{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Bootstrapper's Paradise","title":"Using Data and Machine Learning to Help Restaurants Operate More Profitably","html":"","width":"100%","height":180,"duration":1718,"description":"Matt Wampler, CEO and co-founder of ClearCOGS, talks about how their company uses data and machine learning to help restaurants reduce waste and increase profits. He shares the importance of customer research and finding the right balance between innovation and simplicity. They also discuss ClearCOGS's growth and their approach to making businesses more efficient, finding bottlenecks and processes, and investing in co-founder relationships.\n\nMatt Wampler is the CEO and Co-Founder of ClearCOGS, a company that utilizes POS data and proprietary machine-learning models to help restaurants minimize food waste and optimize inventory management. With his expertise in executive management and a passion for leveraging technology, Matt leads ClearCOGS in revolutionizing the way restaurants save money and improve their operations. Here are a few of the topics we’ll discuss on this episode of Product Launch:\n\nClearCOGS helps restaurants operate more profitably with data and machine learning.\nRestaurants make many operational decisions based on feel, which isn't always accurate.\nAs their system gathers more data, it becomes more intelligent and predictive.\nFocusing on a singular pain point can be more effective than trying to solve multiple problems with a bloated system\nInvesting in maintaining that relationship is essential to business success\nOnboarding for a new customer went from six weeks to one day\nClearCOGS helps restaurants percentage off their food costs and move big needles\n\nResources:\nClearCOGS\nNxtStep\nPodcast Chef\n\nConnecting with Matt Wampler:\nLinkedIn\n\nConnecting with Sean Boyce:\nLinkedIn\nEmail\n\nQuotables:\n02:13 - “So what we do at ClearCOGS synthesize all of that data down, we look at the day of the week, the month of the year, your short-term trends, yearly seasonality, long-term trends, all these external data points just to figure out for this store.”\n07:44 - “Meeting the customer where they are, in my world, at least the world of restaurants, that couldn't...","thumbnail_url":"https://img.transistorcdn.com/B0rBF_s0upJdRqXoy4_4rHlfSlZONOaHnrUmmpOi708/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9kYmIx/MTMxNTExY2VjZjIz/NjEzNWFhNmI4OGZl/ZDliMy5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}