Analytics in the Physical World Ross McKegney CTO, Fadow Ltd 1 April 2009
http://abstrusegoose.com/114
Context <ul><li>We’re all here because we know how powerful web analytics can be </li></ul><ul><li>Today, I’m going to sho...
 
 
 
Quividi Demo
Measuring Impressions <ul><li>What we can measure: </li></ul><ul><ul><li>Time of impression </li></ul></ul><ul><ul><li>Len...
The opportunity <ul><li>Gartner listed the “Real World Web” as one of the top-10 strategic technologies for 2008 </li></ul...
Sensors at our disposal <ul><li>Point of sale devices </li></ul><ul><li>Consumer self-service kiosks </li></ul><ul><li>Dig...
Streitz, et al., 2003
examples
Polo Madison Avenue http://www.fashionwindows.com/windows3/2006/0604.asp
www. microsoft .com/ surface
mi-tu smart dressing rooms http://www.rfidjournal.com/article/articleview/3595/
http://www.biggu.com/applications/
audience measurement
Example: Pharmacy Traffic Measurement © Quividi, 2009
Measuring Conversions (Attention) © Quividi, 2009
 
http://www.trendhunter.com/trends/weight-controlled-bus-stops-fitness-first-wants-people-to-know-their-weight
future
Opportunities <ul><li>Computing is getting smaller, cheaper, faster, and easier to deploy pervasively </li></ul><ul><li>Im...
Challenges <ul><li>First of a kind = higher risk </li></ul><ul><li>Perceived threat to personal privacy </li></ul><ul><li>...
Pattie Maes & Pranav Mistry , MIT Media Lab – Sixth Sense wearable device http://www.ted.com/talks/pattie_maes_demos_the_s...
Analytics in the Physical World Ross McKegney CTO, Fadow Ltd 1 April 2009
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McKegney -- Analytics in the Physical World

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"Analytics in the Physical World" presented to eMetrics 2009 in Toronto, Canada 1 April 2009.

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  • This morning, I gave a presentation at eMetrics 2009 titled 'Analytics in the Real World'. my focus was on the blurring of boundaries between the web and the real world, and the important new role that web analytics practitioners can play in defining goals and metrics for these new systems, given data derived from pervasive sensor technologies. It used to be that the web was its own distinct entity, and that the web and bricks&mortar sides of businesses were kept at arms length. Increasingly, cross-channel scenarios have played out and we've seen the first steps towards measuring in-store behaviour along the lines of looking at things like cross-channel promotions, buy online pickup in store initiatives, etc.. But we need to think much deeper than these basic scenarios.

    Computer vision can now in a cost effective way measure human traffic in your venues, simple pressure sensors can be used to drive digital signage based on how customers interact with your products, and the arguments against RFID are gradually being whittled away. What these sensor technologies provide is the means by which venue operators can measure and react to what is happening, in real time. Processing vast amounts of anonymous data to look for trends, to measure aggregate results, and to trigger events. Sound familiar? This is what web analytics practitioners do on a daily basis in looking at traffic to online sites.

    The slide deck is posted below. Unfortunately, it's not entirely meaningful without the associated dialogue. If you missed the talk, and are confused (and intrigued) by the presentation, contact me and we can discuss. I've spent a lot of time researching sensor technologies, and have a pretty keen sense for how they should be applied in practice. In fact, that's the bulk of the message in the missing dialogue -- that these are all 'cool' technologies, and the hard work here is not on inventing new sensors but rather devising approaches to using technologies such as these to drive your business forward. It's a very exciting space!

    (oh, and if you think I pushed the boundary a bit with my opening cartoon, keep in mind that this was the first presentation of the last day of the conference, in a track titled 'Analytics on the Edge', AND it was April Fool's Day -- so I felt justified to give my intro a bit of an edge. Besides, if you follow it through to the end you see that I come full circle and that in fact there was a very good reason to discuss this scenario).
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  • McKegney -- Analytics in the Physical World

    1. 1. Analytics in the Physical World Ross McKegney CTO, Fadow Ltd 1 April 2009
    2. 2. http://abstrusegoose.com/114
    3. 3. Context <ul><li>We’re all here because we know how powerful web analytics can be </li></ul><ul><li>Today, I’m going to show you how we can collect the same type of information in physical venues </li></ul>
    4. 7. Quividi Demo
    5. 8. Measuring Impressions <ul><li>What we can measure: </li></ul><ul><ul><li>Time of impression </li></ul></ul><ul><ul><li>Length of impression </li></ul></ul><ul><ul><li>Gender (heuristics 90+% accurate) </li></ul></ul><ul><ul><li>Adult/Child </li></ul></ul><ul><ul><li>Opportunity to see </li></ul></ul><ul><li>What can this tell us: </li></ul><ul><ul><li>Basis for CPM calculation </li></ul></ul><ul><ul><li>A/B testing of advertisements (length/content) </li></ul></ul>
    6. 9. The opportunity <ul><li>Gartner listed the “Real World Web” as one of the top-10 strategic technologies for 2008 </li></ul><ul><li>Metrics for success need to be devised, as do new strategies for collecting analytics </li></ul><ul><li>Web analytics practitioners are better placed to lead this charge than the individuals traditionally managing real world venues </li></ul>
    7. 10. Sensors at our disposal <ul><li>Point of sale devices </li></ul><ul><li>Consumer self-service kiosks </li></ul><ul><li>Digital signage </li></ul><ul><li>Mobile phones (consumer & employee) </li></ul><ul><li>Web cameras tied to heuristic algorithms </li></ul><ul><li>RFID chips in products, loyalty cards, etc </li></ul><ul><li>Localized sensors (Microsoft surface, infrared, pressure, etc) </li></ul><ul><li>GPS tracking </li></ul>
    8. 11. Streitz, et al., 2003
    9. 12. examples
    10. 13. Polo Madison Avenue http://www.fashionwindows.com/windows3/2006/0604.asp
    11. 14. www. microsoft .com/ surface
    12. 15. mi-tu smart dressing rooms http://www.rfidjournal.com/article/articleview/3595/
    13. 16. http://www.biggu.com/applications/
    14. 17. audience measurement
    15. 18. Example: Pharmacy Traffic Measurement © Quividi, 2009
    16. 19. Measuring Conversions (Attention) © Quividi, 2009
    17. 21. http://www.trendhunter.com/trends/weight-controlled-bus-stops-fitness-first-wants-people-to-know-their-weight
    18. 22. future
    19. 23. Opportunities <ul><li>Computing is getting smaller, cheaper, faster, and easier to deploy pervasively </li></ul><ul><li>Improved measurement and responsiveness in systems can lower costs and improve customer satisfaction </li></ul><ul><li>The limitations are not technological, but rather our ability to conceive of creative solutions </li></ul>
    20. 24. Challenges <ul><li>First of a kind = higher risk </li></ul><ul><li>Perceived threat to personal privacy </li></ul><ul><li>Deploying technology outside the data center </li></ul>
    21. 25. Pattie Maes & Pranav Mistry , MIT Media Lab – Sixth Sense wearable device http://www.ted.com/talks/pattie_maes_demos_the_sixth_sense.html
    22. 26. Analytics in the Physical World Ross McKegney CTO, Fadow Ltd 1 April 2009

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