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#GeodeSummit - Using Geode as Operational Data Services for Real Time Mobile Experience

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One of the largest retailers in North America are considering Apache Geode for their new mobile loyalty application, to support their digital transformation effort. They would use Geode to provide operational data services for their mobile cloud service. This retailer needs to replace sluggish response times with sub-second response which will improved conversion rates. They also want to able to close the loop between data science findings and app experience. This way the right customer interaction is suggested when it is needed such as when customers are looking at their mobile app while walking in the store, or sending notifications at the individuals most likely shopping times. The final benefits of using Geode will include faster development cycles, increased customer loyalty, and higher revenue.

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#GeodeSummit - Using Geode as Operational Data Services for Real Time Mobile Experience

  1. 1. Using Geode to Enhance Customer Experience
  2. 2. 2Copyright © Capgemini 2015. All Rights Reserved What problem are we trying to solve? Every retailer’s problem to solve… Personalized Service Mobile check- out Optimized Staffing, Tasks and Training Real-time Inventory Tracking Assortment and Price Differentiation Endless Aisle Facilities Control Fault Detection Dynamic Layout Platform Data SecurityAnalytics Virtual Wall Dynamic Labels Smart Digital Signage Customer Devices END POINT CAPABILITY PROCESS CORE PROCESS END POINT CAPABILITY Knowledgeable Sales Associates Heating and Lighting Robotics POC and Mobility Clienteling Apps Customer Employee Physical Store Product Customer Profiling Traffic, Purchase Patterns In-store Interactions Personalized Recommendations What retailer’s “Loyalty 2.0” might be grappling with… 1. Omni-Channel, Seamless Commerce 2. Insights & Data 3. Marketing Resource Management 4. Applied Innovation 5. Unified View of the Customer Next GeneraFon Loyalty Programs requirements… The holy grail of customer loyalty Delivering insights at the point of action in “human time”
  3. 3. 3Copyright © Capgemini 2015. All Rights Reserved Some key dimensions of the problem… §  Retailers have many Big and Fast Data challenges ... §  Data volumes – number of companies storing 100+ TB is growing … … but they are analyzing only a frac%on of their data assets §  Most retailers have 10’s to 100’s of systems churning out data in real Fme - plus external data sources (social media, weather, demographic, spaFal) that they should be using … … but they struggle to store and integrate data, and manage data complexity > today, BATCH rules 0 50 100 Structured Semi-Structured Unstructured Not Analyzed Analyzed Volume Velocity Variety
  4. 4. 4Copyright © Capgemini 2015. All Rights Reserved Just imagine: urban living in 2025 and the requirements of the retail environment. Consumer engagement Taking part in a dialogue with consumers, jusFfying their trust in the industry Transparency Keeping consumers informed about products’ key a`ributes, ingredients, nutrients and provenance as well as their environmental and societal impacts The last mile of distribu%on – both to the retail store and to the consumer Reconsidering the assumpFon that it is an area where companies operate independently of each other, and exploring opportuniFes to collaborate to improve speed, efficiency and consumer saFsfacFon Enabled by Modularized Technology Business agility and rapid collaboraFon require the transformaFon from rigid and purpose-built IT structures into mulF-use, component-based technology capabiliFes which allow for easy assembly or disassembly according to business needs.
  5. 5. 5Copyright © Capgemini 2015. All Rights Reserved Everything changes before tomorrow Click Call Read Click Location Push Engage Lifestyle Location Demand ?
  6. 6. 6Copyright © Capgemini 2015. All Rights Reserved Why tomorrow is too late I’m at the store in Charlotte Human-Time Social Analytics Store Master Customer Master Human-Time Business Analytics Customer Transactions Product Master Store ABC Jane Doe Jane Hey Shovel fans: Offer Code! Great: On our way! Jane’s Friends
  7. 7. 7Copyright © Capgemini 2015. All Rights Reserved Geode Cluster How we are using Geode for this Data Lake Geode Geode Geode Geode Geode Geode Write Back Batch Analytics Services Reference Data Enterprise History Cache Fast Speed Consumer Data Human-Time analytics Bulk Enterprise Data
  8. 8. 8Copyright © Capgemini 2015. All Rights Reserved Advantages of a no-RDBMS architecture § Geode/Gemfire handles the transactional integrity § Data Lake stores the history § Developer model is much more suited to Java developers than RDBMS § Enables multiple other engines to be added to the Batch part § Neo4J for network/graph analytics § Cost
  9. 9. 9Copyright © Capgemini 2015. All Rights Reserved What could tomorrow look like? The Present Tomorrow? Retailers send consumers coupon books generated on a monthly basis. Retailers figure out consumer shopping patterns, and deliver offers the day before the consumer plans to shop. No coupons – the consumer has an electronic wallet that pops up in-store. Customers get printed coupons when they go through the check out line. Consumers scan products as they shop, interact with “recommender” systems they like, and skip the check out line. Apple Pay. -  Recipe Help “It looks like you’re making chili – but you didn’t pick up any kidney beans – did you forget?” -  Healthy Choices “I see you just selected potato chips – would baby carrots be a better choice?” -  Market Share Shifting “There’s an extra 20% off if you choose Doritos instead of Lays today.” -  Managing Perishable Store Inventory “I know you like lettuce – there’s special pricing if you buy today.” Hint: Store ordered too much lettuce, it’s going to spoil if not sold soon. Customers push their carts up one aisle and down the other. “I see that it’s been several weeks. Have you been busy? Since you are one of our best customers, would it help if we were to suggest some items to tide you over, and deliver them by Uber right now?” “The weather forecasters are predicting a blizzard. Do you need some extra supplies? Do you need bottled water for baby formula? We can deliver…”
  10. 10. 10Copyright © Capgemini 2015. All Rights Reserved Enterprise Big Data Reference Architecture – Business Data Lake Hadoop – Foundational Elements REST HTTP/S Stream SPARK-ML (Formerly Mahout) ClassificationClustering Collaborative Filtering Processing & Computation In-Memory Process SPARK R SPARK YARN Business Data Lake Zones Interactive Analytics Visualization & Reporting Analytical Tools, Simulation & Languages Enterprise Solutions (API – XML – Json) Localized Data Sources Deep Learning Machine Learning, Evolutionary/Genetic Programming, Complex Event Processing & Enterprise Automation Tachyon Load & Refine Scoop Flume Hive NFS WebHDFS Pig Speed Layer Zone 0 Zone 1 Zone 5 Zone 2 Zone 3 Zone 4 Cleanse d Ideation SandboxEnriched Detailed Modeled Aggregate ModeledStagin g Metadata Security Knox Ranger Kerberos Provision, Monitor, Manage Ambari Zoo Keeper Schedul e Oozie Deployment ChoiceLinux Windows On-Premises Cloud Hue NO SQL- Slider HIVE - TEZ Phoenix HBase Solr (Search) Landin g SOURCES Geolocation IT Systems Sensor & Machine Server Logs Web & Social Clickstream Unstructured Apache Geode
  11. 11. The new digital divide •  The new digital divide: the gap between consumer’s digital behaviors and expectaFons – in contrast to the readiness and ability of retailers to deliver on the desired experiences. –  Percentage of shoppers 100% connected – growing quickly –  ExpectaFons – evolving rapidly –  “Showrooming” – e.g. fear that digital drives consumers online (it’s a myth) –  Micro-characterisFcs – geographic, demographic, ethnic, social, age, gender –  Ability of retailers to harness technology that – •  Permits applicaFons to be built quickly, dismantled quickly •  Delivers consumer behavior-influencing acFons in real Fme •  Drills down to individual consumers •  Integrates data – from inside and outside the enterprise
  12. 12. 13Copyright © Capgemini 2015. All Rights Reserved ….and how could we do this? Unleash Data and Insights as-a-service Make Insight-driven Value a Crucial Business KPI Empower your People with Insights at the Point of Action Develop an Enterprise Data Science Culture Master Governance, Security and Privacy of your Data Assets Enable your Data Landscape for the Flood coming from Connected People and Things Embark on the Journey to Insights within your Business and Technology Context 1 2 3 7654
  13. 13. Join the Apache Geode Community! •  Check out: http://geode.incubator.apache.org •  Subscribe: user-subscribe@geode.incubator.apache.org •  Download: http://geode.incubator.apache.org/releases/

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