---
name: create-new-saved-audiences-from-audience-insights
description: "Use real audience reports to plan a new test. Save clear settings and keep their limits visible."
category: Content Factory — Promote
stage: Promote
definitive_article: /social-amplification
status: needs-work
---

# Create new saved audiences from Audience Insights

Your reports may show a group you have not served well yet. This guide helps you turn that clue into a clear test plan. Start with what the report can really show, not a guess about each person.

**The path:** Dated insight → relevant hypothesis → saved settings → bounded next test.

**Use this when:** A campaign needs a new relevant audience hypothesis and current aggregate reporting provides a useful clue.

## Inputs
- Actual Page/ad reporting with dates, metric definitions and available aggregate breakdowns.
- The tested audience/creative map, real service area and goal.
- Access to inspect or save supported audience settings; relevant permissions and eligibility for any first-party source. A saved audience does not authorize spend.

## First-run prompt

> Review these aggregate audience reports and propose a relevant new audience test. Label missing or suppressed data, read current supported settings and distinguish suggestions from controls. Save only within the supplied scope and return the exact definition, version and next decision; do not launch it automatically.

## Steps
1. Open the exact current reports and note their source, date range and metric. Some demographics, interests or page affinities may not be available. Do not recreate a retired Audience Insights screen or infer individual identities from aggregate results.
2. Compare observed useful engagement or customer outcomes with the current targeting plan. A demographic skew among viewers is not proof that other people are poor customers, and a small sample may not support a change.
3. Choose a specific business hypothesis, such as an adjacent area the business actually serves or a relevant available topic. Do not turn every age or gender pattern into exclusions, especially where the offer’s ad category restricts targeting.
4. Read the current controls and any automated expansion labels. A saved collection of suggestions is not an exact membership list. Keep warm/custom audience sources separate from a saved targeting preset.
5. Record a clear name and definition, such as service-area-topic-date, without embedding private customer names. Save the source report, settings, source rights and size/eligibility status. An estimate is not guaranteed delivery at $1/day.
6. If saving is authorized, create or update only the intended preset and read it back. Check for an existing equivalent before creating a duplicate. Keep active campaign targeting unchanged unless that distinct action is in scope.
7. Queue a proposal for a bounded switch test against an appropriate baseline. A preset may legitimately stay unused while budget, evidence or scope is pending; it is not a failed guide run because no ad launched.
8. Review prior test history before retiring a preset. Mark an outdated hypothesis or unavailable setting in the log; do not delete a shared audience used by active campaigns or declare a segment permanently bad from one weak test.

## Definition of done (QA checklist)

- [ ] The insight comes from a dated available report with sample limits.
- [ ] The hypothesis fits the offer and service area without unsupported exclusions.
- [ ] Preset, custom audience, suggestion and strict control are distinct.
- [ ] Saved settings are named, deduplicated and read back within scope.
- [ ] The next test proposal has a goal and budget decision; it is not an automatic launch.

## Example(s)

**Fictional teaching example:** A shop’s report shows useful visits from Maple Town, where it also provides service. The operator checks the current settings and saves a Maple Town preset under the authorized setup scope. An interest is marked as a suggestion because the interface can expand it. The preset’s estimate is not called a known list of customers. Its test remains queued until the existing campaign owner assigns a budget and date. No active Cedar Town campaign is overwritten.

## Handoff and Content Factory context

Pass the versioned preset and hypothesis to [execute switch boost to target new audiences](https://local-service-spotlight.github.io/task-library/?task=execute-switch-boost-to-target-new-audiences#task-execute-switch-boost-to-target-new-audiences). The campaign owner determines whether and when its separate test fits the budget.

This task is in Promote within the [Content Factory](https://blitzmetrics.com/content-factory/). Produce gathers real source material; Process makes useful assets; Post places and checks them; Promote distributes suitable work within its own scope. Use the proof and checked posts from earlier stages; a Promote task does not automatically redo all four stages. Use the actual next step above; catalog neighbors are not prerequisites.

## When this runs

Run when new evidence suggests a useful hypothesis, or within the existing campaign review. No daily audience-creation schedule is required; old presets need review when settings or service areas change.

## First-run setup and continuity

Open the supplied task file and its linked source. Verify the project’s real inputs, account, access and output folder before work. This Markdown file is a guide; it does not install an app, connect an account, supply a subscription or create a schedule. Carry out work already authorized; do not ask for the same approval again. Keep any unsupplied destination or new action outside that scope clearly pending.

Save source IDs, versions, decisions, checked outputs and next owner in the project tracker. Before a retry, check the saved state and other workers’ changes. A model name does not guarantee memory or a running timer. Repeated work needs an actual configured trigger and durable state; one-off work can be started by the prompt above.

Keep agent media muted with volume zero before playback. If mute cannot be verified, use captions, metadata or still frames. State the limit: silent visual checks do not prove spoken-word accuracy or audio quality. Do not start sound through the user’s speakers unless explicitly asked.

## Write up the real run

For every actual attempt, [write its meta article](https://blitzmetrics.com/meta-article-prompt/) with this recipe and revision, trigger, steps performed, output evidence, measured result, gaps and next owner. Failed, blocked and partial attempts also get a written record. A draft can satisfy writing; publishing it follows the existing job scope.

Keep one stable execution ID across retries and edits. A separately scoped child task may have its own ID linked to its parent. Writing the parent’s meta record is part of that run, not an endless new chain. The [recipe and meta-article guide](https://localservicespotlight.com/meta-articles/) explains this distinction. Teaching examples are not real executions and must not enter the run count.


<!-- factory-layer:start -->

## Factory chain (do not treat this as isolated)

- **Phase:** Promote
- **Importance:** 5/5 `●●●●●` — frequency 4, revenue 5, gating 4 (runs every factory cycle or weekly; ads/revenue path; gates a phase)
- **Suggested previous station:** `create-facebook-instagram-campaigns-with-location-targeting`
- **Suggested next station:** `execute-switch-boost-to-target-new-audiences`
These stations are generated neighbors, not verified prerequisites or handoffs. Follow the task’s actual Inputs and Handoff sections.
- **Handoff:** Organic metrics CSV (or Ads Manager export), pixel ID, creative filenames, kill/scale log. Next week's ranking starts from this file.

## Model routing (same factory, any engine)

- **This task's lane:** `computer` — Logged-in UI (Meta Ads Manager, Descript, GSC, wp-admin when REST is blocked). Requires a supported browser tool and the right account access.
- **Single-engine path:** Use the capable AI app you already have. Check its required tools and access before starting. Pass the actual result and proof to the next task.
- **Optional multi-engine:** A tested local model may draft; a stronger model may review facts and voice. Use supported tools for approved actions. Pick models from observed quality and cost; a model name does not prove it can run this task.
- **Never** store the working state in one vendor's memory. Files in the Content Library are the bridge.

<!-- factory-layer:end -->
## Definitive article & links

- Canonical article: https://blitzmetrics.com/social-amplification
- Exact task: [Create new saved audiences from Audience Insights](https://local-service-spotlight.github.io/task-library/?task=create-new-saved-audiences-from-audience-insights#task-create-new-saved-audiences-from-audience-insights)
- Writing standard: [Article Guidelines](https://localservicespotlight.com/article-guidelines/)
- [Dollar-a-Day method](https://blitzmetrics.com/dollar-a-day/)
- [Goals, Content, and Targeting](https://blitzmetrics.com/gct-business-strategy/)
- [Social Amplification Engine](https://blitzmetrics.com/social-amplification/)
- [Meta Advantage+ audience help](https://www.facebook.com/business/ads/meta-advantage-plus/audience)

## Review and evidence still needed

The source contributor status is preserved. It is not certification of this draft or proof of account access, completed work or a live outcome. The worked example teaches the method and is explicitly fictional.
- No current Meta account reports or saved-audience UI were inspected in this batch.
- Missing insight fields remain unavailable; they cannot be supplied by model inference.