Nomad Go Success Story – Accelerating Innovations for Large Coffee Chain
Updated: Jun 16
Large coffee chain accelerates product innovation for operational efficiencies and customer experience enhancements.
· Structure: Quick Service Restaurants, Coffee Chain
· Locations: Innovation lab used for rollouts to retail sites
· End goal: Accelerated innovations for operational efficiencies and improved guest experience, reduce reliance on manual processes and staffing
· Solution: Computer vision and artificial intelligence for measuring and improving new product and service innovations with greater precision
· Data insights: Speed of service, dwell time, customer counts/movements/flow, employee productivity/movement/activity, production/product journey/counts/detection
As mobile orders surge and digital transformation are in full swing across the majority of quick-service restaurants in the USA, one large coffee chain is leading the way by accelerating its product innovations with operational efficiencies and customer experience enhancements using computer vision and artificial intelligence from Nomad Go.
With the goal of operational excellence specifically around streamlining food and beverage production, and the need to improve the speed of service, quality product, and overall exceptional service, despite staffing shortage challenges, this large coffee chain tapped into computer vision and artificial intelligence for innovation around front and back of the house operation improvements.
The push to digital transformation during and post-pandemic has this large coffee chain rethinking its business formats, processes, and its ongoing dependency on digital channels. Their customers continued to demand fast service, high product quality, and exceptional service regardless of service or delivery format.
In the meantime, capacity restrictions, labor costs, and labor shortages have and continue to challenge their current operations and as well, for new product innovations.
To innovate faster, better and cheaper, this industry leader wanted automated data collection and deeper insights from innovation tests. This would allow them the ability to expand testing and innovation beyond one standalone lab environment and improve results around launching new products, processes, and services.
An automated, computer vision-based, visual intelligence system for collecting test data in the innovation lab during test runs was utilized to measure, analyze and improve for key customer, employee, and product data points.
Improving Customer Experience - speed of service, dwell time, customer counts, customer movements and activity as well as customer flow data and insights were captured, analyzed, and explored for modifications and enhancements to improve the overall guest service experience.
Enhancing Employee Efficiencies - employees' productivity and utilization, as well as dwell time, movement, and activity data and insights, were captured, analyzed, and explored for modifications and enhancements to improve the overall staffing efficiencies and employee productivity.
Product and Production Efficiencies - production times per product, product journeys (product paths), employee utilization per product, product count and detection, and other product data points were measured, analyzed, and explored for modifications and enhancements to improve product and production efficiency.
This large, brand was looking for a way to see test results in real-time and compare results across different tests with a desire for rapid ideation and comparison of variables. Having a flexible system that could move and adapt to different tests, accommodating different formats, layouts, experiments were critical for success. It needed to be a system that could be easily deployed beyond the lab for field testing in real store environments and that didn’t require major retrofit of existing stores.
The solution needed to collect accurate, reliable data from the field without deploying significant human resources and it needed to scale the innovation process beyond one isolated lab, capture a 360-degree view of field stores so that they could learn about hidden process efficiencies, best practices, and areas for improvement.
The highly flexible Nomad Go system included affordable iOS smart devices that were flexible and accommodated the testing environments and formats of different size, shape, spacing. No other infrastructure was needed, simply the edge-based processing devices and the Nomad Go app.
Not only did this large organization accelerate their innovation cycle and improve their learnings for a faster go-to-market process, but they also saved money with less labor needed to run tests. The in-store, in-lab environment was digitized accelerating digital transformation for their operations. Key highlights included: (1) reduced data collection time by nearly 800 hours per month and (2) are now running experiments and getting results four times faster than prior to using computer vision.
About Nomad Go
Nomad Go is the only end-to-end computer vision solution that provides real-time data so companies can understand and respond instantaneously to how people are using physical spaces.
Traditional systems are costly and time-intensive to deploy. The Nomad Go solution deploys quickly and cost-effectively, allowing companies to get real-time insights about physical spaces out of the box instantly. See how it works at: https://www.nomad-go.com/how-it-works