Customer Insights: a catalyst for a more integrated and data-driven organization

1. CUSTOMER
insights
Developing a Customer Insights Strategy
An Overview
April 2009
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2. Overview
• Need for an actionable approach
• So you know where to start
• So you know what to focus on
• So you can ensure a plan that aligns with the strategy
• Real-world use of a Customer Insights Framework
• Clear objective
• Framework to guide thinking and activity
• Examples of outputs
• Recommended Approach
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3. Strategic Objective
Illustrative Example
Define “deep”: for example,
versus current understanding
Define highest leverage points:
of customers (through existing
for example, common metric for
data, reports, etc.)
customer profitability
“Develop a deep understanding of our customers, using a
disciplined approach, that will be leveraged across the
organization and create competitive advantage for the
Company and its customers.” Define in terms of
differentiation: what will do
Define , document, fund: now that you, nor the
common “customer competition, could do before?
framework”; repeatable
process to turn data into
insights; institute required Define new, value-add for
skills, education and customers: for example, more
support structures “predictive” alerts and tools
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4. Customer Insights Framework
Deepen knowledge of
customers, plus uncover
and clarify opportunities. Apply to Company and other data
Research, secondary and sources to improve targeting and
primary, includes in-depth increase success of promotions
interviews, focus groups, and development of products.
surveys, and industry
analyst sources. Improved
Results
Experimentation
Choose an opportunity based on analytics and
research, design one or more experiments,
measure and document the results, feedback to
refine future experiments. Repeat.
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5. Framework Value
Results include:
• Deeper, actionable
understanding of customers
• Targeted, high-return
marketing campaigns
• Insights-driven product
strategies & improvements
• Sharpened focus on growth:
Improved
• Improved activity metrics
Results (average deposits, fee-based
product usage)
• Improved share of wallet
(cross-sell and up-sell)
Experimentation • Deeper, more sustainable
relationships with your
customers, based on driving
their real business
objectives
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6. Examples of Applying the Framework
• Predictive model
• Predicts likelihood of desired and sustained customer behavior in response to a
marketing campaign
• 2000+ variables considered; 24 variables included in final model
• The Model assigns a „score‟ to each customer based on likelihood to respond
• Model identified over 80% of the responders in the top 50% of the population
• Attitudinal segmentation
• six segments identified: two near-term priority segments, two next-wave (and begin
to nurture), two low priority
• Used to prioritize and drive product decisions for next-gen product release
• “Quick Hits” Examples
• Customer Life Cycle Model
• “Simple” segmentation
• Based on hypothesis about key customer characteristics – tenure and recent activity levels
• Discovered actionable insights such as “low volume customers are most likely to churn” and
“activity velocity in first 90 days predicts attrition”
• Mapped to Attitudinal segments and used in targeted marketing campaigns
• Activity pattern mining
• Customer-level reporting metrics
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7. Example: Customer Life Cycle
Opportunity Model
0 Tenure Total Lifetime Tenure
Highest Volume Activity
Adoption Levels
Hi Activity Hi Upsell Hi Cross sell
Medium Volume Activity
Med Activity Med Upsell Lo Cross sell
Low Volume Activity
Lo Activity No Upsell
No Activity
Attrition Population
Online Adoption
Lifecycle Phases and Related Optimization Opportunities
ACQUIRED ACTIVATED ATTRITED
Subscription Activation Retention Management (Short Tenure)
Acquisition
Subscription Reactivation Retention Management (Long Tenure)
Service Usage (Low to High)
Up sell Incidence (Low to High)
Cross Sell Incidence (Low to High)
= Optimization Opportunities
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8. Approach for Building Customer Insights Strategy & Capability
DISCOVERY TRANSITION OPERATIONALIZE
Decision Point Decision Point
• Develop Strategic Objective • Refine and formally roll out • Continue hiring plan & ramp of
• Create a Customer Insight initial insights Customer Insights capability
Framework & Life Cycle Model • Syndicate success & build • Continue communicating success &
• Assess existing customer institutional support implementing change mgmt. plans
data and insights • Inform and shape key • Continue to monitor & consult on
•Inventory existing data strategic goal: e.g., “improve Insights usage, potential, and
sources, reports & analyses the long-term forecast” measurements
•Assess against the Model • Refine long term plans •Continue to monitor & drive data
(e.g., score on “value” and and investment portfolio and technology improvements
“quality” dimensions) • Adjust product,
• Gap analysis: missing or promotion, channel • Develop longer range term plans
high-value/low-quality strategies, as applicable and investment portfolio that drives
insights; data quality, expanded use customer insights
• Begin to implement new
accessibility & tech. issues measurement methods (e.g., • Partner with your customers to
• Develop & test initial forecast models, reports, harness customer insights data to
hypotheses: new or improved dashboards) against key achieve mutually beneficial goals
high-value customer insights “customer life-cycle” metrics (e.g., reduce costs, improve cash
mgmt.)
• Based on results, refine • Initial hiring & ramp of key
goals & metrics, as applicable Customer Insights roles;
• Build business case for monitor & drive data and
further investment technology improvements
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9. APPENDIX
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10. Defining Analytics
Optimization What’s the best that can happen?
Need to drive
Predictive Modeling What will happen next?
past this line
to achieve break-
through results
Business Value
Forecasting What if these trends continue?
Statistical models Why is this happening?
Query/drill down Where exactly is the problem?
Reports What happened?
Degree of Analytical Intelligence
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11. Customer Insights: catalyst for a more integrated and
data-driven organization
 Drives much-needed processes for
tapping analytics, gathering insights,
planning, execution and measurement
 Laser-beam focus on building
competitively differentiating knowledge of
Customer data
 Highlights value of capturing, codifying
and distributing “common knowledge”
across the organization
 Adds value to corporate, LOB, and
product line strategies, to customer
service improvements, and to marketing
plans and execution
 Instills discipline to management of ROI
and other measurement processes and
can drive clearer accountability for results
that matter
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