Last November if you were driving a BMW x5 or a Volvo XC60 on the highway ringing Moscow, you might have noticed a digital billboard on the side of the road flash an ad just as you approached, one for a new SUV from Jaguar. If it was evening, you saw an ad with a dark background, helping the car stand out. In bad weather, you saw it maneuvering in the snow. Targeted advertising is familiar to anyone browsing the Internet. A startup called Synaps Labs has brought it to the physical world by combining high-speed cameras set up a distance ahead of the billboard (about 180 meters) to capture images of cars. Its machine-learning system can recognize in those images the make and model of the cars an advertiser wants to target. A bidding system then selects the appropriate advertising to put on the billboard as that car passes. Marketing a car on a roadside billboard might seem a logical fit. But how broad could this kind of advertising be? There is a lot an advertiser can tell about you from the car you drive, says Synaps. Indeed, recent research from a group of university researchers and led by Stanford found that—using machine vision and deep learning—analyzing the make, model, and year of vehicles visible in Google Street View could accurately estimate income, race, and education level of a neighborhood’s residents, and even whether a city is likely to vote Democrat or Republican. Synaps’s business model is to sell its services to the owners of digital billboards. Digital billboard advertising rotates, and more targeted advertising can rotate more often, allowing operators to sell more ads. According to Synaps, a targeted ad shown 8,500 times in one month will reach the same number of targeted drivers (approximately 22,000) as a typical ad shown 55,000 times. The Jaguar campaign paid the billboard operator based on the number of impressions, as Web advertisers do. The traditional billboard-advertising model is priced instead on airtime, similar to TV ads. In Russia, Synaps expects to be operating on 20 to 50 billboards this year. The company is also planning a test in the U.S. this summer, where there are roughly 7,000 digital billboards, a number growing at 15 percent a year, according to the company. (By contrast, there are 370,000 conventional billboards.) With a row of digital billboards along a road, they could roll the ads as the cars move along, making billboard advertising more like the storytelling style of television and the Internet, says Synaps’s cofounder Alex Pustov. There are limits to what the company will use its cameras for. Synaps won’t sell data on individual drivers, though the company is interested in possibly using aggregate traffic patterns for services like predictive traffic analysis and the sociodemographic analysis of commuters versus residents in an area, traffic emissions tracking, or other uses. Out of safety concerns, license plate data is encrypted, and the company says it will comply with local regulations limiting the time this kind of data can be stored, as well. “It’s just like an offline cookie,” says cofounder Aleksey Utkin.
Start-up Synaps Labs has moved targeted advertising from being purely internet-based to being in the real world. It used high-speed cameras placed about 180 metres in front of billboards that would take images of passing cars. The machine-learning technology would then recognise the make and model of the cars, and match them up to drivers an advertiser wanted to target. The selected advert will appear on the billboard as the car drives by. Advertisement