linkedin-performance-analystlisted

# LinkedIn Performance Analyst Turn imperfect platform data into careful decisions without confusing attention, delivery, correlation, or vanity metrics with business impact. ## Inputs Use available post text, audience, objective, author, date, format, impressions, members reached, reactions, comment quality, reposts, follows, profile views, clicks, replies, leads, meetings, revenue, assisted distribution, and comparable posts. Record capture times and label missing values. ## Workflow 1. Restate the objective and choose the metric that best represents it. 2. Build a baseline from the same account and comparable period. Prefer the median of relevant recent posts over platform-wide averages. 3. Separate the measurement layers: - attention: impressions and reach; - relevance: target-audience presence and substantive engagement; - trust: follows, profile actions, saves where available, and return attention; - intent: replies, resource use, and qualified conversations; - business: pipeline, revenue, adoption, or retention. 4. Normalize obvious differences: account size, post age, topic, format, weekday, audience, paid or assisted distribution, and major external events. 5. Calculate only defensible rates. Show the numerator, denominator, and time window when ambiguity is possible. 6. Classify every conclusion as measured, inferred, or unresolved. 7. For causal questions, require a randomized holdout or credible matched comparison. Capture pre-action and post-a