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Value of Location Analytics Manju Mahishi

March 2015

@ArubaNetworks

CONFIDENTIAL © Copyright 2015. Aruba Networks, Inc. All rights reserved

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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


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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


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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

0%

5%

10%

0.10% 1.2% 3.5%

7% 10%

Click Through Rate

Source ABI Research


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Big Data Analytics: Market Sizing


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Improve traffic flow Web analytics Stadium /

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 offers Hospitality

Workspace optimization Location based 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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Retail Big Data Topology (Source: IDC, 2012)


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Decoding Big Data


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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


An Overview of Aruba Analytics and Location Engine (ALE)


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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

MER

IDIA

N

ALE (Network) Meridian w/BLE

MERIDIAN, PARTNERS

MER

IDIA

N

CLEA

RPA

SS A

LE +

PA

RTN

ERS


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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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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


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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

ALE AirWave Visual RF

LOCATION$

ANALYTICS$

PLATFORMS$

Analytics Partner Location Services

MOBILITY CONTROLLERS

INSTANT APs


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ALE Internal Workflow

ALE$Processes$

Decode$the$Received$data$to$

appropriate$format$

Loca6on$$Engine$

Redis$In:Memory$Database$

Calculate$Device$Loca6on$(x,y)$

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$


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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


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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


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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


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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


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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


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ALE v1.3 Dashboard: New GUI


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Choosing Floors to Import from AirWave


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Setting Up Secure WebSocket Tunnel to External Analytics Engines


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“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


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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)


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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


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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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Measured RSSI Variation


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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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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)


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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)


ALE In Action: A Few Case Studies Analytics Partners


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Analytics Example – Hospitality (ALE Integration with APAMA)


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Geofence Analytics Example – Hospitality (ALE Integration with APAMA)


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


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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$markeHng$

campaign$effecHve$

A$daily$review$of$peak$6mes$will$help$evaluate$and$measure$the$results$of$promo6onal$campaigns$and$event$programs$

$

Peak$hours$remain$stable$

between$$$$10:00$AM$O$2:00$PM$$

$$

$

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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Correlation with Point of Sale Information (AisleLabs Sample)


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SkyFii Analytics (ALE Integration Example)


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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


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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 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


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Splunk App – Application Visibility Dashboard


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Splunk App – Station Dashboard


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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 - 1


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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


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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 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


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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 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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