Accurate exposure: vBrand knows exactly how effective your campaign is


In an era in which data has become a key to any decision by large corporations, a great need has arisen for an automated solution that will generate rich statistics for exposure, while enabling advertisers to optimize their buying and advertisers to better price their assets. vBrand does just that for sports sponsorships.

A short pitch: What does the company do?

A: vBrands is an artificial intelligence (AI)-based platform used to measure the value and effect of campaigns based on sponsorships in sport. It scans all the content channels, including TV channels, internet broadcasts, and the social networks, and analyzes the results using big data.

A slightly more thorough explanation

A: vBrand wants to bring the AI and Big Data revolution to marketing in sports. We help advertisers and advertising rights owners to understand the value of their advertising space and the impact that they generate. Today, consumption of sports content is reaching all-time peak audiences. Sports broadcasting rights are split among hundreds of TV channels and digital platforms. Measuring exposure created by event sponsorships is becoming a great challenge for advertisers and advertising space owners. The platform scans TV channels and social networks 24/7, automatically detects whenever a brand appears in a video clip or picture, and provides statistics about the appearance in real time.

Identifying branding on sports with vBrand Photo Credit: vBrand
Identifying branding on sports with vBrand Photo Credit: vBrand

Q: How did you get the idea?

A: We were exposed to the great difficulty experienced by the various players in the sports industry, a $60 billion market unable to correctly price the value of exposure on the one hand and the investment on the other. We realized that there was a great black hole here, but also a great opportunity to build a smart automated system that would bridge this gap. Thanks to our experience in AI and image identification, we possess these capabilities, and were able to devise a system that provides easy and accurate solutions for all the concerns in this sector.

Q: What stage have you reached?

A: We have a working product. A number of international companies are already benefiting from the exposure and actual valuation of sponsorship campaigns in various channels and territories. Our system today analyzes dozens of TV and internet channels and social media 24 hours a day. We are consistently expanding the type of visual information we detect and the supported sources of the information.

Q: Who are your competitors?

A: Our main competitors are sports consultant firms. They usually use manual branding of hundreds of people (some say battalions) watching games and marking when and where the brands appeared in the broadcast. In contrast to our solution, their results are not updated in real time with an interactive dashboard.

Q: How do you plan to make money?

A: By selling packages of measurement data and analysis of variance on an individual or annual basis to a variety of concerns in the sports market, especially advertisers and broadcast rights owners.

Q: Have you already received investments? How many? Who invested?

A: We have raised $2 million so far from Nielsen Funds and Nielsen Innovate.

Q: Who are the company founders?

A: Tamir Rubinsky and Eli Ben-David founded the company.

Q: How many employees do you have? Where are your offices?

A: We have eight employees in Israel and a number of representatives in Europe and the US. Our offices are located in Tel Aviv.

vBrand is taking part in the Startup Arena, Geektime’s startup competition taking place for the ninth consecutive year in the framework of the Geektime Conference. Past participants include companies such as Kaltura, Cyactive, and SalesPredict, among others. The 2016 Startup Arena competition this year is sponsored by Altshuler Shaham Benefits.


  1. Hi
    Its great to know such kind of real time solution for Sponsorship value using Computer Vision. As being a computer vision student ,I am curious to know what state of the art algorithms of deep learning you guys are using for logo detection.Which give results in near real time .


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