Netra is creating the "Quantified Store" using existing in-store video, machine vision, AI and deep learning. Netra's appearance-based approach provides greater accuracy and enables cross-camera search & analytics.
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
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