Customer Event Hub – a modern Customer 360° view with DataStax Enterprise (DSE)
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The document discusses the concept of a Customer 360° view using DataStax Enterprise, emphasizing the importance of real-time personalization and enhanced customer service. It outlines challenges like data silos and the limitations of traditional databases in achieving a dynamic customer profile, proposing graph databases as a solution to connect and analyze multiple customer-related data sources. Additionally, it highlights various use cases and key benefits of implementing a Customer 360° approach to improve customer engagement and operational efficiency.
Customer Event Hub – a modern Customer 360° view with DataStax Enterprise (DSE)
- 1. BASEL BERN BRUGG DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. GENF HAMBURG KOPENHAGEN LAUSANNE MÜNCHEN STUTTGART WIEN ZÜRICH Customer Event Hub Customer 360° view with DataStax Enterprise (DSE) Guido Schmutz – 21.6.2017 @gschmutz guidoschmutz.wordpress.com
- 2. Guido Schmutz Working at Trivadis for more than 20 years Oracle ACE Director for Fusion Middleware and SOA Consultant, Trainer Software Architect for Java, Oracle, SOA and Big Data / Fast Data Head of Trivadis Architecture Board Technology Manager @ Trivadis More than 30 years of software development experience Contact: guido.schmutz@trivadis.com Blog: http://guidoschmutz.wordpress.com Slideshare: http://www.slideshare.net/gschmutz Twitter: gschmutz 2
- 3. COPENHAGEN MUNICH LAUSANNE BERN ZURICH BRUGG GENEVA HAMBURG DÜSSELDORF FRANKFURT STUTTGART FREIBURG BASEL VIENNA With over 600 specialists and IT experts in your region. 14 Trivadis branches and more than 600 employees 200 Service Level Agreements Over 4,000 training participants Research and development budget: CHF 5.0 million Financially self-supporting and sustainably profitable Experience from more than 1,900 projects per year at over 800 customers 3
- 4. Agenda 4 1. Customer 360° View – Introduction 2. Customer 360° View – Challenges 3. Customer 360° View – DataStax Enterprise (Graph) to the rescue
- 5. Customer 360° View - Introduction 5
- 6. Why Customer 360° View? “Get closer than ever to your customers. So close that you tell them what they need well before they realize it themselves.” Steve Jobs, Apple q Enhance customer service q Provides real-time personalization q Opens doors to new applications q Increases security q Lowers operational costs q Increases operational efficiency 6
- 7. Customer 360°: Experience Expectations 7
- 8. “The Amazon effect” – why can’t I do .... as easy as buying a product on Amazon 8 Each time a customer is exposed to an improved digital experience, their engagement expectations are reset to a new higher level. Source: Forrester
- 9. Customer 360°: Experience Expectations Consistent Across all channels, brands and devices Personalized To reflect preferences and aspiration Relevant In the moment to customer’s needs and expectations Contextualized To present location and circumstances 9
- 10. Customer 360 – Key Use Cases • Customer micro segmentation • Next Best Offer • Campaign Analytics • Geo-Location Analytics • Recommendation Models • Churn Modeling & Prediction • Rotational / Social Churn • Customer Lifetime Value • Sentiment Analytics • Price Elasticity Modeling • Proactive Care Dashboard • Customer Lifetime Value • Subscriber Analytics • QoS Analytics • Real-Time Alerts Target Marketing & Personalization Churn Prevention & Customer Retention Proactive Care 10
- 11. From Static to Dynamic, Real-Time Micro-Segmentation Age Gender Average Spend Price Plans Usage History Data, Voice, Text Billing History Device Upgrade Traditional Segmentation Age Gender Average Spend Price Plans Usage History Data, Voice, Text Billing History Device Upgrade Device History Other products / services Bundling preferences Offer History Campaign Adoption History Call Center Tickets Location Social Influence Applications Used Content Preferences Usage Details Roaming Analysis Travel Patterns QoS History Household Analysis Lifetime Value Churn Score Clickstream Info Channel Preferences Survey Real-Time Micro- Segmentation 11 Individualization Engaging customers as a segment of one in real-time by listening, capturing, measuring, assessing, and addressing intent across every enterprise touchpoint. Source: Forrester
- 12. From Static to Dynamic, Real-Time Micro-Segmentation Age Gender Average Spend Price Plans Usage History Data, Voice, Text Billing History Device Upgrade Traditional Segmentation Age Gender Average Spend Price Plans Usage History Data, Voice, Text Billing History Device Upgrade Device History Other products / services Bundling preferences Offer History Campaign Adoption History Call Center Tickets Location Social Influence Applications Used Content Preferences Usage Details Roaming Analysis Travel Patterns QoS History Household Analysis Lifetime Value Churn Score Clickstream Info Channel Preferences Survey Real-Time Micro- Segmentation 12
- 13. A Sample Customer 360° Profile Who are you? Where are you? What have you purchased? What content do you prefer? Who do you know? What can you afford? What is your value to the business? How / why have you contacted us? 13
- 14. Customer 360° View - Challenges 16
- 15. Key Challenges in Driving a Customer 360° View Data Silos New Data Sources Costs of Data Processing Data Volumes • Multiple Data Silos • Often store overlapping and conflicting information • Data growing rapidly • Internet of Things will add to that substantially • Semi/Un-Structured Data Sources • Streaming / Real-time data • Critical for building a true 360° view • Cost prohibitive • Cost of storing data in relational database systems per year Clickstream Location/GPS Call center Records Social Media 17
- 16. Customer 360° View - Traditional Flow Diagram Enterprise Data Warehouse ETL / Stored Procedures Data Marts / Aggregations Location Social Clickstream Segmentation & Churn Analysis BI Tools Marketing Offers Billing & Ordering CRM / Profile Marketing Campaigns 18
- 17. Customer 360° View - Traditional Flow Diagram Enterprise Data Warehouse ETL / Stored Procedures Data Marts / Aggregations Location Social Clickstream Segmentation & Churn Analysis BI Tools Marketing Offers Billing & Ordering CRM / Profile Marketing Campaigns Limited Processing Power Does not model easily to traditional database schema Limited Processing Power Storage Scaling very expensive Based on sample / limited data Loss in Fidelity Other / New Data Sources High Voume and Velocity 19
- 18. Customer 360° View: Why status quo won’t work? • Most organizations have a static version of the customer profile in their data warehouse • Mainly structured data • Only internal data • Only “important” data • Only limited history • Activity data – clickstream data, content preferences, customer care logs are kept in siloes or not kept at all Data Analyst Data Analyst Data Analyst Data Analyst Data Analyst Detailed Customer Activity Data sits in silos! 20
- 19. Journey of Customer through multiple Siloed Systems 21 Social Media Call Center Complaint Management Marketing Coupons Warehouse CRM Shipping Billing Order Processing Web Application
- 20. Journey of Customer through multiple Siloed Systems 22 Social Media Call Center Complaint Management Marketing Coupons Warehouse CRM Shipping Billing Order Processing Web Application
- 21. Customer 360° View – DataStax Enterprise (Graph) to the rescue 23
- 22. Why using Graph for Customer 360° View 24 Traditional RDBMS • Multiple Data Locations => siloes • Not all information related • difficult to access all the different information and to relate to each other Graph Database • Connect all customer-related information • Model multi-connected customer relationships • Special questions graphs can answer • Performance & Scalability Partner Id FirstName LastName BirthDate ProfileImageURL Id CustomerId When Where Type Description Contacts Address Id PartnerId Street StreetNr ZipCode City Country Id Name Price Description ImageURL Products CustomerId ProductId Order Id CustomerId When Where InteractionType Description Customer Service Id ProductId Comment UserId Product Reviews Id PartnerId TwitterId FacebookId Customer Geo Point Customer Address Product ownerOf (since) interestedIn (since, degree) lives (since) ActivityparticipatesIn Termuses uses Employee interactsWith reference id name id when where text id firstName lastName birthDate profileImageURL uses id name imageURL id street streetNr zipCode city id name alternateNames location population elevation Country neighbour belongsTo id name iso2 iso3 isoNumeric capital area population continent uses ActivityType id name belongsTo id name at knows(since) mentions uses
- 23. Customer 360° View - Example 25
- 24. Geo Point Customer Address Product ownerOf (since) interestedIn (since, degree) lives (since) ActivityparticipatesIn Termuses uses Employee interactsWith reference id name id when where text id firstName lastName birthDate profileImageURL uses id name imageURL id street streetNr zipCode city id name alternateNames location population elevation Country neighbour belongsTo id name iso2 iso3 isoNumeric capital area population continent uses ActivityType id name belongsTo id name at knows(since) mentions uses Customer 360° View - Example 26
- 25. Hadoop ClusterdHadoop Cluster DSE Cluster Batch Data Ingestion into Customer Hub Billing & Ordering CRM / Profile Marketing Campaigns Location Social Click stream Sensor Data Weather Data Mobile Apps Emails v File Import / SQL Import Cassandra DSE Graph 27 high latency Event Stream
- 26. Batch Data Ingestion into Customer Hub DSE Graph DSE GraphLoaderRDBMS Groovy Script Click Stream 28 Geo Point Customer Address Product ownerOf (since) interestedIn (since, degree) lives (since) ActivityparticipatesIn Termuses uses Employee interactsWith reference id name id when where text id firstName lastName birthDate profileImageURL uses id name imageURL id street streetNr zipCode city id name alternateNames location population elevation Country neighbour belongsTo id name iso2 iso3 isoNumeric capital area population continent uses ActivityType id name belongsTo id name at knows(since) Transformation CSV / JSON Partner Id FirstName LastName BirthDate ProfileImageURL Id CustomerId When Where Type Description Contacts Address Id PartnerId Street StreetNr ZipCode City Country Id Name Price Description ImageURL Products CustomerId ProductId Order Id CustomerId When Where InteractionType Description Customer Service Id ProductId Comment UserId Product Reviews Id PartnerId TwitterId FacebookId Customer Real-Time Insights?
- 27. Streaming Ingestion into Customer Event Hub Microservice Cluster Microservice State { } API Stream Processing Cluster Stream Processor State { } API Billing & Ordering CRM / Profile Marketing Campaigns Location Social Click stream Sensor Data Weather Data Mobile Apps Email File Import / SQL Import Event Stream Event Hub Event Hub Event Hub Event Stream Event Stream Hadoop ClusterdHadoop Cluster DSE Cluster Cassandra DSE Graph 32
- 28. Process native Event Streams Twitter Tweet-to- Cassandra Cassandra Tweet Graph Tweet-to-Graph Customer Reference Click Stream Cllck Stream Activity-to- Graph DSE Cluster 33 Geo Point Customer Address Product ownerOf (since) interestedIn (since, degree) lives (since) ActivityparticipatesIn Termuses uses Employee interactsWith reference id name id when where text id firstName lastName birthDate profileImageURL uses id name imageURL id street streetNr zipCode city id name alternateNames location population elevation Country neighbour belongsTo id name iso2 iso3 isoNumeric capital area population continent uses ActivityType id name belongsTo id name at knows(since) Event Hub Stream ProcessingSensor
- 29. Benchmark Single vs. Scripted Insert 34 • One Event ends up in many modifications of vertex and edges • many round-trips need if done with single API calls • batch API calls into a Groovy script provides 3 – 5x performance gains DSE Graph Tweet-to-Graph Tweet User publishes Term * uses* DSE GraphTweet-to-Graph … ... Geo Point Customer Address Product ownerOf (since) interestedIn (since, degree) lives (since) ActivityparticipatesIn Termuses uses Employee interactsWith reference id name id when where text id firstName lastName birthDate profileImageURL uses id name imageURL id street streetNr zipCode city id name alternateNames location population elevation Country neighbour belongsTo id name iso2 iso3 isoNumeric capital area population continent uses ActivityType id name belongsTo id name at knows(since)
- 30. Process Change Data Capture Events CDC Customer-to- Cassandra Cassandra Customer DSE Graph Customer-to- Graph CustomerCDC AddressCDC ContactCDC Aggregate-to- Customer DSE Cluster 39 Geo Point Customer Address Product ownerOf (since) interestedIn (since, degree) lives (since) ActivityparticipatesIn Termuses uses Employee interactsWith reference id name id when where text id firstName lastName birthDate profileImageURL uses id name imageURL id street streetNr zipCode city id name alternateNames location population elevation Country neighbour belongsTo id name iso2 iso3 isoNumeric capital area population continent uses ActivityType id name belongsTo id name at knows(since) Partner Id FirstName LastName BirthDate ProfileImageURL Id CustomerId When Where Type Description Contacts Address Id PartnerId Street StreetNr ZipCode City Country Id Name Price Description ImageURL Products CustomerId ProductId Order Id CustomerId When Where InteractionType Description Customer Service Id ProductId Comment UserId Product Reviews Id PartnerId TwitterId FacebookId Customer Event Hub Stream ProcessingSensor
- 31. Streaming Oriented Ingestion into Customer Hub Microservice Cluster Microservice State { } API Stream Processing Cluster Stream Processor State { } API Billing & Ordering CRM / Profile Marketing Campaigns Location Social Click stream Sensor Data Weather Data Mobile Apps Email File Import / SQL Import Event Stream Event Hub Event Hub Event Hub Event Stream Event Stream Hadoop ClusterdHadoop Cluster DSE Cluster Cassandra DSE Graph 40
- 32. How to implement an Event Hub? Apache Kafka to the rescue • publish-subscribe messaging system • Designed for processing high-volume, real time activity stream data (logs, metrics, social media, …) • Stateless (passive) architecture, offset-based consumption • Initially developed at LinkedIn, now part of Apache • Peak Load on single cluster: 2 million messages/sec, 4.7 Gigabits/sec inbound, 15 Gigabits/sec outbound Reliable Data Ingestion in Big Data/IoT Kafka Cluster Consumer Consumer Consumer Producer Producer Producer Broker 1 Broker 2 Broker 3 Zookeeper Ensemble
- 33. Apache Kafka Connect • Scalably and reliably streaming data between Apache Kafka and other data systems • not an ETL framework • Pre-build connectors available for Data Source and Data Sinks • JDBC (Source) • Cassandra (Source & Sink) • Oracle GoldenGate (Source) • MQTT (Source) • HDFS (Sink) • Elasticsearch (Sink) • MongoDB (Sink) Reliable Data Ingestion in Big Data/IoT Source: Confluent
- 34. Declarative Dataflow Definition & Execution 43 Apache NiFi StreamSets
- 35. Hadoop ClusterdHadoop Cluster DSE Cluster Streaming Ingestion into Customer Hub OLAP Microservice Cluster Microservice State { } API File Import / SQL Import OLTP Parallel Processing Cassandra Cassandra Stream Processing DSEFS Billing & Ordering CRM / Profile Marketing Campaigns Location Social Click stream Sensor Data Weather Data Mobile Apps Email Event Stream Event Hub Event Hub Event Hub Event Stream Cassandra Replication 44
- 36. Hadoop ClusterdHadoop Cluster DSE Cluster Streaming Ingestion into Customer Hub OLAP Microservice Cluster Microservice State { } API File Import / SQL Import OLTP Parallel Processing Cassandra Cassandra Stream Processing DSEFS Billing & Ordering CRM / Profile Marketing Campaigns Location Social Click stream Sensor Data Weather Data Mobile Apps Email Event Stream Event Hub Event Hub Event Hub Event Stream Cassandra Replication 45
- 37. Questions which can only be answered by Graph 46 Dependencies • Failure chains • Order of operation Matching / Categorizing Highlight variant of dependencies Clustering Finding things closely related to each other (friends, fraud) Flow / Cost Find distribution problems, efficiencies Similarity Similar paths or patterns Centrality, Search Which nodes are the most connected or relevant Source: Expero
- 38. Questions which can only be answered by Graph - Visualize Customer 360 49 Source: Expero
- 39. Questions which can only be answered by Graph - Visualize Customer 360 50 KeyLines Cytoscape LinkuriousJS SigmajsD3 Source: Expero
- 40. Guido Schmutz Technology Manager guido.schmutz@trivadis.com @gschmutz guidoschmutz.wordpress.com