Advanced Analytics Audits

Advanced analytics audits that explain why your reports disagree

When GA4, advertising platforms, the CRM, and a dashboard report different results, the hard part is knowing which difference matters. Shibey’s advanced analytics audit starts with a specific business question and traces the number from user action through collection, processing, configuration, and reporting.

You get an explanation of what is broken, what is an expected difference between systems, and which corrections would change a decision. We begin with the disputed metric, the expected comparison, and the date range so the investigation produces a usable repair plan rather than a long list of settings.

Begin with the number that does not make sense

A useful audit starts with a question. Why does the analytics platform show more leads than the business received? Why did purchases fall after a website release? Why do two dashboards disagree even when they appear to use the same dates?

Those questions identify the relevant events, sources, and user journeys. Each check has a reason tied to the decision the report supports.

The audit covers GA4, Google Tag Manager, connected advertising measurement, application events, and reporting layers where they are relevant and access is available. The exact scope is established around the data flow being investigated.

Collection, identity, commerce, attribution, and reporting logic

Event definitions and actual triggers

The review compares the intended action with the point at which an event is generated. A click, an attempted submission, and a confirmed submission are different behaviors. Naming them clearly is important, but the implementation must also trigger at the correct point. Otherwise, the report remains misleading even with a well-organized event list.

Duplicate and missing collection

Multiple tags, plugins, or application handlers can send the same activity more than once. Events can also disappear on certain templates or fail when a user follows an alternative route. Shibey examines representative journeys and repeated actions to understand whether the collection matches the expected frequency and coverage.

Transaction and revenue information

Commerce measurement needs consistent transaction identifiers, values, currencies, and relevant item details. The audit reviews how those fields are populated and how refunds or other lifecycle events are represented where included. A revenue mismatch needs a defined comparison with the source system rather than an assumption that every difference is a tagging error.

Acquisition and cross-domain behavior

Campaign parameters, referral handling, and transitions between domains can affect how activity is attributed. The review considers the actual journey and the capabilities of external booking or checkout providers. It identifies implementation gaps while distinguishing them from the platform’s own attribution rules and reporting limitations.

Consent, configuration, and dashboard definitions

Collection can vary with consent choices and browser conditions. Property settings, time zones, filters, and report calculations also affect the numbers people see. Shibey reviews those layers where relevant so the audit does not stop at a tag firing successfully in a single test session.

Distinguish a broken implementation from an expected difference

Two platforms may count different things. A CRM may contain manually added leads, an order system may include later cancellations, and an advertising platform may credit an outcome according to its own attribution settings. Those differences need explanation before one total is used to judge another.

Shibey aligns the comparison window, definitions, and available records as far as the scope allows. Confirmed defects are separated from expected differences and unresolved questions. This makes the findings more useful than a blanket statement that the analytics is inaccurate.

The audit also considers the confidence of the evidence. A reproducible duplicate event is a stronger finding than a single unexplained change in a summary report.

Receive a prioritized plan tied to the affected decisions

Each meaningful finding should explain the observed behavior, the intended definition, where the issue occurs, and which reports or decisions it affects. Recommended corrections should include a way to verify that the change worked.

Priority reflects business impact as well as technical severity. A duplicated primary lead event may deserve attention before a missing low-value interaction. A broken transaction identifier may undermine purchasing analysis across several reports at once.

Focused tag repairs can continue through GTM debugging and QA. If the foundation needs rebuilding, GA4 setup or Google Tag Manager configuration may provide the implementation path.

Account for history when the tracking changes

Correcting an event changes future collection, but it may not repair records already processed. Historical reports can therefore retain the effects of the original issue. A useful audit identifies the affected period and explains which comparisons need caution.

For example, if a form-completion event has actually represented every submit-button click, fixing it may produce a lower count. That does not necessarily mean the website is generating fewer inquiries. It may mean the measurement now describes successful submissions more accurately.

Documenting that change helps report users avoid turning a technical correction into an incorrect business conclusion. Dashboard annotations or revised definitions can be included where the reporting tools support them.

What you receive from the analytics audit

The audit provides a source and property inventory, verified discrepancies, event and parameter findings, reporting-definition issues, affected decisions, and a prioritized correction plan. Evidence identifies where the mismatch begins: website behavior, collection, processing, configuration, query logic, or dashboard presentation.

This service fits teams facing conflicting reports, unexplained trend changes, attribution questions, or an inherited implementation they do not trust. It is an investigation of defined questions, not a blanket promise that every number across every platform will become identical.

Questions about analytics audits

Can you audit a setup created by another provider?

Yes, provided relevant access and context are available. The audit evaluates the implementation and evidence rather than assuming how the original provider intended it to work. Existing documentation, change history, and measurement plans can make the review more efficient.

Will the audit fix the problems it finds?

Implementation can be included or scoped separately. A diagnostic engagement identifies and prioritizes the work; a remediation engagement makes and verifies the changes. The distinction should be clear before the project begins so deliverables match expectations.

Can all historical data be corrected?

No. Some collection errors cannot be repaired inside already processed platform data. The audit can identify the affected period and suggest appropriate reporting treatment where feasible. It should not promise to recreate information that was never collected.

Why do we need an audit if the tags are firing?

Tag execution does not establish that the correct action was measured, that parameters were valid, or that the destination interpreted the event as intended. An audit connects those layers and examines the business definition behind the number.

What affects audit cost and duration?

The number of platforms, important journeys, event types, reporting layers, and known discrepancies determines the effort. Access quality and available documentation also matter. A focused investigation into one outcome differs from a broad review of an entire measurement program.

Find the source of the reporting mismatch

Share the report or metric that does not make sense, the comparison you expected, the date range, and any releases or configuration changes near the discrepancy. Include access to the relevant collection and reporting layers where possible. Shibey can trace the difference and produce a prioritized plan tied to the decisions it affects.