Product documentation
Describes what Deeplitic currently collects, stores, exports, and exposes. The implementation and deployment configuration are the source of truth.
Research & methods
Make the measurement contract visible.
Good analytics content should make its question, evidence, and limits easy to inspect. This page explains how Deeplitic separates product facts, editorial comparisons, and original research before anything becomes a public claim.
Three layers of evidence
Useful content has a clear job.
Editorial analysis
Explains trade-offs in privacy-first measurement, event design, reporting, and operations. These are reasoned guides, not vendor-neutral benchmark results.
Original research
Will only report findings from a defined dataset or study: the question, sample, collection window, analysis method, caveats, and date reviewed will be shown together.
Research checklist
What a publishable research note contains.
Readers should be able to reproduce the reasoning even when the underlying traffic cannot be made public.
- Question and scopeState the decision the work is meant to inform and what is outside the study.
- Source and sampleIdentify the dataset, population, inclusion rules, and time window.
- Method and definitionsDefine events, dimensions, aggregation, exclusions, and calculations before showing a result.
- Limitations and review dateExplain uncertainty, known blind spots, and when the findings should be checked again.
Current public library
Start with the questions teams already ask.
Today the public library is intentionally strongest in product education and comparative analysis. It covers privacy boundaries, event planning, campaign measurement, exports, and the operational choices around a quieter analytics stack.
As first-party studies become available, they will be published here with their evidence and limitations instead of being hidden behind a vague “data-driven” label.