Developing a Customer Insights Strategy

Developing a Customer Insights Strategy

An Overview

April 2009

CUSTOMER

i n s i g h t s

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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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Strategic ObjectiveIllustrative Example

“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.”

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Define “deep”: for example, versus current understanding of customers (through existing data, reports, etc.)

Define highest leverage points: for example, common metric for customer profitability

Define in terms of differentiation: what will do now that you, nor the competition, could do before?

Define new, value-add for customers: for example, more “predictive” alerts and tools

Define , document, fund: common “customer framework”; repeatable process to turn data into insights; institute required skills, education and support structures

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Customer Insights Framework

Deepen knowledge of customers, plus uncover and clarify opportunities. Research, secondary and primary, includes in-depth interviews, focus groups, surveys, and industry analyst sources.

Apply to Company and other data sources to improve targeting and increase success of promotions and development of products.

Choose an opportunity based on analytics and research, design one or more experiments, measure and document the results, feedback to refine future experiments. Repeat.

Experimentation

Improved

Results


Framework Value

Experimentation

Improved

Results

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Results include:

• Deeper, actionable understanding of customers

• Targeted, high-return marketing campaigns

• Insights-driven product strategies & improvements

• Sharpened focus on growth:

• Improved activity metrics (average deposits, fee-based product usage)

• Improved share of wallet (cross-sell and up-sell)

• Deeper, more sustainable relationships with your customers, based on driving their real business objectives

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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


Example: Customer Life CycleOpportunity Model

Retention Management (Short Tenure)

Retention Management (Long Tenure)

Lifecycle Phases and Related Optimization Opportunities

ACQUIRED ACTIVATED ATTRITED

AcquisitionSubscription Activation

Subscription Reactivation

Service Usage (Low to High)Up sell Incidence (Low to High)Cross Sell Incidence (Low to High)

No Activity

Low Volume Activity

Medium Volume Activity

Highest Volume Activity

Ado

ption

Le

ve

ls

0 Tenure Total Lifetime Tenure

Attrition Population

No Upsell

Lo Cross sell

Hi Cross sellHi UpsellHi Activity

Online Adoption

= Optimization Opportunities


Med Activity Med Upsell

Lo Activity

Approach for Building Customer Insights Strategy & Capability

• Develop Strategic Objective

• Create a Customer Insight Framework & Life Cycle Model

• Assess existing customer data and insights

•Inventory existing data sources, reports & analyses•Assess against the Model (e.g., score on “value” and “quality” dimensions)

• Gap analysis: missing or high-value/low-quality insights; data quality, accessibility & tech. issues

• Develop & test initial hypotheses: new or improved high-value customer insights

• Based on results, refine goals & metrics, as applicable

• Build business case for further investment

• Continue hiring plan & ramp of Customer Insights capability

• Continue communicating success & implementing change mgmt. plans

• Continue to monitor & consult on Insights usage, potential, and measurements

•Continue to monitor & drive data and technology improvements

• Develop longer range term plans and investment portfolio that drives expanded use customer insights

• Partner with your customers to harness customer insights data to achieve mutually beneficial goals (e.g., reduce costs, improve cash mgmt.)

• Refine and formally roll out initial insights

• Syndicate success & build institutional support

• Inform and shape key strategic goal: e.g., “improve the long-term forecast”

• Refine long term plans and investment portfolio • Adjust product, promotion, channel strategies, as applicable

• Begin to implement new measurement methods (e.g., forecast models, reports, dashboards) against key “customer life-cycle” metrics

• Initial hiring & ramp of key Customer Insights roles; monitor & drive data and technology improvements

DISCOVERY TRANSITION OPERATIONALIZE

Decision PointDecision Point


APPENDIX


Defining Analytics

Optimization What’s the best that can happen?

Predictive Modeling What will happen next?

Forecasting What if these trends continue?

Statistical models Why is this happening?

Query/drill down Where exactly is the problem?

Reports What happened?

Bu

sin

ess V

alu

e

Degree of Analytical Intelligence

Need to drive

past this line

to achieve break-

through results


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


Business

Developing a Customer Insights Strategy