---
name: execute-switch-boosts-to-new-audiences
description: "Test a proven post with a new group. Keep the old test clear and the new cost small."
category: Dollar a Day Campaigns
stage: Promote
definitive_article: /dad
status: needs-work
---

# Execute switch boosts to new audiences

A post worked with one group of people. You want to see if it helps another group too. This guide helps you make that test without losing track of the first result.

**The path:** Proven post → new group → separate test → compare results.

**Use this when:** An existing post has a qualified paid result and a new relevant audience hypothesis fits the available budget.

## Inputs
- Original post/ad IDs, full-window result and pre-set target; current budget/end date of any still-running winner.
- The next audience’s source, fit, allowed settings and eligibility.
- Authority for the specific new test or replacement, aggregate budget ceiling, dates and current account roles.

## First-run prompt

> Prepare a switch-boost test using this verified winning post and the proposed new audience. Keep IDs, budgets and dates separate from the old test. Reuse the original post only where the platform supports it. Act within the provided test scope, then return read-back and the due comparison.

## Steps
1. Recheck that the original qualifies against the written goal, with enough evidence for the proposed next test. A high reaction count alone does not make it a conversion winner.
2. Name why the new audience might benefit from the same content. Use a real service area, relevant interest or eligible warm group. Check any list rights and unsupported settings before preparing the row.
3. Decide whether the scope is an additional test or a replacement. For an additional test, keep the original record and its existing end date unchanged. For a replacement, record the old stop and new start explicitly; neither choice grants extra spending authority.
4. Where supported, select the existing original post and confirm its ID in preview. Do not promise all comments, reactions or placements will carry across accounts or platforms. Label a required new creative as a derivative with its own ID.
5. Keep the creative, goal and planned window comparable while changing the chosen audience variable. Record unavoidable differences such as placement, time, audience overlap or currency; a normal auction comparison is not randomized proof.
6. Price the new test at the method’s $1/day for seven days where feasible. Read current minimums and true spend controls. Add the old and new commitments before any authorized activation.
7. After saving or launching within scope, read back both records. Check the correct post, audience, status, budget and end date. Monitor early delivery; do not edit the original’s audience by accident or duplicate the same active test on a retry.
8. Compare the full windows using the same metric. Keep, stop or hold the new test with a due date. A failure in this audience/window is useful evidence, not proof the post can never work elsewhere.

## Definition of done (QA checklist)

- [ ] The original winner has linked evidence and retains its exact record.
- [ ] New audience fit, rights and available settings are checked.
- [ ] Additional-test versus replacement scope and total cost are explicit.
- [ ] Original-post reuse or a new derivative is verified in preview/read-back.
- [ ] The final comparison includes confounds and a bounded next action.

## Example(s)

**Fictional teaching example:** A repair tip beat its view-cost target in Cedar Town. The shop also serves Maple Town, so the next test uses the same eligible post there. The original still has three days left in its approved run. The log records both commitments before the seven-day Maple test is activated. Maple’s cost is higher, but its run began during a holiday. The report stops that test under its rule and records the timing limit; it does not declare all Maple customers a poor fit.

## Handoff and Content Factory context

Send the results to [analyze cost per result and engagement](https://local-service-spotlight.github.io/task-library/?task=analyze-cost-per-result-and-engagement#task-analyze-cost-per-result-and-engagement). Proven stage-specific posts can feed [sequence content from awareness to conversion](https://local-service-spotlight.github.io/task-library/?task=sequence-content-from-awareness-to-conversion#task-sequence-content-from-awareness-to-conversion) when eligible audience sources and that next scope exist.

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

One new audience hypothesis per bounded test. Batch or recurring switch tests are optional and must fit the existing budget and review capacity; no automatic sequence of paid launches is implied.

## 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 5, revenue 5, gating 3 (runs every factory cycle or weekly; ads/revenue path)
- **Suggested previous station:** `execute-switch-boost-to-target-new-audiences`
- **Suggested next station:** `sequence-content-cold-warm-conversion`
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

- Maintained method: https://blitzmetrics.com/dollar-a-day/
- Source mapping needs reconciliation: the preserved `definitive_article: /dad` points to the coaching program. The method page above is `/dollar-a-day/`; this record does not certify the coaching page as a task recipe.
- Exact task: [Execute switch boosts to new audiences](https://local-service-spotlight.github.io/task-library/?task=execute-switch-boosts-to-new-audiences#task-execute-switch-boosts-to-new-audiences)
- 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/)
- [Meta Advantage+ audience help](https://www.facebook.com/business/ads/meta-advantage-plus/audience)
- [Meta budget help](https://www.facebook.com/business/help/190490051321426)

## 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.
- Current account support for existing-post reuse and audience controls was not tested.
- A real winning record and authority for the new test must be supplied.