National Pizza Chain Improves Service Speeds and Scales Growth with AI

Background

Client

Pizza Chain

Project

Improve Speed of Service and Scale Growth

Ascent South Lake Union

A national “build-your-own” pizza chain had the need to streamline food production, improve employee efficiency and increase overall customer service satisfaction amid a growing demand for their pizza offerings and with continued labor shortage challenges. To grow efficiently, this QSR chain took advantage of computer vision and artificial intelligence to streamline processes and improve their service.

  • Structure: National Fast-Casual Pizza Restaurant Chain

  • Locations: 100 units

  • End goal: Improve server efficiencies and overall customer service

  • Solution: Touchless, computer vision system for measuring queue times and speed-of-service, customer satisfaction, and employee efficiency, at scale across a portfolio of restaurants

  • Data insights: Speed of service (queue times including line waiting to place order, time from placing order to payment and then time from pay for order to pick up order), as well as customer satisfaction and employee efficiency (time for employees to produce an item and time for employees to ‘reset’ after completing the order)

Introduction:


The “build-your-own” pizza chain was experiencing significant increased demand for their pizza offerings during the pandemic and now coming out of it. Stretched thin, this fast-casual restaurant chain needed help scaling their growth. In addition, getting and maintaining staff was a challenge and so they needed a way to streamline food production and improve server efficiencies as well.


Customer complaints were building due to increased queue lines and so they looked to NomadGo with the idea to use computer vision for better data and insights at their physical locations to improve food preparation processes, employee efficiency and ultimately improve service to their customers.


With a 360 view of each store, corporate was able to open new locations while ensuring quality and service standards were maintained. A regional manager no longer had to go out and physically visit a store unless the data identified a reason to.

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The Challenge

Before Nomad Go

Challenge / Goals:

Growing demand and ongoing pressures to operate within thin margins, this national chain of fast casual restaurants needed to find an easy and affordable way to track performance at-scale and find new, more efficient ways to prepare pizza and serve their customers.


Specially they were looking to:

  • Shorten queue times

  • Streamline food preparation / employee processes / employee efficiency

  • Track store performance at scale without significant resources

  • Improve customer satisfaction and ultimately retention to protect revenues

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The Solution

After Nomad Go

Solution

An automated, computer vision-based, visual intelligence system for collecting key data points was utilized to measure, analyze and improve for key customer service, employee and product preparation processes.

  • Improving Customer Experience – measure queue times (speed of service) and customer satisfaction

  1. Time in line waiting to place order

  2. Time from place order to pay for order

  3. Time from pay for order to pick up order at counter

  4. Total speed-of-service (combination of three from above)

  5. Customer rated speed of service using Nomad Go SmartSurvey on a scale of 1- exceptional, 2-good, 3-fair or 4-poor/bad

  • Enhancing Employee Efficiencies – measure employee efficiency across the portfolio

  1. Time for employees to prepare the menu item

  2. Time for employees to ‘reset’ after completing the order

  3. Track performance across all restaurants in real-time

  4. Analysis of inefficiencies causing customer churn across the portfolio

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The solution needed to collect accurate, reliable data in real-time without deploying significant human resources and it needed to scale across their restaurant portfolio to capture a 360-degree view of food preparation and queue lines so that they could understand inefficiencies and areas for improvement.


The highly flexible Nomad Go system included affordable iPhone devices that were flexible and accommodated the need for customer purchase journey data capture and employee production data capture. No other infrastructure was needed, simply the edge-based processing devices and Nomad Go app.

Results

    Results
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With the help of visual intelligence, this QSR pizza chain was able to quickly measure, analyze and improve server efficiencies and speed of service, resulting in increased customer satisfaction and retention. Sales growth continued and they were able to reduce churn for a healthy profit margin.


“We have been long-time users of Nomad Go to help us gain real-time insights about physical activity through our stores, from occupancy to speed-of-service. We were impressed with how accurately it was able to capture guest count data and scale to multiple locations quickly. With the effectiveness of the tools that Nomad Go builds, we expect innovative solutions such as HealthySpaces to continue to help restaurant operators run great and safe restaurants.”


Matt Avila, Director of Operations

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Conclusion

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. Learn more https://www.nomad-go.com/

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We are excited about the opportunity Nomad Go’s technology offers our properties, our clients, and our tenants. With over 200 owned assets and over 2,000 properties under management globally, we are looking forward to expanding the use of Nomad Go and seeing improved business and environmental impact across our portfolio.

 Adrian De Smul

Senior Director of Customer Experience
& Innovation at Greystar

Image by Stephen Dawson

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