Research & Analysis Checklist for E-Commerce

Create a tiered list of direct and indirect competitors by scraping page 1 SERPs for your head terms and cross-referencing Amazon, Walmart, and niche marketplaces. This narrows ongoing monitoring to the brands your customers actually compare you with.

Use BuiltWith to log ecommerce platforms, review apps, analytics tags, and A/B tools. Pair with job postings and Wayback Machine snapshots to infer roadmap maturity and testing velocity.

Monitor SKU-level prices and availability using Keepa, CamelCamelCamel, or custom scripts with scheduled checks and Google Sheets. Flag stockouts to time ad spend and capture demand spillover.

Pull ads from Meta Ads Library and TikTok Creative Center weekly, tagging hooks, formats, and CTAs. Correlate bursts of new creatives with promo periods to anticipate calendar moves.

Use Similarweb and public review velocity as directional indicators for top of funnel and product-market fit. Triangulate with estimated ad spend from third party tools for context, not absolutes.

Blend Google Trends, Amazon search volume tools, and your own GA4 seasonality to forecast demand waves. Feed peaks into inventory purchasing and campaign calendars.

Use VisualPing or Distill.io to watch competitor PDPs for assortment changes, bundle launches, and price drops. Send alerts to Slack so your team reacts within hours, not days.

Export queries from Google Search Console and combine with Amazon and Walmart autosuggest and Helium 10 data. This grounds your research in both web and marketplace demand.

Group queries with NLP clustering or manual tagging into awareness, consideration, and purchase. Prioritize product-led queries for PDPs and guide-led queries for blog and comparison pages.

Log featured snippets, shopping units, and video carousels for target terms using manual checks or SERP APIs. Identify opportunities where competitors rank with guides while you rely only on PDPs.

Assign each high-intent query cluster to a specific SKU or bundle, then update titles, bullets, and FAQs accordingly. Re-measure impressions and CTR in Search Console after 14 to 28 days.

Track rankings for head terms on Google and organic visibility on Amazon using tools like Ahrefs and Helium 10. Focus on the top 20 terms that drive 80 percent of revenue.

Extract recurring questions from customer reviews, returns, and chat logs with light NLP. Turn top concerns into PDP FAQs, comparison tables, and objection-handling bullets.

Run Screaming Frog to catch broken links, thin content, and orphaned PDPs. Add internal links from high authority guides to top SKUs and measure organic revenue lift in GA4.

Analyze GA4 revenue and units before and after past price changes or promo windows by product. Fit a simple elasticity model to inform discount depth without killing margin.

Log coupons, bundles, and shipping promos by date from email scrapes and ad archives. Predict monthly cadences and prepare counter-offers or unique value props to avoid margin wars.

Calculate SKU-level landed cost including marketplace commissions, FBA fees, storage, and returns. Use this to decide which SKUs should be DTC only, marketplace only, or hybrid.

Track Buy Box ownership, sudden price drops, and new seller IDs on Amazon with Helium 10 Alerts or custom monitors. Escalate quickly to protect brand equity and retailer relationships.

Review competitor variation families and ASIN bundles to spot gaps in size, color, and price tiers. Test bundles that raise AOV while solving real use cases customers mention.

Place periodic orders from key competitors and time delivery, unboxing, and return friction. Feed findings into your own SLA commitments and post-purchase messaging.

Rank products by gross margin, return rate, and repeat purchase potential. Push discounts on items that seed bundles or subscriptions, not on fragile margin drivers.

In your BI tool or GA4, compare first 90-day repeat rates and AOV by channel and campaign. Shift budget from channels with low payback to those that compound LTV.

Create recency, frequency, and monetary tiers in Klaviyo or your CDP. Align flows for win-back, VIP early access, and replenishment based on segment behavior, not hunches.

Use keyword extraction or topic modeling on reviews and support logs to pinpoint quality issues and unmet needs. Prioritize product and content updates by impact and frequency.

Calculate 6 and 12 month LTV per acquisition source and top SKU. Set CAC targets that maintain a healthy LTV to CAC ratio and prevent overspending on low retention offers.

Track churn precursors like shipping delays, stockouts, and unresolved tickets. Trigger proactive outreach or credits when risk thresholds are met to protect retention.

A/B test PDPs with and without UGC modules and measure add to cart and conversion. Double down on collection tactics for SKUs with the strongest lift.

Field quick surveys using Van Westendorp and Jobs to be Done frameworks to validate positioning and acceptable price ranges before changes go live.

Pipe Shopify or platform data, Amazon Seller Central, ad platforms, and email metrics into BigQuery or Snowflake using Airbyte or native connectors. This simplifies analysis and removes spreadsheet drift.

Document events and parameters for PDP views, add to cart, checkout steps, and refunds. Implement server-side tagging to reduce signal loss and improve attribution fidelity.

In Looker Studio or Power BI, chart revenue, CAC, ROAS, LTV, stockouts, and buy box share with green and red thresholds. Include trendlines and annotations for test periods.

Schedule scripts that flag outliers in conversion rate, refund spikes, and feed errors. Route alerts to Slack with links to deep dives so owners act within the same day.

For new products, verify certifications, MAP agreements, and chargeback exposure. Check risk indicators like late shipments and defect rates before listing expansion.

Review cookie consent, email opt-in records, and data retention across analytics and CRM tools for GDPR and CCPA. Avoid fines and ad platform penalties.

Log hypotheses, power calculations, success metrics, and guardrails for every A/B test. Add a pre-mortem checklist to reduce false positives and wasted dev cycles.