Shanghai Breakout: Location Analytics – Key Considerations and Use Cases

The Value of Location Analytics
Manju Mahishi
December 2014

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© Copyright 2014. Aruba Networks, Inc.
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Today’s Agenda
• Goal: Understand the value of location
analytics for enterprises and public
venues
• And how Aruba ALE together with key
partner solutions can help with various
analytics use cases and drive business
value

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Understanding Analytics

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Location Based Services in
Enterprises
• Location Context and
Traffic Pattern Analytics
is becoming
increasingly important
across enterprises and
public venues to
support various
optimization efforts and
mobile engagement
with contextually
relevant information

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Why Location Data Matters
• Improve User/Customer Engagement
– Add context to customer purchase patterns
– Targeted engagement based on location
– Improve Ad effectiveness by > 2X
• Improve Operational Efficiencies
– Staffing Efficiency – Don’t wait for queues
to build – Proactively staff based on traffic
• Workspace Optimization
– Identify “hot zones” or lightly utilized
spaces to save costs
• Location as context for access
control and security
10%
5%
0%
1.2%
0.10%
3.5%
7%
10%
Click Through Rate
Source ABI Research

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Big Data Analytics:
Market Sizing

Location Analytics Across Verticals
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Improve traffic flow
Web analytics
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Stadium /
Arena
Increase ad revenues
Optimize traffic flows
Airports /
Malls
A/B Testing
Optimize staffing
Understand buying
patterns
Sentiment analysis
Retail
Improve customer
engagement
Identify high value
customers
Real time offers
Hospitality
Differentiated service
based on location
Workspace optimization
Access policy
management
Enterprises

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Retail Analytics Landscape:
Key Trends and Initiatives
SHELF SPACE OPTIMIZATION
CUSTOMER MARKETING
(SEGMENTATION, TARGETING,
PERSONALIZATION)
FRAUD DETECTION &
PREVENTION
INTEGRATED / STATISTICAL
FORECASTING
LOCALIZATION,
CLUSTERING
(DEMOGRAPHIC DATA)
MARKETING MIX MODELING
(A/B TESTING)
PRICING OPTIMZATION PRODUCT
RECOMMENDATION
REAL ESTATE
OPTIMIZATION
SUPPLY CHAIN ANALYTICS;
INVENTORY OPTIMIZATION
TEST & LEARN WORKFORCE ANALYTICS
(STAFF OPTIMIZATION)
MULTI-CHANNEL
ANALYTICS (ONLINE,
OFFLINE)
LOCATION ANALYTICS,
REAL TIME ENGAGEMENT
VIDEO ANALYTICS

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Decoding Big Data

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Retail Big Data Topology
(Source: IDC, 2012)

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Analytics: Key Takeaways
• Analytics is multi-faceted, complex with
many use cases still evolving
• Several ecosystem players
• Most “real world” implementations require
integration with other data sources
(Sensors, Loyalty databases, POS, etc.) to
create more meaningful data
– May need a SI involvement to put things
together
• Aruba’s ALE provides rich mobility
“context” to analytics and Big Data / mining
systems
• ….but this becomes truly useful only when
combined with multiple data sources to
drive analytics use cases and contextually
relevant user engagement

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An Overview of Aruba Analytics and
Location Engine (ALE)

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Engagement
Platform
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Mapping LBS Use Cases to Aruba’s
Solutions
LBS
Mobile
App
Guest
Access,
Branded
Portals
Indoor
Mapping
Services
Indoor Location
Engine
Contextual
Engagement:
Proximity
Notifications
Analytics,
Data Mining
MERIDIAN
ALE (Network)
Meridian w/BLE
MERIDIAN,
PARTNERS
MERIDIAN
CLEARPASS
ALE +
PARTNERS

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Analytics and User / Customer
Engagement
Contextual Data:
User, Device, Application &
Location
ENGAGEMENT
Location / User Specific
Experiences
DATA
MINING /
ANALYTICS
Sensors
Other
Data
Sources
CRM
Venue Traffic
Patterns, A/B
Testing,
Demographic
Analysis, etc.
ALE
MARKETING, AD
PLATFORMS

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Real time location
engine
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Analytics and Location Engine (ALE):
Key Functions
ALE
1
Unified context for
each user (user name, IP,
MAC, device type, App
visibility, etc.)
2
4
Seamless, secure
connectivity to
analytics platforms
High performance
Northbound APIs
(publish/ subscribe,
polling)
3

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ALE imports Visual RF
maps, Decodes AMON,
Computes Location,
Provides Context APIs
Controllers Create
AMON Messages
AP’s Create Virtual
Beacon Report (VBR)
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ALE System Overview
Probing Clients
LOCATION
ANALYTICS
Visual RF
AirWave ALE
Analytics Partner
Location Services
MOBILITY
CONTROLLERS
INSTANT
APs

Publish the received data
using Publish/subscribe API
(Google Protobuf/0MQ)
North Bound API
Forward
decoded User,
Device, App
data
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Data from
Controller (AMON)
or IAP (HTTPS)
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ALE Internal Workflow
ALE Processes
Decode the
Received
data to
appropriate
format Location
Engine
Redis In-
Memory
Database
Calculate Device
Location (x,y)
Client RSSI data
Floor Maps
from Visual RF
(Airwave)
Write the
received/computed
data to DB
Polling API
(REST)
ALE Virtual Machine

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Data Aggregated & Exposed by ALE
• Presence feed
• Indicating a device has been detected in range of the WLAN
• Events when a device is detected crossing a Geofence
• Entering or leaving a zone
• Device information
• Model, OS (as available from DHCP and browser user-agent)
• User information from authentication to the network:
• Type of authentication, username
• Applications used
• As detected by monitoring data-plane traffic from the device
• Destination URLs
• As detected by monitoring data-plane traffic from the device

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Traffic Pattern Analytics
Enabled by ALE
 Presence (Inside Venues / Conference Rooms)
 Capture Rates (Inside versus Walk-Bys)
 Dwell Times by Geofence
 Repeat versus New Visitors
 User Classification (Employees versus Guests)
 User Engagement (Macro level)

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ALE Location Calculation Overview
• Location is based on RSSI (from Probes, Data Frames)
– All APs will report RSSI for the probes (Virtual Beacon Report (VBR))
– RSSI from Data Frames (for associated clients) is sent via RTLS feeds
directly from AP’s (or Air Monitors)
• Location calculation based on Path Loss Models
• Path Loss = Received signal – client transmit power
• Path Loss = k + 10 n log(d)
– Where K is the path loss at 1 meter.
– K is different for 2.4 and 5.0 GHz radios.
• If we know the path loss, distance can be estimated
– If we get distance from 3 APs, we can uniquely triangulate
– With 2 APs, there are 2 points of intersection, so there is ambiguity
– ALE returns the AP coordinates (x,y) as proxy to client location when fewer
than 3 AP’s are available for location calculation (“Single AP” location
feature can be enabled via configuration)
• In real life RSSI can fluctuate
– Aruba’s location engine uses outlier detection and dampening algorithms
•

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Location Accuracy & Latency
(Summary)
• Factors impacting Accuracy
– AP density, type, mounting type
• Higher the AP (and Air Monitor) density, the better the location accuracy
• Recommended AP / AM density is one every 50 ft (2500 sq ft coverage)
– Client probing behavior, RSSI Variations, Device type, OS type
• Factors impacting Latency
– Client probe frequency (iOS vs Android)
– Network settings: AP/controller timers
• Impact to Use Cases:
– In general, Wi-Fi based locationing from ALE lends itself to use cases where
traffic trends / patterns can be analyzed over a period of time

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Design Considerations for Locationing
• It is imperative to start with a good understanding
of business requirements
• What are the key use cases and “true” business
requirements?
• Traffic Pattern Analytics inside venues?
• Self directed museum tours?
• Push Notifications by Zone (or with more granularity)?
• Ability to locate specific venue (conference room,
restaurant, etc.) within a large venue (statically) or an
app that provides turn by turn directions
(dynamically)?
• Knowledge of the use case is key to understanding
location accuracy, latency requirements – and
designing the network to support the use cases
• For “micro-locationing ” or proximity detection and
indoor turn by turn direction use cases, a client
based solution (BLE) is recommended

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Key Location Analytics Enabled by ALE
Public Venues
(Traffic Patterns,
Engagement)
Enterprise:
Workspace
Optimization
Smart Energy
Management
Integration with
Machine Data
Systems
Location Based
Security Policies
SDN Enablement
(Context APIs)

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ALE In Action: A Few Case Studies
Analytics Partners

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Hospitality Location Analytics Example
(ALE Integration with APAMA)

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Geofence Analytics Example – Hospitality
(ALE Integration with APAMA)

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Retail Analytics Example
Market
Benchmarks
Queue
Management
Interior
Counting
Marketing
Effectiveness
Draw
Rate
Shopper
Demographics
Foot
Traffic
= Perimeter Counting
Sales
Loyalty / Intercept
Abandonment
= Mobile Analytics
(Wi-Fi, BLE)
Source: ShopperTrak

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Retail Traffic Analytics Reporting (Sample)
ShopperTrak
Sample Report
(Generated for a
Retail Store in
Spain; integrating
with ALE)

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Traffic Pattern Analysis
(AisleLabs Sample Data)
Operations
Information can assist
with planning day-to-day
shopping center
management
operations, such as
staffing
Is a specific marketing
campaign effective
A daily review of peak
times will help evaluate
and measure the results
of promotional
campaigns and event
programs
• Peak hours remain
stable between
10:00 AM - 2:00 PM
Compared to the rest of
the Saturdays, guest
numbers climbed at
10:00 AM for week #3
and for 6:00 PM for week
#4 perhaps due to
promotional campaigns.
© 2014 Aislelabs

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CUSTOMER u Hungry Jack’s
CATEGORY u Fast Food
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Case Study:
Hyper-local marketing campaign (SkyFii)
Turn around a geo-targeted promotion in a short space of time
to drive traffic in-store to redeem an offer.
OBJECTIVE u
MEDIA u
RESULTS u
35%
conversion
rate
6,067 people received the offer
2,097 were tracked into store
1,642 sales were recorded

X
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Location as Context for Access Policies
Restrict resources by
location for compliance
Restrict guest access to
inside “Geo-fence”
ClearPass
Policy Mgr
Location as
Policy
Definition
Services / SDN
Controller
Device Location
Update / Gepfence
Event
Aruba WLAN
(Access Policy Enforcement based on Location)
XML
API
Dynamic Policy
Update/Enforcement
(CoA)
Finger Print

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Machine Data Analytics
ALE – Splunk Integration
Applications
SDK
splunk>
Splunk
Forwarder
Log
Files
Streaming data
Devices
Devices
Devices
ALE
Development Kit:
- Interact with the data in Splunk
- Control, manage, script
- SDK support for Perl, Python, Ruby etc.
- Develop custom applications
- 1000s of applications already available
Splunk Engine:
- No RDMS(stored natively)
- Parse/Index/Store the data
- Runs scripts, queries, dashboards
- Cluster & Cloud enabled
- Hunk for Hadoop
- Splunk can be heirarchical (allows distributed
searches)
Data Feed:
- Files & Directories (remote)
- TCP/UDP unstructured data feed
- Forwarders (Universal/Light/Heavy)
- Gather data from network
- Forward (un-indexed) to Splunk Engine
- Compression, SSL, Configurable Buffering
- Feedback from the engine

Key 3rd Party Location Analytics Partners - 1
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Partner Details
• Expertise: Real time / streaming data analytics
• Focus on Finance industry; new to retail location analytics
• Highly customizable; Integration with other data sources; High cost
• Suitable for large enterprises (e.g. Hyatt Resorts & Hotels)
• Retail foot traffic analytics
• Integration with video camera feeds; other data sources (POS, Loyalty
databases, etc.)
• Customizable reports, alerts; predictive analytics
• Omni-channel KPIs
• Presence Analytics
• Mainly operate in APJ, LATAM, SA
• Standard KPIs: Dwell time, People counts, First Time vs Repeat Visitors, etc.
• Retail and Casual Restaurants (e.g. Westfield Malls)
• Small startup, based in Spain
• Solution focus: Retail Presence Analytics
• Standard Retail Traffic Analytics KPIs: Visitor frequency, Dwell time by zones
• Integration with video feeds
• End to end platform for shopping mall marketing and analytics
• Customizable analytics of shopper behavior
• Social Wi-Fi
• Engagement solutions (with BLE / SDKs)

Key 3rd Party Location Analytics Partners - 2
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Partner Details
• Well know for retail analytics (global list of customers). 20 Year experience
• Started with stereoscopic methods for foot traffic counting; new to Wi-Fi
• integration with other data sources: POS, etc.
• Highly consultative sales / engagement process
• Cloud-based Retail / QSR traffic analytics
• Basic KPIs; some integration with other data sources (POS, etc.)
• Customizable reports including benchmarking, A/B Testing
• Low cost of entry
• Retail traffic analytics; Based in Finland
• Standard KPIs: Engagement; dwell times; identifying loyal customers, etc.
• APIs to external marketing software, Google Analytics, etc.
• Recently acquired by Brickstream
• Started with Wi-Fi only solution (Like Eulid)….now have Beacons for
Engagement, and integration with video feeds for people counting
• Similar store analytics KPIs as others (dwell times, paths, etc.)
• Business intelligence for workspace optimization
• Can integrate multiple data sources (Wi-Fi, secure card readers, other
sensors)
• Predictive analytics

POC – 2 to 3 months
Refine Use Cases
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Summary: Analytics – A Journey
1
2
3
Identify Key Use Cases,
Business Value
Proposition
Tune Network, Identify
Key Partners for POC,
Design Use Cases
4
Build Internal Processes to
consume and act on the data.
Refine Use Cases

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Summary: Key Purpose of ALE
• Context Aggregation and Export
– User, Role, Device, Location, Application
– Meta Data: [URL, Session]
– Real Time Traffic Flows
• ….To Drive key business use cases:
– Traffic Pattern Analytics in Retail and other enterprises
(Presence, Dwell Times by zones, etc.)
– Network / IT Analytics
– Location context for access / security policy
management
• ALE is NOT
– An “indoor Navigation” / “Blue Dot” solution
– A solution for proximity engagement requiring less than
5 m accuracy
A.L.E

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ALE: Key Resources
• Detailed API Document
• Sample Feed Reader Code (0MQ) in C and Java
• Source Code for “ALE Demonstrator App” (Android) on
GitHub
– Shows how to consume both REST and 0MQ APIs
• Help with API programming
• Streaming Data from ALE server for Adapter development
(from Sunnyvale LAB)
• Help with POCs
• …Whatever help you need, we are available!

Shanghai Breakout: Location Analytics – Key Considerations and Use Cases

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Shanghai Breakout: Location Analytics – Key Considerations and Use Cases