A/B Test Orchestration with Edison

Most product orgs ship the wrong things 40% of the time. Edison reads every support ticket, NPS comment, and sales‑call transcript — then prioritizes the 3 features that will move retention the most.

The A/B Test Orchestration problem most teams have

• 40 % of A/B tests run beyond 30 days, costing the company an average of $120 k in wasted traffic.
• 25 % of test traffic is allocated to experiments that never reach statistical significance, meaning 1 in 4 dollars spent on traffic is lost.
• Manual test design and approval take 2–3 weeks, delaying revenue‑impacting changes by 15 % of the product roadmap.

How Edison owns A/B Test Orchestration end‑to‑end

Edison turns raw data into a clean experiment roadmap. First, he ingests user‑feedback synthesis from support tickets and NPS scores, mapping user pain points to potential test hypotheses. Next, feature impact scoring ranks each hypothesis by projected lift on key metrics—engagement, churn, and ARR. Finally, Edison orchestrates the A/B test: he writes the experiment code, configures traffic allocation, sets statistical thresholds, and monitors results in real time.

This process eliminates the 40‑minute manual setup cycles, reduces the 25 % traffic waste, and slashes the 2‑week approval window to 24 hours.

A concrete Edison workflow

Scenario: A B2B SaaS with 12 k monthly active users wants to test a new onboarding flow that promises a 5 % lift in activation.

• Before: The product team drafts a test plan, spends 5 days with engineers, and spends 2 weeks waiting for stakeholder sign‑off. The test runs for 45 days, uses 20 % of traffic, and the final lift is only 3 % after the team scrambles to debug.
• Edison’s actions:
  1. Day 0: Edison reviews 2 k support tickets and 3 NPS comments, identifies “slow sign‑up” as a top pain point.
  2. Day 0‑1: He scores the hypothesis, assigns a 6.5/10 impact score, and writes the experiment configuration.
  3. Day 1‑2: Edison pushes the code to staging, runs a 5 % traffic split, and sets a 95 % confidence threshold.
  4. Day 3‑5: The test completes with a 5.2 % lift, 85 % of traffic saved by early stopping.
• After: Activation rises by 5.2 %, the team re‑allocates 15 % of the test budget to other experiments, and the release cycle shrinks from 45 days to 5 days.

Why Edison wins vs. hiring

Edison delivers the same product‑growth insight a human VP would, but without the salary, ramp, or downtime. He scales with your team, not your headcount.

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