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營運週報與指標 E-commerce Weekly Review

AIBarsMITSkill 語言: 英文
授權已核對不含腳本低風險
演示真實執行紀錄 · claude-sonnet-5-5 · 2026年10月
Here are my store numbers for the last two weeks. Please write my weekly review. Week 1: sessions 12,400; orders 310; net revenue 15,200; ad spend 3,100; returns 14 orders. Week 2: sessions 14,900; orders 312; net revenue 14,500; ad spend 4,300; returns 21 orders. By source in week 2: paid social 6,300 sessions (was 3,400), search 4,800 (was 5,200), email 1,900 (was 2,100), direct 1,900 (was 1,700). I started a new paid-social campaign on Monday of week 2 and ran a 10% off email on Wednesday of week 1. The checkout page got a new payment option on Friday of week 2.
Skill: ecommerce-weekly-review

Weekly Review: Week 2 vs Week 1

Decision for you: Hold the new paid-social spend at its current level for one more week. Don't scale it and don't cut it. Traffic rose 20% but orders were flat, and the data can't yet tell you whether the new paid visitors buy at all.

Headline

Net revenue fell 4.6% (15,200 → 14,500) even though sessions rose 20%. Ad spend rose 39%, so blended ROAS dropped from 4.90 to 3.37.

Key metrics

Metric (definition) Week 1 Week 2 Change
Sessions 12,400 14,900 +20.2%
Orders 310 312 +0.6%
Conversion rate (orders / sessions) 2.50% 2.09% −0.41 pts (−16%)
Net revenue 15,200 14,500 −4.6%
AOV (net revenue / orders) 49.03 46.47 −5.2%
Ad spend 3,100 4,300 +38.7%
CPA (spend / orders) 10.00 13.78 +37.8%
Blended ROAS (net revenue / spend) 4.90 3.37 −31%
Return rate (returned orders / orders in the same week) 4.5% (14) 6.7% (21) +2.2 pts

Decomposing revenue as sessions × conversion × AOV: sessions rose 20%, conversion fell 16% and AOV fell 5%. More traffic was cancelled out by weaker conversion and a lower AOV.

What changed and why

  1. Paid social drove all the traffic growth (likely).

    • Paid social sessions went from 3,400 to 6,300 (+2,900), while every other source combined went from 9,000 to 8,600 (−4%).
    • Paid social is now 42% of sessions, up from 27%.
    • This is a mix effect. If paid-social visitors convert worse than search, email and direct, overall conversion falls even if no source changed.
    • I can't confirm this because I don't have orders by source.
  2. The extra spend bought almost no extra orders (confirmed, with a caveat).

    • Spend rose by 1,200 and orders rose by 2.
    • Not all of the extra spend went to paid social, and the orders may have come from other sources, so treat the incremental CPA as a rough signal.
    • It points to the new campaign's visitors converting poorly or arriving too early in their buying journey.
    • One week is too short to judge, since some of these buyers may convert later.
  3. Week 1 is not a clean baseline (confirmed).

    • The 10% email promo on Wednesday of week 1 probably lifted that week's orders.
    • Email sessions were also higher in week 1 (2,100 vs 1,900).
    • Part of the conversion drop may be the promo ending, not only the new traffic.
    • A discount would normally lower AOV, but AOV was higher in week 1, so I can't tie the AOV drop to the promo. Product mix or the new traffic are other possibilities (hypothesis).

Search (−8%) and email (−10%) sessions also dipped. That's modest and could be normal variation, but it's worth watching. Direct rose 12%, which may be a small halo from the paid campaign (hypothesis).

Problems and risks

  • Ad efficiency: CPA is up 38% and ROAS is down 31% after one week of a new campaign. That's a flag, not yet a verdict.
  • Returns: 21 vs 14 returned orders. These are small counts, so some of the rise could be chance. Two other explanations are worth checking:
    • Week 1 promo orders may be coming back as returns in week 2.
    • Returns may be concentrated in paid-social buyers or in a few products.
  • Payment option: It launched Friday of week 2, so it covers only about 3 days. Don't credit or blame it yet. Check payment failures and checkout completion before and after.

Actions

Action Owner By Check next week
Pull orders and revenue by source (especially paid social) You Before the next review Is paid-social conversion well below the others?
Keep paid-social spend flat this week You Now Does CPA settle, or keep rising?
Break down returns by product, source and order date (promo or not) You Next review Is the rise concentrated or spread out?
Compare checkout completion and payment failures before and after Friday You Next review Is there a lift, a problem, or no difference?

Data notes

  • Missing data: orders by source, funnel steps (add-to-cart, checkout start, completion), new vs returning customers, product mix, spend by channel, and whether net revenue already excludes returns. Without these, the explanations above stay at "likely" or "hypothesis".
  • Sample size: the whole comparison rests on about 310 orders per week, so small differences can be noise. One week is not a trend, so I'd also look at the previous 3–4 weeks if you have them.
  • Definitions: I used net revenue as you gave it, and I computed return rate against same-week orders, not against the original order cohort.
  • Attribution: use one analytics source for session counts by channel, and check that its numbers match your ad platform's.

Suggested experiment

The open question is whether paid social brings real incremental buyers. Run the campaign for two more weeks while tracking orders by source. Then, if its conversion is still low, cut its budget by about half for one week and watch total orders. If total orders don't drop, the campaign isn't adding much. Decide the threshold beforehand, for example "a drop of fewer than 10 orders means not incremental", so you don't read the result after the fact.

Real run in an isolated folder with only this skill installed. Only the Skill and Read tools were enabled, so no command was run and no file was written. The store and all numbers are invented and the model had no access to a real store. The ratios in the answer were recomputed by hand afterwards and matched. Explanations are labeled as confirmed, likely or hypothesis because only these totals were provided.

它做什麼

只分析你提供的資料,絕不用「業界平均值」去填補空缺。它先把指標口徑定下來(造訪、轉換率、客單價、新舊客戶、廣告效率、貢獻利潤、退貨率),檢查兩個時間段是否可比、統計口徑有沒有變,然後把營收拆成造訪量、轉換率與客單價來解釋變化,再依來源、裝置、商品與漏斗步驟深挖變動的那個因素。它會留意結構效應,說明每個結論依據了多少訂單,並把每條解釋標成已確認、較可能或假設。

輸出

一頁式報告:一句話結論、關鍵指標表、附證據的主要發現、問題與風險、帶負責人與期限並說明下週如何檢驗的行動項、資料說明,以及針對最大未解問題的一個建議實驗。

適合什麼場景

每週與每月的店鋪復盤、「銷量為什麼掉了」這類問題,以及決定該盯哪些數字。

說明與風險

低風險:純指令檔,沒有腳本,不連網、不寫檔。這是營運復盤,不是財務、稅務或法律意見。它只依據你貼上的數字,所以在你補充缺少的拆分資料之前,解釋只會停留在「較可能」或「假設」,並且會說明缺少哪些資料。請勿貼上客戶個人資料,用彙總數即可。各平台回報的數字可能因歸因重疊而不一致,它會說明用的是哪個來源。AIBars 原創(MIT)。已用虛構的兩週資料試用過一次,其中的比率事後用手算核對過。