Slide 1 — Q3 2026 revenue and orders grew, while returns and ad spend also increased
Points
- Revenue: SAR 1.2m in Q3 2026, versus SAR 1m in Q2.
- Orders: 8,000 in Q3, versus 7,200 in Q2.
- Management decision: next-quarter budget must choose between raising ad spend and addressing rising returns.
Suggested visual: KPI cards for revenue, orders, returned orders, and ad spend.
Speaker note: Open by framing Q3 as growth with two watch-outs: returns and marketing cost.
Slide 2 — Revenue growth coincided with more orders and higher average order value
Points
- Revenue: SAR 1.2m versus SAR 1m.
- Orders: 8,000 versus 7,200.
- Average order value: SAR 150 versus about SAR 139.
Suggested visual: Three mini bar charts comparing Q2 and Q3 for revenue, orders, and average order value.
Speaker note: Show the reported growth drivers without inferring causation.
Slide 3 — Returns increased alongside orders, but return drivers are missing
Points
- Returned orders: 320 in Q3 versus 216 in Q2.
- Total orders: 8,000 in Q3 versus 7,200 in Q2.
- Missing: return reasons, return cost, and return rate are not provided.
Suggested visual: Bar chart of returned orders Q2 versus Q3, with total orders shown as context.
Speaker note: Flag returns as a quality watch-out, but mark that the report does not explain why returns rose.
Slide 4 — Ad spend rose, but the report does not show ad-driven revenue
Points
- Ad spend: SAR 180,000 in Q3 versus SAR 150,000 in Q2.
- Revenue: SAR 1.2m in Q3 versus SAR 1m in Q2.
- Missing: ad-attributed sales, ROAS, and incremental revenue are not provided.
Suggested visual: Side-by-side bar charts for ad spend and revenue, with a “missing ROAS” tag.
Speaker note: Present spend and revenue together, but avoid claiming ads caused the revenue increase.
Slide 5 — Kitchenware and textiles were the top categories, but category economics are missing
Points
- Top-selling categories: kitchenware, then textiles.
- Total revenue: SAR 1.2m, with no category split provided.
- Missing: category-level sales, margin, and inventory data are not provided.
Suggested visual: Ranked category leaderboard with kitchenware first and textiles second.
Speaker note: Use this to show demand concentration, while noting that budget decisions need missing category economics.
Slide 6 — The Jeddah warehouse opened to reduce delivery delays, but impact is unconfirmed
Points
- A small Jeddah warehouse opened in August.
- Purpose: reduce delivery delays.
- Missing: reliable data on the warehouse’s effect on delivery time is not yet available.
Suggested visual: Timeline with August warehouse opening and a blank “delivery-time impact” marker.
Speaker note: Explain the operational action, but make clear that its effect cannot be confirmed yet.
Slide 7 — The next-quarter budget choice is between rising returns and higher ad spend
Points
- Returns evidence: returned orders rose from 216 to 320.
- Ad-spend evidence: ad spend rose from SAR 150,000 to SAR 180,000.
- Missing: return cost, ad ROAS, and warehouse delivery-time effect are not provided.
Suggested visual: Two-column comparison: “Address returns first” versus “Raise ad spend first” with evidence and missing data.
Speaker note: Frame the trade-off: rising returns and unproven ad impact require a decision, but the report lacks the cost data to quantify either option.
Slide 8 — Management ask: choose the budget priority and approve missing-data follow-ups
Points
- Decision needed: raise ad spend or address rising returns first for next-quarter budget.
- Missing data to approve: return reasons/cost, ad attribution/ROAS, category sales/margin, and warehouse delivery-time effect.
- Next-quarter budget amount: not provided in the report.
Suggested visual: Decision table with “Decision,” “Evidence,” and “Missing data required.”
Speaker note: Close by asking management to choose a priority and approve the data needed to make the budget decision measurable.