shanghai breakout: location analytics – key considerations and use cases

The Value of Location Analytics

Manju Mahishi

December 2014

2CONFIDENTIAL

© Copyright 2014. Aruba Networks, Inc.

All rights reserved#AirheadsConf

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

3CONFIDENTIAL



Understanding Analytics

4CONFIDENTIAL



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

5CONFIDENTIAL



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

6CONFIDENTIAL



Big Data Analytics: Market Sizing

7CONFIDENTIAL



Improve traffic flow

Web analyticsStadium /

Arena

Location Analytics Across Verticals

Increase ad revenues

Optimize traffic flowsAirports /

Malls

A/B Testing

Optimize staffing

Understand buying

patterns

Sentiment analysisRetail

Improve customer

engagement

Identify high value

customers

Real time offersHospitality

Differentiated service

based on location

Workspace optimization

Access policy

management

Enterprises

8CONFIDENTIAL



Retail Analytics Landscape: Key Trends and Initiatives

SHELF SPACE OPTIMIZATIONCUSTOMER 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 OPTIMIZATIONTEST & LEARN WORKFORCE ANALYTICS

(STAFF OPTIMIZATION)

MULTI-CHANNEL

ANALYTICS (ONLINE,

OFFLINE)

LOCATION ANALYTICS,

REAL TIME ENGAGEMENTVIDEO ANALYTICS

9CONFIDENTIAL



Decoding Big Data

10CONFIDENTIAL



Retail Big Data Topology (Source: IDC, 2012)

11CONFIDENTIAL



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

12CONFIDENTIAL



An Overview of Aruba Analytics and Location Engine (ALE)

13CONFIDENTIAL



Mapping LBS Use Cases to Aruba’s Solutions

LBSGuest

Access, Branded Portals

Mobile Engagement

App

Platform

Indoor Mapping Services

Indoor Location

Engine

Contextual Engagement:

Proximity Notifications

Analytics,

Data Mining

ME

RID

IAN

ALE (Network)

Meridian w/BLE

MERIDIAN,

PARTNERS

ME

RID

IAN

CL

EA

RP

AS

SA

LE

+

PA

RT

NE

RS

14CONFIDENTIAL



Analytics and User / Customer Engagement

Contextual Data:User, Device, Application &

Location

ENGAGEMENTLocation / User Specific

Experiences

DATA

MINING /

ANALYTICS

Sensors

Other

Data

Sources

CRM

Venue Traffic

Patterns, A/B

Testing,

Demographic

Analysis, etc.

ALE

MARKETING, AD

PLATFORMS

15CONFIDENTIAL



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

16CONFIDENTIAL



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

ALEAirWaveVisual RF

LOCATION ANALYTICS

Analytics Partner

Location Services

MOBILITY

CONTROLLERS

INSTANT

APs

17CONFIDENTIAL



ALE Internal Workflow

ALE Processes

Decode the Received data to

appropriate format Location

Engine

Redis In-MemoryDatabase

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

18CONFIDENTIAL



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

19CONFIDENTIAL



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)

20CONFIDENTIAL



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

•

21CONFIDENTIAL



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

22CONFIDENTIAL



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

23CONFIDENTIAL



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 PoliciesSDN Enablement

(Context APIs)

24CONFIDENTIAL



ALE In Action: A Few Case Studies

Analytics Partners

25CONFIDENTIAL



Hospitality Location Analytics Example (ALE Integration with APAMA)

26CONFIDENTIAL



Geofence Analytics Example – Hospitality(ALE Integration with APAMA)

27CONFIDENTIAL



Retail Analytics Example

Market

Benchmarks

Queue

Management

Interior

Counting

Marketing

Effectiveness

Draw

RateShopper

Demographics

Foot

Traffic

= Perimeter Counting

Sales

InterceptLoyalty /

Abandonment

= Mobile Analytics

(Wi-Fi, BLE)

Source: ShopperTrak

28CONFIDENTIAL



Retail Traffic Analytics Reporting (Sample)

ShopperTrak

Sample Report

(Generated for a

Retail Store in

Spain; integrating

with ALE)

29CONFIDENTIAL



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

30CONFIDENTIAL



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 effectiveA 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

31CONFIDENTIAL



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

Hungry Jack’sCUSTOMER u

Fast FoodCATEGORY u

32CONFIDENTIAL



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)

X

Finger Print

33CONFIDENTIAL



Machine Data AnalyticsALE – Splunk Integration

Applications

SDK

splunk>

Splunk

Forwarder

Log

Files

Streaming data

DevicesDevices

DevicesALE

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

34CONFIDENTIAL



Splunk App – Application Visibility Dashboard

35CONFIDENTIAL



Splunk App – Station Dashboard

36CONFIDENTIAL



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

37CONFIDENTIAL



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

Key 3rd Party Location Analytics Partners - 2

38CONFIDENTIAL



Summary: Analytics – A Journey

1

2

3

Identify Key Use Cases,

Business Value

Proposition

Tune Network, Identify

Key Partners for POC,

Design Use Cases

POC – 2 to 3 months

Refine Use Cases

4

Build Internal Processes to

consume and act on the data.

Refine Use Cases

39CONFIDENTIAL



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

40CONFIDENTIAL



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

Technology

aruba networks

confidential copyright

offlinelocation analytics

aruba ale

traffic pattern analytics

various analytics use

airheadsconfdecoding

big data mining