Who: Canopy Labs, which offers a platform that lets businesses easily analyze customer data to gain more insight into their customers’ product interests, spending habits and more. Canopy’s customers include the Toronto Argos, WagJag and the Canadian Opera Company.
How much: $1.5-million from BDC IT Venture Fund, Peter Thiel’s Valar Ventures and several angel investors. Canopy recently graduated from the Y Combinator program.
Mini-interview with Wojciech Gryc, Canopy Labs’ CEO:
What does your product do? What problem/point of pain does it solve?
Our product is a platform that automates analysis of customer data to predict customer value (i.e. how much they’re likely to spend), customer sentiment (i.e. how happy they are), and customer loyalty (i.e. are they likely to purchase again). We liken it to providing the type of technology that powers Amazon or NetFlix recommender systems, but with the needs of SMEs in mind.
The use case is as follows: a sales or marketing team member will upload the customer data to our platform. The platform will then analyze the data without any coding necessary, and will provide the results in (1) a dashboard format, and (2) a format that can be imported to the team’s internal business intelligence tool.
The problem we’re solving is that customer analytics is crucial for successful marketing and sales campaigns, but often too expensive for SMEs. SMEs now depend on analytics consultants, who are slow and don’t scale well, or very expensive IT solutions. Furthermore, this space is expected to have a shortage of 150,000 workers (according to McKinsey & Co.), so automation is crucial.
How do customers generate ROI?
The platform will provide predictions around each customer that a business has. These model results get updated in near real-time, whenever the business provides new data or is running one of our apps (which automate data submission). ROI is then generated by knowing exactly which customers should be contacted for various campaigns.
For example: our loyalty models will tell you which customers are likely to purchase again, and which ones are not. Thus, the low scoring customers become great leads for a retention or churn campaign.