Tinus le Roux knows fans via Fancam he knows when they attend and how often they attend. On this podcast Sean & Tinus discuss the differences of computer vision and facial recognition and why it is important to sports business.
On this podcast you'll learn about:
- Tinus le Roux on how Fancam started and what it offers
- How Fancam got into the US sports market
- What computer vision does and why is it important
- How Fancam differs in capturing data to measure fan engagement
- How Fancam help sports teams close sponsorship deals
- Where they see Fancam going in the next 18 months
Listen to Tinus le Roux on Sports Geek
Tinus le Roux quote
Every time we take one of these pictures, people go look at it and they spend a lot of time there and then learn more about sports and sponsorship. Then we realize that's actually a sponsorship asset.
Tinus le Roux
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Resources from the podcast
- Connect with Tinus le Rouxe on Twitter @tinusleroux and LinkedIn
- Follow Fancam on Twitter @Fan_Cam, Facebook and LinkedIn
- Some related articles you might want to read more about Fancam:
- Some Sports Geek episodes you might be interested in:
- Tinus Le Roux on digital activations and developing campaigns, way back on episode 15
- Yinzcam story with Priya Narasimhan
Highlights from podcast with Tinus le Roux
4:30 Tinus le Roux chats about Fancam and how it started
7:03 Working on 360 imaging and VR before Fancam started
19:24 Expanding Fancam installation in different stadiums
20:20 How they are analysing data
20:59 The importance of computer vision in fan engagement
25:14 Why Fan Cam is not doing facial recognition
27:48 Tinus’ insights on managing database for ticketing
31:22 Tinus on their plans for Fancam in the next 12-18 months
32:48 How they work on the pitch for their clients
34:00 How Fan Cam can help you benefit in sponsorship deals
41:31 Sports Geek Closing Five
49:40 Digital to Dollars – Go to DigitaltoDollars.com
51:45 Subscribe to Sports Geek News. Go to SportsGeekHQ.com/sgn
Some tweets/posts you may have missed
Nice work with the Mets Fancam
Know anyone who went to the Mets game yesterday?
Never been to Citi Field?
Zoom in to explore…
https://t.co/Ix1PyyD7hV— FANCAM (@Fan_Cam) August 5, 2018
Good to see how it all started
7 years ago.
Man with a broken back capturing our first Fancam.@BobSkinstad @jamestaylorsurf pic.twitter.com/InS3RGNukM— Tinus le Roux (@tinusleroux) May 23, 2017
Follow Tinus on his birding expeditions
Very lucky to get this shot of a Yellow-streaked Greenbul.
Limited distribution and another forest bird that usually keeps itself well hidden. pic.twitter.com/8V9QPMQF7D— Tinus le Roux (@tinusleroux) July 6, 2018
Sean meeting podcast listeners in NYC including Tinus.
It's ok I've been joined by fellow #stevesmithing participants for a #sportsbiz Meetup with people from 🇦🇺 🇺🇸 🇳🇿🇿🇦 pic.twitter.com/0WrZaeJxqB
— Sean Callanan (@seancallanan) August 3, 2018
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More from Tinus le Roux
So for those having listened or planning to listen to my chat with @seancallanan , I thought to add to it by providing a few more practical use cases of computer vision in sports – and specifically how we're using it at @Fan_Cam .
Hope it helps… https://t.co/FE4zfITmpK— Tinus le Roux (@tinusleroux) August 20, 2018
Let's start by cutting out he buzzwords to get to the core opportunity for #SportsBiz people:
"If you could have a computer look at your crowd, what would you ask that computer?"
Here are some of the questions we're getting (and answering) at present:
— Tinus le Roux (@tinusleroux) August 20, 2018
Average Age..
– how old is my crowd?
– how does it change from one game to the next?
– why?
– how does this compare to league averages?
– how fast is my crowd growing older/younger?
– let me know when you guys see a spike, so we can act quickly to address it pic.twitter.com/aFKhQvAKEH— Tinus le Roux (@tinusleroux) August 20, 2018
Age distribution..
– how many millennials are coming to our games?
– we changed ticket brokers, can you pick up a change in demographics – we're concerned they're not reaching our target demo
– what's the crowd composition that delivers highest merch revenue? pic.twitter.com/9v1fv3oUNH— Tinus le Roux (@tinusleroux) August 20, 2018
Gender distribution
– what's the male/female split per age group?
– what's the effect of the weather/pricing on female attendance?
– how can we use these trends to predict distribution and plan our game day activations and merch stock accordingly? pic.twitter.com/pPM6ZXvGZR— Tinus le Roux (@tinusleroux) August 20, 2018
Combined data
– Please have a look at our season ticket seats. Are the same people sitting in them every game and if not (because that's the answer) how many games do they typically attend?
– show me which seats are always empty.— Tinus le Roux (@tinusleroux) August 20, 2018
– combine game data with 'bums in seats' data and help us predict how Gen X males react to a blowout. when do they leave?
– look at our seat packages sold on the secondary market, how many families bought those tickets?
– what's the optimum price point that draws most families?— Tinus le Roux (@tinusleroux) August 20, 2018
– how many people are paying attention to the jumbotron ads?
– which ad played best for Gen X females?
– how many people are looking at their phones?
– how does the differ from one generation to the next?— Tinus le Roux (@tinusleroux) August 20, 2018
There are obviously a 1000 more questions 'we can ask the computer', but these are some of the ones we're seeing in the industry today and I thought those who listened to the podcast would find it helpful to further their thoughts on the topic.
— Tinus le Roux (@tinusleroux) August 20, 2018
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