C3 IoT is an analytics platform founded in 2009 by Tom Siebel, a seasoned software executive who pioneered CRM technologies. C3 IoT has a strong team of data scientists, and is building machine learning algorithms to create value for customers. C3 IoT does not focus on hardware and / or sensoring, but rather the platform that gathers disparate data sources and performs analytics to deliver insights. Upon founding, C3 IoT applications focused on the energy grid, due to the amount of sensor information that existed; however, very quickly Tom Siebel found that the algorithms and applications he could develop were broadly applicable beyond energy, and the company rebranded to expand focus. The company has raised over $200M of funding from well-regarded investors such as TPG Growth, and is looking to build out use cases in a market that is crowded with large incumbents such as GE and Salesforce.
Example Use Cases
C3 IoT has defined multiple applications on its platform that create value for customers; the two critical prerequisites for delivering this value are data unification and intuitive machine learning prediction. As previously mentioned, the company started on the energy grid. C3 IoT’s Energy Management Application uses machine learning to save companies energy usage costs. To do so, C3 IoT spends a few weeks unifying a companies data through its canonical data set (i.e., mapping customer data to its machine learning models in a format that is compatible). After data unification, C3 IoT can create intuitive summary views on its platform, as depicted in Figure 1. These summary views give operators a quick window into energy consumption and help identify potential savings opportunities.
Figure 1: C3 Energy Management Application
Once C3 IoT has gathered and unified the data, the company can utilize its machine learning algorithms. C3 IoT’s talented team of data scientists have the ability to quickly define learning models that create value for companies. Figure 2 below shows a building’s actual energy consumption as compared to an energy consumption prediction that is the result of machine learning algorithms. This information can help organizations identify anomalies at the building or resource level and course correct / save money.
Figure 2: Energy Forecasts on C3 IoT’s Energy Mangement Application
Challenges / Outlook
C3 IoT’s challenge moving forward will be to define and execute additional use cases alongside customers in a crowded market. For example, C3 IoT recently announced a CRM application that leverages historical sales data and external data and machine learning models to forecast sales performance and qualify leads. This is obviously an application that is of interest to large incumbents like Salesforce. C3 IoT’s ability to execute and win in these expansion areas will be primarily incumbent on speed and data. If C3 IoT can quickly define learning models that deliver value for customers and onboard new customers as quickly as possible, the organization can build a differentiated edge as compared to companies like Salesforce through a learning and data advantage. I.e., C3 IoT’s algorithms will be smarter as a result of more advanced learning through customer data.
 Interview with company employees