Quick-service restaurant chain innovates customer experience with AI

Quick-service restaurant chain innovates customer experience with AI

Challenges

An international restaurant chain wanted to innovate their quick-service restaurant (QSR) interactions with customers via order-taking automation.

By reducing order-taking efforts at the drive-thru window, the QSR chain could create a more consistent customer experience when demand increased during peak hours. Automating the routine order taking process would assist service leads and reduce delays to help ensure a high level of customer satisfaction.

Solutions

Neal Analytics worked with the customer to modernize the order-taking process by leveraging AI, voice dictation, and intent mapping technology. Our solution combined state-of-the-art AI, Azure Cognitive Services, specialized hardware, and field-tested architecture.

The voice ordering solution is based on integrating three core components: Customer detection, voice transcription, and order intent mapping.

3 components of voice ordering solution

Neal Analytics built a scalable solution to capture, translate, and analyze orders. The solution takes an input of the customer audio from a high-quality microphone array at the drive-thru window.

  • Customer orders at the drive-thru window are recorded by a high-quality microphone array.
  • An AI bot acts as an “intelligent” voice agent to take customer orders, leveraging the solution’s logic process to converse, clarify orders, and move customers through the process.
  • The audio (wav file) of the order is sent to Custom Speech, using acoustic and language models for Speech to Text.
  • The text stream is then sent to the Language Understanding (LUIS) model for entity detection.
  • The solution maps the text to stored menu items and order intent.
  • The solution generates a valid order list of items (item, quantity) to send directly to the restaurant chain’s POS system.

Voice ordering solution architecture diagram

Some key challenges around AI voice ordering included managing the impact of environmental noise, high variance found in customer order styles, and the process of mapping conversational orders with the Language Understanding (LUIS) model.

However, with continuous testing and quick iterations, the Neal Analytics team was able to further train the models to improve  accuracy and mapping.

Results

Neal Analytics integrated AI-enabled audio hardware and a custom voice ordering solution to assist service leads during peak hours. This “digital drive-thru” connected to restaurant chain’s Point of Sale system to generate orders, using logic to map order intent to menu items. Below is an example of the process.

Voice order example

By automating the order-taking process with bots, Neal Analytics was able to help the restaurant chain…

  • Lower customer service costs
  • Provide faster, quality responses
  • Improve the customer experience

The solution is poised for a large-scale roll-out with a major quick-serve restaurant.