Dec 06, 2025·8 min
How to Create a Web App to Track Hypotheses and Learnings
Step-by-step guide to designing, building, and launching a web app to manage hypotheses, run experiments, and capture learnings in one place.
Step-by-step guide to designing, building, and launching a web app to manage hypotheses, run experiments, and capture learnings in one place.
Before you choose a database or design screens, get clear on what problem your experiment tracking web app is solving. Most teams don’t fail at experimentation because they lack ideas—they fail because the context disappears.
Common signals you need a dedicated learning repository:
Write a one-paragraph problem statement in plain language, such as: “We run many tests, but we can’t reliably answer what we tried before, why we tried it, what happened, and whether it changed our decision.” This anchors everything else.
Avoid vanity metrics like “number of experiments logged” as your primary goal. Instead, define success around behaviors and decision quality:
These criteria will guide what features are necessary versus optional.
Experimentation is cross-functional. Define who the app is for in v1—typically a mix of product, growth, UX research, and data/analytics. Then map their core workflows:
You don’t need to support every workflow perfectly—just ensure the shared record makes sense to all.
Scope creep kills MVPs. Decide your boundaries early.
V1 will likely do: capture hypotheses, link experiments to owners and dates, store learnings, and make everything easy to search.
V1 likely won’t do: replace analytics tools, run experiments, calculate statistical significance, or become a full product discovery tool.
A simple rule: if a feature doesn’t directly improve documentation quality, findability, or decision-making, park it for later.
Before you design screens or pick a database, get clear on who will use the app and what outcomes they need. A great experiment tracking web app feels “obvious” because it mirrors real team behavior.
Most teams can start with four roles:
A fast way to validate your workflow is to list what each role must accomplish:
| Role | Key jobs to be done |
|---|---|
| Contributor | Log an idea quickly, turn it into a testable hypothesis, document an experiment plan, update status, capture learnings with evidence. |
| Reviewer | Ensure hypotheses are specific, confirm success metrics and guardrails, approve “ready to run,” decide whether learning is strong enough to act on. |
| Admin | Set up fields/taxonomy, manage access, handle audit needs, maintain templates and integrations. |
| Viewer | Find relevant prior experiments, understand what was tried, and reuse learnings without re-running work. |
A practical “happy path” flow:
Define where a reviewer must step in:
Common bottlenecks to design around: waiting for review, unclear ownership, missing data links, and “results” posted without a decision. Add lightweight cues like required fields, owner assignment, and a “needs review” queue to keep work moving.
A good data model makes the app feel “obvious” to use: people can capture an idea once, run multiple tests against it, and later find what they learned without digging through docs.
Start by defining the minimum fields that turn a loose idea into something testable:
Keep these fields short and structured; long narrative belongs in attachments or notes.
Most teams end up needing a small set of objects:
Model the connections so you don’t duplicate work:
Add lightweight tagging early, even in an MVP:
This taxonomy is what makes search and reporting useful later, without forcing a complex workflow now.
A status framework is the backbone of an experiment tracking web app. It keeps work moving forward, makes reviews faster, and prevents “half-finished” experiments from polluting your learning repository.
Start with a simple flow that matches how teams actually work:
Keep state changes explicit (a button or dropdown), and show the current state everywhere (list view, detail page, exports).
Statuses are more useful when they enforce completeness. Examples:
This prevents “Running” experiments without a clear metric, and “Decided” entries without a rationale.
Add a structured decision record with a short free-text explanation:
For inconclusive outcomes, don’t let teams bury them. Require a reason (e.g., underpowered sample, conflicting signals, instrumentation gap) and a recommended follow-up (rerun, gather qualitative input, or park with a revisit date). This keeps your experiment database honest—and your future decisions better.
A tracking app succeeds or fails on speed: how quickly someone can capture an idea, and how easily the team can find it again months later. Design for “write now, organize later” without letting the database become a dumping ground.
Start with a small set of screens that cover the full loop:
Use templates and default fields to reduce typing: hypothesis statement, expected impact, metric, audience, rollout plan, decision date.
Add small accelerators that compound over time: keyboard shortcuts (create new, add tag, change status), quick-add for owners, and sensible defaults (status = Draft, owner = creator, dates auto-filled).
Treat retrieval as a first-class workflow. Provide global search plus structured filters for tags, owner, date range, status, and primary metric. Let users combine filters and save them. On the detail view, make tags and metrics clickable to jump to related items.
Plan a simple first-run experience: one sample experiment, a “Create your first hypothesis” prompt, and an empty list that explains what belongs here. Good empty states prevent confusion and nudge teams toward consistent documentation.
Templates turn “good intentions” into consistent documentation. When every experiment starts from the same structure, reviews get faster, comparisons get easier, and you spend less time deciphering old notes.
Start with a short hypothesis template that fits on one screen and guides people toward a testable statement. A reliable default is:
If we [change] , then [expected outcome] , because [reason / user insight] .
Add a couple of fields that prevent vague claims:
Your plan template should capture just enough detail to run the test responsibly:
Keep links as first-class fields so the template connects to the work:
Provide a few experiment-type presets (A/B test, onboarding change, pricing test), each pre-filling typical metrics and guardrails. Still, keep a “Custom” option so teams aren’t forced into the wrong mold.
The goal is simple: every experiment should read like a short, repeatable story—why, what, how, and how you’ll decide.
A tracking app becomes truly valuable when it preserves decisions and reasoning, not just results. The goal is to make learnings easy to scan, compare, and reuse—so the next experiment starts smarter.
When an experiment finishes (or is stopped early), create a learning entry with fields that force clarity:
This structure turns one-off writeups into an experiment database your team can search and trust.
Numbers rarely tell the full story. Add dedicated fields for:
This helps teams understand why metrics moved (or didn’t), and prevents repeating the same misinterpretations.
Allow attachments on the learning entry itself—where people will look later:
Store lightweight metadata (owner, date, related metric) so attachments remain usable, not just dumped files.
A dedicated field for process reflection builds compounding improvement: recruitment gaps, instrumentation mistakes, confusing variants, or mismatched success criteria. Over time, this becomes a practical checklist for running cleaner tests.
Reporting is useful only if it helps the team make better decisions. For an experiment tracking web app, that means keeping analytics lightweight, clearly defined, and tied to the way your team actually works (not vanity “success rates”).
A simple dashboard can answer practical questions without turning your app into an experiment metrics dashboard full of noisy charts:
Make every metric clickable so people can drill down into the underlying experiment documentation instead of arguing about aggregates.
Most teams want to see outcomes by:
These views are especially helpful for hypothesis management because they reveal repeated patterns (e.g., onboarding hypotheses that often fail, or one area where assumptions are consistently wrong).
A “learning feed” should highlight what changed in your learning repository: new decisions, updated assumptions, and newly tagged learnings. Pair it with a weekly summary view that answers:
This keeps product experimentation visible without forcing everyone to read every A/B test workflow detail.
Avoid charts or labels that imply statistical truth by default. Instead:
Good reporting should reduce debate, not create new arguments from misleading metrics.
A tracking app only sticks if it fits into the tools your team already uses. The goal of integrations isn’t “more data”—it’s less manual copy/paste and fewer missed updates.
Start with sign-in that matches how people access other internal tools.
If your company has SSO (Google Workspace, Microsoft, Okta), use it so onboarding is one click and offboarding is automatic. Pair this with a simple team directory sync so experiments can be attributed to real owners, teams, and reviewers (e.g., “Growth / Checkout squad”), without everyone maintaining profiles in two places.
Most teams don’t need raw analytics events inside the experiment tracking web app. Instead, store references:
If you do use APIs, avoid storing raw secrets in the database. Use an OAuth flow where possible, or store tokens in a dedicated secrets manager and keep only an internal reference in your app.
Notifications are what turn documentation into a living workflow. Keep them focused on actions:
Send these to email or Slack/Teams, and include a deep link back to the exact experiment page (e.g., /experiments/123).
Support CSV import/export early. It’s the fastest path to:
A good default is exporting experiments, hypotheses, and decisions separately, with stable IDs so re-import doesn’t duplicate records.
Experiment tracking only works if people trust the system. That trust is built with clear permissions, a reliable audit trail, and basic data hygiene—especially when experiments touch customer data, pricing, or partner information.
Start with three layers that map to how teams actually work:
Keep roles simple for an MVP: Viewer, Editor, Admin. Add “Owner” later if needed.
If a metric definition changes mid-test, you want to know. Store an immutable history of:
Make the audit log visible from each record so reviewers don’t need to hunt.
Define a retention baseline: how long experiments and attachments are kept, and what happens when someone leaves the company.
Backups don’t need to be fancy: daily snapshots, tested restore steps, and a clear “who to call” runbook. If you expose exports, ensure they respect project permissions.
Treat PII as a last resort. Add a redaction field (or toggle) for notes, and encourage linking to approved sources rather than pasting raw data.
For attachments, allow admins to restrict uploads per project (or disable entirely) and block common risky file types. This keeps your learning repository useful without turning it into a compliance headache.
Your MVP’s tech stack should optimize for speed of iteration, not future perfection. The goal is to ship something the team will actually use, then evolve it once workflows and data needs are proven.
For an MVP, a simple monolith (one codebase, one deployable app) is usually the fastest path. It keeps authentication, experiment records, comments, and notifications in one place—easier to debug and cheaper to run.
You can still design for growth: modularize by feature (e.g., “experiments,” “learnings,” “search”), keep a clean internal API layer, and avoid tightly coupling UI to database queries. If adoption takes off, you can split out services later (search, analytics, integrations) without rewriting everything.
A relational database (PostgreSQL is a common choice) fits experiment tracking well because your data is structured: owners, status, dates, hypothesis, variants, metrics, and decisions. Relational schemas make filtering and reporting predictable.
For attachments (screenshots, decks, raw exports), use object storage (e.g., S3-compatible) and store only metadata and URLs in the database. This keeps backups manageable and prevents your DB from becoming a file cabinet.
Both REST and GraphQL work. For an MVP, REST is often simpler to reason about and easier for integrations:
If your frontend has lots of “one page needs many related objects” use cases, GraphQL can reduce overfetching. Either way, keep endpoints and permissions straightforward so you don’t ship a flexible API that’s hard to secure.
Search is the difference between a “learning repository” and a forgotten database. Add full-text search from day one:
If you later need richer relevance ranking, typo tolerance, or cross-field boosting, you can introduce a dedicated search service. But the MVP should already let people find “that checkout experiment from last quarter” in seconds.
If your main bottleneck is getting a working MVP into people’s hands, you can prototype this kind of internal tool with Koder.ai. It’s a vibe-coding platform that lets you build web apps through a chat interface (commonly React on the frontend, Go + PostgreSQL on the backend), with practical features like source code export, deployment/hosting, custom domains, and snapshots/rollback. That’s often enough to validate your workflows (templates, statuses, search, permissions) before investing in a longer-term build pipeline.
An experiment tracking web app succeeds or fails on adoption, not features. Plan your MVP like a product: ship small, test in real workflows, then expand.
Start with the minimum that lets a team document and retrieve work without friction:
If a feature doesn’t reduce time-to-log or time-to-find, defer it.
Ship v1 to a small pilot team (5–15 people) for 2–4 weeks. Ask them to use it for every new experiment and to backfill only a handful of recent ones.
Test with realistic scenarios:
Collect feedback weekly and prioritize fixes that remove confusion: field names, default values, empty states, and search quality.
If you’re using a platform approach (for example, building the MVP on Koder.ai and exporting the code once workflows stabilize), treat the pilot as your “planning mode”: lock the data model and happy-path UX first, then iterate on integrations and permission edges.
Once logging is steady, add higher-leverage upgrades:
Define operating norms:
Document these norms in a short internal page (e.g., /playbook/experiments) and include it in onboarding.
Start when you can’t reliably answer:
If experiments live across decks, docs, and chat—and people repeat work or distrust past notes—you’re past the “spreadsheet is fine” phase.
Use behavioral and decision-quality measures rather than vanity counts:
Keep v1 focused on a shared learning record for cross-functional teams:
Design the record so it reads clearly for all of them, even if workflows differ.
A practical v1 boundary is:
Avoid trying to replace analytics tools or run experiments inside the app. If a feature doesn’t improve documentation quality, findability, or decision-making, defer it.
A simple role model is:
You can map these into MVP permissions as and add more nuance later.
Model what you want people to retrieve later:
Use a small, explicit set such as:
Make state changes deliberate (button/dropdown) and visible everywhere (lists, detail pages, exports). This prevents “half-finished” items from polluting your repository.
Require fields that prevent bad handoffs:
This reduces “we ran it but didn’t define success” and “we have results but no decision.”
Structure learnings so they’re reusable:
Add fields for qualitative context (notes, quotes) and attach evidence where people will look later (designs, dashboards, SQL, exports). Include a “what we’d do differently” field to improve process over time.
A pragmatic MVP stack is:
Key relationships:
This combination optimizes for speed-to-ship while keeping future scaling options open.