Make the numbers behind your decisions consistent, traceable and trustworthy. The engagement connects investigation to implementation, with a baseline, reviewed changes and clear acceptance criteria.
What’s included.
Three steps.
Understand
Agree the task, baseline, source systems and delivery boundary.
Build
Develop and review the code, configuration and integrations in scope.
Verify
Test representative cases, compare with the baseline and hand over.
Recognise the challenge?
Start with the problem you see. Explore what we can change, then open the implementation notes for the detail.
01Purchase-event integrity repair
What you may be seeing
- Our analytics sales total does not match the order system.
- One purchase appears two or three times.
- Completed payments sometimes never appear in reporting.
- Refunds and cancellations make the revenue numbers misleading.
- Refreshing the thank-you page records another purchase.
We make sure orders, refunds and important customer actions are recorded once and agree with the business systems behind them.
Implementation & verification
We reconcile analytics purchases against authoritative order/payment records using stable transaction identifiers. We repair frontend events, data-layer mappings and server event handling so one confirmed transaction produces the intended records. We model refunds, cancellations, currency and agreed revenue components consistently, preserving consent and attribution boundaries. We add automated reconciliation checks and replay duplicate, delayed and failed-payment cases before deployment.
How we verify the work
- Join order IDs to recorded events and classify missing, duplicate and definition-mismatch cases.
- Fix confirmation triggers and server idempotency; handle delayed webhook delivery explicitly.
- Test refreshes, retries, partial refunds, currencies and consent states.
- Report reconciled discrepancy rates, freshness and known measurement coverage gaps.
Tools considered for this work
Delivery: analytics configuration + frontend/backend/SQL (the language used to query a database) development. Collection: GA4 (Google Analytics 4), Google Tag Manager and the existing consent setup. Server option: GTM (Google Tag Manager) server-side or a custom event endpoint when justified. Truth source: Shopify/WooCommerce/Stripe or the existing order system. Verification: BigQuery/SQL (the language used to query a database) reconciliation, Tag Assistant and webhook/event logs.
02Customer-funnel instrumentation installation
What you may be seeing
- We do not know where people leave our checkout.
- Different pages record the same action under different names.
- Our signup funnel breaks when customers change device or return later.
- We track clicks but cannot connect them to completed enquiries.
- A redesign changed the events and made historical comparisons unusable.
We build a reliable view of the steps customers take, including where they stop and which events are missing.
Implementation & verification
We define a versioned event contract for meaningful journey states such as product selection, submission and confirmed completion. We code explicit frontend and backend events with stable identifiers, rather than relying only on guessed button-click tracking. We connect events to business outcomes using appropriate identity rules and document gaps such as anonymous cross-device activity. We validate the full journey and create usable funnel views with device, source and customer-type breakdowns.
How we verify the work
- Model real state transitions and specify event eligibility, timestamps and identifiers.
- Instrument success on confirmed backend outcomes and preserve the same definitions across releases.
- Test single-page routing, retries, returned visits and identity resets.
- Accept on complete, deduplicated journey recording and accurate step-completion rates.
Tools considered for this work
Delivery: tracking configuration + application/SQL (the language used to query a database) development. Analytics options: GA4 (Google Analytics 4) or PostHog; use one agreed source for each decision. Collection: GTM (Google Tag Manager)/data layer or explicit product SDK calls. Outcome source: CRM (a system for managing customer relationships), order/booking database and server events. Verification: browser event inspection, synthetic journeys and event-schema checks.
03Conflicting business-metric reconciliation
What you may be seeing
- Finance and marketing report different revenue totals.
- Teams disagree on what counts as an active customer.
- Different dashboards apply different dates or time zones.
- The same customer appears more than once in our reports.
- Nobody knows which business number to trust.
We resolve conflicting definitions and data sources so teams can explain why a number changed and which version to use.
Implementation & verification
We reconcile the underlying definitions, joins and record grain behind each disputed metric. We code shared transformations and metric definitions with explicit rules for refunds, time zones, customer identity and reporting periods. We expose the reconciled model to existing dashboards and show its provenance and refresh state. We add data checks that flag future divergence instead of allowing teams to silently maintain separate calculations.
How we verify the work
- Compare record-level contributions to disputed totals rather than adjusting the dashboard display.
- Fix one-to-many join inflation, duplicate entities and inconsistent date/revenue rules.
- Publish one versioned metric model with owners and backward-compatible changes where needed.
- Accept on explainable reconciled totals and automated checks for definition or freshness drift.
Tools considered for this work
Delivery: SQL (the language used to query a database)/data-model development + BI configuration. Model: dbt and its Semantic Layer where suitable; governed SQL (the language used to query a database) views as an alternative. Storage: existing BigQuery, Snowflake or PostgreSQL warehouse. Reporting: existing Power BI, Looker/Looker Studio or Metabase. QA: dbt data tests and finance-approved reconciliation fixtures.
04Keep measurement working after a release
What you may be seeing
- A site update silently breaks our tracking.
- Browser and server events disagree or appear twice.
We add repeatable checks for important events and document how tracking should behave for each consent choice. The team has a clear owner and alert route when records stop arriving or change unexpectedly.
Implementation & verification
We add repeatable checks for important events and document how tracking should behave for each consent choice. The team has a clear owner and alert route when records stop arriving or change unexpectedly.
How we verify the work
- Define event fields and expected values, then test the data layer, the structured event information shared by the site and its tracking tools.
- If server-side tagging is appropriate, test the server-managed event route alongside browser events and check duplicate prevention.
- Test allowed, declined and withdrawn consent states against your approved policy; technical configuration does not itself establish legal compliance.
- Keep a release checklist, change history and scheduled data-quality checks in the handover.
Keep every release measurable.
Automated event checks detect missing fields, duplicates and broken journeys. Alerts identify what changed so a person can investigate before the team trusts an incomplete report.
Explore the technical toolkit
Selected around your existing stack, licences, permissions and agreed scope.
Define what better means.
Trust
Reconciliation, event completeness and data freshness.
Decisions
Time to answer a defined operational or customer question.
Outcomes
Qualified conversions, retention and experiment uncertainty.
Targets follow the baseline. These are measurement areas, not promised results.
Engineering terms, explained
- Grounded AI / RAG
- An assistant retrieves relevant approved evidence before generating an answer. Access controls and evaluations still need to be engineered and tested.
- Idempotency
- Retrying the same operation does not create an extra order, payment or record.
- Controlled experiment
- A concurrent comparison of assigned variants, with a defined outcome, observation window and decision rule.
- Core Web Vitals
- Measures of main-content loading, responsiveness to interaction and visual stability.
Technical references: Web Vitals · Azure document access · Structured data guidelines.
Quick answers.
We already have analytics. What would you change?
We check whether it answers your actual business questions. That can mean repairing collection, reconciling reports or connecting activity to orders and qualified leads before adding another dashboard.
Can you run A/B tests on a low-traffic product?
Not every product has enough traffic for a useful result. We assess the sample and decision window first. Where a test is impractical, we use task research and monitored changes, with the limits of the comparison made clear.
Can staff ask questions in plain English?
Yes, within a defined set of data and permissions. We build the governed queries and evidence retrieval behind the answer, then check factual accuracy and access boundaries.