Case study · Data Annotation

Streamlining Autonomous Shopping Through Data Annotation

A global leader in AI-powered stores improved product accuracy and support with a Fusion CX data annotation and support team that grew to 300 people in 18 months, with QA accuracy above 98%.
Streamlining Autonomous Shopping Through Data Annotation
300
team members within 18 months
Client
Provider of autonomous shopping solutions with the most deployed AI-powered stores
Industry
Data Annotation · Autonomous retail
Services
Video-based data annotation, customer credit allocation, back-office tickets, technical support
Delivery
24/7, multiple global locations
The results

Accuracy and speed at scale.

Results within 18 months.
300 | personnel within 18 months 98%+ | quality assurance accuracy 75% | of cases resolved within 10 minutes 25% | lower average handle time
01Challenge

Getting every item and every credit right.

Accurate product selection inside stores was proving difficult, allocating customer credits from video monitoring was complex, and technical glitches such as failed invoices hurt efficiency and reputation.

Product accuracy

Selecting the right products in-store.

Credit allocation

Video-based credits needed expertise.

Tech troubleshooting

Systems and invoices not working as expected.

Staff productivity

Internal teams stretched by back-office work.

02Solution

Round-the-clock support and real-time monitoring.

Fusion CX developed a comprehensive strategy to address the challenges and meet the client's requirements.

24/7 support

Multiple global locations resolving issues promptly.

Back-office tickets

Swift ticket handling for faster resolution.

Real-time monitoring

Continuous analysis and adjustment of operations.

Streamlined processes

Freeing client staff to focus on core tasks.

03Impact

300 people, 98%+ accuracy.

Within 18 months, the team grew from a minimal headcount to 300 personnel and achieved a quality assurance accuracy score of over 98%.

75% of cases were resolved within 10 minutes of arrival, and average handling time fell 25%, improving operational efficiency and the overall customer experience.

Human-in-the-loop annotation keeps AI-powered stores accurate as they scale.

04Key insights

What other programs can take from this.

  • Video-based data annotation needs trained people and strong QA to stay accurate.
  • Taking over back-office tickets frees client teams to focus on core work.
  • Real-time performance monitoring helps large teams keep resolution times low.
Download the full case study

In this case study, you’ll see:

The product-selection, credit-allocation and tech challenges of autonomous stores
How 24/7 support, real-time monitoring and back-office ticket handling helped
The results: 98%+ QA accuracy and 75% of cases resolved within 10 minutes
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Autonomous Shopping Solutions Provider
Case study · Data Annotation

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