By 9 AM today, you've probably already served your biggest coffee rush of the day. Cash register data tells you exactly when you sell coffee - and that's gold for your planning and profitability. Many café owners miss this opportunity because they only look at daily sales, while hourly data shows where you make your money.
Why cash register data by time of day is so valuable
Your register records every sale with a timestamp. That information is packed with patterns that help you with purchasing, staffing, and pricing. Without this analysis you're flying blind - with data you're making smart decisions.
💡 Example:
A café discovers through cash register data that 40% of coffee sales happen between 7:00-9:00:
- Morning rush: €280 coffee sales
- Lunch 12:00-14:00: €180 coffee sales
- Afternoon 15:00-17:00: €120 coffee sales
- Evening after 18:00: €60 coffee sales
Total daily coffee sales: €640
Export data from your cash register system
Most modern cash register systems can export sales data by time of day. Look for reports with "sales per hour" or "transactions by time". You'll need at least one month of data for reliable patterns.
- Square: go to Analytics → Sales → Sales per hour
- Lightspeed: Reports → Sales → Sales by time
- Revel: Analytics → Sales → Hourly Sales
- Local cash register systems: check the manual for "hourly reports"
⚠️ Note:
Make sure you only include coffee-related sales. Filter out other drinks and food for pure coffee data.
Analyze coffee sales by time of day
Divide your day into blocks of 1-2 hours and add up coffee sales per block. This gives you insight into your peak and slow hours - something most kitchen managers discover too late after months of guesswork. Use these time blocks as standard:
- Morning rush: 7:00-9:00
- Quiet morning: 9:00-11:00
- Lunch: 11:00-14:00
- Afternoon: 14:00-17:00
- Evening: 17:00-20:00
- Late evening: 20:00-closing
💡 Example calculation:
Week 1 analysis of a city café:
- Monday 7:00-9:00: 45 coffees × €2.80 = €126
- Tuesday 7:00-9:00: 52 coffees × €2.80 = €146
- Wednesday 7:00-9:00: 48 coffees × €2.80 = €134
Average morning rush: €135 per day
Recognize patterns in your coffee sales
Look for recurring patterns by day of the week and time of day. Weekdays often have different patterns than weekends. Also note external factors like weather, events, or seasons that affect your flow.
- Weekdays: Often high morning rush (7:00-9:00) and afternoon peak (13:00-15:00)
- Weekends: Later wake-up, peak shifts to 9:00-12:00
- Monday: Often lower sales (weekend effect)
- Friday: Sometimes higher sales (TGIF effect)
Practical applications of your time-of-day analysis
With this data you can make concrete decisions that save money and increase sales. Focus on the biggest impact areas: staffing and inventory management.
💡 Staffing example:
If 60% of your coffee is sold between 7:00-11:00:
- Schedule your best barista in the morning
- Extra help between 7:00-9:00
- Fewer staff after 15:00
- Savings: €200-400 per week in labor costs
- Optimize purchasing: Order fresh milk and coffee beans based on expected peaks
- Machine maintenance: Schedule cleaning during quiet hours
- Time promotions: Happy hour during slow periods to boost sales
- Manage inventory: Less waste through better planning
⚠️ Note:
Analyze at least 4 weeks of data before making major changes. One week can be skewed by chance or special circumstances.
Tools for deeper analysis
Excel or Google Sheets work perfectly for basic analysis. For more advanced insights you can use apps that automatically analyze cash register data. Some cash register systems have built-in analytics you're already paying for.
- Excel/Google Sheets: Pivot tables for hour-by-hour analysis
- Cash register system reports: Often free with modern systems
- Business intelligence tools: For larger cafés with multiple locations
- Food cost calculators: Tools that help link sales to costs per time of day
How do you analyze cash register data for coffee sales by time of day?
Export your cash register data
Go to your cash register system and export sales reports with timestamps from at least 4 weeks. Make sure you only select coffee-related products, no food or other drinks.
Divide data into time blocks
Group your sales into logical time blocks of 1-2 hours: morning rush (7:00-9:00), quiet morning (9:00-11:00), lunch (11:00-14:00), afternoon (14:00-17:00), and evening (17:00-closing).
Calculate sales per time block
For each time block, add up total coffee sales and calculate the average per day. Also look at differences between weekdays and weekends to recognize patterns.
Identify your peak and slow hours
Mark your busiest and quietest times. This becomes the basis for staffing, purchasing, and any promotions to stimulate slow hours.
Adjust your operations
Use your insights to better schedule staff, optimize inventory, and time promotions. Monitor results and adjust where needed.
✨ Pro tip
Track your coffee machine temperature drops during peak hours - if it drops more than 3°C between 8-9 AM, you're losing 15-20 seconds per drink during your busiest period. Most cafés don't realize this costs them 30+ customers during morning rush.
Calculate this yourself?
In the KitchenNmbrs app you can do this in just a few clicks. 7 days free, no credit card.
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Frequently asked questions
How much data do I need for reliable patterns?
At least 4 weeks, but preferably 8-12 weeks for seasonal patterns. One week can be skewed by chance, vacation, or nearby events that don't represent normal business.
My cash register system doesn't have hourly reports, what now?
Check if your cash register provider can add this as an update. Many systems have this feature, but it may be hidden in the reports section. Otherwise you can manually group receipts by hour for a week to start.
What if my peaks are very unpredictable?
Then you probably have a lot of random customers like tourists or passersby. Focus on averages and make sure you have flexible staff who can quickly help during unexpected busy times.
📚 Sources consulted
- EU Verordening 852/2004 — Levensmiddelenhygiëne (2004) — Official source
- EU Verordening 853/2004 — Hygiënevoorschriften voor levensmiddelen van dierlijke oorsprong (2004) — Official source
- EU Verordening 1169/2011 — Voedselinformatie aan consumenten (2011) — Official source
- NVWA — Hygiënecode voor de horeca (2024) — Official source
- NVWA — Allergenen in voedsel (2024) — Official source
- Codex Alimentarius — International Food Standards (2024) — Official source
- FSA — Safer food, better business (HACCP) (2024) — Official source
- BVL — Lebensmittelhygiene (HACCP) (2024) — Official source
- Warenwetbesluit Bereiding en behandeling van levensmiddelen (2024) — Official source
- WHO — Foodborne diseases estimates (2024) — Official source
Food Standards Agency (FSA) — https://www.food.gov.uk
The HACCP standards shown in this application are for informational purposes only. KitchenNmbrs does not guarantee that displayed values are current or complete. Always consult the FSA or your local authority for the latest regulations.
Written by
Jeffrey Smit
Founder & CEO of KitchenNmbrs
Jeffrey Smit built KitchenNmbrs from 8 years of hands-on experience as kitchen manager at 1NUL8 Group in Rotterdam. His mission: give every restaurant owner control over food cost.
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