Research & Analysis Checklist for E-Commerce

Aggregate monthly searches from Google Keyword Planner, Ahrefs, and Semrush, then layer in marketplace estimates from Helium 10 or Jungle Scout to size practical demand by category and SKU type. Use a clear methodology for mapping keyword clusters to product families to avoid double counting.

Build a weekly index using Google Trends, TikTok Creative Center, and Pinterest Trends for your top 50 keywords and product attributes. Monitor inflection points and cross-validate with marketplace search trends or brand mentions to confirm signal quality.

Combine historical orders, sessions, and ad spend with retail calendars, shipping cutoffs, and holiday events to create seasonal baselines. Fit a forecast with Prophet or ARIMA, and retain additive channels as regressors to isolate marketing-driven variance from organic seasonality.

Join your keyword universe to your catalog attributes to identify unmet demand by size, color, capacity, or material. Prioritize gaps where search volume is high, CPCs are moderate, and manufacturing lead time is feasible within your planning window.

Combine GA4 user location by category with Google Merchant Center impression share and marketplace regional sales where available to reveal state or DMA level hotspots. Use this to plan 3PL footprints, local promos, and region-specific assortments.

Audit hazmat, refrigeration, oversize, and lithium battery restrictions across carriers and marketplaces using Amazon FBA policies, USPS guidance, and carrier docs. Quantify impacts on shipping cost, lead times, and packaging to ensure accurate contribution margin models.

Triangulate using Google SERPs, Amazon Best Sellers, Walmart top lists, Etsy top shops, and Similarweb category leaders to avoid channel bias. Maintain a living roster with primary domains, marketplace stores, and key product lines.

Scrape competitor PDPs with Apify, Bright Data, or ScraperAPI to collect sizes, colors, packs, materials, and care instructions. Build an attribute frequency table to spot patterns like extended sizes or niche variants you do not offer.

Mine Amazon, Walmart, and DTC site reviews with NLP tools like spaCy plus VADER or AWS Comprehend to tag recurring themes such as fit, durability, or battery life. Rank gaps by sentiment volume and tie them back to specific SKUs or variants.

Use Keepa or CamelCamelCamel for Amazon, and schedule visual snapshots for DTC stores with VisualPing or Hexowatch to log price, coupon use, and stock status. Correlate changes with rank or review velocity to estimate demand response.

Measure copy length, image count, 360 views, UGC presence, and FAQ depth, and validate schema with the Rich Results Test and page speed with Lighthouse. Score competitors on a rubric so you can quantify content gaps and prioritize creative work.

Map where competitors sell across Amazon, Walmart, Target Plus, eBay, Etsy, and Zalando, noting 1P vs 3P models and regional limitations. Use DataHawk or ChannelAdvisor intel to understand assortment differences by channel.

Search USPTO TESS and WIPO for active trademarks, review Amazon Brand Registry status, and track unauthorized sellers via buy box share patterns. Flag categories with high hijacking risk so pricing and enforcement policies can be set proactively.

Run a simple elasticity proxy by correlating price changes with conversion rate and units sold at the SKU or variant level, controlling for ad spend and traffic mix. Use GA4, Shopify order lines, and bid logs to avoid confounding seasonal effects.

Use PriceSpider, Wiser, or TrackStreet to monitor reseller pricing and auto-flag violations by SKU. Escalate through pre-defined tiers such as warning, suspension, and deauthorization to protect margin and brand equity.

Capture hero banners, discount codes, and threshold offers weekly with VisualPing or Hexowatch, then code by mechanics such as BOGO, tiered spend, or category-only. Estimate lift by comparing category rank or traffic changes during promo windows.

Track competitor free shipping thresholds, same-day or 2-day promises, and cutoffs, and simulate shipping costs with Shippo or AfterShip rate APIs. Quantify conversion trade-offs between cost, speed, and threshold to propose viable offers.

Run market basket analysis in SQL or Python mlxtend on order lines to find high-confidence item pairs and triplets. Validate with Amazon Frequently Bought Together cues and design bundles that improve AOV without harming profit per order.

Audit GTIN, MPN, brand, and Google Product Category completeness in Google Merchant Center and ensure PDP schema is valid for Product and Offer types. Use Feedonomics or DataFeedWatch to normalize attributes and push clean feeds to channels.

Create an ICE or PIE scoring model for PDP modules such as size guides, comparison tables, and UGC galleries, then test with Optimizely or VWO. Define success and guardrail metrics before launch to prevent AOV or margin erosion.

Use Ahrefs, Semrush, or STAT to track rank distribution and compute a visibility index for branded and generic terms. Segment by product family and intent stage to find realistic wins rather than chasing vanity keywords.

Monitor Google Ads Auction Insights and Merchant Center impression share, and compare price competitiveness and shipping annotations. Pivot by product type to uncover where feed quality or bids are limiting exposure.

Export Search Terms reports and build n-gram frequency tables to identify converting phrases and negative candidates. Automate weekly clean-up with Google Ads scripts to cut wasted spend and reinforce winners by match type.

Scrape Meta Ads Library and TikTok Creative Center for competitor creatives, then tag hooks, formats, offers, and UGC elements. Use iteration counts and flight durations as proxy for performance to inform your next round of tests.

Use Amazon Brand Analytics, DataHawk, or Helium 10 to monitor Sponsored Products share of voice and keyword CPC trends. Align bids and budgets with target contribution margin rather than ACOS only to avoid overspending on unprofitable rank.

Compare platform-reported ROAS with GA4 modeled conversions and a simple MMM proxy using weekly spend and sales regression. Segment by geo and device to detect over-attribution to last-click channels or brand terms.

Aggregate reviews from Amazon, Walmart, and your DTC site, then run topic modeling with KeyBERT or BERTopic to surface feature clusters. Funnel findings into product requirements and PDP copy to address objections and highlight differentiators.

Set standardized tags in Gorgias or Zendesk for sizing, defects, shipping delays, and price objections, with examples and decision rules. Publish a monthly heatmap by SKU and channel so product and operations can act on recurring issues.

Calculate 30, 60, and 90-day repeat rates in Shopify or your warehouse and compare by acquisition channel and first product purchased. Use latency distributions to schedule replenishment triggers and email flows that match actual behavior.

Create recency, frequency, and monetary segments and layer predictive signals from Klaviyo or custom models such as first AOV, category, and discount depth. Feed these into bidding, suppression lists, and personalized merchandising blocks.

Use Loop Returns, Returnly, or Narvar to tag reasons such as fit, color, or quality and link them back to PDP clarity, size guides, or supplier issues. Quantify the financial impact to prioritize fixes that reduce avoidable returns.

Run keyword detection or lightweight NLP on tickets and live chat to quantify mentions of price, coupon codes, or competitor references. Track trends by SKU and season to inform promo depth and value messaging.

Use Fivetran or Airbyte to pull Shopify, GA4, Google Ads, Meta Ads, Amazon Seller Central, and marketplace data into BigQuery, Snowflake, or Redshift. Maintain source-to-target mapping and freshness SLAs so analysis is both current and auditable.

Create dbt models for orders, line items, customers, products, inventory, and channels with tests for primary keys, duplicates, and referential integrity. Version-control transformations and document models so analysts can reuse logic consistently.

Define revenue, refunds, gross margin, AOV, CAC, blended ROAS, and contribution margin in shared YAML or LookML, including exclusions and time windows. This prevents reporting drift and disagreements during reviews.

Create a Weekly Business Review in Looker Studio, Tableau, or Power BI with leading indicators such as impression share, CTR, add-to-cart rate, and OOS rate. Include clear targets, last 8 weeks trendlines, and owner names for each KPI.

Run daily checks for feed errors, tracking breaks, and spend anomalies using scheduled queries and alerts to Slack or email. Use Prophet or seasonal decomposition to reduce false positives from predictable spikes.

Deploy a CMP such as OneTrust, enable GA4 Consent Mode V2, and consider server-side tagging with GTM to reduce client-side loss while honoring user choices. Enforce PII scrubbing and data retention policies across all pipelines.