Value of Location Analytics

#ATM15 |

Value of Location Analytics

Manju Mahishi

March 2015

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

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#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

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CONFIDENTIAL

© Copyright 2014. Aruba Networks, Inc. All rights reserved

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#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

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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

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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

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Retail Analytics Landscape:

Key Trends and Initiatives

SHELF SPACE OPTIMIZATION

(SEGMENTATION, TARGETING,

CUSTOMER MARKETING

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,

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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 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

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CONFIDENTIAL

© Copyright 2014. Aruba Networks, Inc. All rights reserved

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

ID

IA

N

ALE (Network)

Meridian w/BLE

MERIDIAN,

PARTNERS

MER

ID

IA

N

C

L

EA

R

P

A

SS

A

L

E

+

P

A

R

T

N

ER

S

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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

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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

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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

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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

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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

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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

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#ATM15 |

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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#ATM15 |

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

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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

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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

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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

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CONFIDENTIAL

© Copyright 2014. Aruba Networks, Inc. All rights reserved

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.

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Correlation with Point of Sale Information

(AisleLabs Sample)

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SkyFii Analytics

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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

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Machine(Data(Analytics(

ALE$–$Splunk$Integration

(

Applications

SDK

s

plunk>

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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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)

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Key 3

rd

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)

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CONFIDENTIAL

© Copyright 2014. Aruba Networks, Inc. All rights reserved

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#ATM15 |

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.

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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

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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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