measuring digital signage networks - quividi
DESCRIPTION
"Measuring Digital Signage Networks and Using Metrics to Optimize Your Impact" was presented by Olivier Duizabo, CEO, Quividi, at BroadSign's European Client Summit in London England on June 24, 2013.TRANSCRIPT
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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…
