Data Processing & Reporting Checklist for Digital Marketing

Standardize utm_source, utm_medium, utm_campaign, utm_term, and utm_content with a shared regex and controlled vocab to prevent fragmented reporting. Auto-reject links that don't match the taxonomy before campaigns go live.

Route web events through server-side GTM, respect Consent Mode v2, and minimize client-side load for more reliable conversions and reduced ad blocker loss. Document event-to-platform mappings for each destination.

Align GA4 events and parameters (e.g., purchase, add_to_cart, view_item, refund) with your ecommerce model. Configure data retention, internal traffic filters, and key conversions so reporting matches revenue reality.

Use connectors like Fivetran, Supermetrics, or Airbyte to pull Google Ads, Meta, LinkedIn, TikTok, and Bing into a warehouse with consistent field names. Store raw tables separate from transformed marts.

Ingest Salesforce or HubSpot objects and payment events from Stripe or Shopify to tie pipeline stages and revenue to acquisition sources. Maintain a reliable primary key for user or order level joins.

Generate a persistent campaign_id used across ad platforms, landing pages, and CRM. Maintain a join table that maps platform IDs and naming variants to this canonical identifier.

Convert timestamps to ISO 8601 in UTC and store native timezone for reference. Standardize spend and revenue to a base currency with daily FX rates to avoid distorted ROAS.

Coerce CSVs to explicit data types and convert to Parquet for faster scans. Keep an immutable raw layer and a cleaned staging layer before publishing to analytics marts.

Define primary keys per table (e.g., click_id + date, order_id, lead_id) and run de-dup logic to prevent double counting. Log duplicates and establish escalation thresholds.

Create a mapping table that translates platform-specific fields into shared metrics (e.g., link_clicks vs clicks, sessions vs visits). Publish a single source-of-truth for CPA, ROAS, and attribution windows.

Impute known-null fields, flag incomplete days due to API delays, and cap extreme values via winsorization where appropriate. Track data completeness to avoid misleading trendlines.

Use dbt tests (unique, not_null, relationships) and a pull-request workflow to catch schema drift and logic regressions. Require approvals for changes to core metrics and attribution logic.

Build a fact table that links ad spend with orders or qualified opportunities using campaign_id and attribution windows. Calculate ROAS, CAC, and marketing efficiency ratio at channel, campaign, and audience levels.

Create n-gram tags for search terms and auto-categorize creatives by theme, offer, and CTA. Use these tags for fatigue analysis, negative keyword discovery, and creative iteration.

Build position-based or Markov chain models and benchmark against last-click and data-driven models. Report lift and volatility by channel so budget shifts are grounded in evidence.

Set up Google Ads offline conversion imports and Meta CAPI using SHA-256 hashed emails or phone numbers with user consent. Monitor match rates and dedupe rules to avoid inflating conversions.

Compute 30, 60, 90, and 180-day LTV by channel and campaign start month using CRM and payment data. Feed cohort ROAS back to bidding and budget decisions.

Create lookups for channel, subchannel, objective, audience, funnel stage, geo, and creative type. Enforce the taxonomy with automated checks during ingestion and transformation.

Define measures and dimensions in dbt or a metrics layer with YAML so BI tools pull consistent calculations. Version metrics and annotate changes for stakeholder visibility.

Create Looker Studio, Tableau, or Power BI dashboards with channel, campaign, audience, and geo filters plus rolling date presets. Offer drill-through to raw tables for analyst QA.

Parse ad platform invoices with Tabula or Camelot and use OCR (e.g., Tesseract) for scanned PDFs. Reconcile totals to API-reported spend and flag discrepancies over a set threshold.

Use orchestrators or warehouse schedules to refresh hourly daily facts and daily dimension updates. Track job durations and fail on stale data to maintain dashboard trust.

Run rolling z-score or EWMA checks on spend, CPA, and conversion rates and push alerts to Slack or Teams. Include links to impacted dashboards and suggested triage steps.

Generate weekly executive summaries that highlight top movers, budget pacing, and attribution shifts with plain-language explanations. Tie each insight to a data link for rapid validation.

Document every metric, dimension, and attribution rule along with example values. Host it in a shared wiki so new hires and stakeholders interpret dashboards consistently.

Use least-privilege roles, rotate API keys via a secrets manager, and audit credentials regularly. Separate prod and dev projects to limit blast radius.

Hash identifiers with SHA-256 where allowed, store PII only where legally permitted, and respect user consent flags in downstream processing. Periodically purge expired data.

Throttle requests to avoid rate limit bans, add exponential backoff, and use idempotent tokens or checkpoints to prevent duplicate loads. Log and alert on sustained failures.

Partition and cluster large tables by date and campaign_id, materialize summary tables, and cache BI extracts. Track spend per dashboard to prioritize performance work.

Define row-count, duplicate-rate, and metric delta thresholds for each model and dashboard. Require passing QA before publishing changes to business users.