Web Lab Tool

A tool to design, run, and analyze web experiments for SEO and UX, helping teams decide which variant improves performance.

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Om dette verktøyet

Purpose: analyze web experiments and SEO tests across pages and components for data-driven decisions.
How it works conceptually: collect metric data per variant over a time window, compute deltas, and evaluate significance using established statistical methods. It supports multiple metrics, traffic splits, and segmented views to reflect real user diversity.
Who benefits: growth engineers, SEO analysts, product managers, and data teams seeking quick, rigorous evidence to prioritize changes. Unique value: integrated, end-to-end experiment analysis with automated uplift, significance, and export options, without reliance on external dashboards. Core features: variant definitions, metric selection, time window control, traffic allocation, data source connections, and multi-metric output with confidence intervals and clear recommendations. Use cases: testing a new landing page, comparing headline variants, or evaluating design changes for conversion and engagement.

Hvordan bruke

Provide inputs: define site URL, variants with names and URLs, select metrics and time window.
Choose method: set traffic split and significance model (Frequentist or Bayesian).
Run calculation: compute deltas, uplift, and p/posterior probabilities.
Review outputs: examine significant variants, confidence intervals, and recommended actions.
Export results: download CSV/JSON and save summaries for reports.

How to use web lab tool

Frequently Asked Questions

Finn raske svar

What does the tool measure?
The tool analyzes variant performance across defined metrics within a selected period, computes absolute and relative deltas, estimates uplift, and reports statistical significance (p-values or posterior probabilities) to guide evidence-based decisions about which variant to deploy.
What inputs are required?
Site URL, a list of variants with page URLs, metrics to evaluate, and a time window. Optional filters such as device, geography, and traffic controls refine comparisons and ensure alignment with business goals.
How is significance determined?
Significance is calculated using standard methods appropriate to the metric: for rate metrics a chi-square or Fisher test; for means a t-test; Bayesian methods can be used with posterior probability thresholds, ensuring robust conclusions.
Can I export results?
Yes, results can be exported in CSV or JSON formats for integration with dashboards, reports, or data pipelines, facilitating offline analysis, sharing with stakeholders, and archival of experiment outcomes.

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