RESULTS & OUTPUTS:
CONSIDERATION: • People/Organization: Will require cross • Periodic identification of consumer
• Leverage data from unstructured segments for target treatment plans
brand/organization prioritization of
communications. • Identification of foundational metrics to
campaigns and consumer contacts.
• Unica campaign management can be used baseline customer interaction
to provide automation • Reduction of non-targeted market spend
• This should be an iterative process DATA ELEMENTS:
• Ability to isolate and accommodate specific
• Consumer demographic, behavioral,
messaging based on situational factors. psychographic data
E.g. competitor recalls, wind down • Campaign history across all channels and
dealerships lines of business:
• Campaign history
• Card member services