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Order Count by Time Documentation

Menu Location: Customers > Reports > Order Count by Time

Access Level: Manager and above

Last Updated: 2026-03-01

Overview

The Order Count by Time page provides a time-series log of order counts captured at regular 10-minute intervals. This simple but powerful tool helps track order volume changes in real-time, identify system issues, and analyze ordering patterns throughout the day.

Primary Functions:

  • Track order counts at 10-minute intervals
  • Monitor real-time order volume changes
  • Identify unusual spikes or drops
  • Analyze time-of-day ordering patterns
  • Detect system issues affecting order creation
  • Compare order volume across different days/weeks
  • Export data for trend analysis

Page Layout

Header Section

  • Date Range Selector - Choose dates to view
  • Time Period Filter - Focus on specific hours
  • Graph/Table Toggle - Switch between views
  • Export Button - Download CSV data

Time Series Graph

  • X-axis: Time (10-minute intervals)
  • Y-axis: Order count
  • Multiple days overlaid for comparison
  • Hover for exact counts and timestamps

Data Table

  • Timestamp - Exact time reading was taken
  • Order Count - Number of orders at that moment
  • Change - Difference from previous reading
  • Status - Normal, Spike, Drop indicators

Understanding the Data

What Gets Tracked

  • Automated system checks every 10 minutes
  • Counts total open orders
  • Records timestamp and count
  • Tracks changes between intervals
  • Logs stored indefinitely

Normal Patterns

Steady Growth:

  • Count increases steadily as cutoff approaches
  • Normal healthy pattern
  • Customers adding orders throughout week

Plateaus:

  • Count stays stable for hours
  • Normal outside active hours
  • Expected overnight and early morning

End-of-Week Drop:

  • Count drops to near-zero after orders close
  • Orders moved to next week
  • Normal weekly cycle

Abnormal Patterns

Sudden Spike:

  • Count jumps 50+ orders in 10 minutes
  • Could indicate: System error creating duplicates, Bulk import, Major promotion success

Unexpected Drop:

  • Count decreases mid-week
  • Could indicate: System error, Orders deleted/cancelled in bulk, Database issue

Flatline:

  • No change for extended period when should be growing
  • Could indicate: Website down, Checkout broken, System issue preventing orders

Common Use Cases

Use Case 1: Daily Order Growth Monitoring

Goal: Track if orders are coming in as expected

Steps:

  1. Select today's date
  2. Review graph throughout day
  3. Compare to same day last week
  4. Check for steady growth pattern
  5. Investigate any anomalies
  6. Verify cutoff time approaching normally

Use Case 2: System Issue Detection

Goal: Quickly identify if technical problems affecting orders

Steps:

  1. Notice orders seem low for this time
  2. Open Order Count by Time
  3. Check if count flatlined
  4. Identify when growth stopped
  5. Check what changed at that time
  6. Alert technical team if needed
  7. Monitor for resolution

Use Case 3: Marketing Campaign Impact

Goal: Measure real-time effect of email/promotion

Steps:

  1. Note time campaign sent
  2. Monitor order count after send
  3. Look for spike in next 30-60 minutes
  4. Calculate orders attributed to campaign
  5. Compare to baseline growth rate
  6. Assess campaign effectiveness

Use Case 4: Weekly Pattern Analysis

Goal: Understand when customers typically order

Steps:

  1. Select full week date range
  2. Overlay multiple weeks
  3. Identify peak ordering times
  4. Note quiet periods
  5. Use insights for:
    • Staff scheduling
    • Email send timing
    • Website maintenance windows
    • Customer service coverage

Use Case 5: Cutoff Optimization

Goal: Determine if cutoff time is optimal

Steps:

  1. Review last hour before cutoff multiple weeks
  2. Check if large spike in final hour
  3. Assess if extending cutoff would capture more orders
  4. Compare early-week vs. late-week patterns
  5. Make data-driven cutoff time decision

Interpreting Spikes and Drops

Positive Spikes (Good)

  • After Email Campaign: Expected and desired
  • After Press Mention: Sign of success
  • Weekend Traffic: Higher engagement
  • Payday Patterns: Economic timing

Concerning Spikes (Investigate)

  • Massive Sudden Jump: May be system error
  • Off-Hours Spike: Unusual, check logs
  • Duplicate Pattern: Could be bug
  • Inconsistent with Marketing: Unknown cause

Expected Drops

  • After Cutoff: Orders closed for week
  • Late Night: Low activity period
  • Holiday Closures: Business closed

Concerning Drops (Urgent)

  • Mid-Day Flatline: Website may be down
  • Sudden Decrease: Orders being deleted?
  • Below Baseline: System issue preventing orders
  • Week-over-Week Decline: Business concern

Best Practices

Monitoring

  1. Check daily during active hours
  2. Compare to previous week same day
  3. Set expected range for alerts
  4. Investigate deviations quickly
  5. Document unusual patterns

Problem Response

  1. Notice flatline or drop immediately
  2. Check website functionality
  3. Review system logs
  4. Alert technical team if needed
  5. Communicate with customers if major issue
  6. Document incident and resolution

Analysis

  1. Export data monthly for trends
  2. Create baseline expectations
  3. Factor in seasonal variation
  4. Account for marketing activities
  5. Share insights with team

Quick Reference Card

Task Action/Location
View order count timeline Navigate to Customers > Reports > Order Count by Time
Check today Select today's date
Compare weeks Select multiple weeks in date range
View graph Toggle to Graph View
See exact numbers Toggle to Table View or hover on graph
Export data Click Export button
Identify spike Look for sudden vertical jump
Find flatline Look for horizontal extended line
Check current count Use most recent timestamp
Compare times Overlay multiple days on graph

FAQs

How often is data recorded?

Every 10 minutes automatically. The system runs a scheduled task that counts all open orders and logs the count with a timestamp.

Why would count decrease mid-week?

Usually orders being cancelled or system moving orders to different week. Could also indicate technical issue. Always investigate unexpected decreases.

What's a normal growth rate?

Varies by business size. Small operation might add 5-10 orders/day. Larger might add 50-100/day. Establish your baseline and watch for deviations.

Can I set alerts for unusual patterns?

Depends on your system configuration. Some systems allow threshold alerts. Otherwise, manual daily checks recommended during critical periods.

Why does count spike at certain times?

Often correlates with: Email campaigns sent, Lunch breaks (12-1pm), Evening hours (6-9pm), Weekend mornings, Payday (1st, 15th of month).

Should I be concerned about overnight growth?

Slight overnight growth is normal (5-10 orders). Significant overnight spikes unusual unless you sent late-night email or have international customers.

What if count shows zero?

Either genuinely no orders yet (early in week), or database/logging issue. Verify by checking Orders page directly. If Orders page shows orders but this shows zero, technical issue.

How far back does data go?

Typically retained for 6-12 months minimum. Older data may be archived. Check with your system administrator for specific retention policy.

End of Documentation

For additional help, contact your system administrator or Kiva Logic support.