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## Contents

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*   [Beginning](https://freemwiki.com/index.php?oldid=595317&title=Openai%2F695d0a1a-1d10-8010-bbdc-1d5591edf3f3#)
*   [1 gpt-5-2-thinking](https://freemwiki.com/index.php?oldid=595317&title=Openai%2F695d0a1a-1d10-8010-bbdc-1d5591edf3f3#gpt-5-2-thinking)Toggle gpt-5-2-thinking subsection
    *   [1.1 User: 역할: 너는 마케팅 CRM 고객 분석 20년차 전문가야.](https://freemwiki.com/index.php?oldid=595317&title=Openai%2F695d0a1a-1d10-8010-bbdc-1d5591edf3f3#User:_%EC%97%AD%ED%95%A0_:_%EB%84%88%EB%8A%94_%EB%A7%88%EC%BC%80%ED%8C%85_CRM_%EA%B3%A0%EA%B0%9D_%EB%B6%84%EC%84%9D_20%EB%85%84%EC%B0%A8_%EC%A0%84%EB%AC%B8%EA%B0%80%EC%95%BC.)

    *   [1.2 Assistant: 아래 HTML은 업로드하신 주간 고객피드백PDF 기준으로, 매장별로 오프라인Google / 온라인Grab·Foodpanda 리뷰를 5점 만점 평균점수로 집계하고, 이슈 유형푸드세이프티/대기/누락/콜드푸드/패키징/서…](https://freemwiki.com/index.php?oldid=595317&title=Openai%2F695d0a1a-1d10-8010-bbdc-1d5591edf3f3#Assistant:_%EC%95%84%EB%9E%98_HTML%EC%9D%80_%EC%97%85%EB%A1%9C%EB%93%9C%ED%95%98%EC%8B%A0_%EC%A3%BC%EA%B0%84_%EA%B3%A0%EA%B0%9D%ED%94%BC%EB%93%9C%EB%B0%B1PDF_%EA%B8%B0%EC%A4%80%EC%9C%BC%EB%A1%9C,_%EB%A7%A4%EC%9E%A5%EB%B3%84%EB%A1%9C_%EC%98%A4%ED%94%84%EB%9D%BC%EC%9D%B8Google_/_%EC%98%A8%EB%9D%BC%EC%9D%B8Grab%C2%B7Foodpanda_%EB%A6%AC%EB%B7%B0%EB%A5%BC_5%EC%A0%90_%EB%A7%8C%EC%A0%90_%ED%8F%89%EA%B7%A0%EC%A0%90%EC%88%98%EB%A1%9C_%EC%A7%91%EA%B3%84%ED%95%98%EA%B3%A0,_%EC%9D%B4%EC%8A%88_%EC%9C%A0%ED%98%95%ED%91%B8%EB%93%9C%EC%84%B8%EC%9D%B4%ED%94%84%ED%8B%B0/%EB%8C%80%EA%B8%B0/%EB%88%84%EB%9D%BD/%EC%BD%9C%EB%93%9C%ED%91%B8%EB%93%9C/%ED%8C%A8%ED%82%A4%EC%A7%95/%EC%84%9C%E2%80%A6)

*   [2 References](https://freemwiki.com/index.php?oldid=595317&title=Openai%2F695d0a1a-1d10-8010-bbdc-1d5591edf3f3#References)

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Revision as of 05:30, 7 January 2026 by [Lukegao](https://freemwiki.com/index.php?title=User:Lukegao&action=edit&redlink=1 "User:Lukegao (page does not exist)")([talk](https://freemwiki.com/index.php?title=User_talk:Lukegao&action=edit&redlink=1 "User talk:Lukegao (page does not exist)") | [contribs](https://freemwiki.com/wiki/Special:Contributions/Lukegao "Special:Contributions/Lukegao"))(by freem.ai...)

(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)

_This conversation was exported from ChatGPT_[[1]](https://freemwiki.com/index.php?oldid=595317&title=Openai%2F695d0a1a-1d10-8010-bbdc-1d5591edf3f3#cite_note-1)

## gpt-5-2-thinking[[edit](https://freemwiki.com/index.php?title=Openai/695d0a1a-1d10-8010-bbdc-1d5591edf3f3&veaction=edit&section=1 "Edit section: gpt-5-2-thinking") | [edit source](https://freemwiki.com/index.php?title=Openai/695d0a1a-1d10-8010-bbdc-1d5591edf3f3&action=edit&section=1 "Edit section's source code: gpt-5-2-thinking")]

### User: 역할: 너는 마케팅 CRM 고객 분석 20년차 전문가야.[[edit](https://freemwiki.com/index.php?title=Openai/695d0a1a-1d10-8010-bbdc-1d5591edf3f3&veaction=edit&section=2 "Edit section: User: 역할 : 너는 마케팅 CRM 고객 분석 20년차 전문가야.") | [edit source](https://freemwiki.com/index.php?title=Openai/695d0a1a-1d10-8010-bbdc-1d5591edf3f3&action=edit&section=2 "Edit section's source code: User: 역할 : 너는 마케팅 CRM 고객 분석 20년차 전문가야.")]

역할: 너는 마케팅 CRM 고객 분석 20년차 전문가야. 작업/목표: 고객 피드백 자료를 올려주면, 오프라인, 온라인(딜리버리), 고객 피드백을 긍정/부정 5점 만점 평가점수로 매장별 평가를 해주고, 해야할 일들을 우선순위로 정리해줘. 맥락: 현재는 마케팅 직원이 하나하나 고객 댓글을 읽고 매장별로 개선점을 엑셀로 정리하고 있어서, 시간도 오래걸리고, 어느 매장의 어떤 것을 우선적으로 해결해야하는지 정리가 잘 안돼. HTML 소스로 만들어줘.

### Assistant: 아래 HTML은 업로드하신 주간 고객피드백PDF 기준으로, 매장별로 오프라인Google / 온라인Grab·Foodpanda 리뷰를 5점 만점 평균점수로 집계하고, 이슈 유형푸드세이프티/대기/누락/콜드푸드/패키징/서…[[edit](https://freemwiki.com/index.php?title=Openai/695d0a1a-1d10-8010-bbdc-1d5591edf3f3&veaction=edit&section=3 "Edit section: Assistant: 아래 HTML은 업로드하신 주간 고객피드백PDF 기준으로, 매장별로 오프라인Google / 온라인Grab·Foodpanda 리뷰를 5점 만점 평균점수로 집계하고, 이슈 유형푸드세이프티/대기/누락/콜드푸드/패키징/서…") | [edit source](https://freemwiki.com/index.php?title=Openai/695d0a1a-1d10-8010-bbdc-1d5591edf3f3&action=edit&section=3 "Edit section's source code: Assistant: 아래 HTML은 업로드하신 주간 고객피드백PDF 기준으로, 매장별로 오프라인Google / 온라인Grab·Foodpanda 리뷰를 5점 만점 평균점수로 집계하고, 이슈 유형푸드세이프티/대기/누락/콜드푸드/패키징/서…")]

아래 HTML은 업로드하신 주간 고객피드백(PDF) 기준으로, 매장별로 오프라인(Google) / 온라인(Grab·Foodpanda) 리뷰를 5점 만점 평균점수로 집계하고, **이슈 유형(푸드세이프티/대기/누락/콜드푸드/패키징/서비스태도/위생 등)**을 자동 태깅해서 우선순위 액션을 뽑아주는 “1페이지 대시보드” 템플릿입니다.

*   이번 주 데이터에서 푸드 세이프티(언더쿡·식중독·입원 언급) 같은 “Critical” 케이스가 실제로 존재해, 해당 매장은 점수와 무관하게 최상위 우선순위로 고정되도록 설계했습니다. weekly_reviews_20251229_to_2026…
*   온라인(딜리버리) 영역에서는 미봉인/누락/지연/콜드푸드가 반복적으로 보이며(예: 버거 누락+미봉인), 온라인 운영 프로세스 점검이 우선 과제로 잡히도록 되어 있습니다. weekly_reviews_20251229_to_2026…
*   오프라인에서는 대기시간(예: 1시간, 35분, 40분) 및 응대 태도/위생(쓰레기 냄새) 유형이 확인됩니다. weekly_reviews_20251229_to_2026… weekly_reviews_20251229_to_2026…
*   일부 매장/플랫폼에서 스트로우/커틀러리 등 기본 구성품 누락이 반복 언급됩니다. weekly_reviews_20251229_to_2026… weekly_reviews_20251229_to_2026…

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<div class="wrap">
  <h1>Weekly Reviews – Outlet Scoring & Action Priority</h1>
  <div class="sub">
    오프라인(Google) / 온라인(Grab·Foodpanda) 리뷰를 5점 만점 평균으로 집계하고, 메시지 키워드로 이슈를 자동 분류해
    “어느 매장의 무엇부터” 해결해야 하는지 우선순위로 보여줍니다.<br/>
    <span class="muted">*Critical(푸드 세이프티 등) 이슈가 있으면 점수와 무관하게 최상단 고정</span>
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            <thead>
              <tr>
                <th style="min-width:190px;">Outlet</th>
                <th>Flags</th>
                <th>Offline (Google)</th>
                <th>Online (Delivery)</th>
                <th>Overall</th>
                <th>Top Issues</th>
                <th style="min-width:120px;">Priority</th>
              </tr>
            </thead>
            <tbody id="rows"></tbody>
          </table>
        </div>

        <div class="foot">
          <b>Scoring logic</b><br/>
* Offline/Online Score: 해당 채널 평균 별점(1~5).<br/>
* Priority: 이슈 유형별 가중치 × (불만강도=6-별점) × 빈도 기반 지표.<br/>
* Critical: “언더쿡/식중독/입원/살모넬라 등” 키워드 감지 시 최우선 처리.
        </div>
      </div>
    </div>

    <div class="right">
      <div class="card">
        <div style="display:flex;justify-content:space-between;gap:10px;align-items:flex-start;">
          <div>
            <div class="muted" style="font-size:12px;">This week playbook</div>
            <div style="font-size:16px;margin-top:4px;"><b>Recommended workflow</b></div>
          </div>
        </div>

        <div class="hr"></div>

        <ol class="actions">
          <li><b>Critical first:</b> Food safety/contamination/undercook은 24시간 내 CAPA(원인·조치·재발방지) 완료.</li>
          <li><b>Online ops:</b> Seal/packing checklist + item-scan + “missing item” 재발 매장 집중 점검.</li>
          <li><b>Speed & comms:</b> 피크 타임 WIP 제한 + 예상 대기시간 고지 + 픽업존 동선 정리.</li>
          <li><b>Service basics:</b> 응대 톤/클레임 처리 스크립트 + 매니저 에스컬레이션 룰.</li>
          <li><b>Close loop:</b> “NOT REPLIED” 고위험 건은 우선 응답 + 보상/재발방지 안내 템플릿 적용.</li>
        </ol>

        <div class="hr"></div>

        <div class="muted" style="font-size:12px;">
          필요 시, 다음 단계로 “Owner/ETA/Status” 칼럼까지 붙여서
          <code class="inline">Action Tracker</code> 형태로 확장 가능합니다.
        </div>
      </div>
    </div>
  </div>
</div>

<script>
/**
 * Data source: weekly_reviews_20251229_to_20260104.pdf
 * Offline = Google, Online = Grab/Foodpanda
 * Note: priority_score is a heuristic indicator; Critical flag overrides sorting.
 */
const DATA = ${json_data};

const ISSUE_TO_ACTIONS = {
 "Food safety / undercooked": [
 "즉시: 해당 SKU(치킨/패티) 조리온도·타이머·홀딩타임 전수 점검 (매니저 서명).",
 "원인: 피크타임 생산량/스태핑/그릴 로딩/두께 편차/온도계 교정 여부 확인.",
 "재발방지: QA 체크(첫 30분·피크 중 2회) + 교육 리마인드 + 감사(spot check) 2주 운영."
 ],
 "Long wait / queue": [
 "피크타임 WIP 제한(버거/프라이 동시 처리 기준) + 병목 스테이션 재배치.",
 "예상 대기시간 안내(카운터/픽업존) + 픽업 호출 프로세스 정리(번호/이름).",
 "인력: 피크 2시간 구간(주말/행사) 스태핑 보강 + 러너(픽업존) 지정."
 ],
 "Missing item / condiments": [
 "패킹 체크리스트(2-step: packer + checker) 도입/강제.",
 "소스/스트로우/커틀러리 등 ‘기본 구성품’은 번들로 묶어 자동 투입.",
 "재발 매장은 POS 아이템 스캔→백 확인(visual) 루틴 교육."
 ],
 "Cold food": [
 "핫/콜드 분리 패킹(음료 위에 프라이/치킨 금지) + 단열/환기 기준 점검.",
 "라이더 픽업 대기 시 보관 위치(워머/선반) 표준화.",
 "장시간 지연 주문은 리메이크 룰(임계시간) 적용."
 ],
 "Packaging leak / spill": [
 "컵/소스/쉐이크 씰링 자재 점검 + 봉투 적재 방식 교육.",
 "액체류는 별도 캐리어/칸막이 고정, 스태커블 구조로 변경.",
 "‘스필’ 빈발 시간대/메뉴 추적해 포장 스펙 수정."
 ],
 "Service attitude / rude": [
 "클레임 응대 스크립트(3-step: 사과→확인→해결) 교육 + 매니저 에스컬레이션 룰.",
 "피크타임 스트레스 구간에 ‘프론트 리더’ 배치해 톤 관리.",
 "직원 칭찬 건은 주간 Kudos로 공유해 긍정행동 강화."
 ],
 "Cleanliness / smell / pests": [
 "쓰레기/트레이/픽업존 청소 주기 재정의(피크 30분 단위).",
 "백오브하우스 폐기물 동선·보관·배출 시간 점검 (냄새 원인 차단).",
 "해충(조류/벌레) 유입 포인트(출입문/외부 좌석) 차단 및 시설 점검."
 ],
 "App / order status": [
 "‘준비완료’/‘픽업 가능’ 상태 업데이트 타이밍 표준화.",
 "라이더/고객 대기 발생 시: 상시 담당자 지정 + 커뮤니케이션 문구 통일.",
 "취소/환불 이슈는 플랫폼별 SOP(증빙/응답 템플릿) 적용."
 ],
 "Menu quality / taste": [
 "특정 재료(피클/소스/산미) 불만은 레시피 범위 내 조정(옵션 안내 포함).",
 "품질 편차(오버/언더, 식감) 원인: 홀딩·배치·리메이크 기준 점검."
 ]
};

function fmtScore(v){
 if (v === null || v === undefined || Number.isNaN(v)) return <code><span class="muted">n/a</span></code>;
 const cls = v >= 4.4 ? "good" : (v <= 3.6 ? "bad" : "mid");
 return <code><span class="score ${cls}">${v.toFixed(2)}</span></code>;
}

function topIssuesHtml(issues){
 if (!issues || !issues.length) return <code><span class="muted">-</span></code>;
 return issues.slice(0,3).map(x => <code><span class="pill">${x.tag} <span class="muted">(${x.count})</span></span></code>).join(" ");
}

function buildActions(topIssues){
 const uniq = [...new Set((topIssues||[]).map(x=>x.tag))].slice(0,4);
 const lines = [];
 for (const tag of uniq){
 const actions = ISSUE_TO_ACTIONS[tag];
 if (actions && actions.length){
 lines.push(<code><li><b>${tag}</b><ul class="actions">${actions.map(a=></code><li>${a}</li><code>).join("")}</ul></li></code>);
 }
 }
 if (!lines.length) return <code><div class="muted">특이 이슈가 적어, 표준 운영 유지 + 칭찬 리뷰 중심으로 로열티 강화 활동 추천</div></code>;
 return <code><ol class="actions">${lines.join("")}</ol></code>;
}

function examplesHtml(examples){
 if (!examples || !examples.length) return <code><div class="muted">No sample comments</div></code>;
 return examples.slice(0,3).map(ex => {
 const tags = (ex.issue_tags || []).map(t=><code><span class="pill">${t}</span></code>).join(" ");
 return <code>
 <div class="ex">
 <div class="meta">
 <span class="pill">${ex.channel}</span>
 <span class="pill">${ex.platform}</span>
 <span class="pill">★ ${ex.rating}</span>
 <span class="muted">Ticket ${ex.ticket}</span>
 <span class="muted">${ex.created}</span>
 </div>
 <div class="chips">${tags || </code><span class="muted">-</span><code>}</div>
 <div class="msg">${escapeHtml(ex.message || "")}</div>
 </div>
 </code>;
 }).join("");
}

function escapeHtml(s){
 return (s||"")
 .replaceAll("&","&amp;").replaceAll("<","&lt;")
 .replaceAll(">","&gt;").replaceAll('"',"&quot;").replaceAll("'","&#039;");
}

function calcKPIs(list){
 const totalOutlets = list.length;
 const critical = list.filter(x=>x.critical_food_safety).length;
 const totalReviews = list.reduce((a,x)=>a + (x.total_n||0), 0);
 const avgOverall = totalOutlets ? (list.reduce((a,x)=>a + (x.overall_avg||0),0)/totalOutlets) : 0;
 return { totalOutlets, critical, totalReviews, avgOverall };
}

function renderKPIs(list){
 const {totalOutlets, critical, totalReviews, avgOverall} = calcKPIs(list);
 const el = document.getElementById("kpis");
 el.innerHTML = <code>
 <div class="kpi"><div class="t">Outlets</div><div class="v">${totalOutlets}</div></div>
 <div class="kpi"><div class="t">Critical outlets</div><div class="v"><span style="color:var(--bad)">${critical}</span></div></div>
 <div class="kpi"><div class="t">Total reviews</div><div class="v">${totalReviews}</div></div>
 <div class="kpi"><div class="t">Avg overall score</div><div class="v">${avgOverall.toFixed(2)} <small>/ 5</small></div></div>
 </code>;
}

function sortData(list, mode){
 const arr = [...list];
 if (mode === "priority"){
 // Critical first, then higher priority_score, then lower overall score
 arr.sort((a,b)=>{
 if (!!a.critical_food_safety !== !!b.critical_food_safety) return (b.critical_food_safety?1:0) - (a.critical_food_safety?1:0);
 if ((b.priority_score||0) !== (a.priority_score||0)) return (b.priority_score||0) - (a.priority_score||0);
 return (a.overall_avg||0) - (b.overall_avg||0);
 });
 } else if (mode === "offline"){
 arr.sort((a,b)=>(a.offline_avg ?? 99) - (b.offline_avg ?? 99));
 } else if (mode === "online"){
 arr.sort((a,b)=>(a.online_avg ?? 99) - (b.online_avg ?? 99));
 } else if (mode === "overall"){
 arr.sort((a,b)=>(a.overall_avg ?? 99) - (b.overall_avg ?? 99));
 } else if (mode === "volume"){
 arr.sort((a,b)=>(b.total_n||0) - (a.total_n||0));
 }
 return arr;
}

function needsFixFirst(list){
 // heuristic: any avg <= 3.8 or any critical
 const bad = list.filter(x=>x.critical_food_safety || (x.overall_avg ?? 5) <= 3.8);
 const good = list.filter(x=>!(x.critical_food_safety || (x.overall_avg ?? 5) <= 3.8));
 return [...bad, ...good];
}

function render(){
 const q = (document.getElementById("q").value || "").toLowerCase().trim();
 const sortMode = document.getElementById("sort").value;
 const onlyCritical = document.getElementById("onlyCritical").checked;
 const negOnly = document.getElementById("negOnly").checked;

 let list = DATA.filter(x=>{
 if (onlyCritical && !x.critical_food_safety) return false;
 if (!q) return true;
 const hay = [
 x.outlet,
 ...(x.top_issues||[]).map(t=>t.tag),
 ...(x.examples||[]).map(e=>e.message)
 ].join(" ").toLowerCase();
 return hay.includes(q);
 });

 list = sortData(list, sortMode);
 if (negOnly) list = needsFixFirst(list);

 renderKPIs(list);

 const tbody = document.getElementById("rows");
 tbody.innerHTML = list.map(x=>{
 const flags = x.critical_food_safety
 ? <code><span class="pill critical"><b>CRITICAL</b> Food safety</span></code>
 : <code><span class="muted">-</span></code>;

 const issues = topIssuesHtml(x.top_issues);
 const priority = x.critical_food_safety
 ? <code><span class="pill critical"><b>Critical</b></span></code>
 : <code><span class="pill">Score <span class="score mid">${(x.priority_score||0).toFixed(1)}</span></span></code>;

 const detailId = <code>d_${x.outlet.replace(/\s+/g,'_')}</code>;

 return <code>
 <tr>
 <td><b>${escapeHtml(x.outlet)}</b><div class="muted">${x.total_n} reviews</div></td>
 <td>${flags}</td>
 <td>${fmtScore(x.offline_avg)} <span class="muted">(${x.offline_n})</span></td>
 <td>${fmtScore(x.online_avg)} <span class="muted">(${x.online_n})</span></td>
 <td>${fmtScore(x.overall_avg)} <span class="muted">(${x.total_n})</span></td>
 <td>${issues}</td>
 <td>${priority}</td>
 </tr>
 <tr>
 <td colspan="7" style="padding-top:0;border-bottom:1px solid var(--line);">
 <details id="${detailId}">
 <summary>Show details: action plan + sample comments</summary>
 <div class="detail">
 <div class="chips">
 ${(x.top_issues||[]).map(t=></code><span class="pill">${t.tag} <span class="muted">(${t.count})</span></span><code>).join("") || </code><span class="muted">No issue tags</span><code>}
 </div>

 <div style="margin-top:6px;"><b>Recommended actions (priority order)</b></div>
 ${buildActions(x.top_issues)}

 <div style="margin-top:12px;"><b>Sample comments (worst-first)</b></div>
 ${examplesHtml(x.examples)}
 </div>
 </details>
 </td>
 </tr>
 </code>;
 }).join("");
}

document.getElementById("q").addEventListener("input", render);
document.getElementById("sort").addEventListener("change", render);
document.getElementById("onlyCritical").addEventListener("change", render);
document.getElementById("negOnly").addEventListener("change", render);
document.getElementById("reset").addEventListener("click", ()=>{
 document.getElementById("q").value="";
 document.getElementById("sort").value="priority";
 document.getElementById("onlyCritical").checked=false;
 document.getElementById("negOnly").checked=false;
 render();
});

render();
</script>
</body>
</html>

원하시면, 이 HTML을 다음 형태로 더 “실무용”으로 업그레이드해드릴게요(질문 안 하셔도 됩니다. 저는 바로 작업 가능합니다).

*   Owner / ETA / Status(Open/In progress/Done)까지 붙여서 “주간 Action Tracker”로 확장
*   매장별로 Offline vs Online 갭(Δ) 자동 표시 + “원인 가설”까지 추천
*   “직원 이름 언급(칭찬 직원 리포트)” 자동 추출(예: One Fullerton에서 특정 직원 칭찬처럼) weekly_reviews_20251229_to_2026…

## References[[edit](https://freemwiki.com/index.php?title=Openai/695d0a1a-1d10-8010-bbdc-1d5591edf3f3&veaction=edit&section=4 "Edit section: References") | [edit source](https://freemwiki.com/index.php?title=Openai/695d0a1a-1d10-8010-bbdc-1d5591edf3f3&action=edit&section=4 "Edit section's source code: References")]

1.   [↑](https://freemwiki.com/index.php?oldid=595317&title=Openai%2F695d0a1a-1d10-8010-bbdc-1d5591edf3f3#cite_ref-1 "Jump up")["CRM 고객 피드백 대시보드"](https://chatgpt.com/share/695d0a1a-1d10-8010-bbdc-1d5591edf3f3). ChatGPT. Retrieved 2026-01-07.

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