© 2014 Cognizant

BBVAOpen4U Innova Challenge

SpendWise Genie ApplicationDec 1, 2014

SpendWise. Be wise

© Cognizant Technology Solutions 2014. All rights reserved.  Cognizant owns all rights in all its trademarks, trade names or logos, Patents, Copyrights and any other intellectual property rights used in the presentation. Cognizant acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in the presentation. Except as expressly permitted, neither this presentation nor any part of it may be reproduced, stored in a retrieval system, transmitted or modified in any form or by any means, electronic, mechanical, printing, photocopying, recording or otherwise, without prior written permission of Cognizant Technology Solutions. Cognizant disclaims and makes no warranties or representations as to the accuracy, quality, reliability, suitability, completeness, usefulness of the presentation.

© 2014 Cognizant 2

What’s in store …• Background

• Introduction

• The Experience

• User Journey

• Key Insights

• “People Like Me” & “Offers for Me”

• Key Business Benefits Delivered

• How it Works

• Solution Architecture – Business & Data Flows

• Technical Architecture – Application Design Components

• Cloud Based Predictive Model Design

• Recommended Road Map

• Solution Evaluation Parameters

• Appendix

© 2014 Cognizant

Background

Banks face the challenge of leveraging large volumes of disparate data for increasing customer engagement (consumers & Bank affiliated merchants)

Merchants find it challenging to provide new and existing customers with target offers at the right place & time and increase sales

Customers need to manage personal finances, monitor spend and save money on their purchases through relevant offers

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SpendWise. Be wise

Cognizant believes that Banks can address these questions by leveraging advanced Cloud Based Data Analytics and the Digital Banking–driven ecosystem ( including Social Media Feeds )

This submission is an attempt to solve these business problems by leveraging the power of Cognizant’s Pioneering SMAC (Social - Mobile - Analytics - Cloud) Framework

Our Solution

Challenges

© 2014 Cognizant 4

Introduction“SpendWise Genie”, is a multipurpose Mobile App that gives consumers rich insights into benchmarking their spend behavior with people of similar demographic profile( by location and merchant category). The App also uses the power of real time predictive algorithms for relevant offer presentment and empowered decision making

BBVA App For Customers

Value Proposition

SpendWiseGenie

Spend Comparison with

Peers across segments Responsible

Spending (for Consumers*)

+Increased Sales

(for Banks & Merchants)Best Offers

Prediction supported with real-time data ( Maps/ratings)

SpendWise. Be wise

People Like Me

Offers For Me

Leverages the power of BBVA API’s

Non BBVA customers can also benchmark their spend

behaviour through this App

© 2014 Cognizant 5

User Journey

SpendWise Genie is a Smart “Spend Benchmarking & Real Time Offer Presentment” App that leverages BBVA APIs & advanced predictive algorithms

View

• Minimal input details (Age group, Gender etc.) for Non BBVA customers

• Interactive charts & visualizations to highlight spend patterns across category-location- time continuum

1

Plan

• Empowered spend planning across categories and peer groups

3

Compare

2

• Compare own spend pattern with peer segment and other BBVA Customers to understand deviations or identify high spend categories

Explore

4

• Receive relevant offers and reduce spend (Prediction based on consumer preferences, merchant location, discount offered etc. )

© 2014 Cognizant 6

Key Insights

“SpendWise Genie” is designed to answer the following:-

What is your spend pattern across different

spend categories & time-periods?

How does your spend compare to your peer

segment?

How can you plan & optimize your spend across categories ?

What are the most relevant offers which

you can utilize ?

© 2014 Cognizant 7

“People like me” AppWith “People Like Me” learn how consumers spend in your same category

People Like Me

Rich interactive charts represent the spend pattern across time cohorts

The consumer also picks spend category and subcategoryThe user provides his age, gender & location information and also the time period for which he wants to view spend patterns

For App demo refer to

pps file

© 2014 Cognizant 8

The “Offers for me” AppWith “Offers For Me”, review relevant and timely offers with supporting information ( maps, reviews and Ratings)

Offers for me

The user provides his age, gender & location information

The consumer specifies anticipated spend range or can be presented offers for the selected spend category

The selected merchant location is displayed on an interactive map where consumer has the option to get driving directions from current location (GPS)

The consumer can also view ratings and reviews of the selected merchant, available with ‘Google Places’

The top merchant offers pertinent to the spend category and anticipated spend range are presented to the consumer as a listing

For App demo refer to

pps file

© 2014 Cognizant 9

Key Business Benefits delivered

Promotes Responsible Spending - Know whether you are over spending / or can afford to spend more in certain categories

People Like Me

Offers For Me

Save on spend by redeeming offers

Drive / Reach new customers (by motivating those who underspent in specific categories)

Drivel Sales through increased Footfall and attract new customers

Design Future Offers/ Campaigns based on the performance / take-up of offers

Deliver the power of data to customers and increase loyalty by being a source of personal finance planning information

Drive Spend – thus increasing benefits for the bank and its merchants

Increased Customer Loyalty through an effective Offer Presentment Program

Consumer Merchant Bank

© 2014 Cognizant 10

Solution Architecture Business & Data Flow

BBVA Data

AnalyticsBBVA Host

BBVA API’s Recommendation rules to push relevant offers based on offer

score & customer segment mapping

Easy Visualization of Spend

Patterns

Existing Consumer

Transaction

Aggregated BBVA Consumer Data | Individual Consumer Data

Review Feed

Analyzer

Regular feed of Social Posts Google

Places APIs

High prediction accuracy for

spend category & range

Merchant locations with favorable ratings & reviews

SpendWise. Be wise

Advanced Predictive Model

Cloud Hosted Model

© 2014 Cognizant

Services

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Technical Architecture Application Design / Details

Mobile Android Application

Ionic js

Angular JS

Cordova / Phone gap

Offers Service hosted in

Google app engine

Prediction Service via Advanced predictive

models

BBVA Data API

Utilities

Auditing

Logging

Caching

Exception

Handling

Con

necti

vit

y

Locator Service via

Google Maps API

Social Sentiments via Google Places

API

Solution Building

BlockDescription

Mobile Android application

UI was developed using ionic and angular js. High charts was used as charting framework. Mobile app

was packaged using Cordova.

Offers ServiceThis service was built to filter and display the offers

specific to user interest. This service is hosted in Google app engine.

Prediction Service

This service was built using Advanced Predictive Models.

Locator ServiceDetails of the merchant providing the offers are

located on Maps and direction from user’s location to merchant location is provided.

Social Sentiments

Reviews and ratings of the merchant are provided using Google Places API

Utilities Common components to address non functional related common concerns across layers

High charts

© 2014 Cognizant

Cloud Based Predictive Model Design

Modeling

Business Understanding

DataUnderstanding

DataPreparation

Evaluation

Deployment

Objective of the model is to predict user spend category and spend amount based on:- The historic spend behavior of user segment (age group & gender);- Location (zip code) based spend patterns as well as seasonality trends – based on day-of-the-week and time-of-the-

dayThe base data* used for model training is based on the BBVA transaction data for:- Top 10 spending Zip Codes using BBVA APIs: Cards Cube and Consumption Pattern- Data filtered for Top 5 Merchant Categories with the highest spend in these 10 zips

Input data* for predictive model training is prepared by combining data from above 2 BBVA APIs to calculate the most probable spend on merchant category for a user segment at a weekday, at a particular hour: - By calculating merchant spend probability at an hour of a weekday using weighted contribution of spend at that hour

and spend by that user segment- For the sake of simplicity, spend behavior on a particular weekday (e.g a Tuesday) across months is assumed to be

similar

Using Advanced Predictive Models for a given Zip Code, User Segment , Day, Hour of Day:- Model 1: Prediction of Merchant Category with the Highest Probability of next User spend; Model Type: Classification Model; Model Accuracy: 89%- Model 2: Prediction of Spend Range for a user for selected/predicted Merchant Category; Model Type: Regression

Model; Refer MSE (Mean Square Error Values in Appendix)

Model Output i.e. the predicted category and the predicted spend is used to pull offers from offers database filtered for :- Selected Zip Code, Merchant Category, Predicted Spend (Range)- Pull rating/reviews from Google Places for displayed filtered offers

Detailed Evaluation of Models explained in next 2 slides

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CRISP – DM Methodology Followed

* - All local copies of data made to test model accuracy have been purged

© 2014 Cognizant

Offer Prediction Model

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Prediction Models

Prediction of the Merchant Category with the Highest Probability of next User spend in a given:- Zip Code- User Segment (Age Group, Gender)- Week Day- Hour of Day

1

Spend Prediction

Category Prediction

Prediction of the Spend Range for a user for selected/predicted Merchant Category in a given:- Zip Code- User Segment (Age Group, Gender)- Week Day- Hour of Day

2

Model

Model

Model Limitations:Since the current model uses the costumer transaction data as input data from BBVA APIs , which is available only at a segment level (age-group, segment) and not at a customer ID level, the models output is only valid for segment/cohort level predictions, assuming all the customers within that cohort behave in a similar spending manner.

Cloud Based Predictive Model Design

© 2014 Cognizant

How it works…

Prediction of Merchant Category with the Highest Probability of subsequent user spend in a given:- Zip Code - User Segment (Age Group, Gender)- Week Day - Hour of the day

Assumptions:- Training* and prediction limited to Top 5 Spend Merchant Categories (except mx_others):

{(mx_basrsandrestaraunts, mx_food, mx_fashion, mx_auto, mx_hyper (mall)}- High Accuracy prediction for Top 10 spending zips for current model

Model Type: Classification Model; Model Accuracy: 89%Test Cases:

Zip Code

Age Group GenderWeek Day

HourActual Category with

Maximum SpendPredicted Category

"11000" "19-25" "Female" "Fri" 5 " mx_barsandrestaurants1" " mx_barsandrestaurants1"

"11000" "26-35" "Unknown" "Mon" 19 " mx_food1" " mx_barsandrestaurants1"

"11320" ">=66" "Male" "Thu" 1 " mx_food1" " mx_food1"

"11510" ">=66" "Male" "Mon" 17 " mx_travel1" " mx_food1"

"11520" "Unknown" "Female" "Thu" 19 " mx_fashion1" " mx_barsandrestaurants1"

"14300" "19-25" "Female" "Fri" 16 " mx_hyper1" " mx_hyper1"

"11510" "Unknown" "Female" "Mon" 10 " mx_travel1" " mx_travel1"

"11000" "56-65" "Female" "Tue" 6 " mx_beauty1" " mx_barsandrestaurants1"

"11000" "Unknown" "Female" "Fri" 1 " mx_beauty1" " mx_beauty1"

"11590" "<=18" "Male" "Sat" 2 " mx_auto1" " mx_auto1"

1Model

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Evaluation of the Data Mining Model for Spend Category Prediction

* - All local copies of data made to test model accuracy have been purged

© 2014 Cognizant

Prediction of Spend Range for a user for selected/predicted Merchant Category for:- Zip Code - User Segment (Age Group, Gender)- Week Day - Hour of Day

2Model

Assumptions:- Training* and prediction limited to Top 5 Spend Merchant Categories (except mx_others):

{(mx_basrsandrestaraunts, mx_food, mx_fashion, mx_auto, mx_hyper (mall)}- High Accuracy prediction for Top 10 spending zips for current model

Model Count: Separate Model for each of the Top 5 Merchant Categories

Model Type: Regression Model; Test Cases: E.g. For merchant Category – “mx_auto”

Zip Code

Age Group Gender Week Day Hour Actual Spend Predicted Spend

"11590" "19-25" "Female" "Fri" 7 147.33 483.6822

"11320" "36-45" "Male" "Mon" 17 1114.3 1611.875

"11520" "46-55" "Male" "Sat" 12 2723.5 1415.509

"64000" "56-65" "Male" "Wed" 18 168.15 169.7097

"11590" "46-55" "Male" "Wed" 9 1000 1294.2

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How it works….Evaluation of the Data Mining Model for Spend Range Prediction

* - All local copies of data made to test model accuracy have been purged

© 2014 Cognizant 16

Recommended Roadmap…. All zips from BBVA consumers and across Mexico can be incorporated

All the merchant categories can be included in analysis

Real time access to offers database

Real time Social media sentiment analysis to push relevant places / offers to specific segments

Customer level transaction data , if made available, more specific & targeted offers can be built using more sophisticated algorithms

Customers can like/dislike offers to generate valuable insights for future offer design

Incorporation of external data factors (e.g. weather data) to suggest suitable offers / merchants

Integration of payments/offer redemptions through Dwolla, helping merchants track & plan offers

iOS version of the app would be launched

A Spanish version of the app in agenda for future release

© 2014 Cognizant 17

Solution evaluation parameters

Originality

The app is uniquely positioned to address

customer need of spend optimization

through relevant offer presentment based on peer-group past

spend behavior, location, day of the week & time of the

day

Visual Appeal

The app enables the customer to view his peer segment spend

patterns across spend categories at

different drill down levels through rich interactive charts. Also, once a user selects an offer -

relevant merchant details, ratings, reviews, social

sentiment and its location , directions on an interactive

map are presented

Usefulness

1. Spend Tracking & Reporting

2. Peer Benchmarking

3. Spend Prediction & Offer Presentment

4. Spend optimization

Usability across devices

The app uses consumer’s current location along with

other segment parameters to report & recommend offers

based on cloud hosted Advanced Prediction API rule engine. Mexican

mobile market has a prevalence of Android OS (>70% share *),

the app can be currently used across

Android mobiles & tablets

External Data

1. Google Places API (ratings & reviews)

2. Google Maps API 3. Offers database

as a proxy for bank offers / Groupon data

* Source - http://www.statista.com/statistics/245193/market-share-of-mobile-operating-systems-for-smartphone-sales-in-mexico/

© 2014 Cognizant 18

Appendix

© 2014 Cognizant 19

Mocked Up Offers Database

The offers database consists of following variables: Actual Merchant Details in Mexico

• Merchant Name• Merchant Address• Merchant Zip Code• Merchant Latitude & Longitude• Merchant Sample Image Link• Merchant Category

Mocked-up Offers

• Offer Details Spend Amount in Mex$ Discount% Savings in Mex$

Sample offers data

© 2014 Cognizant

Predictive Model Evaluation (1/2)

Model Description # of Instances Model Type Classification Accuracy

Model 1: Category Prediction 21,161 ClassificationIn Sample Validation: 89%

Out Sample Validation: 64%

Model Description # of Instances Model TypeSquare Root of Mean Squared

Error (Develpoment Sample)

Model 2: Spend Prediction

(mx_auto)72,737 Regression 1,824

Model 2: Spend Prediction

(mx_barsandrestaraunts)361,616 Regression 1,470

Model 2: Spend Prediction

(mx_hyper)133,114 Regression 5,036

Model 2: Spend Prediction

(mx_food)253,337 Regression 1,017

Model 2: Spend Prediction

(mx_fashion)79,648 Regression 2,557

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© 2014 Cognizant

Predicted Category mx_auto mx_barsandrestaurants mx_hyper mx_food mx_fashion

mx_auto 100% 0% 0% 0% 0%

mx_barsandrestaurants 0% 93% 1% 6% 0%

mx_hyper 0% 0% 84% 16% 0%

mx_food 0% 8% 2% 89% 0%

mx_fashion 0% 98% 0% 0% 2%

Prediction Training Confusion Matrix: In Sample ~ 89%1Model

High Prediction Accuracy for 4 merchant categories

Poor Prediction Accuracy for 1 merchant category

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Predictive Model Evaluation (2/2)

Predicted Category

mx_auto

mx_barsandrestaurants

mx_hyper

mx_food

mx_fashion

Low

Low

Prediction Accuracy: Out Sample Validation ~64%

Accuracy Level

High

High

High

1Model

Based on 50 Out Sample Validation Test Cases

© 2014 Cognizant

Visit link for app download :- https://bbvaopen4u.cognizant.com/SpendWiseGenie

Visit link for app demo :- https://bbvaopen4u.cognizant.com/SpendWiseGenie/SpendWiseGenieDemo.ppsx22

Thank you