Post-Black Friday analysis: 5 reports to run immediately

It's Saturday morning, November 30th. Black Friday is over. You're exhausted. The last thing you want to do is pull reports and analyze data.

Do it anyway. Right now, while everything is fresh in your mind and your team's minds. Wait two weeks and you'll forget critical details—that payment issue at 2 PM, that Instagram post that crushed it, that moment traffic spiked but conversion tanked and you're not sure why.

According to post-event analysis research from retail operations teams, insights captured within 48 hours of event completion are 3-4x more actionable than insights from analysis conducted 2+ weeks later due to memory decay, lost context, and lack of correlation between observed problems and data patterns.

These five reports take 2-3 hours total to run and analyze. They'll give you everything you need for next year's planning, reveal what worked and what didn't, and identify problems you need to fix before your next promotional event. The ROI on these few hours is enormous—next year's Black Friday will be dramatically better because of what you learn today.

Let's get to it.

📊 Report 1: Hour-by-hour performance decomposition

What it shows: Exactly when things happened during the event—traffic spikes, conversion changes, revenue peaks, problems.

How to build it:

Pull hourly data for Black Friday (and Cyber Monday if that's part of your event) showing:

• Revenue per hour

• Traffic per hour

• Conversion rate per hour

• Orders per hour

• Average order value per hour

Export to spreadsheet, create line charts for each metric. You're looking for patterns and anomalies.

What to analyze:

Peak hour identification: What hour generated most revenue? Most traffic? Highest conversion? These often differ—highest traffic doesn't always equal highest revenue if conversion drops during traffic spikes.

Example findings from a Shopify store:

• Highest traffic: 11 AM (8,400 visitors)

• Highest conversion: 2 PM (3.8% vs 2.1% average)

• Highest revenue: 1 PM (€43,200)

Why does this matter? Next year's email sends, ad scheduling, and team availability should align with proven high-performance hours. If 1-3 PM consistently produces best results, concentrate resources there.

Problem hour identification: Look for hours where traffic was strong but conversion tanked, or conversion was normal but traffic disappeared.

Example: 6-7 PM showed normal traffic (5,200 visitors) but conversion dropped to 1.2% (vs 2.3% event average). Investigation showed mobile checkout had intermittent payment processing errors during that hour costing an estimated €15K in lost revenue.

🎯 Action item: Schedule post-mortem meeting next week reviewing hour-by-hour chart with team. Ask "what happened here?" for every anomaly. Someone will remember the Instagram story that went viral at 10 AM, or the site slowdown at 4 PM, or the inventory stockout on bestseller at 8 AM. Capture these stories while fresh.

Traffic source performance by hour:

Extend this analysis showing hourly performance by source. Email traffic might peak 8-10 AM (when people read morning emails) while social peaks 7-9 PM (evening browsing). Paid search might show steadier all-day performance.

Understanding these patterns enables better scheduling next year—send emails for 7 AM delivery capturing morning browsers, boost social ad spend evening hours when that channel performs best.

💰 Report 2: Traffic source ROI analysis

What it shows: Which marketing channels delivered profitable returns and which destroyed value.

How to build it:

Create table showing for each traffic source:

• Total traffic

• Conversion rate

• Revenue generated

• Marketing spend (if tracked)

• Revenue per visitor

• Cost per acquisition (CPA)

• Return on ad spend (ROAS) if applicable

Example output:

What to analyze:

Winners: Email crushed it—4.2% conversion, strong revenue, minimal cost. Organic and direct also strong (no acquisition cost). These channels deserve increased investment next year.

Marginal performers: Paid search delivered 2.6x ROAS. Is that acceptable? Depends on your margins and target ROAS threshold. If you need 3x to be profitable, paid search underperformed.

Losers: Social paid at 1.7x ROAS likely unprofitable after margins. Drill deeper—was it all social or specific platforms/campaigns? Facebook might have worked while TikTok failed, requiring platform-level analysis.

⚠️ Critical caveat: Attribution is messy. Someone might click social ad, leave, read email, then purchase. Email gets credit in last-click attribution but social contributed. Use these numbers directionally, not absolutely. That said, massive differences (37x email vs 1.7x social) represent real performance gaps despite attribution noise.

Action items:

• Double email investment next year (bigger list, more sends, better segmentation)

• Review paid social strategy—either fix or dramatically reduce spend

• Investigate paid search underperformance—was it budget depletion, bid strategy, keyword targeting?

• Consider testing new channels that didn't run this year based on competitor activity

📦 Report 3: Product performance analysis

What it shows: Which products carried the event and which disappointed.

How to build it:

Pull product-level data for Black Friday showing:

• Units sold

• Revenue generated

• Conversion rate (product page visits to purchases)

• Percentage of total event revenue

• Average discount depth (if applicable)

• Inventory status (stocked out? oversupplied?)

Sort by revenue descending. Your top 20 products likely drove 60-80% of event revenue.

What to analyze:

Unexpected winners: Products that dramatically overperformed expectations. These deserve prominent placement next year and possibly deeper inventory investment.

Example: A store selling kitchen equipment expected their stand mixer to dominate (historical bestseller). Instead, a €45 knife set generated 2.4x expected revenue becoming #2 product. Investigation: gift-buying behavior, perfect price point for gifts, Instagram influencer mentioned it.

Disappointing performers: Products you stocked deeply expecting strong performance that underdelivered.

Example: Store invested heavily in winter jacket inventory (featured in ads, homepage placement, 30% off promotion). Sold only 60% of forecast. Why? Unseasonably warm November reduced cold-weather product demand. Next year: weather-dependent inventory depth with contingency plans.

Stockout analysis: Products that sold out before event ended. Calculate lost revenue: (Hours stocked out) × (Hourly sales rate before stockout).

Example: Bestselling product stocked out at 2 PM Friday, didn't replenish until Monday. Lost 70 hours at estimated €280/hour = €19,600 lost revenue. Next year: 2x inventory depth for proven winners plus rapid restock capability.

Discount effectiveness: Compare products at different discount levels. Did 30% off generate proportionally more volume than 20% off? Or did deeper discounts unnecessarily compress margins?

According to promotional effectiveness research, discount depth beyond 25% often generates diminishing returns—30-40% off promotions produce <15% incremental volume versus 25% off while destroying 20-60% more margin. Shallower discounts often optimal.

💡 Product mix insight: Build category-level view. Electronics might have carried the event while apparel underperformed. This informs next year's advertising focus, homepage layout, and inventory allocation.

🛒 Report 4: Checkout funnel analysis

What it shows: Where customers dropped out during purchase process and why.

How to build it:

Pull funnel data showing:

• Product page views

• Add-to-carts

• Checkout initiations

• Payment step completions

• Orders completed

Calculate drop-off rates between each step.

Example funnel:

• Product views: 156,000

• Add-to-cart: 18,200 (11.7% add-to-cart rate)

• Checkout initiated: 12,400 (68.1% of carts)

• Payment info entered: 8,900 (71.8% of checkouts)

• Orders completed: 7,300 (82.0% of payments entered)

Overall conversion: 7,300 / 156,000 = 4.7%

What to analyze:

Biggest drop-off point: In this example, cart-to-checkout transition (32% abandoned) represents largest leak. Why?

Common causes:

• Unexpected shipping costs appearing at checkout

• Account creation requirements

• Complicated checkout forms

• Site performance issues (slow loading)

• Mobile checkout problems

Device-specific funnel analysis: Run same funnel segmented by device. Mobile often shows worse checkout completion than desktop revealing device-specific friction.

Example: Desktop checkout completion 85%, mobile checkout completion 68%. That 17-point gap represents significant opportunity—fixing mobile checkout could boost total revenue 8-12%.

🎯 Action protocol: For every major drop-off point (>25% loss), document hypotheses about why. Test checkout yourself on multiple devices. Ask team members to complete purchases. Watch session recordings if available. Schedule UX improvements before next promotional event.

Payment method analysis: If you offer multiple payment options (credit cards, PayPal, Apple Pay, etc.), analyze success rates by method.

Sometimes one payment method shows higher failure rates indicating processor issues or customer confusion with that option. Next year: improve successful methods, reconsider problematic ones.

📊 Report 5: Customer segmentation analysis

What it shows: Who bought during the event—new customers vs returning, high-value vs low-value, etc.

How to build it:

Segment Black Friday customers into:

• New customers (first purchase ever)

• Returning customers (prior purchase history)

For each segment, calculate:

• Count of customers

• Percentage of total orders

• Average order value

• Total revenue contribution

• Conversion rate (if traffic segmented by customer type available)

Example analysis:

(Returning customers placed 1.27 orders each on average during event)

What to analyze:

New customer acquisition effectiveness: 4,850 new customers represents your future customer base. But at what cost? Divide total marketing spend by new customers acquired calculating cost per acquisition.

If CAC = €18 and you need 2.5x LTV:CAC for profitability, these new customers must generate €45 lifetime value. Track November 2024 cohort's performance over next 12-24 months validating profitability.

Returning customer performance: Returning customers showed higher AOV (€82 vs €68) and placed multiple orders indicating higher engagement. They're your valuable audience.

Question for next year: How do you activate more of your existing customer base during Black Friday? Consider VIP early access, exclusive offers, or loyalty rewards driving higher returning customer participation.

Geographic segment performance: If you ship multiple countries/regions, analyze by geography. Some markets might dramatically outperform others indicating regional marketing opportunities or logistics challenges.

First purchase product category: For new customers, which product categories drove acquisition? These are your "gateway" products deserving prominence in next year's acquisition marketing.

Post-Black Friday analysis captures learning while fresh transforming this year's experience into next year's improvements. Run hour-by-hour performance decomposition identifying peak periods and problems. Analyze traffic source ROI determining which channels delivered profitable returns. Review product performance revealing unexpected winners and disappointing underperformers. Examine checkout funnel identifying friction points and abandonment causes. And segment customer analysis understanding acquisition effectiveness and customer quality.

Don't wait. Saturday morning after the event, schedule 3-4 hours, pull these reports, share with team, document findings, and create action items for next year. That investment returns 10-20x through better planning, fixed problems, and captured opportunities next season.

The stores that dominate Black Friday year after year aren't lucky—they learn systematically from every event and implement those learnings. Two years of systematic learning compounds into unbeatable operational excellence and marketing efficiency.

Want these post-event metrics delivered automatically? Try Peasy for free at peasy.nu and get daily reports throughout your event and recovery period—track sales, conversion, and top products with automatic week-over-week comparisons for instant post-event analysis.