Sales Decline Diagnostic

Sales Decline Diagnostic activated — beginning diagnostic workflow.

The strongest preliminary diagnosis is a conversion problem, with inventory availability and the mobile buying experience as the leading internal causes. Higher prices and weaker customer experience may be compounding it. Advertising costs and economic pressure remain plausible contributors, but the evidence does not establish either as the dominant cause.

This analysis uses your newly attached document and external research for the stated market: Egyptian women’s modest-fashion e-commerce, primarily urban middle- and upper-middle-income customers, during May–August 2026. Your internal figures are treated as supplied evidence, not independently audited.

The store lost 747 monthly orders between May and August. Higher order value partially cushioned the revenue decline.

Using orders ÷ sessions for unrounded conversion, a sequential calculation gives:

This decomposition depends on the order of calculation. It identifies where revenue changed, not which business decisions caused it. It nevertheless makes clear that traffic loss alone cannot explain the deterioration.

There are three qualifications

Channel scope: The document says Shopify represents 78% of sales, yet website sessions, orders, AOV, and reported revenue reconcile almost exactly. Confirm whether the table covers Shopify alone or combines channels. Until then, this calculation applies provisionally to the table’s scope.
Revenue definition: Confirm whether revenue represents placed orders or fulfilled sales after cancellations and refunds. Otherwise, COD failures could be counted twice when explaining the decline.
Seasonality: May and August both have 31 days, but may differ commercially. August’s approximately 21.8% year-over-year revenue decline strengthens the evidence of deterioration, without fully controlling for product mix or seasonal timing.

Structural data quality is adequate. Diagnostic readiness is adequate for prioritizing tests, limited for allocating losses to individual causes.

Which explanations are supported

Confidence below refers to causal attribution, rather than whether the reported change occurred.

Expected benefits are less wasted traffic, clearer identification of purchase friction, and better targeting of recovery work. The available data cannot support a credible revenue-recovery or ROI forecast.

The main implementation risks are overstocking weak variants, introducing checkout defects, and changing several factors simultaneously so their effects cannot be separated. Use limited exposure, rollback options, and explicit success measures.

First-week sequence

Days 1–2: Reconcile channel and revenue definitions; obtain daily funnel and stock records; correct documented ad-to-stock mismatches.
Days 3–4: Validate checkout tracking and launch the controlled mobile test. Start customer and COD-failure analysis.
Days 5–7: Review implementation quality and early evidence. Set the next decision date based on the test’s required sample size; one week may not be enough to establish an effect.

Over the following month, retain improvements supported by completed and fulfilled purchases. Once availability and checkout are stable, test pricing or promotional changes on a limited set of products. Assess profitability after advertising, fulfillment, cancellations, and returns before increasing spend.

The three highest-value data requests are

Daily channel and funnel data: sessions, product views, cart additions, checkout starts, purchases, device, and deployment dates.
SKU/size commercial history: availability, price, discount, product traffic, orders, and category revenue.
Order and customer outcomes: customer ID, first/repeat purchase status, order and delivery dates, carrier, cancellation/refund reason, net revenue, and relevant costs.

These could change the diagnosis materially. For example, stable conversion among available products would strengthen the stockout explanation; a failed controlled checkout test would weaken the redesign explanation; deterioration isolated to paid traffic would raise advertising quality as a priority.