---
name: kill-underperformers-scale-winners
description: "Put ad funds behind results you can prove."
category: Strategy & Measurement
stage: —
definitive_article: /9-triangles-framework-scalable-home-service-businesses/
status: complete
---

# Kill underperformers, scale winners

Put ad funds behind results you can prove. This guide helps your team stop weak tests and make small changes to strong ones. Start with the checked facts, test limits and actions you are allowed to take.

**The path:** Checked diagnosis → Exact change plan → Authorized action → Readback

**Start when:** A test’s agreed review window has closed and an evidence-backed pause or scale decision is due.

## Inputs

- [Diagnose weak results](https://local-service-spotlight.github.io/task-library/?task=analyze-why-underperformers-fail#task-analyze-why-underperformers-fail) and the winners list with source metrics and sufficient evaluation windows.
- [Goals, Content, Targeting: the result, proof and people for this work](https://blitzmetrics.com/gct-business-strategy/) targets, existing spend authority and the account’s applicable campaign rules.
- Current ad IDs, budgets, audience settings and prior changes; a supported account editor and change owner.

## Steps

1. Recheck the current account and evidence. Confirm result definitions, conversion delay, sample limits and any recent edits. Do not treat an unresolved source problem as proof that a campaign failed.
2. For each evaluated weak test, propose a pause or one bounded repair using the agreed stop rule. Preserve the asset and history; pausing an ad is not deleting its source page. Document why a retry is justified or exhausted.
3. For a winner, confirm the result still meets its target at useful volume. Prepare a gradual increase or a new audience test with explicit amount, total cap, end/review date and owner. The source’s small [Dollar a Day, small paid tests of proven content](https://blitzmetrics.com/dollar-a-day/) tests are a method, not a universal platform budget guarantee.
4. Check the current provider’s budget and delivery rules. Do not claim a twofold increase is always safe or that every edit resets learning. Google’s [budget-change guidance](https://support.google.com/google-ads/answer/10487143?hl=en) explains that a day’s spending limit can reflect its highest selected daily budget.
5. Apply only the exact changes covered by existing authority through the supported account workflow. If authority or required evidence is missing, save the plan and name the blocker. Freed budget may remain unspent; it is not permission to increase another campaign.
6. Read back the changed IDs, status, budget and schedule. Keep before/after evidence and the actual save time; a proposed action or successful click alone is not a verified change.
7. Set the agreed review condition and compare the resulting performance after adequate data arrives. Carry every pause, test and scale result into the next report, including failures or no action.

## Definition of done (QA checklist)

Quality assurance (QA) means checking the result against its agreed requirements; see the [Article Guidelines](https://localservicespotlight.com/article-guidelines/).

- [ ] Each decision has a target, sufficient evidence and an exact bounded action.
- [ ] Only authorized changes occurred and readback matches the plan.
- [ ] Budget totals, test limits and next review are recorded; no profit or guaranteed scaling claim is made.

## Example(s)

**Fictional teaching example. This is not a client result or proof of a completed run.**

In a fictional account, a reviewed $7 test misses its agreed goal and is paused. Another passes, so the team prepares a $30 test spread over 30 days. The owner has not authorized that new spend yet; the scale remains a proposal. The unused $7 limit is not silently moved elsewhere.

## Handoff and Content Factory context

[Write the agreed weekly report](https://local-service-spotlight.github.io/task-library/?task=submit-weekly-maa-report-every-friday#task-submit-weekly-maa-report-every-friday) receives the action log; the assigned measurement task checks the next agreed window.

This work supports Promote in the [Content Factory: Produce, Process, Post and Promote](https://blitzmetrics.com/content-factory/). It follows the source, editing and publishing work of Produce, Process and Post; the actual campaign evidence feeds the next review.

## Start with an agent

Give the AI worker this recipe, the real Inputs, the desired result and the actions already authorized. Ask for the saved output, source evidence, passed and failed checks, and the next owner. A skill is a set of instructions. Loading it does not prove access, install a scheduled job or complete the work. Use the [current installation guide](https://localservicespotlight.com/install/) only if reusable setup is needed; a ZIP contains instructions and is not an access grant.

Use the actual supported app and verified source access. A model name is not a promise of browser control, memory or unattended work. For recurring work, inspect the real job’s trigger, owner, timezone and observed firing; this recipe creates no schedule. Before any media playback, mute the player and set volume to zero. A silent review does not prove sound was heard.

## Record the real execution

Open the run record when work begins. Keep one execution ID, the exact starting recipe revision, input sources and actual state. Write a [meta article, the record of one real execution](https://blitzmetrics.com/meta-article-prompt/) with results, checks, failures and next owner. Link it to this task in the [Task Library](https://local-service-spotlight.github.io/task-library/). Writing is required; public release follows existing authority.

Reuse the same execution ID for internal reviews, meta writing, revisions and retries. A blocked run stays open with its dependency and owner, without an invented finish time. Keep historical public examples as dated evidence; article counts alone are not verified execution frequency. Use supported findings to propose and verify a recipe improvement.


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

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

- **Phase:** —
- **Importance:** 3/5 `●●●○○` — frequency 3, revenue 3, gating 2 (supporting)
- **Suggested previous station:** —
- **Suggested next station:** —
These stations are generated neighbors, not verified prerequisites or handoffs. Follow the task’s actual Inputs and Handoff sections.
- **Handoff:** Save this task’s actual output, source, date and checks where the receiving owner can read them. Follow the Inputs and Handoff sections for its next step.

## Model routing (same factory, any engine)

- **This task's lane:** `any` — Any capable chat model (Claude, ChatGPT, Grok, Gemini). SOP following, drafts, checklists.
- **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 source guide](https://blitzmetrics.com/9-triangles-framework-scalable-home-service-businesses/)
- [This task in the Task Library](https://local-service-spotlight.github.io/task-library/?task=kill-underperformers-scale-winners#task-kill-underperformers-scale-winners)
- [Article Guidelines](https://localservicespotlight.com/article-guidelines/)
- [Definitive article and task recipe standard](https://blitzmetrics.com/definitive-article-guide/)
- [How recipes and run records work together](https://localservicespotlight.com/meta-articles/)

## Review and evidence still needed

The inherited contributor status is `complete`. It is preserved, not promoted by this rewrite. That label alone does not verify document readiness, account access, an actual execution or a client outcome.

A relevant real execution, source evidence, independent reviewer and accepted handoff remain to be recorded. The fictional example teaches the method and does not fill a real-run evidence gap. The opening and full method need a named semantic reviewer; the task-specific path is source guidance, with visible desktop/mobile presentation still to check.