Regenerative Ideation

Outcome-Based Thinking

Empathy Prototyping

Outcome Based

Thinking

Empathy

Empathy

Regenerative Ideation

Regenerative Ideation

Prototyping

1 2 3 4 5

Problem

Definition

Solution

Design

Model

Design & Build

Dashboard

Development

Deployment

& Scaling

LIFECYCLE OF A TYPICAL DATA SCIENCE PROJECT

W H A T W O U L D Y O U D O ?

1

Problem

Definition

E X A M P L E 1 : S E T T I N G T H E C O N T E X T

Context: A large bank is noticing high levels of churn in some of the credit card segments. Finance team’s

direction is to focus on reducing churn to half

Challenge:

• Existing process is time intensive and manual

• No way to identify potential customers who would churn

Output:

• A predictive model for identifying customers who are likely to churn

Context: A large bank is noticing high levels of churn in some of the credit card segments. Finance team’s

direction is to focus on reducing churn to half

Challenge:

• Existing process is time intensive and manual

• No way to identify potential customers who would churn

Outcome:

• Reduce churn by 50% (to 11%)

• Estimated savings of ~1.1MM to marketing initiatives

• Output: A combination of predictive model + BI report for business

E X A M P L E 2 : D E S I G N I N G T H E S O L U T I O N

2

Solution

Design

E X A M P L E 3 : T E S T I N G H Y P O T H E S E S

Model

Design & Build

3

E X A M P L E 4 : D E S I G N I N G U S E R S T O R I E S

4

Dashboard

Development

E X A M P L E 5 : C O N S U M P T I O N D R I V E N S C A L I N G

5

Deployment

& Scaling

Problem Definition

Analytics Translation

Model Design & Build

Dashboard Development

Deployment & Scale

Outcome

based thinkingEmpathy Regenerative

IdeationPrototyping

Problem summary

Program outcomes

Stakeholder summary

Empathy map

Model diagnostics (for business)

Report usage stats

MI reporting

Factor map

Hypotheses matrix

KPI summary templates

Dashboard mockupsUser story

Drop in sessions

Change logs

Program v2.0

Solution alignment

Decision matrix

Model pilot by category

Prototype ambassadors

Model lifecycle management

?Key Problem(s)

that we will solve

• A statistical scoring process to predict the probability of a customer to churn

• Reduce churn by 50%

• Estimated savings od ~1.1MM to marketing initiatives

What will we deliver?

Tangible Action/Value Delivered

Context: A large bank is noticing high levels of churn in some of the credit card segments. Finance team’s

direction is to focus on reducing churn to half

Challenge:

• Existing process is time intensive and manual

• No way to identify potential customers who would churn

PROBLEM STATEMENT

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Analytics problem

Scoring business customers on propensity to churn/stop using credit cards

Accounting for marketing team’s actionability is critical to make the program successful

Key solution considerations

▪ Multiple models should be built for cascading effect

▪ Allow 3 weeks’ of lag to let marketing team build and design preventive campaigns for ‘High risk’ customers

▪ Design a test-control experiment to showcase efficacy of existing model over natural selection

Stakeholders & Expectations