PredictHQ, a company that aggregates data sets from myriad events and public holidays to help companies forecast demand for their services, has raised $22 million in a series B round of funding led by Sutter Hill Ventures, with participation from Lightspeed Venture Partners, Aspect Ventures, and Rampersand VC.
The San Francisco-based startup meshes data from myriad sources related to events such as concerts, sports, and public holidays and then mixes in proprietary and other “hard to find” data. The company then throws all of this into a big melting pot, channels it into an API, and licenses it to companies like Uber, Domino’s, Quantas, and Booking.com.
So why is this data so useful? Well, it all comes down to predictive insights — knowing how much demand a service is likely to see. During a major music festival or sports event, for example, Uber often employs surge pricing, a mechanism to manage supply (and make more money) when demand is high. Surge pricing often kicks in with little to no warning, as the pricing mechanism simply reacts to a surge in demand. But knowing when to expect a spike in ride requests could allow Uber to alert drivers to be at a specific location at a certain time.
PredictHQ’s secret sauce is in the way it combines data. For example, knowing there’s a rock concert on a specific date in San Diego is useful, but adding in the fact that the American Society of Hematology is holding an exposition in the same area on the same day might suggest an even greater demand for rides. Moreover, Uber could tap other independent data sources — including hyper local weather forecasts — and if a torrential downpour is anticipated as the two major events are about to finish, drivers can be standing by to cash in.
Similarly, by using PredictHQ’s data Domino’s can garner greater insights into how many delivery drivers they might need on a specific evening, or whether they might need to order more ingredients.
Ultimately, PredictHQ is all about helping businesses cut down on losses by adapting their supply and pricing to suit demand.
The story so far
Founded out of Auckland, New Zealand in 2015, PredictHQ exited stealth three years later with $10 million in funding. The very same year, PredictHQ upped sticks and moved its global headquarters to San Francisco, with CEO and cofounder Campbell Brown moving his whole family to the U.S. With another $22 million in the bank, the company said that it’s well positioned to grow its data science team and bring its demand intelligence platform to more industries and markets.
“This funding will be used to grow the team, especially our data scientists who now make up about half of our team,” Campbell told VentureBeat. “We are focused on our correlation and prediction engine that will turn months of complicated data science work into a few hours for our customers.”
Back in September, PredictHQ launched its first industry-specific product called aviation rank, which is designed for airlines. Aviation rank uses machine learning models to forecast which global events are likely to impact the demand for flight bookings — this could be Oktoberfest in Munich, or industry events such as the World Dairy Expo in Madison. The reason why a specific product is required for the airline industry is due to the fact that not all events are created equally — some are more likely to attract inbound air traffic than others. A major music festival or technology conference will likely draw people in from far and wide, whereas a local standup comedy gig probably won’t. By tailoring its product for niches, PredictHQ widens its appeal.
According to Brown, the company will be working on similar product niches in the future, but for now it’s more focused on developing its main product.
“Aviation rank has performed really well for us, snagging us a series of leading airline customers,” Brown said. “But the opportunity in front of us is vast so we are focused on sequencing our investment into our core product and knowledge graph to generate even greater relevance. This creates value for all of our customers and target industries. We will be working on industry relevant products in the future, but we prioritized aviation rank early because airlines have very specific requirements.”
Big data is the driving force behind countless digital services, from issuing life insurance policies to unlocking insights into cities and improving public transport. Pittsburgh-based Gridwise, for example, bypasses ride-hail companies and targets drivers directly via a dedicated mobile app that uses big data and real-time alerts to inform drivers about potential ways to increase their earnings.
It’s clear that there is a growing demand for big data insights that help companies adapt to shifting consumer demand, which is often impacted by events in the real world.
“In today’s hyper connected world, it just doesn’t make sense for businesses to miss out on factoring the significant impact of real-world events into their forecasting, pricing, planning, and other business optimization strategies,” Brown said.