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E-Commerce

Wöchentlicher Shop-Report (E-commerce Weekly Review)

AIBarsMITSprache des Skills: Englisch
Lizenz geprüftKeine SkripteGeringes Risiko
DemoEchter Lauf · claude-sonnet-5-5 · Oktober 2026
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.

Was es macht

Analysiert nur die Daten, die Sie liefern, und füllt Lücken nie mit „Branchendurchschnitten“. Zuerst legt es die Kennzahlendefinitionen fest (Sitzungen, Conversion, Bestellwert, Neu- und Bestandskunden, Werbeeffizienz, Deckungsbeitrag, Retourenquote) und prüft, ob die beiden Zeiträume vergleichbar sind und die Messung unverändert ist. Dann erklärt es eine Veränderung, indem es den Umsatz in Sitzungen, Conversion-Rate und Bestellwert zerlegt und den bewegten Faktor nach Quelle, Gerät, Produkt und Trichterschritt aufschlüsselt. Es achtet auf Mix-Effekte, nennt, auf wie vielen Bestellungen jede Schlussfolgerung beruht, und kennzeichnet jede Erklärung als bestätigt, wahrscheinlich oder Hypothese.

Ergebnis

Ein einseitiger Bericht: eine Schlagzeile, eine Tabelle der Kernkennzahlen, die wichtigsten Befunde mit Belegen, Probleme und Risiken, Maßnahmen mit Verantwortlichem, Frist und Prüfung in der nächsten Woche, Datenhinweise und ein vorgeschlagenes Experiment für die größte offene Frage.

Geeignet für

Wöchentliche und monatliche Shop-Reviews, die Frage „warum sind die Verkäufe gesunken“ und die Auswahl der zu beobachtenden Zahlen.

Hinweise & Risiken

Geringes Risiko: Reine Anweisungen ohne Skripte, ohne Netzwerkzugriff und ohne Dateischreiben. Es ist eine betriebliche Auswertung, keine Finanz-, Steuer- oder Rechtsberatung. Es arbeitet nur mit den eingefügten Zahlen; seine Erklärungen bleiben daher „wahrscheinlich“ oder Hypothese, bis Sie die fehlenden Aufschlüsselungen liefern, und es nennt, welche Daten fehlen. Fügen Sie keine personenbezogenen Kundendaten ein, nutzen Sie Summen. Von Plattformen gemeldete Zahlen können wegen überlappender Zuordnung voneinander abweichen; es nennt, welche Quelle es verwendet hat. Originalwerk von AIBars (MIT). Einmal mit erfundenen Zahlen für zwei Wochen ausprobiert; die Verhältnisse wurden danach von Hand nachgerechnet.