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Customer case study · Face Recognition

SoftBank Mobile Customer Analytics

See the in-store experience as it happens.

Customer SoftBank Mobile

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A SoftBank and Y!mobile retail store seen from its forecourt under a bright sky

The problem

SoftBank Mobile stores needed clearer visibility into the in-store customer experience. Teams could not reliably identify customers waiting too long, recognize signs of frustration, understand repeat-visit patterns, or give priority service to VIP customers. As a result, service quality depended heavily on manual observation, while long waits and missed engagement opportunities could reduce customer satisfaction and loyalty.

The solution

Ayonix Customer Analytics uses AI-powered video analytics and facial-recognition capabilities, subject to customer consent and applicable privacy requirements, to provide real-time retail-service intelligence.

  • Measures customer arrival time, queue time, waiting time, and total visit duration.
  • Alerts staff when a customer has waited longer than the configured service threshold.
  • Detects observable facial-expression signals, such as potential frustration, to prompt timely staff assistance; alerts require human review.
  • Identifies consented returning customers and records visit frequency, preferred store, and historical service activity.
  • Recognizes consented VIP customers at arrival and notifies authorized staff for personalized support.
  • Provides dashboards for customer flow, wait-time performance, staff response time, repeat visits, and service-quality trends.
  • Uses role-based access, retention controls, and auditable logs to support responsible customer-data management.

What we brought

  • Wait-time analytics
  • Expression signals
  • Consented recognition
  • Service dashboards

How we solved it

  1. Arrive
  2. Measure
  3. Alert
  4. Assist
  5. Review

Outcome

  • Faster response to customers who are waiting too long or may need assistance.
  • Better queue management and staff allocation during peak periods.
  • Improved service experience for returning and VIP customers.
  • Reduced risk of customer dissatisfaction caused by long waits or missed engagement.
  • Measurable store-level insights to improve staffing, layout, service processes, and customer loyalty.

Technology: Face Recognition

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