Measuring Digital Signage Networks - Quividi
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Transcript of Measuring Digital Signage Networks - Quividi
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Measuring digital signage networksand using metrics to optimize your impact
Olivier Duizabo, CEO, Quividi
BroadSign ConferenceLondon, June 24th 2013
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Agenda
• Why measure?
• How to measure?
• What do you get?
• Who’s doing it?
• What’s next?
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>> Why? How? What? Who? What’s next?
Why measure?
• Learn what works and what doesn’tGet solid evidence to base your growth upon
• Optimize locationsIdentify places where people pay most attention
• Fine-tune your contentKnow what’s attractive to your key targets
• Value your airtimeMonetize your screen with proven audience data
• Make Digital Signage a trusted mediaDemonstrate ROI
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>> Why? How? What? Who? What’s next?
Why automated measurementwith face detection?
• PrecisePassive/unbiased method, 1/10th sec. precision
• ExhaustiveMeasure all of the audience, 24/7
• Real-timeGet audience data early on, use it on the fly
• Easy to deployAdd the software to your player + a webcam (or IP cam) and you’re done
• Competitive wrt. standard methodsCosts a fraction of traditional methods
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>> Why? How? What? Who? What’s next?
Why Quividi?
• Industry pioneer since 2006• 2Bn faces detected, 6000+ locations• The largest customer base with 150 screen networks
measured across 35 countries
Why? >> How? What? Who? What’s next?
How does the solution work?
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A video analysis software running on your digital signage player and webcam, placed on or below your screen, that:
• Counts faces turned toward cameraFace detection – not nor eye tracking
• Classifies viewers by demographicsBased on facial traits (hair, skin, chin, etc)
• Tracks head movementsKnows when a person is looking or not, counts him as one as long as in the field of view
• Models movement in sceneryIsolates human silhouettes
• Uploads data to a back-office server
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Why? >> How? What? Who? What’s next?
Demonstration
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Why? >> How? What? Who? What’s next?
How does it protect privacy & data integrity?
• No human seeing any image• No video recorded• No face recognition
Video Stream
Local SW(local automated
processing) Encrypted Audience Data
Private Online
Back-office
ChartsReal timeaudience description
made available to CMS
• Data redundancy checks• Alerts on anomaly• Rights management per data
3rd party data(eg Proof of Perf
reports)
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Why? >> How? What? Who? What’s next?
How to deploy it?
• Start with a pilotLearn, compare, experience, challenge
• Take time to analyze the data• Define objectives
Areas of focus, strategic use of audience data, team organization, processes
• Plan deployment on next roll outLarge economies of scale if integrated into new design
• Or build panel Appoint 3rd party to select representative screens and certify your extrapolated data
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Why? How? >> What? Who? What’s next?
What metrics do you get?
Globally # of Opportunities
To See # of Viewers Conversion ratio
viewers / OTS Average Unit of
Audiencenew industry trading currency
For each viewer Dwell (presence) time Attention (gaze) time Gender
(male / female) Age class
(0-8 / 8-35 / 35-65 / 65+) # of gazes
In real time All of the above + Position Distance Currently
watching or not
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Why? How? >> What? Who? What’s next?
What you get: overviews
Data courtesy of www.media-reciprocity.com
No two screens
are alike
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Why? How? >> What? Who? What’s next?
What you get: insight on peculiar days
Data courtesy of JR Railand Ocean Outdoor
Drill down to
discover insight
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Why? How? >> What? Who? What’s next?
What you get: understanding demographic differences
Data courtesy of Green Room Retail
Human groups
have contrasted
behaviors
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Why? How? >> What? Who? What’s next?
Media
Media Viewer
CountMedia Runtime
(Mean) Play CountMedia Runtime
(Total)Media Viewers /
hour runtimeMedia #140 10 860 139 sec. 3 019 116:34:01 93,2Media #436 9 712 383 sec. 766 81:29:38 119,2Media #336 6 289 115 sec. 1 530 48:52:30 128,7Media #344 5 694 46 sec. 1 534 19:36:04 290,5Media #432 5 477 342 sec. 674 64:01:48 85,5Media #435 5 379 414 sec. 404 46:27:36 115,8Media #351 4 406 113 sec. 1 468 46:04:44 95,6Media #6 4 217 94 sec. 2 167 56:34:58 74,5Media #420 3 454 51 sec. 1 523 21:34:33 160,1Media #431 2 964 38 sec. 1 513 15:58:14 185,6Media #348 2 726 91 sec. 1 458 36:51:18 74,0Media #398 2 656 66 sec. 276 5:03:36 524,9Media #353 2 509 35 sec. 1 466 14:15:10 176,0Media #434 2 418 37 sec. 1 036 10:38:52 227,1Media #402 2 019 60 sec. 252 4:12:00 480,7
What you get: ad campaigns comparisons
Media
Media Viewer
CountMedia Runtime
(Mean) Play CountMedia Runtime
(Total)Media Viewers /
hour runtimeMedia #140 10 860 139 sec. 3 019 116:34:01 93,2Media #436 9 712 383 sec. 766 81:29:38 119,2Media #336 6 289 115 sec. 1 530 48:52:30 128,7Media #344 5 694 46 sec. 1 534 19:36:04 290,5Media #432 5 477 342 sec. 674 64:01:48 85,5Media #435 5 379 414 sec. 404 46:27:36 115,8Media #351 4 406 113 sec. 1 468 46:04:44 95,6Media #6 4 217 94 sec. 2 167 56:34:58 74,5Media #420 3 454 51 sec. 1 523 21:34:33 160,1Media #431 2 964 38 sec. 1 513 15:58:14 185,6Media #348 2 726 91 sec. 1 458 36:51:18 74,0Media #398 2 656 66 sec. 276 5:03:36 524,9Media #353 2 509 35 sec. 1 466 14:15:10 176,0Media #434 2 418 37 sec. 1 036 10:38:52 227,1Media #402 2 019 60 sec. 252 4:12:00 480,7
Some ads work
better than others
Why? How? What? >> Who? What’s next?
Who’s doing it: Amscreen
• 6,000 screens across 8 countries in Europe, Africa & Middle East, mostly in gas station stores
• Uses audience data to raise CPM and justify ad rates• Announced 100% equipment with Quividi and a standard webcam
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"It’s revolutionary not because the technology hasn’t been used before but because of the [study’s] sheer scale and size, and because it’s a permanent, rather than temporary, installation. It’s a positive initiative." Carolyn Nugent, head of digital, Kinetic
April 8th 2013
Why? How? What? >> Who? What’s next?
Who’s doing it: Ocean Outdoors
• Specialist of large outdoor digital screens in the UK• High definition cameras to track tens of persons at once• Real-time analytics to identify majority gender and target
content accordingly• Automated campaign reports with proven audience, by crossing
audience data with a proof of performance reports 16
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Why? How? What? Who? >> What’s next?
What to do with audience data?
• Gain insight by building knowledge at the macro and micro level
• Introduce new business models (e.g. pay per view)
• Introduce adaptive loops, depending on – Demographics– Behavior– Nb of viewers– Position
• Benchmark your network
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Why? How? What? Who? >> What’s next?
The 2012 DOOH Audience Report
• Published by the Ministry of New Media, based on Quividi data
• A sample of 69 networks• Average week over 6 months• 18 venue types x screen placements
Venue type 3D screenHigh
impact
Long dwell time
Wander by
Window screen Global
Banking X XBar restaurant X XPharmacies X X XSmall store X X X X XSuperstore X X X XTransportation hub X X X XGlobal X X X X X X
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Why? How? What? Who? >> What’s next?
Typical learning from the Audience Report
• 4.4 seconds of attention time globally– Range varies from 1.5 to 9.6 seconds
• Long dwell time screens in banks– 447 viewers per day– 8.3 sec of attention time – 42% conversion ratio– Daily AUA of 220
• The older people get, the more attentive Child
Young adultAdult
Senior
Average
0
20
40
60
80
100
120
Attention timeDwell time
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Why? How? What? Who? >> What’s next?
What to expect in the future?
• StandardizationDP-AA guidelines, Methodologies, Media planning software
• EmbeddingPreloaded players, CMS, screens with built-in cameras
• More sensors / more insightGlobal behavior on the premises, frequency of visit/look, hand pick of product…
• Integration with other data setPurchases, predictive analysis…