Sales Data Health Check 1. Executive Data Readout What the data clearly shows: Revenue declined from $128,000 in April to $98,000 in July, a 23.4% decline. Traffic fell only 6.5%, while conversion fell much more sharply, from 2.55% to 1.92%. AOV increased from $54 to $59, partially offsetting the deterioration. Main observable deterioration: Conversion performance, accompanied by a substantial deterioration in returning-customer contribution and higher CAC. Data quality: Adequate Where deeper investigation should focus: Conversion deterioration, returning-customer performance, stock availability, acquisition efficiency, and the possible commercial impact of slower fulfillment. Biggest current data gap: The business cannot currently isolate deterioration by product, acquisition channel, or customer cohort, which prevents distinguishing among several plausible internal mechanisms. 2. Data Quality Assessment Overall Quality: Adequate Comparable Periods: Yes, assuming April–July represent complete calendar months and revenue definitions remained unchanged. However, there is no prior-year comparison to control for seasonality. Metric Consistency: Adequate to Strong. The headline metrics are mathematically coherent. For example: April sessions × conversion × AOV: 92,000 × 2.55% × $54 ≈ $126,684 Reported April revenue: $128,000 July: 86,000 × 1.92% × $59 ≈ $97,421 Reported July revenue: $98,000 The small differences are consistent with rounded conversion rates and AOV rather than an obvious contradiction. Segmentation Available: Limited. There is some customer-type information through returning-customer order share and some aggregate acquisition information, but no proper channel, product, or cohort decomposition. Major Limitations: No product-level revenue or conversion. No stockout dates, duration, affected sessions, or lost-order estimate. No conversion by Meta, TikTok, email, direct, or returning traffic. No new-versus-returning revenue or conversion. No cohort retention analysis. CAC definition and channel coverage are not specified. No checkout-stage funnel. No prior-year benchmark. Delivery deterioration is documented but its impact on purchasing or repeat behavior is unknown. 3. Sales Performance Breakdown For this business: Revenue ≈ Website Sessions × Conversion Rate × Average Order Value Metric April July Change Status Interpretation Revenue $128,000 $98,000 −23.4% Deteriorating Material sales deterioration is verified. Website sessions 92,000 86,000 −6.5% Deteriorating Traffic weakened, but considerably less than revenue. Conversion rate 2.55% 1.92% −0.63 pp / −24.7% relative Deteriorating Largest negative movement in the core revenue equation. AOV $54 $59 +9.3% Improving Higher basket value partially offset declining conversion/orders. Returning-customer share 36% 25% −11 pp / −30.6% relative Deteriorating Returning customers represent a substantially smaller share of orders. Meta spend ~$24,000 ~$24,000 Approximately stable Stable Spend itself did not materially decline. CAC ~$31 ~$43 +38.7% Deteriorating Acquisition efficiency deteriorated under the stated CAC definition. Return rate Stable Stable No material change reported Stable Returns are not an obvious deteriorating metric in the supplied evidence. Delivery time ~2 days 3–4 days +1–2 days Deteriorating Operational service performance worsened. Stock availability Better Shortages in top sellers Deteriorated Deteriorating Important products were unavailable during part of the decline period. Approximate order movement Using: Orders ≈ Revenue ÷ AOV April: $128,000 ÷ $54 ≈ 2,370 orders July: $98,000 ÷ $59 ≈ 1,661 orders That implies an approximate 29.9% decline in orders. These figures are approximate because reported AOV values are rounded. Revenue-system decomposition The supplied data strongly indicates that traffic decline alone is insufficient to explain the revenue deterioration. If July traffic had operated with April's conversion and AOV: 86,000 × 2.55% × $54 ≈ $118,422 That would represent only about a 7.5% revenue reduction versus April's reported $128,000. Using July traffic and July conversion but retaining April AOV: 86,000 × 1.92% × $54 ≈ $89,165 July's higher $59 AOV then increases the modelled revenue to approximately: $97,421 Therefore, within the mathematical sales equation, conversion deterioration is the largest observable negative component, while higher AOV materially offsets part of the loss. This is a decomposition of performance, not proof of why conversion declined. 4. Where the Deterioration Appears Area: Conversion Evidence: 2.55% → 1.92%, a 24.7% relative decline. Observed Status: Deteriorating Why It Matters: Conversion deterioration is substantially larger than traffic deterioration and directly reduces orders produced by existing traffic. Requires Deeper Diagnosis: Yes Area: Returning-Customer Performance Evidence: Returning customers fell from 36% to 25% of orders. Observed Status: Deteriorating Why It Matters: The sales decline is not confined to acquiring new customers. A smaller portion of orders is coming from existing customers. Using the approximate order counts above: April returning orders ≈ 2,370 × 36% ≈ 853 July returning orders ≈ 1,661 × 25% ≈ 415 This suggests roughly a 51% decline in returning-customer orders, subject to the rounding and metric-definition limitations noted above. Requires Deeper Diagnosis: Yes Area: Customer Acquisition Efficiency Evidence: CAC increased from approximately $31 to $43, or 38.7%, while Meta spend remained near $24,000 per month. Observed Status: Deteriorating Why It Matters: The company appears to be obtaining fewer new customers per acquisition dollar under the supplied CAC definition. Requires Deeper Diagnosis: Yes This supports investigating advertising performance but does not establish that advertising deterioration caused the overall revenue decline. Area: Product Availability Evidence: Several top-selling products experienced shortages during June and July. Observed Status: Deteriorating Why It Matters: Product unavailability can mechanically prevent otherwise willing customers from completing purchases and can distort aggregate conversion. Requires Deeper Diagnosis: Yes The current evidence does not quantify how much revenue or conversion was affected. Area: Fulfillment Performance Evidence: Delivery increased from approximately 2 days to 3–4 days following a fulfillment-partner change. Observed Status: Deteriorating Why It Matters: This is a material internal operational change occurring during the deterioration period. It could potentially affect customer satisfaction, repeat purchasing, cancellations, or buying decisions. Requires Deeper Diagnosis: Yes No supplied evidence establishes such effects yet. Area: Average Order Value Evidence: $54 → $59. Observed Status: Improving Why It Matters: Customers who purchased spent more per order on average. AOV therefore mitigated rather than caused the revenue deterioration. Requires Deeper Diagnosis: Not immediately. 5. Segment Findings Classification: NOT DETERMINABLE There are signs that different parts of the commercial system deteriorated simultaneously: conversion weakened; returning-customer contribution weakened; acquisition efficiency weakened; stock availability worsened; fulfillment slowed. However, the available data cannot determine whether the decline is broad across products and channels or heavily concentrated in particular products, traffic sources, or customer cohorts. The strongest measurable company-level weakness is conversion. The potentially most severe customer-segment deterioration is returning-customer ordering, but proper cohort and absolute customer data are needed before drawing a retention conclusion. 6. What the Data Does NOT Yet Prove The evidence does not currently establish that: Meta advertising is the primary cause of the sales decline. CAC deteriorated, but channel-level traffic, conversion, CPM, CPC, CTR, ROAS, and new-customer counts are unavailable. Advertising deterioration explains the conversion decline. Website conversion may be affected by traffic quality, product availability, customer mix, merchandising, checkout behavior, or other factors. Stockouts caused the 23% revenue decline. Stock shortages are documented, but their sales impact has not been quantified. Longer delivery times caused declining repeat purchases. Timing makes this worth investigating, but causation is not established. Customer retention fundamentally deteriorated. Returning-customer order share fell sharply, but cohort repurchase rates are unavailable. Customers became more price-sensitive. No pricing-response evidence exists. Market demand declined. Competitors gained share. Economic conditions caused the deterioration. TikTok or Meta platform changes were responsible. Customer preferences changed. These hypotheses require additional internal or external evidence. 7. Critical Missing Evidence Essential Channel-level funnel performance Sessions, orders, revenue, conversion, new customers, CAC, and preferably contribution margin by Meta, TikTok, email, organic/direct, and other meaningful sources. Without this, the company cannot determine whether deterioration originates primarily in paid acquisition or after customers reach the site. Product-level availability and performance Revenue, units, sessions/product views, conversion, and in-stock percentage for major SKUs. This is essential because documented stockouts overlap with the sharpest revenue deterioration. New versus returning customer performance Absolute orders, customers, revenue, AOV, conversion, and repeat-purchase rates. The 36% → 25% order-share change warrants substantially deeper examination. Useful Customer cohorts 30-, 60-, 90-day or comparable repeat-purchase behavior by acquisition month. Website conversion funnel Product view → add-to-cart → checkout → purchase. This would locate where the aggregate conversion deterioration occurred. Optional Customer-support metrics and qualitative customer feedback, especially complaints mentioning delivery or unavailable products, would help interpret subsequent findings but are secondary to the transactional data above. 8. Top Data Collection Priorities 1. Collect: Conversion and revenue by acquisition channel Why: Aggregate conversion combines customers with potentially very different intent levels. What It Will Help Determine: Whether conversion deterioration is concentrated among Meta/TikTok traffic or occurs across email, direct, organic, and returning customers. Priority: Essential 2. Collect: Product-level sales and stock availability For the top SKUs, capture daily or weekly: stock status; product-page traffic; units sold; conversion; revenue. Why: Top-selling products experienced known shortages during the decline. What It Will Help Determine: Whether stockouts coincide with and plausibly account for a material portion of lost orders or conversion. Priority: Essential 3. Collect: New vs returning customer absolute performance Capture orders, customers, revenue, conversion, and AOV separately. Why: Returning-customer share dropped from 36% to 25%, but percentage shares alone can obscure what happened to each group. What It Will Help Determine: Whether deterioration is primarily acquisition-related, repeat-customer-related, or both. Priority: Essential 4. Collect: Repeat-purchase cohorts Compare customer cohorts acquired before and after the fulfillment change. Why: Aggregate returning-customer share cannot distinguish cohort size from retention behavior. What It Will Help Determine: Whether actual customer repurchase behavior deteriorated. Priority: Useful 5. Collect: Conversion funnel by month and channel Track: Product view → Add to cart → Checkout started → Purchase Why: A falling final conversion rate does not identify the point at which customers are being lost. What It Will Help Determine: Whether deterioration occurs mainly in product engagement, cart behavior, checkout, payment completion, or earlier traffic quality. Priority: Useful Diagnostic Handoff Pack Business Model: Direct-to-consumer skincare e-commerce business selling through Shopify. New-customer acquisition comes mainly from Meta and TikTok, while email and returning customers provide a meaningful share of revenue. Sales Problem: Monthly revenue declined from $128,000 in April to $98,000 in July, a verified decline of approximately 23.4%. Analysis Period: April through July. Internal Data Quality: Adequate Main Observable Deterioration: Website conversion rate declined from 2.55% to 1.92%, a 0.63 percentage-point / 24.7% relative reduction. Secondary Deterioration: CAC increased approximately 38.7% from $31 to $43. Returning-customer share declined from 36% to 25% of orders. Website sessions declined approximately 6.5%. Top-selling products experienced stock shortages during June and July. Delivery time increased from approximately 2 days to 3–4 days. Stable / Improving Metrics: AOV increased from $54 to $59, approximately 9.3%. Meta advertising spend remained approximately stable at $24,000 per month. Return rate remained relatively stable. Segment Concentration: Not determinable from current data. Product, channel, and customer-cohort segmentation is insufficient. Relevant Internal Changes: Top-product stock shortages occurred during June and July. Fulfillment partner changed. Delivery performance subsequently deteriorated from approximately two days to three–four days. Key Internal Evidence: Revenue: $128k → $98k, −23.4% Traffic: 92k → 86k, −6.5% Conversion: 2.55% → 1.92%, −24.7% relative AOV: $54 → $59, +9.3% Approximate orders: 2,370 → 1,661, −29.9% Returning-customer order share: 36% → 25% CAC: $31 → $43, +38.7% Essential Missing Evidence: Channel-level traffic, conversion, orders, revenue, CAC, and new-customer performance. Product-level sales, conversion, and stock availability. Absolute new-versus-returning customer performance. Customer cohort repeat-purchase data. Areas Requiring Root-Cause Diagnosis: Determine what caused the conversion deterioration. Determine whether acquisition deterioration is channel-specific. Quantify the sales impact of top-product stockouts. Determine whether returning-customer behavior actually deteriorated versus simply changing in mix. Test whether the fulfillment change and slower delivery correlate with lower repeat purchasing or conversion. The current evidence therefore does not support management's working hypothesis that advertising alone caused the decline. Advertising efficiency clearly worsened, but the internal data simultaneously shows deterioration in conversion, returning-customer contribution, product availability, and fulfillment performance. The next diagnostic system should distinguish among these mechanisms rather than starting from an advertising explanation.