# Local SEO NPS Improvement Strategy for Travel App
**Category:** SEO → Local SEO | **Audience:** Professionals or practitioners using AI for local seo tasks. | **Delivery:** Google Search
**Job:** Improve the named business/performance outcome using a measurable, testable intervention.
## SOURCE INPUT
Use this source brief as the authoritative task/scenario. Preserve its supplied numbers, constraints, assets, frameworks and requested deliverables. Separate facts from assumptions; never invent missing evidence.
> Treat this as the authoritative source brief. Preserve useful specifics; do not invent facts or turn scenario values into verified claims.
>
> > Act as Senior Local SEO Strategist for SEO (experienced, scaled Travel App to 8-figures, experienced technology/product background, generated substantial commercial experience with this system). CONTEXT: Business: Travel App - $5M ARR, team 16, targeting enterprise CTOs, stage pre-seed Current: CAC $154, LTV $2445, churn 12%, traffic 17656/mo, conversion 4.9% Assets: Notion docs + SOPs Goal: improve NPS 32 to 72 in 90 days Constraint: Budget $3k/mo, solo founder Tone: witty GenZ professional FRAMEWORK: Jobs-to-be-Done + structured 4-step. TASK: Complete Local SEO system for SEO / Travel App. - common failure modes about local seo for Travel App - 5 Whys root cause for improve NPS 32 to 72 - Hidden cost of failure - Contrarian insight advanced practitioner insight ## SEO strategy variants
> Variant A — INFORMATIONAL INTENT: Target discovery/learning intent with topical coverage and answer quality.
> Variant B — COMMERCIAL INTENT: Target evaluation/comparison intent with evidence, differentiation and conversion pathways.
> Variant C — AUTHORITY / TOPICAL DEPTH: Target durable topical authority through entity coverage, internal linking and supporting content.
> For each define query intent, content type, SERP objective, information architecture, KPI and decision rule.
## METHOD
**Workflow:** Search intent → audience/problem → SERP/content diagnosis → content strategy → execution → measurement → iteration
**Execution**
- Diagnose the actual task before prescribing when diagnosis changes the answer.
- Make the key strategic/technical/creative decision explicit.
- Use concrete instructions, structures, examples, thresholds or implementation details.
- Distinguish supplied facts, assumptions and validation needs.
- Do not invent capabilities, results, claims or evidence.
**Model / delivery**
Target model(s): ChatGPT-4o, Claude 3.5, Gemini 1.5. Keep the core solution portable; adapt only relevant model behavior. Delivery context: Google Search; respect its real format, audience and constraints.
**Output contract**
Return the smallest complete deliverable that solves the job. Include the result, material assumptions, measurable/verifiable success criteria where relevant, and next decision/action when useful.
**QA**
- search intent is explicit
- topic satisfies user need
- keyword use is natural
- content structure supports discoverability
- success metrics are meaningful
Also check for contradictions, generic recommendations, unusable outputs and constraint violations.
## CONTROLLED VARIANTS
Use variants only as real experiments; never as paraphrases.
**A — Informational:** optimize for learning intent. Preserve core identity/facts. Define the hypothesis, changed variable, expected effect and decision criterion.
**B — Commercial:** optimize for evaluation/decision intent. Preserve core identity/facts. Define the hypothesis, changed variable, expected effect and decision criterion.
**C — Authority:** optimize for topical depth and evidence. Preserve core identity/facts. Define the hypothesis, changed variable, expected effect and decision criterion.
## FINAL GATE
Before answering, ask: Is this specific to the supplied scenario? Is every important instruction actionable? Are assumptions labeled? Is the output genuinely usable? Would a professional get a better result from this than from a generic prompt? If not, revise the weakest part before delivering.