Shanghai Breakout: Location Analytics – Key Considerations and Use Cases
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The document discusses the value of location analytics and Aruba's Location Analytics Engine (ALE) solution. It describes how ALE can aggregate and expose user, device, application and location data to drive analytics use cases and contextual customer engagement. ALE provides a unified context for each user and high performance APIs to integrate with analytics and marketing platforms. The accuracy and latency of location services depends on factors like AP density and client behavior, and the appropriate solution depends on the required use case and business needs.
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CONFIDENTIAL
© Copyright 2014. Aruba Networks, Inc.
All rights reserved
#AirheadsConf
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](https://image.slidesharecdn.com/finallocation-analytics-v1-141210205225-conversion-gate02/85/Shanghai-Breakout-Location-Analytics-Key-Considerations-and-Use-Cases-39-320.jpg)
Shanghai Breakout: Location Analytics – Key Considerations and Use Cases
- 1. The Value of Location Analytics Manju Mahishi December 2014
- 2. 2 CONFIDENTIAL © 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
- 3. 3 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf Understanding Analytics
- 4. 4 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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
- 5. 5 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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
- 6. 6 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf Big Data Analytics: Market Sizing
- 7. Location Analytics Across Verticals 7 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved Improve traffic flow Web analytics #AirheadsConf 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
- 8. 8 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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
- 9. 9 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf Decoding Big Data
- 10. 10 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf Retail Big Data Topology (Source: IDC, 2012)
- 11. 11 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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
- 12. 12 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf An Overview of Aruba Analytics and Location Engine (ALE)
- 13. 13 Engagement Platform CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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
- 14. 14 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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
- 15. 15 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved Real time location engine #AirheadsConf 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
- 16. 16 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved ALE imports Visual RF maps, Decodes AMON, Computes Location, Provides Context APIs Controllers Create AMON Messages AP’s Create Virtual Beacon Report (VBR) #AirheadsConf ALE System Overview Probing Clients LOCATION ANALYTICS Visual RF AirWave ALE Analytics Partner Location Services MOBILITY CONTROLLERS INSTANT APs
- 17. Publish the received data using Publish/subscribe API (Google Protobuf/0MQ) North Bound API Forward decoded User, Device, App data 17 Data from Controller (AMON) or IAP (HTTPS) CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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
- 18. 18 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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
- 19. 19 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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)
- 20. 20 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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 •
- 21. 21 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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
- 22. 22 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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
- 23. 23 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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)
- 24. 24 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf ALE In Action: A Few Case Studies Analytics Partners
- 25. 25 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf Hospitality Location Analytics Example (ALE Integration with APAMA)
- 26. 26 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf Geofence Analytics Example – Hospitality (ALE Integration with APAMA)
- 27. 27 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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
- 28. 28 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf Retail Traffic Analytics Reporting (Sample) ShopperTrak Sample Report (Generated for a Retail Store in Spain; integrating with ALE)
- 29. 29 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf Retail Traffic Analytics Reporting in Shopping Mall (AisleLabs “Flow” Analytics Sample)
- 30. 30 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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
- 31. 31 CUSTOMER u Hungry Jack’s CATEGORY u Fast Food CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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
- 32. X 32 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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
- 33. 33 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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
- 34. 34 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf Splunk App – Application Visibility Dashboard
- 35. 35 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf Splunk App – Station Dashboard
- 36. Key 3rd Party Location Analytics Partners - 1 36 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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)
- 37. Key 3rd Party Location Analytics Partners - 2 37 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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
- 38. POC – 2 to 3 months Refine Use Cases 38 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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
- 39. 39 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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
- 40. 40 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved #AirheadsConf 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!
- 41. 41 CONFIDENTIAL © Copyright 2014. Aruba Networks, Inc. All rights reserved Thank You #AirheadsConf