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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

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
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

status of personalization
Status of Personalization


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

barriers to customer experience management
Barriers to Customer Experience Management


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

answering the tough questions
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?

getting to actionable customer insights
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

challenges with traditional approach
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
oracle ngdata customer analytics solution
Oracle / NGData Customer Analytics Solution

marketing automation


BI and analytics tools

single customer view



master data

data management platform (DMP)

data integration

advertising platforms


Lily Enterprise

ecommerce and sales


real-time decisionengine

Big Data ApplianceCloudera

customer service










Oracle Confidential

turning data into valuable customer dna
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

lily delivers next generation personalization
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
customer dna
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

customer dna1
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

real time delivery engine intelligent interactions
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




Contact Center



Digital Interactions

Human Interactions

Single Customer View


Lily Enterprise

  • Digital DNA & 360 view
  • Predictive Analytics
  • Next Best Action
  • Next Best Product
  • Most Relevant Experience
deliver offers in real time
Deliver Offers in Real-Time

Business Rules

Performance Goals

Marketing Automation






Advertising Platforms

Predictive Models

Lily Enterprise

eCommerce and Sales

Customer Service

Self-learning Feedback Loop

mobile customer experience
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

implementing the solution at hdfc
Implementing the Solution at HDFC

Russell Sangster

Vice President, Professional Services


hdfc bank background
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.
hdfc bank use case real time offers
HDFC Bank Use Case: Real Time Offers


  • 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
hdfc bank real time offer project
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
conceptual view
Conceptual View

Real-Time Offer Flow

real time offer process flow
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

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

architecture and roles
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.


pilot timelines
Pilot Timelines

Downstream processes from the inferences are not factored in the timelines