Visitor Insights Updates to Make Data Analysis Easier and More Insightful

1. Ability to Include or Exclude Staff Data

Why It Matters

For most companies, distinguishing visitors from staff is essential for undistorted visitor behavior analytics. Separating staff data also enables you to evaluate employee effectiveness, such as:

• Time spent at their workplace.
• Frequency of interactions with visitors.
• Changes in visitor engagement when staff are present versus absent.

DISPL automatically tags visitors as staff if their visit duration exceeds 2 hours by default (configurable up to 24 hours in Settings > Main).

Previous Process

Previously, all data shown on the dashboard excluded visitors tagged as staff. To analyze staff behavior, you needed to access and filter the Raw Data manually.

What’s New

You can now filter staff-related data directly in the dashboard:

• Visitors without staff tag: Displays data for visitors excluding staff, for undistorted visitor analytics.
• Visitors with staff tag: Displays data exclusively for staff.
• All visitors: Combines both visitor and staff data.

Why It’s Valuable

This feature simplifies staff analysis and allows you to measure employee performance directly from the dashboard.

2. Visit Duration Filters

Why It Matters

Different locations often require insights based on specific visit duration intervals. For instance, you may want to exclude data from visitors who stopped by briefly to ask a question or window shop, focusing only on those who spent significant time in the location.

Previous Process

Previously, all data was displayed without filtering options. To focus on specific visit durations, you had to download and process the Raw Data manually.

What’s New

You can now filter data by visit duration directly in the dashboard. Simply set a minimum and maximum duration in the filter, click Apply, and view visitors whose visit durations match your criteria.

Why It’s Valuable

This feature helps eliminate outliers (visitors with very short or very long durations) and ensures more reliable analytics.

3. Content Analysis

Why It Matters

When using digital signage, understanding the effectiveness of your content is key to optimizing visitor engagement and determining what works best.

Previous Process

Previously, analyzing content effectiveness required accessing the Raw Data.

What’s New

You can now filter content data directly in the dashboard by selecting the specific content in the Content field. Device selection is optional but available for comparison.

Why It’s Valuable

• Measure Impact: See how visitors responded to specific content, including their engagement levels and demographics.
• Compare Effectiveness: Duplicate dashboard tabs to compare multiple content pieces or analyze the same content across different devices.
• Optimize Campaigns: Use this insight to improve underperforming content and replicate successful campaigns.

4. Additional Data for Raw Data Exports

Why It Matters

You can download raw data by clicking Send Report in the dashboard. The report will be sent to your specified email and includes three tabs: Visitors, Contacts, and Content.

Previous Process

By default, raw data included basic metrics such as tracks, visit duration, visitor characteristics (e.g., gender, age), and staff tags.

What’s New

We’ve added the option to include additional visitor characteristics for deeper analysis:

• Contact Duration: Total time a visitor was actively looking at the sensor, as opposed to their overall visit duration.some text
  • Example: A visitor spends 5 minutes in the location but engages with the sensor for only 30 seconds. This insight can indicate whether products in the sensor’s zone align with the visitor’s interests.
• Glasses/Facial Hair/Hair Color/Type: Helps refine customer profiles and improve targeted content delivery.
• History Face Attributes: Includes detailed facial metrics like angle (pitch/yaw) and expressions. Note: Facial expressions represent visual analysis, not emotional states.
• Face Quality: Measures the accuracy of face analysis on a scale of 0 to 1.
  • 1: Perfect conditions, with the face identified at 100% quality.
  • 0: No identification, often due to poor lighting or misalignment with the sensor.
  • Average Face Quality can also evaluate sensor installation quality—the closer the score is to 1, the better the sensor setup.

How to Enable

To add these attributes, contact our support team to activate them for your account. These additional fields are excluded by default to simplify raw data reports for users who don’t require such a deep dive.