How Your Annotation Team Can Maximize the Best Retail Autonomous Experience

People HATE waiting in lines. And while the retail market has already adopted smart technologies in order to add value to their services, they’re still struggling to offer the best customer experience. This frustration is well known to Standard who teamed up with Circle K convenience stores (which was acquired by Alimentation Couche-Tard) . They’ve already got 15,000 stores, and are planning on building 100 to 200 more stores a year. Standard believes convenience stores will be the first to quickly adopt autonomous checkouts. 

As the demand and popularity of autonomous checkouts grow, customers are not going to choose to shop in any other way, which would logically set the demand in motion for stores to quickly start implementing this check-out free experience. 

For stores, this is beneficial as they’re able to decrease their expenditures by not hiring cashiers and performing inventory checks. And for customers, they benefit from skipping the lines altogether. 

The Challenge 

The biggest question is always how can you translate the data you collect from “the field” (the store) into production-level applications, in the smartest, and most efficient way. The results need to be highly accurate and available in real-time in order to be trusted and adopted by consumers.

In addition, the complexity that is involved in data labeling doesn’t make this task any easier. Translating day-to-day human knowledge into a “language” that AI/ML models can use, and predict, is not a simple task especially when you’ve got changing seasonal/holiday packaging, and the constant influx of new products. And then you’ve got data accuracy, which is crucial for product success. 

Maybe you’ve been contemplating taking the autonomous checkout route? Or, maybe you’re just not sure how to make it work. But this process starts from your data annotation team, and the tools you need that can help you scale to success. Either way, we’ve got you covered, and here’s what you need to know to get started…

5 Must-Haves Every Data Annotation Team Needs to Scale

  1. A diverse toolset: This will ensure you’re addressing all of your use cases. For instance, detection tooling for product detection, object tracking for detecting and tracking consumer movement, as well as pose detection and parenting features to define consumer behavior.
  1. A data management system is a must: A single, secure data management system helps you categorize and organize mass amounts of data. Which will allow multiple global teams to work simultaneously and remotely – in real-time! Providing feedback and task updates to annotators during the labeling process and preventing recurring mistakes ends up saving significant resources
  1. Automation: With the help of ML models you can automatically annotate all your raw data turning the manual labeling process into a simple auditing task. Run model predictions on raw data to visualize detection accuracy, and see exactly where your models need more training
  1. State of the Art Annotation Studio: With AI-assisted applications, QA, and quality control applications you can ensure quality and accuracy all while the work is taking place. This allows you to improve your workforce performance significantly and annotate at scale efficiently.
  2. Task management features: These features allow you to monitor use cases such as shelf monitoring, customer pose detection, and customer gestures. Monitoring and understanding your customer’s actions as well as assessing your stock will keep you ahead of the game, allowing you to monitor in real-time, translate human activity into usable information that you can understand, use, and predict.

When working with millions of data items, workflows become a crucial part of the data labeling process. They allow you to provide real-time communication to annotators, product visibility allowing managers to improve data labeling processes in real-time, and smarter data architecture allowing you to provide orderly data access which is critical for ML development. 

Conclusion

In the retail industry, it’s all about delivering the best customer experience. To accomplish this task, you’re going to need to consider the above to help you accomplish your goal. The simple fact is that AI technology enables retailers to rapidly deliver an amazing new shopping experience to customers, and this is possible to accomplish through your existing stores. Machine learning is your ticket to saving countless hours, by increasing your productivity, revenue, and growth via efficiency, intelligence, and automation. This allows you to reinvest in your customer’s experience. 

Are you struggling to translate your data collected in the field into production-level applications in the smartest and most efficient way? 

Then it’s time to consider implementing Dataloop where you’ll be able to:

  • Take advantage of advanced automation abilities
  • Simultaneously ensure multiple projects are labeled, tasked, and QA’d with our data management platform
  • Provide real-time feedback
  • Use data in order to enhance the customer experience

Take it from our customer, Standard’s who successfully managed multiple projects, regardless of where their workforce was based, and was still able to scale their business into new stores in a very short period of time.

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