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CON 8965

CON 8965. Customer Profile in a Big Data Client Solution Approach: Monetizing Customer DNA. Jim Acker Industry Solutions Manager Oracle Global Business Unit, Financial Services. Trends in Consumer Experience. Using Customer Analytics to Create More Personalized CX.

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CON 8965

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  1. CON 8965 Customer Profile in a Big Data Client Solution Approach: Monetizing Customer DNA Jim Acker Industry Solutions Manager Oracle Global Business Unit, Financial Services

  2. Trends in Consumer Experience Using Customer Analytics to Create More Personalized CX All interactions of each individual customer are turned into a personalized experience: Those channels are already heavy personalized and the customer will expect the same from the financial institution Brands will use more differentiating content or offers to acquire and retain customers, to up-sell and cross-sell Customers will make web / mobile their primary interaction with the financial institution

  3. Status of Personalization 94% of those surveyed believe that "personalization is critical to our current and future success” Source: Econsultancy, Digital Marketing Exchange However, few companies have been able to implement

  4. Barriers to Customer Experience Management 84% regard IT roadblocks and lack of technology as barriers to adopting or improving personalization Source: Econsultancy, Digital Marketing Exchange No Solutions – No Automation – Manual Work – Low ROI

  5. Answering the Tough Questions… Which top hundred customers are likely to buy my product X today? I have a customer - what are the top 3 products he is likely to buy? What is the best channel to connect with my customer, and when? Can I turn around my most valuable potential churners?

  6. Getting to Actionable Customer Insights Big Data and advanced analytics provide an ideal solution for predictive customer insight that is more cost effective, easier to implement and change, andoperates in real-time on ALL your data Traditional Data Warehouse based solutions (DW/BI) are costly, slow to implement and change, work with sample data and provide limited insight Getting from Raw Data to Individual Preferences

  7. Challenges with Traditional Approach Effective Customer Treatment Requires 1:1 Personalization • Male, born in 1948 • Grew up in England • Married twice, children • Successful, wealthy, celebrity • Loves dogs and the Alps

  8. Oracle / NGData Customer Analytics Solution marketing automation internal BI and analytics tools single customer view external contentsites master data data management platform (DMP) data integration advertising platforms identity Lily Enterprise ecommerce and sales batch real-time decisionengine Big Data ApplianceCloudera customer service real-time enrich stream acquire organize learn analyze decide respond Oracle Confidential

  9. Turning Data into Valuable Customer DNA Introducing NGData and Lily Enterprise Identify unique customer behaviors and preferences in real time View thousands of metrics for each customer Continuously monitor customers’ evolving preferences to identify opportunities Bring Analytics to the data – Open towards DW/BI

  10. Lily Delivers Next Generation Personalization From Raw Data to Individual Preferences • Listen Better- Lily works with all types of data- all transactions, all behavior, all context - continuously capturing and automatically making real time observations • Learn Faster - Lily delivers behavior- based models that take into account all context at various levels of granularity, automatically delivering micro-segmentation to the individual customer and multi-contextual recommendations based on predicted customer needs • Execute Smarter – easily integrates with marketing and BI platforms, allowing companies to deliver offers based on smarter dynamically updated predictions for better customer experiences

  11. Customer DNA From Data to DNA – 1000s of metrics determine individual DNA – common, industry and customer metrics See everything together – comparisons with a Set defined by you, and evolving trend scores for each customer

  12. Customer DNA Dynamically created Sets defined by your own rules With Lily’s Customer DNA and Machine Learning Engine, individual product Preferences are available each moment More effective Alerts based on real-time customer metrics Models available, or easily and dynamically add new models from all available metrics Manage Big Data - Breaking down data silos to gain insights on all customer interactions in one place

  13. Real Time Delivery Engine – Intelligent Interactions • Automating decision-making in any channel • I-CX engine recommendations modified based on data collected during the interaction • Self-learning process determines propensity to do something for each customer • Prioritizes and triggers events. Real Time Delivery Engine Recommendations improved in real time during interaction Website IVR Mobile Contact Center Social Sales Digital Interactions Human Interactions Single Customer View Branch Lily Enterprise • Digital DNA & 360 view • Predictive Analytics • Next Best Action • Next Best Product • Most Relevant Experience

  14. Deliver Offers in Real-Time Business Rules Performance Goals Marketing Automation Content Sites Real-time Offers Decision Advertising Platforms Predictive Models Lily Enterprise eCommerce and Sales Customer Service Self-learning Feedback Loop

  15. Website Real Time Offer Personalization ? ? ?

  16. Mobile Customer Experience Location-Based Real Time Offer Personalization Joe can view and look up favorite shops, restaurants,... Joe receives merchant offers in his Bank’s Mobile wallet Joe can redeem coupons through his mobile wallet Mobile Redemption Mobile Information Mobile Wallet

  17. Implementing the Solution at HDFC Russell Sangster Vice President, Professional Services NGData

  18. HDFC Bank : Background • HDFC Bank wants to offer their customers personalized offers, but only at a time when they are most likely to make a relevant spend at the nearest accessible outlet. • The approach was to collect more detailed data about an individual customer’s spending habits, lifestyle choices and combine this with their propensity to buy and factor in the situational variables. • The challenge is assimilating high-volume/high-velocity data streams quickly to be able to take decisions and implement decision on real-time basis. • HDFC wanted a solution to derive real business value from a wide variety of data types from different sources, and to be able to easily analyze it within the context of all their enterprise data.

  19. HDFC Bank Use Case: Real Time Offers Objective • To provide real time offers to HDFC credit card customers based on propensity, geo-location and offer palette • Increase customer spend by providing relevant, targeted offers

  20. HDFC Bank Real Time Offer Project • HDFC is looking to enrich their traditional enterprise data with non-traditional yet potentially valuable data for decision making. • At the core of this project HDFC Bank is gaining Customer Intelligence and making relevant Merchant Funded Offers to the banks Customers in ‘Real Time’ for maximum impact • HDFC Bank is presenting their Credit / Debit Card Customers with applicable Bank and Merchant Offers, based upon the Customer buying behavior, by: • Real time integration of Customer Credit / Debit Card transaction data • Real time analytics to identify and present, to the banks Customers the Merchant and Bank Offer that has been determined to be of the most interest to them • Deliver the relevant offer in real time for maximum impact

  21. Conceptual View Real-Time Offer Flow

  22. Real Time Offer - Process Flow Send real-time offer via SMS based on time, customer’s location and propensity model Transaction data transfer in real-time Card transaction made at a shopping mall <ADV> Dear Preferred customer, We have exclusive offer of 20% savings at Gucci and Sephora near your location! A real time calculation linking type of transaction, location information, offers in vicinity and the propensity associated with the next best action is done. Use offer presented at merchant Bank’s Data Center Real-time/batch based understanding of offer acceptance/rejection and subsequent tweaking of models

  23. Architecture and Roles 4. The next best offer is presented via a text message on their registered mobile number. 1.Approved credit card transactions are captured and replicated to RTD Database. 2. Customers past 1 year transactions details are provided to NGDATA Lily. NGDATA Lily creates Propensity Model for the customers/ the NBO model. Lily does customer identification and location identification to identify the next best spend categories and the merchant categories for this spend. 3.RTD looks up the List of Offers, closest merchant to customer location, checks if customer on DNC list, mobile number is available and the best offer is sent to Customer. If any check fails no offers is made. *

  24. Pilot Timelines Downstream processes from the inferences are not factored in the timelines

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