Se ha denunciado esta presentación.
Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.

The Big Picture: Real-time Data is Defining Intelligent Offers

719 visualizaciones

Publicado el

New research shows that 57% of the buying cycle is completed before a prospect even speaks to a company. Marketers already know this, Ninety-six percent (96%) of organizations believe that email personalization can improve email marketing performance. But where do we get this increasingly personal direction? The answer is likely in your customer data. In order to understand your customer needs contextualized in the moment they feel the need to act you will require a platform that can leverage real-time data. Apache Kudu is a Cloudera component that makes dealing with quickly changing data fast and easy. Companies are leveraging next generation data stores like Kudu to build data applications that deliver smart promotions, real-time offers, and personalized marketing. Join us as we discuss modern approaches to real-time application development and highlight key Cloudera use cases being powered by Cloudera’s operational database.

Publicado en: Tecnología
  • Sé el primero en comentar

The Big Picture: Real-time Data is Defining Intelligent Offers

  1. 1. 1© Cloudera, Inc. All rights reserved. The Big Picture: Real-time Data is Defining Intelligent Offers Sean Anderson, Sr. Solutions Marketing Manager Ryan Lippert, Sr. Product Marketing Manager
  2. 2. 2© Cloudera, Inc. All rights reserved. We empower people to transform complex data into clear and actionable insights DRIVE CUSTOMER INSIGHTS CONNECT PRODUCTS & SERVICES (IoT) PROTECT BUSINESS
  3. 3. 3© Cloudera, Inc. All rights reserved. DRIVE CUSTOMER INSIGHTS CONNECT PRODUCTS & SERVICES (IoT) PROTECT BUSINESS Delivering greater value through improved customer understanding Powering predictive analytics to increase performance and reduce fleet downtime Creating new revenue streams with an advanced anti-fraud solution Cloudera powering data-driven customers
  4. 4. 4© Cloudera, Inc. All rights reserved. Cloudera Use Cases Omni-Channel optimization Customer analysis Sentiment analysis Churn analysis Market spend analysis Next best offer Smart promotions Basket analysis Network threat detection User/entity behavioral analysis Logger Merchant fraud Connect products & services Predictive maintenance Remote monitoring Supply chain optimization Inventory optimization Operations optimization Spend analytics service Drive customer insights Protect business
  5. 5. 5© Cloudera, Inc. All rights reserved. Powering a Variety of Use Cases… Targeted Marketing Smart Promotions Recommendation Engines Omni-Channel Optimization
  6. 6. 6© Cloudera, Inc. All rights reserved. ✓ Breaking down data silos ✓ Sharing data in accordance with privacy and regulatory policies ✓ Becoming iterative, lean and leveraging knowledge across the business Creating True Customer 360
  7. 7. 7© Cloudera, Inc. All rights reserved. Customer 360 Journey • Marketing Systems (Salesforce, Omniture, CRM) • Clickstream Data Primary Data Source • Clickstream • NPS Systems • Support Call Logs • Social Feeds Primary Data Source Understand Your Customer Learn Behaviors Improve Interactions • Shopping Cart Platforms • Geolocation Primary Data Source
  8. 8. 8© Cloudera, Inc. All rights reserved. The Data Journey Collate the Data Sources Micro-Segmentation Drive Personalized Campaigns Devise Micro- segments based on combining multiple factors: • Age • Location • Spending History • Channel Preferences • Content Preferences • Apps Usage • Social Influence • Churn Score • Lifetime Value • Usage Patterns • Data Usage Drive Personalized Campaigns for specific micro-segments Retention campaign for high value customers with iPhone who recently shared a negative social sentiment Upsell campaign for high-data users with family to move over to a family bundle Geo-Location based targeted advertising for specific customer micro-segments
  9. 9. 9© Cloudera, Inc. All rights reserved. How to Iteratively Build a True Customer 360? Customer Data Source Start with ingesting the “best” version of your customer profile Find your common identifiers across datasets: customer name, number, IMEI, IMSI IMEI ChannelsPurchase History Add New Data Source Common Identifier Current Source Enrich with additional demographic information (purchase history or channels) from other systems / sources Deliver A Use Case Deliver a specific use case based on the profile with new data sets: • Customer Lifetime value • Next Best offer • Omni Channel Enrich Your Profile • Enrich your customer profiles with purchase behavior • Continue to enhance with each new use case Location Clickstream Continue to add new data sources iteratively to enhance your customer profile with new use cases Call center Social Media Apps External Data New Data Sources
  10. 10. 10© Cloudera, Inc. All rights reserved. Three Scenarios
  11. 11. 11© Cloudera, Inc. All rights reserved. Three Scenarios – Event Modelling in Real Time Events trigger changes in purchasing preferences among customers and potential customers. However, most NBO frameworks are based on historic data models. Historic models handle a baseline of behavior/information well, but struggle to optimize in the moment for events. By incorporating the real-time behavior of users back into the model on a rolling basis, companies can capture the opportunity these events present.
  12. 12. 12© Cloudera, Inc. All rights reserved. 56% of all customer interactions happen during a multi-channel, multi-event journey. Companies that put data at the center of their marketing and sales decisions improve their marketing returns by 15-20% adding up to 150 to 20 billion in additional revenue. Research states that personalized emails improve click through rates by 14% and conversation rates by 10% Over 96% of organizations believe that email personalization can improve email marketing performance. Three Scenarios - Email Personalization
  13. 13. 13© Cloudera, Inc. All rights reserved. HEB is the largest grocery chain in the state of Texas. When employees couldn't get to work, some stores still operated with as few as five people Hurricane impact projections are often not accurate which means HEB had to plan for the worst and leverage real-time data to make shipping and staffing decisions. Certain items become in high demand during a hurricane, while other experience almost no- demand (Frozen Foods, Flowers) HEB leveraged real-time data to plan special shipments which arrived before state and federal aide. Three Scenarios - Hurricane Harvey
  14. 14. 14© Cloudera, Inc. All rights reserved. The Platform
  15. 15. 15© Cloudera, Inc. All rights reserved. Cloudera Enterprise – The Platform for Customer 360 Location Social Clickstream BI Tools Online & Mobile Apps Billing/ Ordering CRM/ Profile Marketing Campaigns Search EDW N/W Logs Call Center Apps Network Other Structured Sources Internal Systems External Sources BI Solutions Real-Time Apps Search Data Science Workbench SQL Machine Learning Systems Data
  16. 16. 16© Cloudera, Inc. All rights reserved. Key Enabling Capabilities Ideal for real-time analytics on IoT and time series data. Simplifies Lambda architectures for running real-time analytics on streaming data Leading analytic SQL engine running natively in Hadoop. Impala provides the fastest insights, at high-concurrency, with the familiar access necessary for powering BI and analytics across the business. Kudu: Real-Time Offers Impala: Self Service BI Data Science Workbench Collaborative hub for enterprise data science and an integrated development environment for running Python, R, & Scala with support for Spark
  17. 17. 17© Cloudera, Inc. All rights reserved. • Serve real-time data at scale for real-time decision making • Aggregate relational, NoSQL, structured & unstructured data • Stream processing & analytics on changing operational data • Leverage linear performance scalability and predictable TCO • Deliver a secure, low-latency, high-concurrency experience Extract real-time insights from big data OPERATIONAL DATABASE
  18. 18. 18© Cloudera, Inc. All rights reserved. The Underlying Driver What drives a use case to real-time? High Frequency Trading APT Detection Fraud Detection Predictive Maintenance Next Best Offer Inventory Management Shipping/Logistic Systems CRM Systems Employee Management Strategic Planning Real-time data management use cases are defined by a common set of characteristics. • Narrow time window in which to make a decision (automated or manual) • Opportunity for data points to change the decision, and thus the business’s path • Decreasing value of data over time Not all use cases have a pressing need for real-time data. • Broader strategic decisions, for example, do not require real-time data input • Over time, decreases in HW costs and increases in availability of real-time systems will lead most use cases to be conducted in real-time Real Time Some Latency Acceptable
  19. 19. 19© Cloudera, Inc. All rights reserved. Managing Data from Customer Touchpoints Handle real-time data ingest from diverse sources Fundamentall y Secure Data Streams Deployment Flexibility Machine Learning Capabilities Diverse Analytical OptionsCombine data from various sources Customer Data Mgmt. Hub Scale easily & Cost effectively Batch or Real- time Data Streams A comprehensive data management platform to drive business insights from data Data Sources Data Storage & Processing Serving, Analytics & Machine Learning Data Ingest Data Sources Security, Scalability & Easy Management
  20. 20. 20© Cloudera, Inc. All rights reserved. The Right Storage Technology to Meet Your Use Case Real-Time Inputs Real-Time Analytics Input data is pushed into a semi-static model of a well- defined process, resulting in the selection of an optimal strategy given the known variables. Input data itself becomes part of the model, continuously evolving (within boundaries) as behaviors change and new connections are identified.
  21. 21. 21© Cloudera, Inc. All rights reserved. Operational DB: Real-Time Architecture Driving the Model Through Machine Learning Kafka Spark Streaming Kudu Spark MLlib Input Data Addt’l Sources Individual Session Full Model/Learning Genesis Spark 1 Event Occurs 2 Messaging 3 Stream Processing 4 Land in Relational Store 5 Apply ML Libraries
  22. 22. 22© Cloudera, Inc. All rights reserved. Operational DB: Real-Time Architecture MLlib & K-Means: Defining Microsegments via Machine Learning Height Weight Height Weight 1 2 Height Weight 3 Height Weight 4 L M S XL L M S XS Near Custom ?
  23. 23. 23© Cloudera, Inc. All rights reserved. Operational DB: Real-Time Architecture Driving Prediction and Optimization Kafka Spark Streaming Kudu Spark MLlib Input Data Addt’l Sources Individual Session 1 Data Processed Genesis Spark 2 Request Processed/ Kudu Queried 3 4 Results Returned Results Processed 5 Processed Data Returned Full Model/Learning
  24. 24. 24© Cloudera, Inc. All rights reserved. Operational DB: Real-Time Architecture Driving Prediction and Optimization Step 1: Data Processed Apache Spark processes the data from the event (car sensors, manufacturing, wearables, etc), which potentially involves keeping a running list of the last X number of events Step 2: Request Processed/Kudu Queried A Spark application uses the data gathered in step one to query Kudu’s database in a predefined manner to look for similar patterns defined via machine learning Step 3: Kudu Results Returned Kudu returns the results from the query in step 2 back to Spark to determine what needs to be returned to the application Step 4: Results Processed Spark associates the results from Kudu with the information stored from the current event to determine the next step to feed back to the application Step 5: Processed Data Returned The machine-generated, best possible outcome is prescribed and served to the application
  25. 25. 25© Cloudera, Inc. All rights reserved. Operational DB: NBO Use Case Prediction and Optimization Kafka Spark Streaming Kudu Spark MLlib Application Addt’l Sources Individual Session User Shopping Spark Full Model/Learning Data Request Sent For Stream Processing Data Cleaned/Ordered/Processed, Then Delivered to Kudu for Modelling Automated processes based on machine learning enable prediction and optimization at a new level. Illustrative, models will likely have >2 dimensions
  26. 26. 26© Cloudera, Inc. All rights reserved. Visit: Solutions Gallery
  27. 27. 27© Cloudera, Inc. All rights reserved. Thank you Sean Anderson, Sr. Solutions Marketing Manager Ryan Lippert, Sr. Product Marketing Manager

×