Understand InStore Shopper Behavior with Precise Loca7on Analy7cs #CCES14 Webinar Sponsored by
Nov 27, 2014
Understand In-‐Store Shopper Behavior
with Precise Loca7on Analy7cs
#CCES14
Webinar Sponsored by
#CCES14
Welcome Webinar A8endees
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Follow This Webinar On Twi8er
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About Retail TouchPoints
ü Launched in 2007
ü Over 28,000 subscribers
ü To provide executives with relevant,
insightful content across a variety of
digital medium
Free subscription to our weekly newsletter: WWW.RETAILTOUCHPOINTS.COM/SIGNUP
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Consistent Customer Experience
Winning The Ba8le Of Customer
Service Vs. Task: OpKmizing The
Customer-‐Centric Payroll EquaKon
Growing Revenue While Controlling
Labor Cost
CommunicaKon Ma8ers: Solving
the Store ExecuKon Challenge
Conquer the FiRng Room –
Make the Most of Your Most
Valuable Real Estate
Understand In-‐Store Shopper Behavior With Precise LocaKon
AnalyKcs
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Panelists
Debbie Hauss Editor-in-Chief
Retail TouchPoints
MODERATOR PANELISTS
Erin Oldershaw Retail Consultant
SMK Workforce Solutions
Patrick Blattner CPO
iinside
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The leader in passive indoor analyKcs with accuracy within 1 meter
7 Patrick Bla8ner | Chief Product/Data Officer October 22, 2014
PRECISE INDOOR ANALYTICS
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• Our founders were leaders on the original Apollo Space Mission and Space Shuttle program
• On the team that launched the first GPS satellite into space
• We have an extensive patent portfolio with over 30 patents on proximity within indoor location
iinside Steeped History in LBS
iinside is a WirelessWERX company and has been in business for over15 years with 30 global patents around zone technology
FOUNDED BY FORMER NASA ENGINEERS
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Observed Devices
Basket Cart Employee
Wearables
Mobile Phones
Nodes
Bluetooth 2.0 & 4.0
We also have small Quarter size tags that can adhere to carts, baskets, and Employee Badges to capture 100% of tagged items.
Tag
Base StaKon
Nodes are Chained off of Base Stations and talk to secure servers
We see over 500+ device types
HOW WE SEE DEVICES – NON APP DEPENDENT Both Passive Listening Nodes with Beaconing Capability
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Each sensor is about the size of a deck of cards
Small Sensors are Easy to Deploy Throughout the Store An easy-‐to-‐install, low-‐cost soluKon, to the most comprehensive and accurate indoor analyKcs using Bluetooth technology. We simply place our small nodes within locaKons inside the store and size the zones and start collecKng data.
EASY INSTALLATION 50,000 Sq. Foot Store Setup in One Night
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Close Proximity with Bluetooth
ACCURACY MATTERS
4b
iinside uses Bluetooth technology to passively monitor Bluetooth enabled devices that appear within proximity of our sensors.
Filtering Out Noise / Non-‐Shoppers
iinside is capable of filtering out employees and pass through customers
Employee TSA Agent Front Staff
Shopper Traveler Visitor
From 4b. Across to 50 b. Across
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61% 39%
12
67% 33% EMPLOYEE AND PASS THROUGH SHOPPERS
SHOPPER VISITS
STORE 1
WHY IT MATTERS– PROVEN CLEAN DATA SETS Proven by 3rd Party Audit
BAD DATA Employees and Pass Through Shoppers Shoppers < 10 Seconds Persons > 2 Hours
GOOD DATA Shoppers Dwelling > 10 Seconds
STORE 2
Within 5% aber 24 hours | Within 2.5% aber 48 hours
SHOPPER VISITS
EMPLOYEE AND PASS THROUGH SHOPPERS
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CROSS MARKET VALUE & ROI Where We Leverage Our Accuracy
MASS TRANSIT
TSA, Baggage Check-‐in, Carousel, and Concourse
InstallaKons
End to End
CITY FOOT TRAFFIC
Embedded Upstream in Signs
City Behavior
BIG BOX & GROCER
Full Store, Store in Store, Cross Store
Store Behavior
WORLD TOWERS
MulK-‐Floor Queuing
Trip Time
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WEB ANALYTICS FOR THE PHYSICAL WORLD Specifically Looking at Retail
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VISUAL IMPACTFUL ANALTYICS WITH VALUE
First LocaKon Visited
Most Visited LocaKon
Average Dwell
First LocaKon Visited
Most Visited LocaKon
Cross Chain & Single Store Views
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FIRST LOCATION VISITED Awareness & Intent from Offline PromoKons
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FIRST LOCATION VISITED Awareness & Intent from Offline PromoKons
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FOOTPRINT / DENSITY
70%
30%
Brand and Category Interest/Square Foot Efficiencies
EXAMPLE Typical Big Box Retail
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70%
30%
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FOOTPRINT / DENSITY
70%
30%
Brand and Category Interest/Square Foot Efficiencies
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LABOR & FINANCE Efficiencies for Staffing
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SHOPPER vs EMPLOYEE CONCENTRATION Engagement Efficiencies
Shopper Employee
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SHOPPER vs EMPLOYEE CONCENTRATION Engagement Efficiencies
Shopper Employee
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CROSS SHOPPING Referring Traffic
• Format stores to make cross shopping easier • Understand Shopper Intent • Promote an Easier Shopping Experience • Increase Customer SaKsfacKon • Increase Likelihood for repeat visits
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RESEARCH BEHAVIOR -‐ IdenKfied Intercept Efficiencies
One & Done We idenKfied a large number of consumers entering the stores, stopping at 1 locaKon for a short period of Kme and then leaving without touching POS
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RESEARCH BEHAVIOR -‐ Trending Intercept Efficiencies
Research vs Intent Driven Shoppers We see research behavior increase leading up to the holidays where consumers enter 1 locaKon and leave without going through checkout. As the holiday/event approaches, consumers are intent driven and research behavior dramaKcally decreases
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RESEARCH BEHAVIOR -‐ QuanKfied Intercept Efficiencies
Intercept to Convert • Are Products Stocked? • Is LocaKon Appropriately Staffed? • Are The Products Priced CompeKKvely • Is Staff Trained EffecKvely?
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SHOPPER SEGMENTATION
Grocery Store
Informs Lane Types
Basket Cart Shopper
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WHY IT MATTERS – The Li8le Things Add Up
Lane Hopping Impact • 18.1% Lane Hop • 9% Lane Hop Twice
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CHECKOUT LANE EFFICIENCIES
Time distribution by checkout lane shows lanes with greatest proportion of low vs high average checkout times
TIME DISTRIBUTION
Grocery Store
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Customer Experience
OperaKng Model
Service Model
ExecuKon Service
Enhancements
Measurement
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Q & A | Panelists
Debbie Hauss Editor-in-Chief
Retail TouchPoints
MODERATOR
Patrick Blattner CPO
iInside
PANELISTS
Erin Oldershaw Retail Consultant
SMK Workforce Solutions
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COMING IN 2015
ERIN OLDERSHAW
SMK Workforce Solutions
ANNE MACKENZIE KOTRABA
SMK Workforce Solutions
Scott Knaul SMK Workforce Solutions