Examples

Design For Impact in action.

Three fictitious examples show how Design For Impact can support statistical collaboration in industry and academia - including one that makes the value of versioning visible.

Review the Tool and Implementation Guide →

Marketing Campaign Optimization

A statistical practitioner is helping a marketing team identify customer segments for an upcoming campaign and decide how to allocate its budget.

Vision — Q₁

Maximize customer engagement and lifetime value through more personalized marketing strategies.

Impact — Q₁

Help the marketing team allocate its budget across customer segments to maximize conversion.

Partnership — Q₁

Deliver within two weeks, before the next campaign-planning cycle begins.

Analysis Pathway — Q₂

Fit a logistic model to estimate conversion probability across customer segments, accompanied by interpretable visualizations that guide budgetary decisions.

Narrative — Q₃

The model identified a customer segment with a significantly higher likelihood of conversion. Marketing shifted its budget toward this segment, increasing overall conversion from 8% to 14% and generating $86,000 in additional revenue.

What this illustrates: The business goal, stakeholder decision, project timeline, analysis, and final result remain connected throughout the engagement.

Environmental Attitudes and Behavior

Environmental psychology researchers and a statistical collaborator are studying why environmental concern does not always translate into sustainable behavior.

Vision — Q₁

Advance understanding of why environmental concern does not always translate into sustainable behavior.

Impact — Q₁

Determine whether the attitude-behavior gap depends on context, measurement, and access to sustainable choices, helping researchers design future studies and more effective interventions.

Partnership — Q₁

All collaborators will appear as co-authors on a paper targeting environmental psychology journals. The team agrees that null or mixed findings remain valuable and will still be submitted for publication.

Analysis Pathway — Q₂

Use multilevel regression to determine how the relationship between environmental concern and behavior varies by context, measurement approach, and access to sustainable choices. Compare results across measures and settings to help researchers identify priorities for future studies and promising targets for behavioral interventions.

Narrative — Q₃

Environmental concern was more strongly associated with self-reported than observed behavior, particularly when sustainable alternatives were difficult to access. Researchers can prioritize access and measurement in their next study and design interventions that reduce practical barriers rather than focusing only on attitudes.

What this illustrates: Academic impact may involve contributions to a body of knowledge, decisions about what to study next, and the design of future interventions rather than an immediate organizational outcome.

Summer Newsletter Layout

A sales director and senior data scientist need to decide which newsletter layout will increase purchases. The team revisits the tool across four versions to record what changed, why it changed, and what comes next.

View All Four Tool Versions (PDF)

Vision — Q₁

Become customers’ preferred brand for durable outdoor clothing.

Impact — Q₁

Help the sales director choose newsletter characteristics that increase purchases.

Partnership — Q₁

Share an initial recommendation with marketing by April 1, then deliver a final recommendation by June 20.

Analysis Pathway — Q₂

1. Version 1: Compare purchase rates across previous newsletter layouts to identify characteristics worth retaining.
2. Version 2: Model purchase probability using newsletter characteristics and customer purchase history.
3. Version 3: Randomly assign customers to two layouts and compare purchase rates.
4. Version 4: Continue randomized testing across additional sends, accounting for repeat recipients and send dates.

Narrative — Q₃

1. Version 1: Prominent product images appear promising, but audience differences prevent a recommendation.
2. Version 2: The team identifies a reliable-comparison problem and recommends a randomized test.
3. Version 3: Early results are too imprecise, so marketing and sales agree to continue testing.
4. Version 4: The expanded test supports prominent product images; sales approves the recommendation and marketing ships the new summer layout.

What this illustrates: Versioning preserves the reasoning behind a changed Analysis Pathway. A promising observation becomes a defensible test, then an implemented decision.

Apply the process to your own collaboration.

Download the Tool + Guide See Implementation Guidance