Team Olo Spotlight: Mingming Zhang

Our Principal Data Scientist, Mingming Zhang, joined Olo two years ago as part of our acquisition of Wisely. There she led the data science team and earned a 2021 Top Women in Restaurant Technology Award for her work on algorithms that accurately predict restaurant wait times and proprietary natural language processing (NLP) that annotates the sentiment of online restaurant reviews.

‍Before Wisely, Mingming was a Data Science Manager at Domino’s, where she was tasked with projects like combating fraud with machine learning.

Scroll to learn how all of that experience informs her team’s work at Olo, what she considers to be the biggest challenge and opportunity for restaurant data, and her thoughts on the role of AI and machine learning in the industry.
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Reflecting on the last two years at Olo, what are you most proud of?  

Mingming Zhang: I’m most proud of the machine learning and data science platform we’ve built. In less than two years, we’ve rolled out a few algorithmic products based on these platforms to our customers.

I’m also proud of the culture shift toward data-driven decision-making. The A/B testing framework we built has enabled our product managers to measure impact and iterate quickly based on data. We can also build case studies backed by rigorous data methods to demonstrate the metrics improvement for better product adoption.
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How does the work of our Data Science team benefit restaurant brands?

MZ: The work of our Data Science and Engineering teams allows brands to reap the benefits of AI with minimal investment. For example, the OrderReady AI machine-learning solution we delivered to help restaurants manage their capacity is built on a modern data and machine-learning stack that Olo invested heavily in building and maintaining. We did it so our customers don’t have to build their own capacity solutions and can invest in their core business.

What is the biggest challenge and biggest opportunity for restaurant data?

MZ: Restaurant data is usually generated across multiple vendors or touchpoints such as online ordering, waitlist, POS, payments, marketing campaigns, KDS, guest feedback, etc. It requires deep business process knowledge to understand how these data points interact with each other and how they impact the key metrics. Without an understanding of restaurant business processes, it can be challenging to turn massive amounts of data into actionable insights.

The biggest opportunity for restaurant data lies in two aspects: driving a personalized guest experience and driving higher operational efficiency. Restaurants can use data to tailor their services, whether on- or off-premise, to individual guest preferences. Personalization could also extend to targeted marketing strategies and loyalty programs based on individual dining history.

The potential for restaurant data to drive higher operational efficiency is equally transformative. Capacity management with an AI solution is a great example. We have also seen restaurant brands successfully use machine learning solutions to manage supply chain and labor schedules. With the emergence of generative AI, restaurants can use voice AI to reduce the cost of taking orders and level up their service.

Machine learning and AI will play a much bigger role in the restaurant industry in the next five years. This is the time to leap in.

How can restaurants use data to enable hospitality at scale?

MZ: Data can revolutionize every aspect of a restaurant business—from personalizing the guest experience to building targeted marketing campaigns, optimizing the menu, and improving day-to-day operations. But for this revolution to happen, restaurants need to foster a culture of data thinking within the company, starting with educating the team on what data can do. Any transformation starts with people’s mindset.

To learn more about Team Olo and apply for one of our job openings, visit our careers page.