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Replace your “spreadsheet of doom” with purpose-built experiment management

Spreadsheets are fine until they aren’t. When experiment tracking outgrows them, you lose institutional knowledge, repeat tests, and spend more time maintaining the sheet than running the programme. Here’s what changes when you switch.

10 signs you’ve outgrown spreadsheets

If several of these sound familiar, a purpose-built system will save you time and prevent knowledge loss.

  • 1Someone leaves and their tab with "the real numbers" vanishes.
  • 2You’ve tested the same thing twice because nobody could find the first result.
  • 3Decisions live in Slack. Rationale lives nowhere.
  • 4The "experiment log" has 14 columns and still doesn’t answer "why did we ship that?"
  • 5Linking research to hypotheses means copy-pasting URLs into a cell.
  • 6Revenue impact is a back-of-the-envelope guess with no audit trail.
  • 7Stakeholders ask for a programme overview and you rebuild a deck from six sources.
  • 8Version control is "Experiment Log v3 FINAL (use this one).xlsx".
  • 9You spend more time maintaining the spreadsheet than running experiments.
  • 10New joiners need a 90-minute walkthrough of the tabs before they can contribute.

Spreadsheets vs Experiment OS

CapabilitySpreadsheetsExperiment OS
Institutional knowledgeLost when people leaveIteration chains, decision docs, research links
Experiment lineageManual, breaks oftenParent–child links, full chain at decision time
Statistical analysisBuild yourself or leave outBayesian analysis, stopping rules, uncertainty
Research to hypothesis linkURL in a cell, if you rememberFirst-class research library, evidence URLs
Revenue impactSeparate model, no traceabilityAt decision time, conservative modelling, audit trail
Setup and maintenanceWeeks to build, ongoing DIYMinutes to start, no template archaeology

Migration checklist

Most teams are up and running in a few days. No consultancy, no implementation project.

  1. Export your experiment log (CSV or similar).
  2. Map columns to Experiment OS: hypothesis, status, results, decision.
  3. Create a project and add hypotheses or experiments from the export.
  4. Link existing research (URLs, docs) to hypotheses where it matters.
  5. Invite the team; they’ll see one source of truth from day one.

Step-by-step migration guide and 10 signs you’ve outgrown spreadsheets.

Stop losing institutional knowledge

Experiment OS gives you one place for research, hypotheses, experiments, decisions, and iteration chains. When someone leaves, the programme doesn’t. When someone asks “why did we test that?”, you can show them. When you prioritise the next test, you’re building on what you learned, not guessing.

Frequently asked questions