HILLSTROM EMAIL EXPERIMENT / 2008

Did the email
make a difference?

A randomised holdout reveals what promotional email actually changed. Explore the evidence behind two campaigns, 64,000 customers and one business decision.

64,000assigned customers
3randomised groups
2 weeksoutcome window
36 passedautomated project tests
01 / THE EXPERIMENT

Follow the outcome.

Same customers, three ways to measure impact.
Choose an outcome to update the evidence.

Purchase conversion

ALL ASSIGNED CUSTOMERS

Rates include all assigned customers. Each group has approximately 21,300 customers.

CAMPAIGN EFFECTS VS CONTROL

Absolute rate changes are in percentage points; relative lift is a percentage of the control rate. All displayed intervals are pointwise 95% intervals, not simultaneous intervals.

WHAT THE EVIDENCE SUPPORTS

Both email assignments increased purchasing in this experiment. The men’s campaign is the stronger candidate for a contemporary replication. Profitability still depends on costs and margins.

02 / INSIDE THE WORKSPACE

From evidence to a decision.

A Python and Streamlit application connects
experiment integrity, inference and economics.

MARKETING EXPERIMENT INTELLIGENCE / STREAMLIT

Actual Streamlit executive overview showing customer counts, conversion rates and treatment-effect intervals
Actual local dashboard capture. This public page provides an interactive results explorer; the full eight-view Python workspace runs separately.
03 / BEHIND THE NUMBERS

Evidence you can inspect.

Open the technical audit ↗
01

Original experimental data

Original Hillstrom customer assignments are preserved. Validation covers all 12 fields, missingness, category coding and outcome consistency. Duplicate-looking records are retained.

02

Inference with uncertainty

Two primary conversion tests use Holm correction. Newcombe intervals describe risk differences; Welch tests and a seeded bootstrap assess mean spending, including zeros.

03

Reproducible calculations

Twelve DuckDB analyses are reconciled against Python. The application and reports share tested calculations. All 36 automated checks passed in the recorded local verification.

PYTHONPANDAS / NUMPYSCIPY / STATSMODELSDUCKDBPLOTLYSTREAMLITPYTEST

Keep the study in context.

This is a 2008 experiment with two-week outcomes, not a current campaign. Assignment is observed; delivery, opens, clicks and actual campaign costs are unavailable. None of the 14 exploratory interaction tests survives Holm correction. The study does not establish individual treatment effects or present-day profitability.

Original-data redistribution permission is unresolved. This page publishes aggregate results and dashboard captures, with no customer-level records. Source acquisition used the publisher’s original HTTP endpoint after its HTTPS certificate failed; the recorded checksum supports byte reproducibility.

EXPLORE THE PROJECT

Take a closer look.

Read the reasoning, inspect the estimates or reproduce the analysis.