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Organizing the
tsunami of videos and
making them
searchable and useful
About Us
• Empowering brick & mortar retailers with actionable
insights using existing in-store video
• $20B total addressable market
• Machine Vision + AI + Deep Learning from MIT CSAIL
• Auto-detect similar objects based on appearance and activity
• 1 PCT and 3 provisional patent applications filed
• Signed LOI to one of the largest camera manufacturers
• 5 pilots with multi-national companies in retail applications
• Mark Cuban (pre-seed funding 6/14)
• Techstars Boston 2015 class
• $500K convertible debt round (7/15)
Overview
Primary Focus
Technology
Customers
Investors
• Founded 2013, 8 employees, U.S. Delaware Corp.
Senior Team
Richard Lee, Co-Founder & CEO
Sales, Marketing, Finance, Retail Innovation
Shaser Bioscience (2010-2015)
• VP Marketing
• Majority acq by SPB for $50M in 2012
P&G / Gillette (2003-2010)
• Global Manager, RFID Strategy
• Marketing Innovations / Brand Management
MIT MBA (2001-2003)
• Research, Auto-ID Center
• Mastercard, New Payment Technologies
PwC, Cambridge Incubator (1994-2001)
Shashi Kant, Co-Founder & CTO
Emergent Semantics, Web Architecture,
Machine Learning
Cognika (2006-2013) - Co-Founder and CTO
MIT MS (2004-2006)
• MIT CSAIL
• Researcher, MIT Auto-ID Center
• ASP Fellow, Systems Engineering
Absolute Software (2000-2003)
• Web Architect
Thinq.com, Trintech, Schlumberger
Why Video Analytics (now)?
Brick and Mortar Retailers are in a
Period of Massive Re-Invention
Finally entering the “age of enlightenment”
Actionable Insights Using
Existing Infrastructure
Technology / Innovation Finally
Delivering on Promise
IRIS+ Technology
Based on Appearance & Activity vs. “Trip-Wire”
Female,
based on
flowing
hair
Large
human
walking
Female,
based on
body
features
Brown hair
Red shirt
Blue pants
White shoes
C T S C
C T S C
Female,
based on
flowing
hair
Small
human
walking
Female,
based on
body
features
Brown hair
Red shirt
Blue pants
“Quantified Store” with IRIS+
1. Shopper Foot Traffic
Nationally, By Region, By Store
Focus on Traffic, Time in Store, Conversion
2. Time Spent in Store
Total, By Department, Dwell,
Interactions
3. Conversions
Number of Transactions,
Abandonments, Basket Size,
Conversion Percentage
IRIS+ Demo for QSR
Marketing interested in Shopper Traffic & Peel-Off
5.4%
5.6%
5.8%
6.0%
6.2%
6.4%
6.6%
6.8%
36,000
38,000
40,000
42,000
44,000
46,000
48,000
50,000
Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul
Monthly Traffic & Peel Off Rate
Passerby Traffic (in 000s) Peel Off%
Total Chain Data
to understand drivers
Store- and Regional Data
to understand winners / laggards
IRIS+ Demo for QSR
Merch / Store Ops interested in Time Spent in Store
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
18.0%
5.60
5.70
5.80
5.90
6.00
6.10
6.20
6.30
6.40
6.50
Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul
In-Store Activity
Time In-Store (min) Salesperson Interaction (%) Display Engagement (%)
Ability to deep-dive
into Time by Activity
Understand which
promotions work
Track Interactions
by Salesperson / Stores
Visualizations for QSR
Data Visualizations help understand what’s working
Target Customers
Initially, Netra will Sell
Through:
• Camera Manufacturers
• Solutions Providers
• Integrators
Signed LOI with camera company & 5+ pilots currently
Eventually, Netra will
Sell to End-Users:
• QSR Chains
• Department Stores
• Cell Retailers
• Mass Merchants
• Grocery Stores
• Convenience Marts
• Gaming
• Municipalities
Competition
Focus on improving Usability / UX vs. Core Technology
Financial Projections
$125MM+ revenues focused on Retail Intelligence only
$000s 2015 2016 2017 2018 2019
IRIS+
streams
339 4,115 28,331 137,379 422,514
Revenues $249 $3,956 $12,127 $41,574 $126,013
Operating
Expenses
$397 $4,210 $11,279 $29,989 $90,451
EBITDA ($157) ($358) ($171) $6,308 $31,274
www.netra.io
info@netro.io
617.272.0955

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Netra Intro

  • 1. Organizing the tsunami of videos and making them searchable and useful
  • 2. About Us • Empowering brick & mortar retailers with actionable insights using existing in-store video • $20B total addressable market • Machine Vision + AI + Deep Learning from MIT CSAIL • Auto-detect similar objects based on appearance and activity • 1 PCT and 3 provisional patent applications filed • Signed LOI to one of the largest camera manufacturers • 5 pilots with multi-national companies in retail applications • Mark Cuban (pre-seed funding 6/14) • Techstars Boston 2015 class • $500K convertible debt round (7/15) Overview Primary Focus Technology Customers Investors • Founded 2013, 8 employees, U.S. Delaware Corp.
  • 3. Senior Team Richard Lee, Co-Founder & CEO Sales, Marketing, Finance, Retail Innovation Shaser Bioscience (2010-2015) • VP Marketing • Majority acq by SPB for $50M in 2012 P&G / Gillette (2003-2010) • Global Manager, RFID Strategy • Marketing Innovations / Brand Management MIT MBA (2001-2003) • Research, Auto-ID Center • Mastercard, New Payment Technologies PwC, Cambridge Incubator (1994-2001) Shashi Kant, Co-Founder & CTO Emergent Semantics, Web Architecture, Machine Learning Cognika (2006-2013) - Co-Founder and CTO MIT MS (2004-2006) • MIT CSAIL • Researcher, MIT Auto-ID Center • ASP Fellow, Systems Engineering Absolute Software (2000-2003) • Web Architect Thinq.com, Trintech, Schlumberger
  • 4. Why Video Analytics (now)? Brick and Mortar Retailers are in a Period of Massive Re-Invention Finally entering the “age of enlightenment” Actionable Insights Using Existing Infrastructure Technology / Innovation Finally Delivering on Promise
  • 5. IRIS+ Technology Based on Appearance & Activity vs. “Trip-Wire” Female, based on flowing hair Large human walking Female, based on body features Brown hair Red shirt Blue pants White shoes C T S C C T S C Female, based on flowing hair Small human walking Female, based on body features Brown hair Red shirt Blue pants
  • 6. “Quantified Store” with IRIS+ 1. Shopper Foot Traffic Nationally, By Region, By Store Focus on Traffic, Time in Store, Conversion 2. Time Spent in Store Total, By Department, Dwell, Interactions 3. Conversions Number of Transactions, Abandonments, Basket Size, Conversion Percentage
  • 7. IRIS+ Demo for QSR Marketing interested in Shopper Traffic & Peel-Off 5.4% 5.6% 5.8% 6.0% 6.2% 6.4% 6.6% 6.8% 36,000 38,000 40,000 42,000 44,000 46,000 48,000 50,000 Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Monthly Traffic & Peel Off Rate Passerby Traffic (in 000s) Peel Off% Total Chain Data to understand drivers Store- and Regional Data to understand winners / laggards
  • 8. IRIS+ Demo for QSR Merch / Store Ops interested in Time Spent in Store 6.0% 8.0% 10.0% 12.0% 14.0% 16.0% 18.0% 5.60 5.70 5.80 5.90 6.00 6.10 6.20 6.30 6.40 6.50 Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul In-Store Activity Time In-Store (min) Salesperson Interaction (%) Display Engagement (%) Ability to deep-dive into Time by Activity Understand which promotions work Track Interactions by Salesperson / Stores
  • 9. Visualizations for QSR Data Visualizations help understand what’s working
  • 10. Target Customers Initially, Netra will Sell Through: • Camera Manufacturers • Solutions Providers • Integrators Signed LOI with camera company & 5+ pilots currently Eventually, Netra will Sell to End-Users: • QSR Chains • Department Stores • Cell Retailers • Mass Merchants • Grocery Stores • Convenience Marts • Gaming • Municipalities
  • 11. Competition Focus on improving Usability / UX vs. Core Technology
  • 12. Financial Projections $125MM+ revenues focused on Retail Intelligence only $000s 2015 2016 2017 2018 2019 IRIS+ streams 339 4,115 28,331 137,379 422,514 Revenues $249 $3,956 $12,127 $41,574 $126,013 Operating Expenses $397 $4,210 $11,279 $29,989 $90,451 EBITDA ($157) ($358) ($171) $6,308 $31,274