The role of Big Data and Modern Data Management in Driving a Customer 360 from the Cloud
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The document discusses building a customer 360 view using big data and modern data management. It describes key challenges in creating a customer 360 like data silos, large and growing data volumes, and new data sources. It then presents an architecture using an Enterprise Data Hub to ingest diverse data sources and enable analytics to build a holistic view of individual customers. The approach advocates starting with core customer data and iteratively expanding the view by adding new data sources and delivering specific use cases.
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The role of Big Data and Modern Data Management in Driving a Customer 360 from the Cloud
- 1. 1© Cloudera, Inc. All rights reserved. The Role of Big Data and Modern Data Management in Driving a Customer 360 from the Cloud Joel Roland // Sales Engineer
- 2. 2© Cloudera, Inc. All rights reserved. Data is Transforming Business How do you want to leverage your data assets? How do I build a 360 degree picture of my customer to deliver new revenue streams? Customer and Channel
- 3. 3© Cloudera, Inc. All rights reserved. Customer 360 – An Industry Perspective - What is Customer 360? A holistic real-time view of your individual customers Across all products, systems, devices and interaction channels In order to deliver a consistent, personalized, context specific and relevant experience
- 4. 4© Cloudera, Inc. All rights reserved. Key Challenges in Driving a Customer 360 DATA SILOS DATA VOLUMES NEW DATA SOURCES COSTS OF DATA PROCESSING • Multiple Data Silos • Often store overlapping and conflicting info • Issue compounded with multiple business units Care Product Catalog CRM Ordering Billing Legacy Enterprise Inventory OSS Network Customer Care Product Catalog Ordering Billing CRM Legacy Enterprise Inventory Supply Chain PoS • Data growing at ~100% YoY • Capturing new sources of insights and customer interactions Clickstream Location/ GPS Call center Records Social Media • Semi/ Un-Structured Data Sources • Streaming/ Real-time data • Critical for building a True 360 view • Cost prohibitive • $30,000 and $100,000 (USD) per TB – Cost of storing data in relational database systems per year
- 5. 5© Cloudera, Inc. All rights reserved. Consumer activity data sits in silos • 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, is kept in BU silos or not kept at all Customer 360 view: Why status quo won’t work AnalystData Analyst Data Analyst DataAnalystData AnalystData
- 6. 6© Cloudera, Inc. All rights reserved. Customer 360 – Traditional Data Flow Diagram Location Social Clickstream ETL/Stored Procedures Enterprise Data Warehouse Segmentation & Churn Analysis BI Tools Marketing Offers Data Marts / Aggregations Billing/ Ordering CRM/ Profile Marketing Campaigns
- 7. 7© Cloudera, Inc. All rights reserved. Customer 360 – Traditional Data Flow Diagram Location Social Clickstream ETL/Stored Procedures Enterprise Data Warehouse Segmentation & Churn Analysis BI Tools Marketing Offers Data Marts / Aggregations Billing/ Ordering CRM/ Profile Marketing CampaignsOther/ New Data Sources – Mobile, Sensors, Apps, Network Logs, Files Does not model easily into traditional database schema Limited Processing Power Limited Processing Power Storage scaling very expensive. Not designed for ELT Loss in Fidelity Manual work. Few automated system feeds. Based on sample/ limited data
- 8. 8© Cloudera, Inc. All rights reserved. A New Way Forward…
- 9. 9© Cloudera, Inc. All rights reserved. EDH based Architecture for Effective Data Mgmt. Enterprise Data Warehouse Enterprise Data Hub Data Sources DataIngest– StreamingorBatch Business Intelligence/ Reporting Tools Network Usage CRM Inventory Clickstream Sensors Machine Logs Social Billing Ordering Structured Unstructured /Semi-Structured OPERATIONS Cloudera Manager Cloudera Director DATA MANAGEMENT Cloudera Navigator Encrypt and KeyTrustee Optimizer BATCH Sqoop REAL-TIME Kafka, Flume PROCESS, ANALYZE, SERVE UNIFIED SERVICES RESOURCE MANAGEMENT YARN SECURITY Sentry, RecordService FILESYSTEM HDFS RELATIONAL Kudu NoSQL HBase STORE INTEGRATE BATCH Spark, Hive, Pig MapReduce STREAM Spark SQL Impala SEARCH Solr SDK Partners Deployment Flexibility On-Premises Appliances Engineered Systems Public Cloud Private Cloud Hybrid Cloud
- 10. 10© Cloudera, Inc. All rights reserved. Customer 360 – Flow with EDH Location Social Clickstream BI Tools Online & Mobile Apps Billing/ Ordering CRM/ Profile Marketing Campaigns Ingest Search EDW Sqoop or Native connector to Impala SQL via Impala Solr HBase N/W Logs Call Center Apps Network Other Structured Sources
- 11. 11© 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, email, ID ID ChannelsPurchase History Add New Data SourceCommon IdentifierCurrent 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 OPERATIONS Cloudera Manager Cloudera Director DATA MANAGEMENT Cloudera Navigator Encrypt and KeyTrustee Optimizer BATCH Sqoop REAL-TIME Kafka, Flume PROCESS, ANALYZE, SERVE UNIFIED SERVICES RESOURCE MANAGEMENT YARN SECURITY Sentry, RecordService FILESYSTEM HDFS RELATIONAL Kudu NoSQL HBase STORE INTEGRATE BATCH Spark, Hive, Pig MapReduce STREAM Spark SQL Impala SEARCH Solr SDK Partners
- 12. 12© 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, email, ID ID ChannelsPurchase History Add New Data SourceCommon IdentifierCurrent 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 OPERATIONS Cloudera Manager Cloudera Director DATA MANAGEMENT Cloudera Navigator Encrypt and KeyTrustee Optimizer BATCH Sqoop REAL-TIME Kafka, Flume PROCESS, ANALYZE, SERVE UNIFIED SERVICES RESOURCE MANAGEMENT YARN SECURITY Sentry, RecordService FILESYSTEM HDFS RELATIONAL Kudu NoSQL HBase STORE INTEGRATE BATCH Spark, Hive, Pig MapReduce STREAM Spark SQL Impala SEARCH Solr SDK Partners
- 13. 13© Cloudera, Inc. All rights reserved. Customer 360 – Key Use Cases Churn Prevention & Customer Retention Targeted Marketing & Personalization Proactive Care • Churn Modeling & Prediction • Rotational/ Social Churn • Customer Lifetime Value • Sentiment Analytics • Price Elasticity Modeling • Customer micro-segmentation • Next Best Offer • Campaign Analytics • Geo-Location Analytics • Recommendation Models • Proactive Care Dashboard • Customer Lifetime Value • Subscriber Analytics • QoS Analytics • Real-Time Alerts
- 14. 14© Cloudera, Inc. All rights reserved. Thank you
- 15. 15© Cloudera, Inc. All rights reserved. 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? The Foundation of a “Segment of One”
Notas do Editor
- Before I go into detail on the Customer 360 - At Cloudera, what we see is three areas of opportunities within businesses generally are: Data-Driven Products – How can I build better data-driven products and services, at lower cost? Security, Risk, and Compliance – How do meet compliance regulations and preserve data security to minimize our corporate risk profile? And for today, focusing on Customer and Channel – How do I build a 360 picture of my customer?
- Customer 360 is not necessarily something new. Many industries such as the Telecommunications & Insurance have been doing this for many years. The thing that has changed however diverse set of data sets that available Richness that this data brings And as we use data to build a profile about the customer, it enables us to Understand better how are customers So that we can better serve and meet their needs And these factors are driving how a modern Customer 360 view is built: Look at the new sets of data sources and emerging data sets to build out a real-time holistic view Could be Location Based Data Clickstream Data External data such as Mobile & Social Media feeds And for organizations that deliver via omni-channel customers are now demanding a seamless experience across channels If your customer profile or view is based only on data from a single channel, what do you think experience is like in other channels? This needs to drive the buildout of the profile, to extend across the channels that are being delivered to. And all of this is done to ensure that: You can deliver a consistent experience across channels You can make personalized recommendations to the right customers You can deliver targeted experience by knowing the behavior and patterns of your customer
- However there are still many challenges for customers in building their 360 degree view out. And consistently across the board we are seeing the same issues: Data Silos Data is spread across multiple sources, often no consolidated view. And a company with multiple business units often has duplicates sets of common data; but this data is often conflicting, from system to system and BU to BU making it hard to identify which is correct. 2. Volume of Data As systems are generating more and more data (often doubling year on year), keeping this data is posing a challenge. 3. New Data Source Gone are the days of storing just batch, structured relation data. Many systems now are generating semi- or unstructured data and often in real time. And its often these that are critical for complete view. 4. Cost of Data Storage and Processing Traditional systems and architectures are expensive to storage large amounts of data (between 30,000 – 100,000 per TB depending on the system). To be able to keep the volume of data required and process it is not economical at those rates.
- And if those challenges are not addressed- what you are left with is a customer view that is not a full and complete representation. And generally what that means is that its: Only focused on Structured Data Only contains internal data Only has what someone has deemed “important” (but is that really the case) And Only Limited history is kept. And this approach is not sustainable and won’t organizations address the complex challenges they are facing.
- First, Lets take a look at a Traditional Data flow common today… On the top left you see some of the traditional systems with structured data – Billing & Ordering Systems, Product catalogues, CRM Profile Data & a lot of info from Marketing campaigns & Surveys – and these are very well structured On the bottom left you see some of the newer sets of data sources – including sources such as Location data, social media streams (for example Facebook, Twitter and Linked In) or could be website clickstream data. This new data is either not collected today or a only very small subsets are; or data is highly aggregated and then all of this is commonly fed into the Data Warehouse
- But the problem with this approach is that it has many issues with it: First of all these systems are really not able to handle the data that is being generate – both from the volume of data that is coming in and also the streaming aspect that they commonly have to deal with. Plus - some of these new sources may not be easily be able to be ingested into traditional database schemas From a processing point of view, systems that are doing the ETL and stored procedures have limited processing power; and the data marts end up need to do the aggregations What you end up with is a loss of granularity with your data that is in your data warehouse. You no longer have the full set of data, you only have aggregations of data that have passed through the ELT processing.
- So, there is a new way forward and a new way to address these challenges:
- But lets contrast this with a Cloudera Enterprise Data Hub. It provides a new approach and a number of key differentiators to the traditional architectures: It can ingest data from multiple sources; streaming as well as Batch processing while enabling you to can keep unlimited data online without needing to archive Economically feasible to store more data (cost can be 10x cheaper than traditional systems) Powered to predictably process large data sets Ability to build your data asset at linear scale Collect data in native format – enables agility 2. Diverse users can get direct access to all business relevant data, through the best tool for the them. That could be SQL, search, or your existing BI or analytics tools such as MicroStrategy, Tableau etc. Users who previously had no way to benefit from data can now find and generate insights. 3. Finally All of this can be done with confidence, thanks to Cloudera’s enterprise-grade security, governance, and management tools Either on-premise or the cloud. While this afternoon will talk about Cloudera tools in the cloud, certainly many customers are choosing Cloud to deploy solutions such as Customer 360. Cloud enables a new infrastructure paradigm with flexibility & easy scalability An when combined with Cloudera’s automated tools makes deploying managing and growing clusters in the cloud quick & easy. And the great thing is because Cloudera’s solution can be deployed in a similar manner across all Cloud Providers you can avoid any vendor lock in or even have Cloudera in Multiple Cloud’s simmultaniously
- And so then in contrast, the Data flow architecture with Cloudera is simplified; addressing the key challenges as: All the sources are ingested into one location We are able to handle all the different types of data Standard way to process and analyze both batch and streaming data While Enabling multiple use cases off a single platform
- Cloudera we like to say “Think Big, Start small and Iterate Often” Start with ingesting the “best” version of your customer profiles from a transactional system or an existing data warehouse Identify your common identifiers across datasets: customer name, number, IMEI, IMSI Enrich with additional demographic information from other systems Deliver your first use case with this information, e.g.: Lifetime value modeling, Device and plan modeling, Next device offer
- Continue to add datasets – such as purchase behavior - and explore common identifiers across your datasets As you explore those new datasets, enrich your customer profile with the additional information Continue to deliver additional use cases,
- Although there are many applications that can be applied to Customer 360 – I wanted to go highlight some of the common ones that we see time and time again: Targeted Marketing & Personalization So - making sure you are focusing the right things to the right customers. As you can imagine most people don’t have unlimited marketing and advertising dollars – so ensuring this is spend to the maximum efficiency is critical…. And this can be done through activities such as Offering personalized product offerings or derive specific upsell/ cross-sell opportunity to and existing customer Or proactively present the right offer, to the right person at the right time based on some event that has happened. Proactive Care Which is all about improving the customer service experience. Organizations are building intelligence and analytics tools so as to proactively identify issues and fix it or offer a solution before it impacts the customer. And Not only does this provides a compelling customer experience; but it also deflects and prevents calls to the customer care centers thereby lowering costs. Finally -> Churn Prevention & Customer Retention. Given the impact of customer churn affecting the Insurance and Telco industry today, we are seeing Big Data & analytics to bring together various data points including - quality of service, network performance, billing information, details on calls to the care centers, and social media sentiment analysis to design and build an effective model to predict and prevent customer churn