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Time-of-day heatmap

The time-of-day heatmap visualizes customer activity along the time axis. While the standard heatmap is a static “all-day summary,” the time-of-day heatmap lets you watch density and movement change continuously, like a time-lapse video. You can quickly identify when and where the store gets crowded and which times suffer reduced operational efficiency.

Report layout

Time-of-day heatmap demo
Time-of-day heatmap analysis view
Customer activity changes over time visualized as a time-lapse

In-depth Visit-based analysis

  • Analyze the Visit-based heatmap by time of day.

Examples

  • Identify zones that get crowded at specific times and manage congestion.
  • Find zones with relatively low visits and place strategic merchandise there to redistribute traffic.

Activity visualized over time

  • Use filters to view changes in customer activity at one-hour intervals.
  • Track how red and blue color distributions shift by time of day to follow congestion changes in the space.

Examples

  • Track how peak times shift precisely (for example, 12:30 lunch-box corner → 12:50 checkout).
  • Discover localized hotspots that occur only at specific times and run target marketing.

Dynamic resource allocation

  • Optimize the placement of staff, checkouts, and inventory based on visit density by time of day.

Examples

  • Maintain minimum staffing during quiet hours, then redeploy staff to focus on customer service at peak times.
  • Optimize checkout operations around lunchtime to minimize wait times.

Time-targeted marketing

  • Plan your marketing timing using time of day + zone activity + customer group (gender / age group) behavior.

Examples

  • Plan marketing around the zones and times specific customer groups visit.
  • If women in their 30s actively visit the baby-products corner around 10 a.m., plan a “Mom’s Time” flash sale at that hour.

Expected outcomes

Operational optimization

  • Flexibly adjust staff, facilities, and inventory based on traffic by time of day.
  • Reduce waste and maximize efficiency.

Better marketing performance

  • Plan marketing around the times and zones where customers gather.
  • Deliver the right message and increase marketing conversion.

Better customer experience

  • Anticipate crowded times and pre-staff in advance.
  • Minimize the stress of waiting and increase shopping satisfaction.

Use cases

  • Peak-time operations — Identify path bottlenecks during peak hours and reposition staff.
  • Time-of-day campaigns — Design different promotion messages for lunch, evening, and other windows.
  • Event impact measurement — Track traffic changes during the hours of an event.

Advanced tips

Track changes by hour and set baselines

  • Compare with historical data:
    • Compare with the heatmap from the same day of the week and the same hour in the prior week or month.
    • Diagnose how external factors — promotions, display changes, weather — affect visit traffic.
  • Confirm a normal traffic range:
    • Compare customer activity at a specific hour with average visit patterns to use as a baseline that determines whether a hotspot or cold zone is temporary or structural.
    • If a normally red zone turns blue at a certain hour, it signals the need for an operational check.

Time-of-day comparison by target

  • Compare time of day by gender and age group:
    • Separate the target groups and compare their time-of-day heatmaps.
    • Confirm that different customer types show interest at different hours in the same space, and design tailored strategies.

Example workflow

The following is a multi-step analysis flow built around the time-of-day heatmap.

  1. Find the rhythm — From the time-of-day heatmap, spot that the “appliances corner” gets crowded at 3 p.m. on weekends.
  2. Trace the path — Use [Representative path] to see which routes brought customers to the zone.
  3. Diagnose performance — Use the [Zone Interest Analysis] matrix to determine whether the corner is a “leaky bucket” (high visits, low interest).
  4. Validate conversion — Use [Purchase conversion rate] to confirm whether sales actually occur at that hour.
  5. Execute and measure — Based on the validated evidence, invest resources at that hour (POP repositioning, staffing, etc.).