Location Analytics Applications and Architecture


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Presentation to IBM's Information on Demand conference 2013, Las Vegas, NV.
It describes location analytics - data sources, types of analysis and solution architecture.

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Location Analytics Applications and Architecture

  1. 1. © 2013 IBM Corporation A Deep Dive into “First of A Kind” Big Data Telco Solution Session #: ISA-3638 Sambit Sahu, Arvind Sathi, Tommy Eunice, Mathews Thomas Ken Kralick IBM
  2. 2. Please note IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice at IBM’s sole discretion. Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision. The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract. The development, release, and timing of any future features or functionality described for our products remains at our sole discretion. Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user’s job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here. 2
  3. 3. Acknowledgements and Disclaimers Availability. References in this presentation to IBM products, programs, or services do not imply that they will be available in all countries in which IBM operates. The workshops, sessions and materials have been prepared by IBM or the session speakers and reflect their own views. They are provided for informational purposes only, and are neither intended to, nor shall have the effect of being, legal or other guidance or advice to any participant. While efforts were made to verify the completeness and accuracy of the information contained in this presentation, it is provided AS-IS without warranty of any kind, express or implied. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, this presentation or any other materials. Nothing contained in this presentation is intended to, nor shall have the effect of, creating any warranties or representations from IBM or its suppliers or licensors, or altering the terms and conditions of the applicable license agreement governing the use of IBM software. All customer examples described are presented as illustrations of how those customers have used IBM products and the results they may have achieved. Actual environmental costs and performance characteristics may vary by customer. Nothing contained in these materials is intended to, nor shall have the effect of, stating or implying that any activities undertaken by you will result in any specific sales, revenue growth or other results. © Copyright IBM Corporation 2013. All rights reserved. • U.S. Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. • Please update paragraph below for the particular product or family brand trademarks you mention such as WebSphere, DB2, Maximo, Clearcase, Lotus, etc IBM, the IBM logo, ibm.com, [IBM Brand, if trademarked], and [IBM Product, if trademarked] are trademarks or registered trademarks of International Business Machines Corporation in the United States, other countries, or both. If these and other IBM trademarked terms are marked on their first occurrence in this information with a trademark symbol (® or ™), these symbols indicate U.S. registered or common law trademarks owned by IBM at the time this information was published. Such trademarks may also be registered or common law trademarks in other countries. A current list of IBM trademarks is available on the Web at “Copyright and trademark information” at www.ibm.com/legal/copytrade.shtml If you have mentioned trademarks that are not from IBM, please update and add the following lines: [Insert any special 3rd party trademark names/attributions here] Other company, product, or service names may be trademarks or service marks of others. 3
  4. 4. Agenda •  Motivation and Background •  IBM Research activities •  Advanced Analytics Platform •  Life Style Analytics 4
  5. 5. Telco and cross industry data to create a unified view of the customer. Mobility information is becoming increasingly valuable….. Structured Repeatable Linear Monthly sales reports Profitability analysis Customer surveys Other     Industries   Other   Data   Industry  Reports   Retail   Social     Media  Data   Customer   • Segment   • Social  Network   • Demographics     • Sex,  Age  Group,  etc   • Tenure   • Rate  plan   • Credit  RaBng,  ARPU   Group   Device   • Class   • Manufacturer   • Model   • OS   • Media  Capability     • Keyboard  Type   TransacBons   • Voice,  SMS,  MMS   • Data  &  Web  Sessions   • Click  Streams   • Purchases   • Downloads   • Signaling,  AuthenBcaBon   • Probe/DPI   Network   • Availability     • Throughput/Speed   • Latency   • LocaBon   • FaciliBes     Interface   • Discovery   • NavigaBon   • RecommendaBons   Product/Service   • SubscripBons   • Rate  Plans   • Media  Type   • Category/ClassificaBon   • Price   Starts,  Stops   Success  Rates   Errors   Throughput   Setup  Time   ConnecBon  Time   Usage   Recency   Frequency   Monetary   Latency   Telco Data Cross Industry Data 5
  6. 6. Building Context and Intent from Location data •  Deriving location: location information may be derived using multi- modal information –  CDR data, tower data, device data, Wi-fi etc. –  Accuracy of location information depends on data fidelity etc. •  Building context: making sense of the location information –  Correlate location information with business data –  Various other correlation rules may be used to build a rich context •  Inferring intent: infer consumer level intents by leveraging location and mobility patterns Deriving Location Inferring IntentBuilding Context 6
  7. 7. 7 Data Cell tower locations Wi-fi locations Device locations Device usage data – apps, web sites Customer data – demographics Refined locations Mobility Patterns Hang outs Hang outs correlated with business locations Mode of transportation Traveling buddies Analytics 7
  8. 8. 8 Type of Location Analytics….. Habitual Journey Patterns Demographic customer profiles Common origins and destinations Direction of travel Level of Mobility + Segmentation Aggregated Mode of transport Average journey times Travel pattern anomalies Accurate Location Congestion Real time traffic incident flags Optimal route planning Foot traffic Customer wait times Individual mode of transport Possible with event data More detailed data required VCC Board Morph Update, June 2011
  9. 9. First-of-a-Kind Program §  Experimental technology-based solutions engagements §  Testing tomorrow’s innovations on today’s business problems §  Yielding prototype solutions across a range of industries §  Creating valuable intellectual capital for IBM’s portfolio §  Value to IBM Clients –  Early market advantage –  Access to world class researchers 9
  10. 10. FOAK Deliverables •  Early thought leadership and experiences with new technologies •  Working prototype of an innovative solution not yet available in the marketplace •  The know-how to improve a business process or solve a problem •  Software components, methodologies and tools •  Press & media coverage 10
  11. 11. Agenda •  Motivation and Background •  IBM Research activities •  Advanced Analytics Platform •  Life Style Analytics 11
  12. 12. Two Scenarios: Aggregate and Individual •  Aggregate Anonymized Analytics: Sensing City-scale people movement from Telco data and leveraging for Transit Optimization •  Enriched Consumer Profile: Customer Analytics with Mobility Profiles from Telco Data 12
  13. 13. Enriched Consumer Profiles for Enabling Telco Data Monetization •  We develop enriched consumer profiles by deriving insights about consumer preferences, life style, and intent from location, mobility and call data joined with use case appropriate data sources. •  Enriched consumer profiles are utilized to enable new services and effective campaign through targeted segmentation. 13
  14. 14. Two Scenarios: Aggregate and Individual •  Aggregate Anonymized Analytics: Sensing City-scale people movement from Telco data and leveraging for Transit Optimization •  Enriched Consumer Profile: Customer Analytics with Mobility Profiles from Telco Data 14
  15. 15. Sensing City Scale People Movement from Telco Data Cities Demonstrated: Istanbul (Turkey), Dubuque (USA) for Transit Optimization and a series of subsequent client pipeline Challenge Cities have very little real understanding of where citizens, goods and transportation move during the day. Without this information it is difficult to accurately plan and manage the usage of roads and infrastructure. Solution Using a variety of real time data from “smart phones”, GPS devices, terminals, traffic cameras, public transportation schedules and transit data, develop models of zonal density, flow of goods and origin / destination pairs. From these models, drive processes to manage this flow against a specific objective. Benefits Evaluates the efficacy of existing transit system and transportation infrastructure; provides the structure for design incentive strategies to win new riders – information, incentives, services; optimize fleet operations in situations where demand outpaces supply; manage revenue through better zoning and permits. comprehensive solution that will address the management of congestion, fleet management, people attending events, and multimodal transit 1515
  16. 16. Sensing People Movement from Telco Data 16
  17. 17. Identifying Meaningful Locations Where People Live Where People Work Istanbul Movement Analysis - 4.7 million phones w. 3B+ events/week - Accurate detection of home, work & meaningful locations 17
  18. 18. Traffic Monitoring Uses basic analytics building blocks already seen to display time based traffic flow levels mapped to city road system. A snapshot at 8:30am: 18
  19. 19. Commuter Pain Index 19
  20. 20. Feeder Bus Route Optimization for M4 Metro Line on Anatolian side of Istanbul Feeder bus routes based on demand to 4 metro stations on Kadikoy-Kartal metro line 20
  21. 21. Optimal Bus Stop Location Design •  Stops are added by considering the greatest potential demand for transit and accessibility at origin and destination •  Some stops are added to far places in which demand to the area already served by existing stops is potentially large 21
  22. 22. Two Scenarios: Aggregate and Individual •  Aggregate Anonymized Analytics: Sensing City-scale people movement from Telco data and leveraging for Transit Optimization •  Enriched Consumer Profile: Customer Analytics with Mobility Profiles from Telco Data 22
  23. 23. Consumer Analytics with Enhanced Consumer Profiles •  Derive advanced location/mobility attributes and patterns from Telco data to enrich consumer profiles with mobility context •  Derive predictive model about consumers location and mobility patterns •  Leverage enriched consumer profiles for data monetization opportunities by correlating and joining other data sources •  Build an operational asset on IBM Big Data platform to enable Telco to extract mobility attributes and patterns efficiently 23
  24. 24. Set of example mobility attributes •  Base set of example mobility attributes – Home and work location – Weekday top locations – Weekend top locations – Meaningful location detection – Classification of where and when time spent – Detecting tourism pattern – Detecting specified habits related to mobility –  Trip purpose – Anomaly in mobility from baseline patterns – Detecting who’s who in the household based on mobility pattern •  Advanced predictive models (Next Best Location) – Likely place a person would be at a future time – Likelihood of a person going to a Mall during this weekend – When this person is likely to be a tourist 24
  25. 25. 25 Enhanced Micro-segmentation with Mobility Model Mobility Patterns Buying Patterns Social Patterns Demographics • Gender • Age group • Address • Income Historical buying patterns Social network influencers Mobility Model • Location and movement pattern (space, time) • Meaningful location detection • Meaningful location classification • Trip purpose • Estimated Duration of stay • Estimated Duration of travel • Mode of travel • Calling patterns • Detecting tourist patterns • Detecting student patterns • Estimated demographic profile of user of phone • Anomalies in regular patterns Enhanced Attributes for Customer Segmentation
  26. 26. Retailer Customer Profile Real Time Targeted Advertisement for IPTV AAP (Advanced Analytics Platform) 3 - AAP catches the new football interest flag, his frequent sports shopping, and in realtime matches Tom’s profile with an offer for 20% off coupon to an Nike store. 4 - Tom is also an existing SMS Opt- In mobile cust. 5 – Tom receives targeted IPTV advertisements based on his IPTV, mobility and social profiles 2 - Tom is channel surfing, mostly sports channels, primarily football games where Nike advertises a lot (AAP enhances his customer profile, after 10 football games viewed in 1st month, with an interest flag as a “football fan”) Enhanced Cust. Profile Interest / Mobile # / Email 1- Tom activates IPTV service with the America 50 package and adds the ESPN sports ala carte option (we have an initial customer profile with his fixed # and a mobile#) A la carte option Sports Packages tom@gmail.com   212-­‐201-­‐1234   Language Package 26
  27. 27. Location Based Real Time Offering on Mobile Phone Lisa 4 - AAP catches that Lisa is entering a mall, and matches her “Fashion” interest flag and “Perfume” preference, sends in realtime an offer for 20% off coupon for Byonce fragrance at Sephora in that mall. 5 - Lisa receives an SMS/email/App notification that her mobile app account contains a new offer for Beyonce perfume. Beyonce Fan Page 2 - She follows a friend’s post on FB and clicks the Like button on the Beyonce Fan Page. 3 - Lisa’s IPTV viewing & mobile clickstream behaviors set her Interest flag to “Fashion” and one preference to “Perfume”. 6 - Lisa uses the mWallet app on her smartphone to purchase some perfume at POS via NFC. 1- Lisa is a mobile subscriber with Telco and downloads the mobile app and agrees to receive offers related to her interests. AAP (Advanced Analytics Platform) Retailer Customer Profile Enhanced Cust. Profile Interest & Preference IPTV a la carte option & Mobile Features/Apps IPTV Lang Pkg & Mobile Pkg 27
  28. 28. Agenda •  Motivation and Background •  IBM Research activities •  Advanced Analytics Platform •  Life Style Analytics 28
  29. 29. 1 2 3 Advanced Analytics Platform End-use Applications Analytics Visualization Big Data Analytics Warehouse Predictive Analytics Sens e Analyze Act Search / Explore KPIs Dashboards Drill-Downs Reports Marketing Campaigns Rules Engine Behavioral Analysis Outcome Optimization Propensity Scoring Model Creation Structured / Unstructured Data Data Governance Data Integration ETL/ELT ChangeCapture DataQuality/Validity/Security-Privacy Format/UnitConversion Consolidation/De-duplication DataRepositories Network Data Customer Behavior Data Customer Data ProductDataNetworkTopology Data ContinuousFeed Sources Usage Data Reference Data Historical Analysis Data Demographics Segmentation Location Past Actions Propensity Scores Behaviors Predictive Model Deployment Actionable Insight Stream Processing Streaming Data Operational Systems 4 5 AAP Capabilities High Performance Historical analysis (Big Data Platform) Model Based Analytics - behavioral scoring, micro segmentation, correlation detection analysis Real-time scoring, classification, detection and action Visualize, explore, investigate, search and report Take action on analytics IBM’s Advanced Analytics Platform (AAP) Supports Use Cases across the business with New Era Capabilities Create new Services and Business Models Transform Operations Build Smarter Networks Personalize Customer Engagements 1 1 2 3 4 5 5 29
  30. 30.   Social  Informa-on       Loca-on  Informa-on       Customer  database   informa-on         InfoSphere Streams Low Latency Analytics for streaming data InfoSphere BigInsights Hadoop-based low latency analytics for variety and volume IBM Netezza BI and Ad Hoc Analytics Structured Data Customer database Coremetrics Low Latency Analytics for streaming data Data sources….. 30
  31. 31. The carmel frappuccino in starbucks is just heavenly. IBM  BigInsights  Text  Analy-cs   Accelerators   BigInsights   Custom  analyBcs   First  Name:  Joe   Last  name:  Smith   Address:  1234  Anyroad   ….   [  X  ]  Likes  coffee   [  X  ]  Likes  frappuccino   [        ]  Likes  cappuccino   ….   [  posiBve]  SenBment  coffee       Social Media Profile Creation 31
  32. 32. URL Analysis- Extract Implicit User Profile analysis" URL Analysis: for each user, report the most meaningful interests to describe her profile. Large scale analysis Update users profiles" Consume" Adaptive user segmentations: create new users segmentation by clustering similar interests Data Cleansing 32
  33. 33. 3 3 CDR Relationship Analytics 33
  34. 34. Collecting and analyzing in real-time millions of events from multiple sources to detect the right time to respond to the event CDRs Billing CRM Location Account Mgt Internet Network Millions of events per second Microsecond Latency Dropped Calls Outgoing International Calls Call Duration Extra Call Contract Expiration Entered new cell New Top-Up 5 minutes left on pre-paid EDW Invoice Issued Predictive Models 3 dropped calls in 10 minutes Customer is close to a store Customer entered a shopping area Invoice paid + called competitor Smart phone browsing pattern Customer is watching a video Congested Cells Invoice Paid Acquired new products Change contracts Brand Reputation Customer Sentiment from Social network Customer is roaming Customer is at home Campaign ManagementInvoke appropriate campaign Score Real-time Stream Analytics 34
  35. 35. 35 Campaign Management
  36. 36. Campaign Response 36
  37. 37. Agenda •  Motivation and Background •  IBM Research activities •  Advanced Analytics Platform •  Life Style Analytics 37
  38. 38. Determining Buddies, Hangouts, Life Style Example Lifestyle Attributes for marketing demonstration §  Subscriber Lifestyles §  Popular Locations §  Subscriber Pairings Who Are You? Homebody Daily Grinder Delivering the Goods Globetrotter Nomad 10 Top Hangouts Best Buddies Next Steps •  Given the lifestyles, popular locations, and best buddy data => predict where individuals or groups of similar individuals will be and when. •  Use time series modeling and clustering we can create time/location based marketing campaigns targeted at homogenous groups in specific locales. 38
  39. 39. © 2012 IBM Corporation Buddies, Hangouts, Globtrotters Areas of mobility analytics n  Individual Lifestyle and Usage profiles n  Popular Locations with specific profiles n  Who are the Buddies n  Predicting where people go Who Are You? Homebody Daily Grinder Delivering the Goods Globetrotter Sofa Surfer 10 Top Hangouts Mobile ID Buddy Rank 2702 1 1256 2 8786 3 4792 4 8950 5 39
  40. 40. What are Profiles •  Lifestyle Profiles are defined by marketing analysts for specific use cases or marketing programs •  Usage Profiles are created using data mining algorithms and define how a person uses services during the day •  Location Affinity is created with algorithms and determines preferred locations for individuals throughout the day and week •  Together these uniquely define a person with relation to how the retailer or marketer might want to market to them 40
  41. 41. Creating Groups of Mobility Profiles Enables Better Prediction for Certain Groups l  profiles breakdown like this l  Homebody, doesn't visit too many unique locations l  Daily Grinder, back and forth to work, quiet weekends, makes stops along the way l  Norm Peterson, inside the lines, no deviations l  Delivering the goods, no predictable patterns, many different locales during the day l  Globe Trotter, either not in town, or keeps their phone turned off l  Rover Wanderer, spends evenings at various location (sofa surfers www.couchsurfing.org) l  “Other”, is a group hard to categorize 41
  42. 42. By Profile, when is it easy or difficult to predict where they will be? Profile Day Time Predictability Daily Grinder Thursday Dinner Highest Daily Grinder Friday Afternoon Lowest Homebody Saturday Night Highest Homebody Wednesday Morning Lowest These are the 2 most predictable profiles, yet there is diversity in their predictability. To best communicate with Daily Grinders, contact them on Thursday Afternoons just before dinner 42
  43. 43. Preferred Locations of by profile type at Lunchtime Weekdays (Central Stockholm) Delivering the Goods Night Shifters Daily Grinders 43
  44. 44. What analysis is available (Anonymous Data) From the mobility profiles, summarized, anonymous analysis is available l  Summarized to ensure anonymity, analysis of popular locations by time of day and profile of subscribers is possible l  For retailers this information can help understand what types of people are nearby at lunch time l  What types of people prefer which areas. Some obvious results are Globe Trotters go to airports, Daily Grinders go to office buildings. Other non-obvious results show up also. l  Are there predictable patterns that we can use to target certain groups in the future? 44
  45. 45. What Makes this Possible? l  Using the power of Netezza and modeling capabilities of SPSS we can literally throw all the data at data mining algorithms and create discrete clusters of subscribers by activity, mobility l  Apply the data mining outputs to the entire subscriber base by creating detailed specific analyses for each subscriber refined by the mobility profiles 45
  46. 46. Enriched Consumer Profile Hub Customer  Profile  Hub   IPTV     -­‐   SubscripBon  Billing   -­‐ VOD  Billing  &  viewed   -­‐   channel  viewing  history   -­‐ -­‐  contents  purchased   -­‐ Logs  &  Tuning  Events   -­‐   package  subscripBon   Mobile   -­‐   LocaBon   -­‐   URL+App  Transac-ons   -­‐   xDRs  and  inb.  roaming   -­‐   RAN  (incl.  HLR/VLR)   -­‐   Top  Up   -­‐   Pkgs   -­‐   Billing   -­‐   SMS,  browing  URLs   Other:   -­‐  Devices   -­‐  Dealer  Network   -­‐   Contact  Center   -­‐   Call  Recordings   -­‐   Trouble  Tickeing   -­‐   Campaign  Results  (Imagine)   -­‐   Loyalty   -­‐   CompeBBon  Website   -­‐   Retail  Transac-ons   Fixed   -­‐   CDR   -­‐   URL  (IP)   -­‐ Radius  (IP-­‐Cust)   -­‐   Pkgs   -­‐   Billing   Historical   TransacBons/     Events   Partners/Retailers   AdverBsers   Other/Internal   GIS   -­‐   Business  map  and  numbers   -­‐   Point  of  Interest  maps     Consumers  of  new  Insights   Feedback   Social     Media  Data   46
  47. 47. Agenda •  Motivation and Background •  IBM Research activities •  Advanced Analytics Platform •  Life Style Analytics 47
  48. 48. Thank You Your feedback is important! •  Access the Conference Agenda Builder to complete your session surveys o  Any web or mobile browser at http://iod13surveys.com/surveys.html o  Any Agenda Builder kiosk onsite 48