[{"data":1,"prerenderedAt":227},["ShallowReactive",2],{"use-case:linkedin-post-engagement-analysis:en":3,"related-use-cases:linkedin-post-engagement-analysis:social-research:en":192},{"id":4,"title":5,"authorized":6,"body":7,"capabilities":14,"category":15,"contentStructure":16,"context":17,"conversation":18,"dataStory":41,"description":101,"evidence":102,"extension":131,"goal":132,"limitations":133,"locale":140,"meta":141,"navigation":6,"path":142,"performedAt":121,"platforms":143,"prompt":145,"published":6,"redacted":6,"relatedArticles":146,"relatedCases":147,"results":148,"screenshots":159,"seo":163,"slug":164,"sourceType":165,"stem":166,"steps":167,"tools":189,"translationKey":164,"updated":113,"__hash__":191},"useCases\u002Fuse-cases\u002Flinkedin-post-engagement-analysis\u002Fen.md","Analyze 95 LinkedIn posts for a content-strategy shift and replicable engagement patterns",true,{"type":8,"value":9,"toc":10},"minimark",[],{"title":11,"searchDepth":12,"depth":12,"links":13},"",2,[],[],"social-research",null,"A LinkedIn content team currently needs to decide which publishing patterns to repeat. It needs an account-level view of recent public posts so recommendations are based on comparable evidence rather than one standout or weak post.",[19,22,25,27,29,31,33,35,37,39],{"role":20,"content":21},"user","@skill:Social Signal Retrieval Analyze the posting activity of this account: https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fxx. The goal is to find a replicable method for posts with high readership.",{"role":23,"content":24},"assistant","Understood. I’ll first run a deep comparison: high- versus low-engagement post characteristics, opening patterns, and publishing cadence. I’ll then deliver a complete quantitative Markdown report.",{"role":23,"content":26},"The data is clear. I’ll add one essential control: engagement velocity (engagement per day), so a newly published post is not misclassified as weak simply because it has had less time to accumulate interaction.",{"role":23,"content":28},"The data is sufficient. Next I’ll extract the full text of key posts for a post-by-post breakdown and adaptable examples; account identity, links, and company details will remain redacted.",{"role":23,"content":30},"The analysis is complete. I’m upgrading the earlier findings to a quantitative report based on all 50 posts; before delivery, I’ll read the existing report and replace it with the updated Markdown version.",{"role":23,"content":32},"The full quantitative Markdown report was delivered, based on 50 posts from 2025-11-07 through 2026-08-07. It includes two validated higher-engagement content lines, an engagement-velocity adjustment, topic priorities, publishing observations, three adaptable drafts, and a post-level appendix.",{"role":20,"content":34},"Add more page-level data.",{"role":23,"content":36},"I’ll use the pagination_token returned on page one to fetch pages two and three, then compare older material with recent posts instead of treating the latest 50 as the account’s entire history.",{"role":23,"content":38},"Page two returned 45 posts and a further token. Page three returned zero posts, so this returnable pagination chain is exhausted. The combined sample contains 95 posts from 2024-05-17 through 2026-08-07, spanning 812 days.",{"role":23,"content":40},"I’ll update the Markdown report and monthly trend chart with the complete sample, adding an early-versus-recent comparison, a monthly trend table, and a Bottom 15 counterexample section. Content shape and engagement both change around June 2026; that is temporal alignment, not proof that a single series caused the growth.",{"userQuestion":42,"problemToSolve":43,"answer":44,"publicDisclosure":45,"targetTitle":46,"metrics":47,"comparison":64,"decisionShift":80,"reusableSteps":90,"nextActions":96},"How should this LinkedIn account post for higher engagement, and can high readership be copied directly?","Compare content types across the returnable post history instead of copying one viral post, and keep missing impressions from being treated as reads.","Across 95 returnable posts, App rankings averaged 15.3 likes and hashtag-only posts averaged 2.1. Readership cannot be verified because impressions were null on all 50 recent posts.","The user authorized publication of redacted engagement metrics, content types and the research conclusion. Account name, profile and company details remain redacted.","95 returnable LinkedIn posts",[48,52,56,60],{"label":49,"value":50,"note":51},"Returnable sample","95","Pagination chain ended",{"label":53,"value":54,"note":55},"Ranking avg likes","15.3","7 posts",{"label":57,"value":58,"note":59},"Hashtag-only avg likes","2.1","14 posts",{"label":61,"value":62,"note":63},"Impressions","null","Reads unverifiable",{"title":65,"description":66,"unit":67,"targetLabel":68,"targetValue":69,"items":70},"Average likes by content type in the recent 50 posts","Bars start at zero. This is an account-internal comparison, not a LinkedIn benchmark and not readership.","average likes","App rankings",15.3,[71,74,77],{"label":72,"value":73},"Growth cases",6.6,{"label":75,"value":76},"DTC \u002F brand marketing",4,{"label":78,"value":79},"Hashtag-only posts",2.1,[81,84,87],{"label":82,"body":83},"Initial concern","Copy the highest-readership post directly.",{"label":85,"body":86},"What the data showed","Impressions were missing. The gap is between image-led data rankings and hashtag-only posts.",{"label":88,"body":89},"Revised judgment","Retest rankings and single-app cases; stop hashtag-only posts. This is an account ranking, not a platform algorithm rule.",[91,92,93,94,95],"Confirm available metrics and treat missing fields as unknown.","Follow pagination instead of treating page one as the full history.","Compare by content type, not only the top post.","Adjust new posts by engagement per day.","Keep temporal alignment separate from causation.",[97,98,99,100],"Keep posting image-led data rankings instead of hashtag-only posts.","Retest the single-app growth-case format.","Do not promise readership from likes.","Keep the proxy-metric boundary visible while impressions stay missing.","A Social Signal Retrieval run that paginated through all 95 returnable posts of a LinkedIn account, compared early and recent content, and extracted replicable patterns without making readership promises.",[103,106,114,122,125,128],{"title":104,"body":105},"The execution established scope, metric definitions, and missing fields","Page one returned 50 posts from 2025-11-07 through 2026-08-07. Each was checked for likes, comments, reposts, publish time, text, and image fields; impressions were null on all 50. Following the page-one token returned 45 posts from May 2024 to October 2025; page three returned zero. The report therefore uses likes+comments+reposts and engagement per day, while explicitly treating readership as unverified.",{"title":107,"body":108,"image":109},"Full pagination revealed a measurable shift in content shape","Across the 45 early posts, average likes were 0.20, 88.9% had zero likes, 95.6% were hashtag-only, mean text length was 139 characters, and image rate was 6.7%. Across the 50 recent posts, the corresponding figures were 5.9, 8.0%, 30%, 1,368 characters, and 68%. The comparison shows engagement and content format changing together; it does not isolate any one field as the cause.",{"src":110,"alt":111,"caption":112,"capturedAt":113},"\u002Fevidence\u002Flinkedin-post-engagement-analysis\u002Fmonthly-engagement-evolution-95-posts.webp","Monthly average likes and average text length across 95 LinkedIn posts, showing a clear inflection point in June 2026","Trend for the complete sample: purple is monthly average likes; the dashed orange line is average text length on the separate right axis. The visible inflection is temporal association, not a causal conclusion.","2026-08-19",{"title":115,"body":116,"image":117},"Fifty posts exposed a clear content-type gap","App-ranking posts averaged 15.3 likes across seven posts and carried 36% of all likes, while fourteen hashtag-only posts averaged 2.1. The comparison identifies a relative high-engagement line inside this account, not a platform benchmark.",{"src":118,"alt":119,"caption":120,"capturedAt":121},"\u002Fevidence\u002Flinkedin-post-engagement-analysis\u002Fposts-overview.webp","Overview of 50 posts from a LinkedIn account: total likes, comments, reposts, top posts, content-type breakdown and time trend","The engagement overview of the 50 fetched posts, including the Top 5 posts and the content-type breakdown.","2026-08-12",{"title":123,"body":124},"The time series corrected the earlier single-post conclusion","June 2026 reached 98 total likes and 12.2 average likes; July average likes reached 13.7. May had 17 total likes and 4.2 average likes. The “Global Mobile App Revenue Top 30” ranking series began in June, and its first post earned 46 likes. With missing impressions and uncontrolled variables, this supports retesting the content line—not a readership, reach, or causal claim.",{"title":126,"body":127},"Grouping and age adjustment produced an actionable, bounded replication target","All eight high-engagement posts included images and had a 2,025-character median length; the 24 low-engagement posts had a 1,034-character median, and 14 hashtag-only posts generated just 30 interactions combined. After age adjustment, a single-App case post ranked first at 1.33 interactions per day. The report prioritizes App rankings and single-App cases and recommends stopping hashtag-only posts; this is account-level evidence, not a LinkedIn algorithm rule.",{"title":129,"body":130},"The deliverable is traceable to post-level data rather than a generic recommendation","The updated Markdown report includes a 95-post appendix ranked by total engagement and engagement per day, content-type summaries, early-versus-recent comparison, a monthly trend table, Bottom 15 counterexamples, key-post text breakdowns, a data anchor → tension → structured points → takeaway → CTA formula, a topic matrix, and three drafts requiring human rewriting and fact checks. It records missing impressions, pagination scope, and the non-causal nature of the comparison.","md","Analyze the available LinkedIn post history, compare content patterns over time, and identify repeatable formats while stating the limits of engagement data.",[134,135,136,137,138,139],"Impressions (the \"readership\" the user wanted to replicate) were null on the initial 50 posts, so engagement metrics remain a proxy, not reach; extra pages do not turn that proxy into readership.","The 95 posts are complete only for this retrieval’s returnable pagination chain, not necessarily the account’s permanent full LinkedIn history; deletion, visibility, and endpoint scope can change what is returned.","4 posts had missing like_count and were treated as missing, not zero.","This is a single-account snapshot; patterns are not a platform-wide benchmark.","Engagement velocity depends on capture date and publish time. Without impressions, traffic-source, or audience-composition data, it cannot establish true reach efficiency.","Weekday and time-of-day observations come from a non-random account sample; topic, image choice, text length, and account growth were not controlled. The early-versus-recent comparison also cannot turn correlation into causation.","en",{},"\u002Fuse-cases\u002Flinkedin-post-engagement-analysis\u002Fen",[144],"LinkedIn","@skill:Social Signal Retrieval Analyze the posting activity of this account: https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fxx. The goal is to find a replicable method for producing posts with high readership",[],[],[149,150,151,152,153,154,155,156,157,158],"Top post: Appark global App revenue Top 30 (ChatGPT $314M leading) - 46 likes \u002F 6 comments \u002F 2 reposts on 2026-06-11.","App-ranking posts (7) averaged 15.3 likes and carried 36% of total likes, while hashtag-only posts (14) averaged 2.1 likes - a significant content-type gap.","The complete returnable sample contains 95 posts: 50 on page one (November 2025 to August 2026), 45 on page two (May 2024 to October 2025), and zero on page three, spanning 2024-05-17 to 2026-08-07.","Early posts (May 2024–October 2025) averaged 0.20 likes: 88.9% had zero likes, 6.7% had images, average text length was 139 characters, and 95.6% were hashtag-only. Recent posts (November 2025–August 2026) averaged 5.9 likes: 8.0% had zero likes, 68% had images, average text length was 1,368 characters, and 30% were hashtag-only.","June 2026 was the monthly inflection point: average likes rose from 4.2 in May to 12.2, while total likes rose from 17 to 98. The ranking series launched that month and its first post earned the account’s high of 46 likes. The alignment supports further testing of that line; it is not causal proof.","High-engagement posts were consistently image + data-anchored (rankings, growth rates, rank jumps); low-engagement posts were mostly image-less hashtag-only posts.","The earlier single-post reading was corrected: the account’s early history was almost entirely short hashtag posts, while recent material shows a clear relative high-engagement line. This is an account-level change, not a LinkedIn-wide rule.","All eight high-engagement posts had images. Their median text length was 2,025 characters and they commonly used a data anchor, structured points, and a closing CTA; among 24 low-engagement posts, 14 hashtag-only posts produced only 30 interactions in total and median text length was 1,034 characters.","The peak single-App growth-case post produced 45 interactions and the sample’s highest age-adjusted engagement velocity, 1.33 per day. It is a candidate template for an outlier case, not evidence of readership.","Within this sample, Thursday had the highest interaction total (160) and Friday the lowest (30); 11:00 and 18:00 Beijing time were comparatively strong observed windows, not guaranteed publishing times.",[160,162],{"src":110,"alt":111,"caption":161,"capturedAt":113},"Monthly trend for the complete 95-post sample: purple is average likes; the dashed orange line is average text length, plotted on a separate axis.",{"src":118,"alt":119,"caption":120,"capturedAt":121},{"title":5,"description":101},"linkedin-post-engagement-analysis","authorized-user","use-cases\u002Flinkedin-post-engagement-analysis\u002Fen",[168,171,174,177,180,183,186],{"title":169,"body":170},"Pull the latest posts and pin down metric boundaries","Fetched the first 50 posts via get_linkedin_user_posts. Impressions were null on all 50, so likes, comments, and reposts were the available interaction metrics; readership was not treated as a verifiable result.",{"title":172,"body":173},"Follow pagination tokens to complete the returnable history","Used the page-one pagination_token to retrieve 45 page-two posts, then used its token for page three. Page three returned zero posts, ending the available chain. The final sample is 95 posts from 2024-05-17 through 2026-08-07.",{"title":175,"body":176},"Profile the account and the engagement overview","Confirmed the account profile (a market researcher with several hundred followers and 500+ connections; name and company redacted) and first computed the recent-50 totals: 295 likes (median 4, max 46), 22 comments on 7 posts, 15 reposts on 7 posts; 34\u002F50 posts with images and 34\u002F50 long-form posts. The 95-post sample was then used to check strategy evolution.",{"title":178,"body":179},"Rank top posts and break down by content type","Ranked the 50 posts by likes+comments+reposts and grouped them into four content types: App rankings (7 posts, avg 15.3 likes, 36% of likes), growth case studies (10, avg 6.6), DTC\u002Fbrand marketing (13, avg 4.0), and hashtag-only posts (14, avg 2.1).",{"title":181,"body":182},"Analyze the time trend and content shape","Found a visible turning point: June 2026 had 98 total likes and 12.2 average likes, up from 17 and 4.2 in May; July reached 13.7 average likes. Rankings began in June as content moved from hashtag-only\u002Fgeneric marketing to data-driven rankings and growth cases. These variables moved together and establish temporal association only.",{"title":184,"body":185},"Adjust engagement for post age","Calculated engagement velocity as likes, comments, and reposts divided by days since publication, preventing recent posts from being penalized for their shorter accumulation window. A single-App case post led the sample at 1.33 interactions per day; the measure remains an account-internal comparison.",{"title":187,"body":188},"Extract post text and deliver the complete Markdown report","Extracted the full redacted text of key high-engagement posts and checked their data-led openings, tension, structured points, takeaway, and question CTA. The delivered Markdown report includes a topic matrix, replication formula, three drafts, a 95-post appendix, early-versus-recent comparison, monthly trend table, and Bottom 15 counterexamples.",[190],"$research-social-signals","nuEpcorI3OxO6pojd50_WWOEUk5JbaI5Jsp73go4pgU",[193,198,202,206,210,216,221,222],{"slug":194,"category":15,"title":195,"description":196,"performedAt":197},"grok-for-excel-seo-signal","Assess Grok's opportunity after OpenAI limits Cursor model access","A current X research snapshot examining how OpenAI's Cursor access change may affect Grok's position in coding tools, with native post metrics and explicit coverage limits.","2026-08-31",{"slug":199,"category":15,"title":200,"description":201,"performedAt":197},"wechat-seo-keyword-conflict-article-analysis","Measure how the latest WeChat official-account article is performing","A Social Signal Retrieval run that resolved the WeChat official account (account id and nickname redacted) behind an article, retrieved its latest 10 articles, and benchmarked the target article against the account baseline.",{"slug":203,"category":15,"title":204,"description":205,"performedAt":197},"xiaohongshu-account-growth-diagnosis","Diagnose why a Xiaohongshu account is underperforming and define its direction","A Social Signal Retrieval snapshot of 12 public Xiaohongshu notes, using native account counters and note-level metrics to identify the account's strongest recent topic and format signals.",{"slug":207,"category":15,"title":208,"description":209,"performedAt":197},"zhihu-ai-replacing-programmers-search","Search Zhihu for discussions about programmers being replaced by AI","A current Zhihu search for discussions about programmers being replaced by AI, retaining the 20-result retrieval boundary and 18 accepted results without claiming exhaustive coverage.",{"slug":211,"category":212,"title":213,"description":214,"performedAt":215},"chatgpt-403-error-keyword-research","seo-research","Decide whether the \"ChatGPT 403 Forbidden\" error keyword cluster is worth targeting","A keyword demand-signal analysis of the \"ChatGPT 403\" error family that filtered for 50–500 monthly searches and difficulty under 30, then shaped the results into a two-article troubleshooting cluster instead of standalone pages.","2026-08-20",{"slug":217,"category":15,"title":218,"description":219,"performedAt":220},"deepseek-hardness-x-discussions","Find popular X discussions on DeepSeek Harness","A Social Signal Retrieval run that aggregated X, Xiaohongshu and Zhihu signals around the newly released DeepSeek Harness, and turned them into a priority-ranked posting plan with risk boundaries.","2026-08-14",{"slug":164,"category":15,"title":5,"description":101,"performedAt":121},{"slug":223,"category":212,"title":224,"description":225,"performedAt":226},"seo-keyword-opportunity-demand-research","Research real keyword-opportunity needs with Reddit and X signals","A cross-platform social-signal study that combined public Reddit and X discussions into an evidence matrix, identifying the decision confidence, risk checks, and demand context people need from a keyword-opportunity product.","2026-08-07",1788262611126]