LinkedIn Ads audience segment waste review workbench

Build me an app for the recurring LinkedIn Ads optimization review, where I decide which audience segments are quietly wasting budget and then act on it. I am a paid social manager running this monthly or quarterly across live campaigns. The app is the workbench for that review: it shows delivered performance broken out by audience segment, and it is where the resulting targeting edits get proposed, checked and applied.

At the top of the app I pick an ad account, one or more campaigns, and a date range. Populate the ad account picker with LinkedIn Ads Search Ad Accounts and the campaign multi-select with Search Campaigns for the chosen account, defaulting to active campaigns. Default the date range to the last full calendar month. Remember my last selection so reopening the app lands me back where I was.

The main surface is a set of tabs, one per demographic breakdown: job function, seniority, industry, company size, job title, and location. Each tab is a table of segments for the selected campaigns and date range, sourced from Get Ad Analytics with the matching demographic pivot. Rank every table by spend descending. Columns: segment name, spend, impressions, clicks, click through rate, conversions where available, cost per result, share of spend, share of results, and the gap between those two shares. Make that gap the thing that jumps out, since a segment taking a quarter of the budget and returning a twentieth of the results is the whole point of the review. Highlight rows where share of spend runs well ahead of share of results, and let me sort by any column.

Conversion data is not present for every demographic pivot. When conversions are missing for a breakdown, fall back to click through rate and cost per click as the result measure rather than rendering blank columns, and label the table so I know which measure is in play. Analytics endpoints are throttled hard, so pull each pivot as a single call across the whole range using timeGranularity rather than looping day by day, and cache the results for the selected account, campaigns and range so switching tabs does not refetch.

Add a compare mode toggle. When it is on, fetch the equivalent previous period of the same length and show this period next to it, with delta columns for spend, cost per result and the share gap, so I can see which segments got worse rather than which ones merely look bad. Flag segments that deteriorated most since last period.

Show a persistent note on screen explaining that LinkedIn hides reporting rows for audiences under 300 members, so some segments will read as zero and are not necessarily dead. Put it near the tables, not buried in a help page.

From any row I can decide to narrow or exclude that segment. When I do, the app builds the proposed targeting change for the affected campaign, resolving segment names into targeting entities with Find Targeting Entities and Get Targeting Facets. Before anything is applied, it checks the proposed targeting with Get Audience Counts and Get Supply Forecasts and shows me the resulting audience size and forecast impressions, clicks and spend next to the campaign's current numbers. Because LinkedIn returns counts under 300 as zero and campaigns need a matched audience of at least 300 to serve at all, block the change outright if the resulting audience is at or near that floor, and never propose narrowing a campaign whose audience is already close to it. Only after I confirm in a review dialog does the app apply the change with Update Campaign, using a partial update against that campaign's targeting.

Every decision gets written to the General Input Database with Execute SQL: the ad account, campaign, breakdown, segment, the action taken (narrowed, excluded, left alone, increased), the date, my written reasoning, and the segment's metrics at the time of the decision. Create the table on first use. When I open the app, show a Since last review panel that reads back the decisions from the previous review and pulls current numbers for those same segments, so I can see whether each edit actually helped, hurt, or did nothing. Let me file a decision as left alone with reasoning too, since deciding not to act is worth recording.

Add a Propose targeting changes button that kicks off a background agent across every campaign in the selected ad account. The agent lists campaigns with Search Campaigns, reads the demographic breakdowns for each with Get Ad Analytics across all six pivots, and finds two things: segments spending real money with nothing to show for it, and segments that are producing results but are underfunded relative to what they return. For each proposed change it validates the resulting audience with Get Audience Counts, and discards any recommendation that would push a campaign to or near the 300 member serving floor. It writes a ranked change list back into the app, each item carrying the campaign, breakdown, segment, proposed action, the numbers behind it, the validated audience size, and plain reasoning. The list lands in a review queue in the app where I approve or reject each item. Approving routes it through the same confirm and apply path as a manual change, so Get Supply Forecasts and the audience floor check still run before Update Campaign is called, and the decision is logged the same way. The agent never edits a campaign itself; it only recommends.

Keep the read and review side usable for someone with view only access to the ad account, and disable rather than hide the apply actions when the connected account cannot edit campaigns. Show the agent's progress while it runs and keep the last generated change list visible until a new run replaces it.