Location Analytics – Key Considerations and Use Cases

#ATM15 |
Value of Location Analytics
Manju Mahishi
March 2015
@ArubaNetworks
The Value of Location Analytics
Manju Mahishi
January 2015

CONFIDENTIAL © Copyright 2015. Aruba Networks, Inc. All rights reserved2#ATM15 |
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

3
CONFIDENTIAL
© Copyright 2014. Aruba Networks, Inc.
All rights reserved
Understanding Analytics

4 CONFIDENTIAL © Copyright 2015. Aruba Networks, Inc. All rights reserved#ATM15 |
Location Based Services in Enterprises
• Location / Traffic
Pattern Analytics is
becoming increasingly
important across
enterprises and public
venues to support
various operational and
marketing initiatives
and mobile engagement
with context

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
0%
5%
10%
0.10%1.2%
3.5%
7%
10%
Click Through Rate
Source ABI Research

Big Data Analytics:
Market Sizing

Improve traffic flow
Web analyticsStadium /
Arena
Location Analytics Across Verticals
Optimize traffic flows
Airports /
Malls
A/B Testing
Optimize staffing
Understand buying patterns
Sentiment analysis
Retail
Improve customer
engagement
Real time offersHospitality
Workspace optimization
Location based Access
Policy managementEnterprises

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

Retail Big Data Topology
(Source: IDC, 2012)

Decoding Big Data

Analytics: Key Takeaways
• Analytics is multi-faceted, complex, with many use
cases still evolving and 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
business insights and contextually relevant user
engagement

12
CONFIDENTIAL
All rights reserved
An Overview of Aruba Analytics and
Location Engine (ALE)

Mapping LBS Use Cases to Aruba’s Solutions
LBS
Guest
Access,
Branded
Portals
Mobile
Engagement
App
Platform
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

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

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

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

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
Forward decoded User,
Device, App data
North Bound API
Floor Maps
from Visual RF
(Airwave)
Data from
Controller (AMON)
or IAP (HTTPS)
Write the
received/computed
data to DB
Publish the received data
using Publish/subscribe API
(Google Protobuf/0MQ)
Polling API
(REST)
ALE Virtual Machine

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

ALE Northbound APIs
• Two types of Northbound APIs:
• Publish/Subscribe
• Uses Google Protocol Buffering (“Protobuf”) for encoding and TCP
based ØMQ transport
• External Analytics engines can subscribe to various “topics”:
• Location
• Presence
• Applications, Destination URLs
• Campus, building, floor, etc.
• Polling Based: REST API
• Supports standard REST queries for various events/objects
• Example: http://<ip>/api/v1/station will return a list of all stations
• Return data format is JSON

ALE Software Delivery
• ALE Product is delivered as a VM only (OVA File)
• Supported/Tested on VMware ESX/ESXi 5.0 and higher
• Can be deployed with various different hardware
configurations (for CPU, Memory, Hard Disk) based on
scale requirements
• VM has CentOS 6.4 pre-installed with all the needed
dependencies
• ISO Image option is also available
• ALE licensed on per-AP basis

ALE Server Sizing Guidelines
Notes on Server Sizing:
• Maximum number of controllers per ALE instance = 4
• Maximum number of AirWave servers per ALE instance = 1
• Max number of APs per ALE instance = 2K
• Maximum number of clients per ALE instance = 32K
• Client counts includes mix of associated and unassociated devices
• Recommended Grid Size (Floor Plan in AirWave) = 10 x 10 ft
Configuration Number of
AP’s/Clients
CPU Cores RAM Hard
Disk
SMALL 500 / 8000 4 16 GB 160 GB
MEDIUM 1,000 / 16,000 8 24 GB 320 GB
LARGE 2,000 / 32,000 16 48 GB 1 TB

ALE: Simple Configuration Requirements!
• Controller Configuration
– Each controller must be configured to send data to ALE
• ALE Configuration
– ALE must know about each controller (this is used to initially “pull” the current
information)
– ALE must know about the Airwave (AMP) server, so that it can pull in the maps
and AP placement data
• IAP Configuration
– Each IAP Virtual Controller (VC) needs to be configured to send data to ALE
– Each IAP (not just VC) needs to be placed on the map also

ALE v1.3 Dashboard: New GUI

Choosing Floors to Import from AirWave

Setting Up Secure WebSocket Tunnel to External
Analytics Engines

“Map - less” Support for Small Locations with Instant AP’s
• Assume a small venue deployment with IAP’s (coffee
shops, small retail stores, etc.)
– 1 - 2 AP per location
• No Maps are needed from Airwave in this scenario (with
ALE 1.3)
• IAP’s begin sending data from every location
• ALE realizes data is being generated from single AP’s
• Switches to “Map-less” mode and generates events
appropriately

Geofencing Support (ALE 1.3)
PoC Area
Cubicals
Key Highlights
• Draw regions in Airwave
• Regions equate to Geofences in ALE
• ALE generates events of ZoneIn and ZoneOut and provides
dwell times (through Geofence notify APIs)

Excluding Regions from Location Calculation
• Assume a Mall
environment
• Given the openness of
area, there is a
probability a client gets
triangulated in the
Atrium
• To avoid this, ALE
does not place clients
in any region drawn in
Airwave that begins
with an
_UNDERSCORE
1. Draw a
region
2. Region Name
should begin with
underscore

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

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

Measured RSSI Variation

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

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)

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

35
CONFIDENTIAL
All rights reserved
ALE In Action: A Few Case Studies
Analytics Partners

Analytics Example – Hospitality
(ALE Integration with APAMA)

Geofence Analytics Example – Hospitality
(ALE Integration with APAMA)

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

Retail Traffic Analytics Reporting in Shopping Mall
(AisleLabs “Flow” Analytics Sample)

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

Correlation with Point of Sale Information
(AisleLabs Sample)

SkyFii Analytics
(ALE Integration Example)

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

Machine Data Analytics
ALE – Splunk Integration
Applications
SDK
splunk>
Splunk
Forwarder
Log
Files
Streaming data
DevicesDevicesDevices
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 hierarchical (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

Splunk App – Application Visibility Dashboard

Splunk App – Station Dashboard

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 - 1

Key 3rd Party Location Analytics Partners - 2
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

Summary: Analytics – A Journey
1
2
3
Identify Key Use Cases,
Business Value
Proposition
Tune Network, Identify Key Partners
for POC, Design Use Cases
Develop ALE Adaptor (API
Programming)
POC – 2 to 3 months
Evaluate couple of solutions
Refine Use Cases
4
Build Internal Processes to
consume and act on the data.
Refine Use Cases

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

ALE: Key Resources
• Detailed ALE 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
• Secure link to streaming Data from ALE server (Sunnyvale
LAB) for Adapter development
• Help with Splunk / ElasticSearch + Logstash (ELK)
integration
• Help with POCs
• …Whatever help you need, we are available!
ALE Demonstrator App
(Android)

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