Data Processing & Reporting Checklist for E-Commerce

Create a canonical data model that aligns Shopify, Amazon, and marketplace fields to common keys like product_id, sku, order_id, and customer_id. Document required fields and data types so downstream transformations stay stable.

Pull data on a schedule from Shopify Admin API, Amazon SP-API, GA4, Google Ads, Meta Ads, and Klaviyo using Fivetran or Airbyte. Set hourly or daily syncs and capture deletions or soft-deletes where available.

Convert created_at and updated_at to UTC to avoid time zone drift by region and daylight changes. Revalue order totals and ad spend with daily FX rates from the ECB or Open Exchange Rates for consistent reporting.

Auto-detect delimiters, quote chars, and encodings for supplier price lists and marketplace exports using Python csv or Pandas/Polars inference. Build column mapping rules so renamed fields or added columns do not break loads.

Use updated_at watermarks or change tokens to fetch only new or changed records, then merge on stable keys with UPSERT semantics in BigQuery, Snowflake, or Redshift. This reduces load and prevents duplicate orders or line items.

Normalize variant mappings so every SKU links to a stable product_id and option set across channels. Collapse duplicate SKUs from legacy systems, and persist a crosswalk table for marketplace ASINs and store SKUs.

Split gross revenue into product price, taxes, discounts, gift cards, and refunds to derive net revenue per line. Store each component in separate fields to drive profit waterfalls and to match finance statements.

Distribute shipping costs and processor fees proportionally by item price or weight so product margins reflect true landed cost. This enables SKU-level contribution margin, not just order-level estimates.

Transform bundle line items into their underlying components using a maintained bill of materials. This supports accurate inventory decrement, cost rollups, and replacement part analytics.

Normalize tax amounts across Shopify, Avalara, or custom tax tables into a consistent set of fields and jurisdictions. Flag tax-exclusive vs tax-inclusive pricing to avoid double-counting in profit and tax reports.

Record receipts, picks, transfers, cycle counts, and returns with signed quantities and timestamps. Derive on-hand and available-to-promise from movements instead of relying only on snapshots, which often mask issues.

Apply regex checks for internal SKU patterns and verify UPC/EAN/GTIN using GS1 check digit algorithms. Flag bad codes early to prevent feed rejections in Google Merchant Center and marketplace listings.

Maintain a lookup that assigns each product a Google Product Category and product_type. This improves feed quality scores, campaign segmentation, and search visibility for Shopping and Performance Max.

Join gclid, fbclid, and UTM parameters from GA4 or server-side tag captures to sessions and orders. Where click IDs are missing, use last non-direct channel attribution with lookback windows tailored to your sales cycle.

Aggregate spend from Google Ads, Meta Ads, and TikTok Ads at the same granularity as attributed revenue. Produce blended MER and channel ROAS with filters for new vs returning customers and match to finance cutoffs.

Load supplier price lists from CSV or PIM and track start and end dates for each SKU cost. Recompute order COGS using the cost effective on the order date to prevent margin drift after price changes.

Compute acquisition month cohorts, recency-frequency-monetary scores, and lifecycle stage for each customer. Use shipping address geocoding to assign regions for regional merchandising and fulfillment planning.

Integrate price monitoring feeds or scraping APIs to compare your SKU prices with key competitors and Buy Box data. Flag SKUs priced above target thresholds or violating MAP rules, and route to pricing updates.

Use OCR and table detection tools like Tesseract, Amazon Textract, or Google Document AI to parse headers and line items. Normalize vendor names, invoice numbers, and item codes, then store clean tables with foreign keys to vendors.

Parse UPS, FedEx, or DHL invoices to identify base charges and surcharges such as residential or fuel. Join to WMS shipment data by tracking number to audit overbilling and feed accurate shipping cost into contribution margin.

Extract Amazon and eBay settlement statements and map fees to orders and SKUs. Include referral, FBA, storage, and refund fees so profitability by channel is complete and finance tie-outs are faster.

Compute expected totals from extracted line items, tax, and shipping and compare to stated totals. Raise a discrepancy record when the delta exceeds tolerance to trigger manual review before posting to finance reports.

Implement heuristics that carry column headers across pages and manage page footers or watermarks. Use layout-aware parsing for rotated or stacked columns to minimize rework and data loss.

Store original PDFs and extracted CSV or Parquet in S3 or GCS with stable paths and metadata. Record processing version, parser model, and timestamp so results are reproducible and auditable.

Report gross sales, discounts, tax, refunds, and net revenue for each channel with consistent definitions. Use Looker Studio, Tableau, or Power BI with filters for region and product category to serve finance and marketing.

Combine COGS, shipping, payment fees, marketplace fees, and attributed ad spend to show true margin. Add slicers for bundles, variants, and fulfillment method so product teams can spot unprofitable SKUs quickly.

Calculate days on hand and stratify inventory by 30-60-90 day bands with cost exposure. Recommend markdowns or liquidation for slow movers and highlight stock at risk ahead of season rollovers.

Join returns, reasons, and RMA resolutions to suppliers and SKUs to surface quality issues. Track refund rate by channel and campaign so ad spend is not scaled on high-return products.

Integrate Klaviyo or similar engagement data, linking sends and flows to orders with clear attribution windows. Split results by first purchase vs repeat to avoid inflating performance with existing customer orders.

Use simple time series baselines with promotions and seasonality flags to project next 4-8 weeks of volume. Add ad spend pacing vs target MER so teams adjust bids before budgets overshoot.

Automate a text summary that explains week-over-week changes in revenue, MER, and top movers by SKU. Pull metric deltas from the warehouse and produce channel-level highlights to share with leadership and investors.

Send Slack or email alerts when inventory covers fewer than X days of sales or when ROAS falls below threshold. Include links to the impacted SKUs and campaigns so owners can act immediately.

Monitor rolling baselines with seasonality adjustments to spot sudden spikes in cancellations or CAC drift. When anomalies are detected, attach context such as channel and product family to reduce noise.

Publish definitions for revenue components, attribution windows, and margin metrics with examples. Track lineage from sources to reports so teams understand how a number is produced and can audit quickly.

Hash emails and phone numbers for analytics and encrypt raw identifiers at rest. Respect opt-outs and enforce EU and California consent rules on event data while preserving aggregate reporting.

Use dbt tests or Great Expectations to validate row counts, nulls, and referential integrity before report refresh. Define idempotent backfills and exponential retries for flaky APIs to keep dashboards stable.