Why You Need Sensor Fusion In Your 2016 Retail Analytics Strategy

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ShaunKirbyChiefTechnologyOfficerCiscoConsul:ngServices

MODERATOR:AliciaFiorle9aSeniorEditor,RetailTouchPoints

New Horizons in Retail How the Internet of Everything Changes the Game

Cisco Consulting Services

September, 2015

7 © 2013-2015 Cisco and/or its affiliates. All rights reserved. Cisco Consulting Services

By 2020, about 50 billion objects connected to the

Internet

In 2012 alone, we created more data than previous

5,000 years

An estimated 77 billion apps will be downloaded

during 2014

This year, the number of mobile-connected devices will exceed the number of

people on earth

2/3 of the world’s mobile data traffic will be video by 2015

Globally, machine-to-machine IP traffic will grow 20-fold from 2012 to 2017

Digital Disruption Digital

Disruption 53% of young people would trade their sense of smell for technology

50% of shoppers want to integrate mobile into

their grocery experiences

500M people urbanized in the next 5 years

Mobile apps $77 billion industry in 2017

2/3 of the world’s mobile data traffic will be video

this year

By 2020, ~50 billion objects connected

to the Internet

Digital Disruptors

Convergence of Mobile, Social, Cloud, and Data Is Driving Digital Disruption


Sensor Fusion: From “Sense & Respond” to Sense, Predict, and Act

Sensors & Actuators

Secure Connectivity

Programmability

Manageability

Extensibility

Real-Time, Predictive Analytics

Machine Learning

Stream Processing

Business Rules

Next Gen Collaboration P2P

P2M

M2M

Visualization

Orchestration

The IoE Enabled Organization:

Ø  Hyperaware

Ø  Predictive

Ø  Agile

Cloud

Fog

Flexible, Scalable Infrastructure


Value Is Not in the Number of Things, But the Connections of People, Process, Data, and Things

Things

People

Data

IoE $19.0 trillion

Process


Sensor Fusion in Retail Delivers Significant Business and Financial Impact

Checkout Optimization

Out of Stocks Mobile Customer Promotions

Mobile Customer Service

Supply Chain Optimization

$70B retailer with 2,000 large

format, general merchandise and

grocery stores

$2B Revenue

$100M SGA

$200M Working Capital

Value ChainTM Impact Opportunity

Real Time Loss Prevention

$50B CPG Company

$1.5B Revenue

$300M SGA


Fog Platform Filtering Local Rules Recognition

Wi-FiGesture

Recogni=on

AssociateMobile

DigitalSignage

CustomerMobile

VideoCameras

Central Compute Platform

Big Data Repository

Fast Data Processing

Action Enablement

Wisdom from the Cloud

Business Systems

eCommerce & Semantic Web

DoorHinges

IRMo=on

ParkingSpaceSensor

Wi-FiBadge

WeightMat

Wi-FiTags

ShelfWeightSensors

Sensor Fusion in Retail Lights up a Myriad of Dark Assets


Trip Wires •  Count shoppers entering and leaving

Area Counts •  Count shoppers in specific areas of interest

Queue Depth •  Count shoppers waiting for service

Dwell Times •  Measure time waiting for checkout or

service

Pilot Implementation: Stereoscopic Cameras


Wide Area Counts •  Count shoppers in broad areas

Door Activity •  Measure refrigeration door opening &

closing

Shelf Stock •  See holes on shelves

Modular Integrity •  Assess planogram compliance

Pilot Implementation: HD & PTZ Cameras


Active Wi-Fi Tags •  Track carts throughout store

Aisle Beacons •  Pinpoint carts entering and leaving specific

aisles

Checkout Beacons •  Associate carts with market basket

Market Basket Fusion •  Replay and analyze shopping trips

Pilot Implementation: Wi-Fi Tracking


Current State With IoE

Cashier Hours/Day: Cost/Day: Average Queue Length: 95% Queue Length: Average Wait Time: 95% Wait Time: Utilization

150 $ 1800

2.5 2

1:13 3:49 70%

Weekend Weekday 200

$ 2400 2.7 3

1:31 4:01 78%

140 $ 1680

1.6 1.7

0:29 1:55 77%

Weekend Weekday 180

$ 2160 1.9 2.5

1:09 4:00 86%

Economics: Checkout Optimization: $100M / yr. Savings for 1500 Store Chain

An estimated annual savings of $50-$70K

can be achieved at one large store in a year

2-3 FTE Free per large store to redirect to other

tasks

95-percentile wait time cut by ½ on weekdays

Up to 50%+ reduction in average wait time


Multi-day view

0

0.25

0.5

0.75

1

0

0.25

0.5

0.75

1

Current System reported shelf stock level IoE Solution reported level

Single day view

Shelf inventory depleting while system inventory increasing Shelf inventory zero

Case Study: On-Shelf Availability (OSA)

Out of Shelf

Stoc

k Le

vel


Metrics Current State With IoE Benefit

Lost sales: Driver: 1. Opportunistic picks

$100M $60M $40M (incr. rev)

Stocking labor: Drivers: 1.  Inventory checks reduced 2.  False picks avoided

$80M $35M $45M (reduced cost)

Vendor audits: Driver: 1. Cost of store audits

$30M $0 $30M (reduced cost)

Soft benefits include: 1)  Increased customer service satisfaction from increased product availability 2)  Increased store management satisfaction from OSA process simplification

Economics: On-Shelf Availability (OSA)

1500 Store Chain


Market Basket Fusion Analytics Opens New Windows on Conversion

Cart Speed Slow Fast


25M Wi-Fi Location Points

847,615 Visits 7.9M Scans

72,490 Confidently Identified Cart Trips Between 5 min and 3 hrs duration

20,007 Cart Trips Spent >30s in Dept. X

4461 Purchased

$42 Ave. Basket (Carts + Non-Carts)

$61 (Carts Only)

$114

$27 in Dept. X

27.6% Conversion

22.3% Conversion Lingered 4m 15s

Lingered 2m 22s $93

6.2% of All Carts Converted

Case Study: Merchandising Conversion


Handbags

Women’s

Boys’

Pha

rmac

y Men’s

Glasses

Tops

Grocery

Girls Shoes

Girls’

Improving Conversion Through Sensor Fusion Example Results & Outcome

Max

Min

Potential Value of

Conversion Campaigns

Example Findings: § “Conversion

Cliff”


Handbags

Women’s

Boys’

Pha

rmac

y Men’s

Glasses

Tops

Grocery

Girls Shoes

Girls’

Improving Conversion through Sensor Fusion Example Results & Outcome

Max

Min

Potential Value of



Cliff”

o Showrooming in Grocery


Handbags

Women’s

Boys’

Pha

rmac

y Men’s

Glasses

Tops

Grocery

Girls Shoes

Girls’


Max

Min

Potential Value of



Cliff”


o Social media opportunity in Women’s


Handbags

Women’s

Boys’

Pha

rmac

y Men’s

Glasses

Tops

Grocery

Girls Shoes

Girls’


Max

Min

Potential Value of



Cliff”


o Social media opportunity in Women’s

o Extensive search throughout


Sales Impact from Promotions in a Particular Department

Fri Sat Mon Sun Tue Wed Thu Average

30.0% 28.5%

22.6%

25.4%

Sun Mon

27.2%

32.0%

28.5% 27.2%

31.6%

27.7%

31.4%

20.4%

23.5%

27.8% 26.0%

22.6% 24.6%

31.2%

3/24/14 3/25/14 3/26/14 3/27/14 3/28/14 3/29/14 3/30/14

6.0% 7.6% 7.7% 7.0% 7.2% 6.9%

9.7%

% Visiting Department % of Those Purchasing % Of All Shoppers Purchasing

Promotion Dates


Shelf Set A/B Testing with CPG Collaboration Planogram A Planogram B

Original SKU Location New SKU Location

7.3% C

onve

rsio

n R

ate

Caucasian Females 25-34

Positive Mood

11.1%

Con

vers

ion

Rat

e

Caucasian Females 25-34

Positive Mood

1 Week Cumulative Results


Hot Spots in the IoE Technology Stack Architecting Dynamic Experiences Levels

Application (Reporting, Analytics, Control)

Data Abstraction (Aggregation & Access)

Data Accumulation (Storage)

Edge Computing (Data Element Analysis & Transformation)

Connectivity (Comm & Processing Units)

Physical Devices & Controllers (The “Things” in IoE)

Collaboration & Processes (Involving people and bus. processes)

1

2

3

4

5

6

7 Data at

Rest

Data in Motion

4. Orchestration & Visualization - SPOG

1. Precision Indoor Location 2. Video - the Supersensor

3. Wearables


1. Precision Indoor Location

On the brink of pervasive, precise indoor location

Strategies that Work •  Drive Mobile App /

Site Engagement •  Target Mobiles and

Wearables as the experience platform

•  Fuse with sensing across channels to achieve breakthroughs

•  Adopt infrastructure that protects from new threats and helps manage increased complexity

Scrambling & Encryption

Today: 7-10m (aisle)

2015: 1-3m (aisle)

Future: <1m (product/shelf)

Previously: 7-10m (zone)

Angle of Arrival Wi-Fi

Beacons

Sensor Fusion

Low Cost Active Tags

Device Proliferation

Rogue Beacons

Location Insight

Competitor

Crowdsourcing


2. Computer Vision and Video Analytics

Cameras become the “super-sensor” and everything “sees”

Lensless Cameras Deep Focus

Deconvolution 4K / 8K Video Understanding

Demographics •  Ethnicity •  Mood •  Body language

Eulerian Video Magnification

Autocalibration

•  Extensible with new software •  Unobtrusive and low maintenance •  Already widespread •  Transparency •  Symbiosis, not espionage

Strategies that Work •  Tap fog computing to scale

to pervasive video without drowning in data

•  Converge surveillance, security, and analytics initiatives with common infrastructure

•  Position cameras to collect multiple metrics

•  Adopt technology that can be extended with new computer vision easily

•  Look for auto-calibrating technologies


3. Wearables

Free, continuous channel engagement

•  Wearables not the whole platform •  Fads vs. sticky devices •  Context and content required to

realize value

Strategies that Work •  Build audio and semantics

into apps to engage in busy store environment

•  Revive augmented reality innovation

•  Tap health data as vast new source of shopper insight

•  Crowdsource valuable insights into shelf condition, operations, and other store health indicators

•  Consider new horizons for gamification


4. Single Pane of Glass (SPOG) for IoE

?

Little Geospatial Context

Diverse Analytics Tools Complex Connections Among Previously Dark Assets

Wildly Disparate Datasets and Sources

```

TOTAL IOE Value at Stake: $19.0 Trillion

•  Visualization

•  Orchestration

Strategies that Work •  Virtualize data sources •  Adopt visualization tools

with 3D spatial capability and “infinite” zoom

•  Provide intuitive control over data

•  Embed collaboration with the SPOG

•  Focus on seamless integration of new devices and creation of new visualizations

The end of the dashboard era…Minority Report, here we come!


In Summary

1.  There’s money to be had – top and bottom line from IoE to digitize everything

2.  Rapid prototyping / modeling techniques using real data in live environments to demonstrate value

3.  Platform architecture •  Avoid point solutions •  Open, multi-sensor fusion •  Data at the edge

Thank you.

#RSPS15

Q&A//Panelists

@CiscoConsul:ng

ShaunKirbyChiefTechnologyOfficerCiscoConsul:ngServices

MODERATOR:AliciaFiorle9aSeniorEditor,RetailTouchPoints

#RSPS15

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