I'm Jacob Cacheria. Every number on this page comes from a data pipeline I built: 34,982 public written reviews web scraped of Whatnot's iOS and Android App Store reviews (Dec 2019 to Jul 2026), collected with Python via public app-store APIs, audited to zero data-quality defects, classified into complaint themes, and rendered with hand-built SVG.
Skills in play: Python and pandas ETL, web scraping, advanced SQL, data-quality auditing, KPI and metric design, A/B testing and causal-inference thinking, data visualization, and communicating insights to technical and non-technical audiences.
What I foundApril 2026 ratings dipped to 3.43★ (37% negative), and your users named the cause: an 835-upvote review describes a UI update that blocks the stream view during shows.
What I would doPut review themes in release QA as guardrail metrics, so the next regression dies in staged rollout instead of your reviews.
Why it helpsIt turns a lagging signal into a leading one: a bad release is caught at partial rollout instead of dragging the App Store rating down for a month, protecting new-user downloads and the roadmap's credibility.
What I foundYour support team replies same-day (median) but covers only 12.7% of reviews, and 303 users edited their review after receiving a reply. Replies visibly move sentiment.
What I would doMake reply coverage, lag, and post-reply re-rating owned CX KPIs, so you can see the effect each reply has on the customer.
Why it helpsIt lets CX prove its own ROI and route effort where it moves the needle: once you know what a reply is worth by theme, you can shift reply capacity toward the complaints where it recovers the most rating and retention.
What I foundRefund pressure is climbing: the share of reviews asking for their "money back" is up about a quarter year over year (0.7% to 0.8% of all reviews), and 2026's #2-upvoted complaint (230 upvotes) is not seeing shipping costs before purchase.
What I would doTrack refund language as a leading indicator of refund cost, and test upfront price transparency.
Why it helpsIt gives finance an early-warning gauge on a real cost line, so refunds get forecast and intervened on before they land, and earlier shipping-cost disclosure lifts both checkout conversion and contribution margin.
Everything on this page came from limited public data, only the reviews anyone can read. With your internal data (orders, support tickets, payments, and session and experiment logs) I could turn these signals into owned KPIs, live dashboards, and product changes I can actually test. There's far more to find once I'm on the team.