copyright © 2014, oracle and/or its affiliates. all rights reserved. | con 8965 jim acker industry...

Copyright © 2014, Oracle and/or its affiliates. All rights reserved. |

CON 8965

Jim AckerIndustry Solutions ManagerOracle Global Business Unit, Financial Services

Customer Profile in a Big Data Client Solution Approach: Monetizing Customer DNA

Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 3

Trends in Consumer ExperienceUsing Customer Analytics to Create More Personalized CX

Customers will make web / mobile their primary interaction with the financial institution

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


Status of Personalization

However, few companies have been able to implement

Source: Econsultancy, Digital Marketing Exchange

of those surveyed believe that "personalization is critical to our current and future success”

94%


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

84%


I have a customer - what are the top 3 products he is likely to buy?

Answering the Tough Questions…

Which top hundred customers are likely to buy my product X today?

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 from Raw Data to Individual Preferences

Traditional Data Warehouse based solutions (DW/BI) are costly, slow to implement and change, work with sample data and provide limited insight

Big Data and advanced analytics provide an ideal solution for predictive customer insight that is more cost effective, easier to implement and change, and operates in real-time on ALL your data


• Male, born in 1948

• Grew up in England

• Married twice, children

• Successful, wealthy, celebrity

• Loves dogs and the Alps

8

Challenges with Traditional ApproachEffective Customer Treatment Requires 1:1 Personalization

Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 9Oracle Confidential

real-time decisionengine

data

in

tegr

ation

Oracle / NGData Customer Analytics Solution

stream organize analyze decide respond

internal

external

batch

real-time

master data

marketing automation

contentsites

customer service

ecommerce and sales

BI and analytics tools

acquire learn

identity

enrich

advertising platforms

Big Data ApplianceCloudera

data management platform (DMP)

Lily Enterprise


Turning Data into Valuable Customer DNAIntroducing NGData and Lily Enterprise

Identify unique customer behaviors and preferences in real timeView thousands of metrics for each customer

Continuously monitor customers’ evolving preferences to identify opportunitiesBring Analytics to the data – Open towards DW/BI


Lily Delivers Next Generation PersonalizationFrom 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

See everything together – comparisons with a Set defined by you, and evolving trend scores for each customer

From Data to DNA – 1000s of metrics determine individual DNA – common, industry and customer metrics


Customer DNADynamically created Sets defined by your own rules

More effective Alerts based on real-time customer metrics

Models available, or easily and dynamically add new models from all available metricsManage Big Data -

Breaking down data silos to gain insights on all customer interactions in one place

With Lily’s Customer DNA and Machine Learning Engine, individual product Preferences are available each moment


Real Time Delivery Engine

Recommendations improved in real time during interaction 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.

Website Mobile SocialIVR

Digital Interactions

Human Interactions

BranchContact Center Sales

• Digital DNA & 360 view• Predictive Analytics• Next Best Action• Next Best Product• Most Relevant Experience

Lily Enterprise


Deliver Offers in Real-Time

Marketing Automation

Content Sites

Advertising Platforms

eCommerce and Sales

Customer Service

Predictive Models

Business Rules Performance Goals

Real-time Offers

Decision

Self-learning Feedback Loop

Lily Enterprise


?

?

?

Website Real Time Offer Personalization


Mobile Customer ExperienceLocation-Based Real Time Offer Personalization

Mobile Information Mobile Wallet Mobile Redemption

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


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

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


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


Real-Time Offer FlowConceptual View


Real Time Offer - Process Flow

<ADV>Dear Preferred customer, We have exclusive offer of 20% savings at Gucci and Sephora near your location!

Card transaction made at a shopping mall

Transaction data transfer in real-time

A real time calculation linking type of transaction, location information,

offers in vicinity and the propensity associated with the next best action is

done.

Bank’s Data Center

Send real-time offer via SMS based on time,

customer’s location and propensity model

Real-time/batch based understanding of offer acceptance/rejection and subsequent tweaking

of models

Use offer presented at merchant


Architecture and Roles

1.Approved credit card transactions are captured and replicated to RTD Database.

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.

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.

4. The next best offer is presented via a text message on their registered mobile number.

*


Pilot Timelines

Week Week 1Week

2Week

3Week

4Week

5Week

6Week

7Week

8Week

9Week

10Week

11Week

12

Use Case Confirmation

Infrastructure availability and connectivity

Software installation and configuration

Business discovery

Test cases planning

Development \ Deployment

Pre-Production system testing

Data Preparation

Data Loading and Model Tuning

POC Go Live

Downstream processes from the inferences are not factored in the timelines

copyright © 2014, oracle and/or its affiliates. all rights reserved. | con 8965 jim acker industry...

Documents

data slide

oracle andor

individual customer

personalization slide

monetizing customer

raw data

sample data

valuable customer dna