Expand README with complete pricing A/B test analysis brief

[Copilot]

This updates the README from a one-line summary to a complete analysis brief for the $39 vs $59 pricing experiment, focused on revenue optimization, segment-level price sensitivity, and test-duration sufficiency. It also clarifies the traffic split as approximate to avoid a misleading exact 99% total.

• Problem framing

  • Added a dedicated Pricing A/B Test Analysis section describing experiment context and allocation.

• Analysis scope

  • Added explicit Objectives: revenue-first pricing decision, segment sensitivity (source/device/OS/geography), and duration reliability.

• Methodology

  • Added Approach bullets covering SRM/balance checks, statistical comparison of conversion and RPU, segment analysis, and time-series stability/novelty review.

• Deliverables captured in README

  • Added Key Questions Answered section with the three decision-driving questions.
  • Added Dataset section describing the two-table join (assignment/conversion + geography).
  • Added Tech Stack section listing Python/pandas/NumPy/SciPy/statsmodels/matplotlib/seaborn.

## Key Questions Answered
1. Which price drives higher revenue per user?
2. Are there segments where the higher price actually wins?
3. Was the test duration sufficient, and when should it have been concluded?

Original prompt

Pricing A/B Test Analysis

Analysis of a pricing experiment run by Company XYZ to determine whether
raising software price from $39 to $59 would increase revenue. 66% of users
saw the original price, 33% saw the higher price.

Objectives

• Recommend the optimal price point ($39 vs $59) based on revenue, not just conversion
• Identify user segments with distinct price sensitivity (by source, device, OS, geography)
• Evaluate whether the test ran long enough to draw reliable conclusions

Approach

• Data validation and randomization checks (sample ratio, group balance)
• Conversion rate and revenue-per-user comparison with statistical testing
• Segment-level analysis across marketing channel, device, OS, and country
• Time-series review of conversion stability and novelty effects

Tech Stack

Python · pandas · NumPy · SciPy · statsmodels · matplotlib · seaborn

add this to readme

Key Questions Answered

1. Which price drives higher revenue per user?
2. Are there segments where the higher price actually wins?
3. Was the test duration sufficient, and when should it have been concluded?

Dataset

Two tables: user-level test assignments (price shown, conversion outcome,
traffic source, device, OS) joined with user geography (city, country, lat/long).