# Marketing Release Annotation Log

Canonical page: https://waitlist.crany.ai/marketing-templates/marketing-release-annotation-log

By Tangenix. Published 2026-10-24. Category: Acquisition measurement.

Record marketing and website changes beside reporting windows so reviewers can distinguish release facts, measurement changes and causal hypotheses.

## Task

Preserve operational context for an analytics time series outside a controlled experiment.

## When to use

Use this when several campaigns, page releases or tracking changes can overlap in the same reporting period. Record when a change actually became visible and which reports it may affect.

## Choose another approach when

You need an experiment interference analysis, want to retrofit an explanation to every graph movement, or plan to label an unpublished draft as a release.

## Inputs

- Verified publication or configuration-change evidence with time and timezone.
- The affected public surfaces, campaign labels and reporting definitions.
- Known provider data incidents, reporting delays and other concurrent changes.

## Template

    Annotation ID / owner: [reference]
    What actually changed: [short factual description]
    Change type: content / placement / product access / measurement / external context
    Actual effective time or range / timezone: [not merely commit time]
    Affected surface / population: [bounded scope]
    Release evidence: [live URL, provider result or verified operational record]
    Relevant reporting windows: [reports a reviewer may inspect]
    Definition or tracking impact: [event, filter or consent changes]
    Concurrent changes / data incident: [separate factual entries]
    Expected effect: [hypothesis, explicitly unproven]
    Observed movement: [report reference and available cutoff]
    Alternative explanation: [traffic mix, lag, missing data or unknown]
    Correction history: [append when the fact changes]
    A graph annotation explains context; timing alone does not establish that this release caused a change.

## Illustrative example

This is not a Crany customer result. Replace example details with verified facts.

    Fictional example: a founder deploys a revised demo page in the evening, after a campaign link was shared earlier that day. The annotation records the real deployment time and keeps the earlier placement as a separate event.
    A later report shows more visits, but the log does not credit the page revision automatically. Audience mix, the campaign placement and the reporting cutoff remain visible as possible explanations. A separate tracking repair receives its own annotation because it changes what is counted.
    The reviewer can now choose appropriate comparison windows. Nothing in the log turns this uncontrolled sequence into an A/B test or proves that the revised page increased qualified signups.

## How to use

1. Record factual effective times and affected reporting scope from verified releases.
2. Separate changes to the experience from changes to the measurement system.
3. Review temporal context and competing explanations before interpreting a movement.

## Checks

- A planned release time is not substituted for a verified public effective time.
- Tracking repairs are visible rather than silently counted as audience growth.
- An explanation remains a hypothesis when only coincident timing supports it.

## What to measure

Track missing release evidence, ambiguous timezones and unexplained measurement discontinuities. Use annotations to improve interpretation and select follow-up investigations. Counting completed annotations is not a campaign performance metric or proof of an effect.

## Research and sources

Google Analytics describes annotations for events, launches and data-impacting changes. This original provider-independent log adds an evidence trail; it creates no annotation in an actual analytics account and makes no causal claim from temporal alignment.

- [Google Analytics: About annotations](https://support.google.com/analytics/answer/15884203?hl=en) — checked 2026-10-24. Record launches, contextual events and data-impacting changes alongside report dates. Temporal association alone does not establish causality.

Prepared with AI assistance. Sources support the stated principles; they do not endorse Crany or guarantee performance.

## About Crany

Crany is an AI marketing co-founder in development for software founders and vibe coders. It is designed to strategize, execute approved marketing and improve based on results. These free resources are usable independently of app access. The public waitlist is at https://waitlist.crany.ai/.