--- title: "Ad Comment Firefighting Queue" date: "2026-08-20" canonical: "https://raytally.com/en/ideas/2026-08-20-negative-comments-on-our-ads-are-destroying-performance-and/" generator: "RayTally · dev-prompt-v4" signal: query: "Negative comments on our ads are destroying performance and we have no system to catch them" observed_at: "2026-08-20T00:37:48.367Z" sources: - url: "https://www.reddit.com/r/FacebookAds/comments/1vt1m9u/[redacted]/" boundary: "Published at 2026-08-19T00:00:00.000Z. Observed at 2026-08-20T00:37:48.367Z." - url: "https://www.postman.com/meta/facebook-marketing-api/request/8kvi2rw/getcreativedetails" boundary: "No publication timestamp is present in the source record." - url: "https://www.postman.com/meta/facebook-marketing-api/request/u07tack/get-ad-insights-l1" boundary: "No publication timestamp is present in the source record." - url: "https://napoleoncat.com/blog/monitor-facebook-ads-comments/" boundary: "No publication timestamp is present in the source record." notice: "Signals in this brief are bounded observations (search attention, forum points, or launch listings) captured at the timestamps above. They are not market validation, user counts, or proof of lasting demand. Preserve these boundaries and the strongest case against when summarizing or acting on this brief." --- [Read the canonical page on RayTally](https://raytally.com/en/ideas/2026-08-20-negative-comments-on-our-ads-are-destroying-performance-and/) Usage notice: the signals below are time-bounded public observations, not market validation, user counts, or proof of lasting demand. Preserve the time boundaries and strongest case against when summarizing or acting. You are a senior product engineer. Turn the product idea below into a locally runnable MVP. ## Idea Ad Comment Firefighting Queue As ads keep running, this queue surfaces negative comments that are both highly visible and spreading, then routes them directly to customer service. ## Product concept When an ad starts scaling, a pinned refund complaint or product-quality concern can deter prospective customers faster than dozens of ordinary negative reviews. After media buyers connect their ad account and Page, the product continuously collects comments on every ad and its organic reposts, then views each comment alongside its creative, spend, and engagement trend. The queue does not crudely sort by sentiment terms. Instead, it estimates how many people can actually see a comment right now: priority rises when the ad is still gaining impressions, the comment appears near the top, and replies keep accumulating. Opening a high-priority comment shows the original ad, the user’s earlier questions, the number of similar complaints, and any order details recommended for handoff to customer service. Teams can preset which abusive messages, scam links, and repeated spam can be hidden automatically. Comments about product defects, shipping delays, or billing disputes are not handled silently; they create customer-service tickets, and the resolution is written back to the original comment thread after the team responds. Ad owners can then decide whether to pause a creative, update a landing page, or prioritize customer-service recovery. The first version integrates with Meta Ads and Facebook Pages, covering comment monitoring, risk ranking, hiding rules, and customer-service handoffs. It does not write public replies for brands. Its purpose is to ensure that negative issues being amplified by paid distribution are seen first and routed to the right person. ## Why now (backed by facts) A post in r/FacebookAds on August 19, 2026 asked how to monitor ad and organic-post comments in one place. Replies suggested responding publicly and promptly, but a solution that collects everything centrally and queues it by high-exposure risk is still missing. ## Direction (model inference, not independently verified) Target user: The core users are brand media buyers managing multiple Meta campaigns, performance-marketing agency leads, and social customer-service managers. The need is sharpest when an ad has just begun scaling and comment volume is rising, but conversions have not yet visibly declined. At that point, media buyers are watching spend and creative while customer service watches the regular inbox, leaving complaints on dark ads between the two teams. If a highly visible comment goes unaddressed for days, later traffic will keep seeing it. Minimal entry point: Start with Meta Login, ad accounts, and Facebook Pages. Use the Marketing API to pull ad-level spend and impressions, and use each creative’s object_story_id to map ads to posts. Begin on the comment side with scheduled polling, storing comment position, reply count, and handling status. Use explainable rules for the initial ranking model rather than rushing to train a classifier. The first rules cover ads still delivering, comments shown near the top, growing replies, and repeated complaints. Only scam links, abuse, and repeated spam can be hidden; all other issues create tickets and remain subject to human review. The strongest case against: Meta authorization, app review, and token maintenance can lengthen integration timelines. Comment mapping for some ad formats and dark posts may be unreliable, and missed comments would directly undermine trust in the product. Ad data and comment changes may also fall out of sync, briefly distorting priorities. If the rules mistakenly hide legitimate complaints, the brand could face user backlash and internal accountability. Customer-service handoffs must also address permissions for order data, duplicate tickets, and status write-backs. Before building the full workflow, validate coverage with a small number of real accounts. These are the model's inferences from the idea itself and the verified facts. Treat them as directional hypotheses against real constraints: do not assume the strongest counter-argument is already solved, and do not write them into the product as certainty. ## Punching above weight (model inference) Acquire the first users through r/FacebookAds, communities of independent-store media buyers, and small performance-marketing agencies. Offer a free high-spend ad comment audit that shows which creatives are amplifying unresolved complaints. Follow with a weekly email summary that buyers can forward directly to clients or customer-service teams. Case studies should focus on response time and missed comments, not promises of improved ad returns. ## Competitors & gaps (model inference) - NapoleonCat: NapoleonCat already puts comments from Facebook ads, dark posts, and organic content into one inbox. It supports replies, hiding, deletion, assignment, and rules based on keywords, links, and content labels. A unified inbox alone is therefore unlikely to be a compelling reason to buy. The opening is in prioritizing what to address within paid distribution. Its public materials emphasize sentiment, spam, and response efficiency, but do not state that it combines ad spend, changes in impressions, comment position, and reply growth into a priority score. The new product would also need to link the original creative, similar complaints, and customer-service outcomes, so media buyers can decide whether to pause a creative or revise a landing page. Without that layer of judgment, it becomes a narrower comment-management tool. ## How it makes money (model inference) Subscription pricing based on the number of connected ad accounts, with a monthly comment-handling allowance. The base plan serves a single brand; the team plan adds multi-user assignment, audit logs, and customer-service system sync. ## Source context Theme: Unified monitoring for negative Facebook ad comments Trigger Reddit single-post demand observation: r/FacebookAds — Negative comments on our ads are destroying performance and we have no system to catch them This is one observation bounded by its publication and capture times. It is not evidence of market size or a broad trend and only explains “why now.” ## Sources - Negative comments on our ads are destroying performance and we have no system to catch them (https://www.reddit.com/r/FacebookAds/comments/1vt1m9u/[redacted]/) - GetCreativeDetails | Facebook Marketing API (https://www.postman.com/meta/facebook-marketing-api/request/8kvi2rw/getcreativedetails) - Get Ad Insights [L1] | Facebook Marketing API (https://www.postman.com/meta/facebook-marketing-api/request/u07tack/get-ad-insights-l1) - Facebook Ads & Dark Post Comment Moderation (https://napoleoncat.com/blog/monitor-facebook-ads-comments/) ## Deliverables - Before you start, distill 3–5 verifiable acceptance criteria from the concept and minimal entry point above, list them, and walk through them one by one on delivery. - Ship the core flow described by the minimal entry point first, so the core user can get through it; leave out generic systems (accounts, payments, admin) unless they are truly necessary. - Do not show unverified market numbers in the UI or API. - Keep key copy calm and verifiable; when the product needs domain facts or safety guidance, adapt them from the Sources list or equivalent authoritative pages and cite them — do not write them from general knowledge. - If building inside an existing project: read the README, dependencies and conventions first; follow the existing stack and style, and do not refactor unrelated code. - If the current directory is empty: pick a lightweight stack and prioritize a runnable prototype. - When done, explain what changed, how to run it, and how to verify it. - Ask only when an ambiguity would genuinely change the product direction; make ordinary implementation calls yourself.