實戰案例 Case Studies

三個廣告帳戶,三個一度看似無解的限制;以下數據均來自實際操作成果,因保密協議不揭露品牌名稱。

Fixing growth at the root, not the symptom — strategy, ad architecture, and the full conversion funnel for direct-to-consumer brands.

All figures from real campaigns. Brand names withheld under NDA.

01

當商品本身,就是最大的過審障礙

When the Product Itself Was the Obstacle

現況 The Situation

Meta 的審核機制持續判定商品照片裸露過多,過不了審。

Meta's review kept flagging the product photography for showing too much skin, so only single-image post ads reliably survived review.

應該扛起帳戶大半流量的 DPA(動態商品廣告),幾乎每天被擋下來。Meta 的審核機制持續判定商品照片裸露過多,過不了審。我接手前,唯一能穩定過審的只剩單圖貼文廣告,因此帳戶失去了動態再行銷與商品目錄驅動的再行銷,實際被消費者看到的商品比例也一路萎縮。

Dynamic Product Ads, the format that should have carried most of the account's weight, were getting rejected almost every day. Meta's review kept flagging the product photography for showing too much skin, so only single-image post ads reliably survived review. That cost the account its dynamic retargeting and catalog-driven remarketing, and shrank how much of the catalog actually reached shoppers.

判斷 The Call

目錄健康度恢復之後,DPA 才真正跑得起來,審核被擋的狀況也從此大幅減少。

With the feed healthy again and the primary images swapped out, the DPA campaigns could finally run, and rejections dropped sharply.

我重新調整了商品目錄架構與分類,排除掉少數幾乎不可能過審的品類,再換掉驅動 DPA 的商品首圖。目錄健康度恢復之後,DPA 才真正跑得起來,審核被擋的狀況也從此大幅減少。受眾端前期每週微調,測出穩定架構後就不再動。出價每週檢視,只有在市場競爭激烈的檔期才調整。素材跟著活動節奏,每週上稿兩到三次。

I rebuilt the product catalog structure and its category groupings, and pulled the handful of product lines that were never realistically going to pass review. With the feed healthy again and the primary images swapped out, the DPA campaigns could finally run, and rejections dropped sharply. On the audience side, I adjusted targeting weekly at first, then left the structure alone once it proved stable. Bids got a weekly look and moved only during competitive periods. Creative went up two or three times a week, timed to the campaign calendar.

成果 The Result
1.5 → 9 ROAS

ROAS 在兩到三個月內從 1.5 拉到 9,並維持到合作結束。

ROAS went from 1.5 to 9 within two to three months and held there for the rest of the engagement.

ROAS 在兩到三個月內從 1.5 拉到 9,並維持到合作結束。這個數字的組成要講清楚:它是 Meta 加 Google(含品牌關鍵字)的綜合結果。去掉品牌字,純廣告曝光帶來的 ROAS 大約落在 4 到 5,這個數字才真正說明帳戶有沒有能力找到新客,而不只是收割已經認識品牌的人。全站流量成長 50%,收益成長 95%,會員成長 80%。

ROAS went from 1.5 to 9 within two to three months and held there for the rest of the engagement. It's worth being precise about what's inside that number: it's the blended result across Meta and Google, including branded search. Strip out brand terms and the pure prospecting ROAS still lands around 4 to 5, which is the number that actually shows whether the account can find new customers rather than just closing out people who already knew the brand. Site-wide traffic grew 50%, revenue grew 95%, and membership signups grew 80%.

+50% +50% 流量成長 Traffic Growth
+95% +95% 收益成長 Revenue Growth
+80% +80% 會員成長 Membership Signups
投放之外的決定 Beyond the Media Plan

先修真正壞掉的地方,即使解法不是廣告投放本身。

One decision from that engagement sat outside media buying entirely.

這次合作有一個決定完全在投放範疇之外:品牌當時毛利是 -15%,一個月內我透過調整定價與營運策略把它收斂到 -7%,同時接受營業額掉了將近 20% 這個代價。這跟我做事的邏輯是同一套:先修真正壞掉的地方,即使解法不是廣告投放本身。

One decision from that engagement sat outside media buying entirely. The brand was running a negative margin of -15%. Within a month of restructuring pricing and operations, I brought that to -7%, accepting a nearly 20% drop in revenue as the deliberate cost of that trade. It's the kind of call I'd make again: fix what's actually broken, even when the fix has nothing to do with the media plan.

02

沒人驗證過的受眾假設

The Targeting Assumption Nobody Had Tested

現況 The Situation

這個品牌的商品是女性性感睡衣類,屬於不少主流廣告平台預設會加嚴審核、限縮觸及的品類。

The brand sold intimate apparel, a category where a lot of mainstream ad platforms apply extra restrictions and cut reach by default.

這個品牌的商品是女性性感睡衣類,屬於不少主流廣告平台預設會加嚴審核、限縮觸及的品類。品牌當時也還在早期階段:客源基數小,成長曲線還沒站穩。

The brand sold intimate apparel, a category where a lot of mainstream ad platforms apply extra restrictions and cut reach by default. It was also early-stage, with a thin customer base and a growth curve that hadn't found its shape yet.

判斷 The Call

整個帳戶最大的單一槓桿,其實是一個沒人質疑過的受眾假設。

The biggest lever in the account turned out to be a targeting assumption nobody had questioned.

客戶原本的假設是受眾只會是女性,前期我也照這個假設設定:鎖定女性受眾,同時處理素材裸露問題讓 DPA 能順利過審。架構穩定後,我用小額預算測試把男性受眾也納進來,結果 ROAS 飛速提升,後續就持續加大男性預算。整個帳戶最大的單一槓桿,其實是一個沒人質疑過的受眾假設。

The client's own assumption was that the audience was exclusively women, so that's where I started: targeting locked to women, creative cleaned up so the product imagery could pass Meta's nudity review and the DPA feed could actually run. Once that was stable, I tested a small budget on male audiences. ROAS jumped almost immediately, so I kept scaling that budget. The biggest lever in the account turned out to be a targeting assumption nobody had questioned.

成果 The Result
1.5 → 3 ROAS

七個月合作期間,全站流量成長 50%,收益成長 100%,ROAS 從 1.5 提升到 3,新客佔比全程維持在 70% 以上。

Traffic grew 50%, revenue grew 100%, and ROAS improved from 1.5 to 3 over a seven-month engagement.

七個月合作期間,全站流量成長 50%,收益成長 100%,ROAS 從 1.5 提升到 3,新客佔比全程維持在 70% 以上。品牌還很年輕,organic 流量不多,這波成長幾乎完全是廣告放大出來的。

Traffic grew 50%, revenue grew 100%, and ROAS improved from 1.5 to 3 over a seven-month engagement. New customers made up more than 70% of the base throughout. The brand was young enough that organic traffic was still thin, so nearly all of that growth came straight from paid.

+50% +50% 流量成長 Traffic Growth
+100% +100% 收益成長 Revenue Growth
70%+ 70%+ 新客佔比 New Customer Rate
7 個月 7 mo 合作期間 Engagement Length
03

先把數據蓋起來,才有成長可言

Building the Data Before the Growth

現況 The Situation

帳戶裡沒有任何「什麼真的有效」的可信數據。

The account had no source of truth for what was actually working.

這是完全的成人產業型態,主流廣告與分析平台幾乎都碰不得。當時沒有聯盟行銷系統,也沒有可信的轉換追蹤,瀏覽器端追蹤又隨著隱私政策改版持續掉訊號。帳戶裡沒有任何「什麼真的有效」的可信數據。

This platform operated in a restricted category with near-zero access to mainstream ad and analytics platforms. There was no affiliate program and no reliable conversion tracking, and browser-side tracking was already losing signal fast as privacy changes rolled out. The account had no source of truth for what was actually working.

判斷 The Call

先從數據下手,而不是先加碼預算

The fix started with data, not spend.

先從數據下手,而不是先加碼預算:與工程團隊合作導入 server-to-server(S2S)轉換回傳,讓轉換不再依賴瀏覽器 cookie 撐過整段轉換路徑。有了準確數據之後,才第一次有條件跑演算法自動出價,這才是真正把 CPA 往下拉的關鍵。同時,從零建立聯盟行銷夥伴系統(夥伴頁面、主動開發、以及維繫長期合作關係的營運機制),並在 Google Ads、X Ads、TrafficJunky、ExoClick、直接購買版位的 media buy,以及其他幾個 DSP 平台上投放,把每月數萬美金的預算分散到真的願意讓這個品類上架的通路。

The fix started with data, not spend. Working with engineering, I implemented server-to-server postback tracking so conversions stopped depending on a browser cookie surviving the funnel. With accurate data in hand, algorithmic bidding became usable for the first time, and that's what actually moved CPA. In parallel, I built the affiliate partner program from scratch: a program page, direct partner outreach, and the account management to keep long-term partners active. Paid media ran across Google Ads, X Ads, TrafficJunky, ExoClick, direct-buy placements, and a handful of other DSPs, spreading a $10K+ monthly spend across the channels that would actually accept the category's ads.

成果 The Result
0 → 30+ 聯盟夥伴 Affiliate Partners

聯盟夥伴從 0 成長到 30+ 個活躍夥伴,投放橫跨 10 個以上廣告平台。

The affiliate network grew from 0 to 30+ active partners, spread across 10+ ad platforms.

聯盟夥伴從 0 成長到 30+ 個活躍夥伴,投放橫跨 10 個以上廣告平台。準確的追蹤數據讓 CPA 大約砍半,落地頁轉換率提升 12%。

The affiliate network grew from 0 to 30+ active partners, spread across 10+ ad platforms. Accurate tracking cut CPA roughly in half, and landing page conversion rate rose 12%.

10+ 10+ 廣告平台 Ad Platforms
+12% +12% 落地頁轉換率 Landing Page CVR
數萬美金 $10K+/mo 月投放規模 Ad Spend Managed
我打造並上線的工具
Tools I’ve Built & Shipped

不只是操盤,這些是我自己設計並上線的產品。

These are products I designed and shipped myself, not just campaigns I managed.

ai-skills-pack

給 AI agent 直接載入的行銷技能包,從追蹤健康度、切角、廣告素材到到達頁優化與成效迭代,全部深度在地化成台灣媒體採購情境。

An AI agent-loadable marketing skills pack covering tracking health, positioning, ad creative, landing page CRO, and campaign iteration, each skill deeply localized for how media buying actually works in Taiwan.

GTM Container Generator

免費掃描網站,診斷 GTM、GA4、Meta Pixel 的追蹤設定有沒有漏收或設錯。

A free scanner that audits a site's GTM, GA4, and Meta Pixel setup and flags what's broken before it costs you data.

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