Consumer Electronics Team Project

1. New Data Project Plan
Anderson, Brykman, Gero, Lakhani, Matthews
PREDICT 480 – SECTION 55

2. Introduction
Company Background
 DERAK is an analytical support team for a young credit card company
 Company is 5 years old
 Latest venture is to consolidate data sources across the organization as well as
collect data from mobile applications to gain customer loyalty
 Why did we select this company?
 The combination of credit card data, social media, GPS, mobile data, etc. offer
exiting opportunities for modeling to give value to the customer

3. Introduction
SWOT Analysis
 Strengths
 Company is becoming analytically mature (Stage III on Maturity Model)
 Diversity of products to spread risk
 Weaknesses
 Not yet a significant brand in Credit Card market
 As a new company, we have limited data from 2008 Financial Crisis
 Opportunities
 Gain new insights on customers through mobile app
 Utilize data for predictive modeling
 Explore uses of GPS data
 Sell customer data to third party companies
 Threats
 Credit Card Market is mature with several key players. Gaining significant market share is
difficult and expensive
 Customer privacy concerns and government regulations

4. Introduction
3 Key Issues to Address with Analytics
1. Limited understanding about our customer
1. We need to know more about our customer in order to offer them more value than
the competition
2. Credit card default risk
1. Increase volume but maintain quality
3. Fraud
1. We need to increase trust with the customer by protecting them financially

5. Introduction
Database and Data preparation plan
 Our database and data preparation plan will help us solve this issues by:
 Helping us to gather more intimate data on our customer to better understand
their shopping preferences, personal interests, and measure customer loyalty
 We plan to utilize the new data to identify possible natural clusters of our
customers that may help use to identify segments that could carry higher
probability of default risk
 Through mobile applications, we can use GPS data to identify transactions that
may be fraudulent in order to better protect our customers

6. Literature and Data Sources Overview
 Document your information and data sources.
 Describe methods relevant to:
 Acquiring
 Storing
 Maintaining
 Accessing the data that you need
 Cite references that you have used to guide your thinking about
data sources and methods, and include these in the reference list
a the end of the paper.

7. Acquiring Data
 Company Databases1
 Demographic information
 Banking plans
 Credit/Debit balances
 Transactional data
 Social media3
 Twitter
 Hashtags
 @Branding
 Facebook
 Status updates
 ‘Likes’
 Contact Network
1 https://customers.microsoft.com/Pages/Download.aspx?id=13928/
2 http://www.sitetechsystems.com/top-10-ways-to-use-gis-in-retail-banking/
2 www.mmaglobal.com/files/mbankingoverview.pdf
3 http://www.bearingpoint.com/ecomaXL/files/0615_WP_EN_Social_CRM_final_web.pdf
 Mobile Phone Data2
 GPS Location
 Mobile client application
 Mobile web
 Short Message Service
(SMS)
 Contact Lists

8. Bank
MobileSocial
media
Data Integration
Central location
Dealing with data issues
and data preparation
 Integration of results from
various data sources into a
central location for analysis1
 Integration of results from
feeds (structured and
unstructured)1
1 https://www.in.capgemini.com/resource-file-
access/resource/pdf/A_Case_for_Enterprise_Data_Management_for_Banking.pdf

9. Storage
 Considerations close to real time or longer term
access1
 Governance for retention periods, data ownership
and entry of new data sources2
 Maintain backups2
 Accessible2
 Document sources and validations taking place2
1 http://www.oracle.com/us/products/middleware/data-integration/oracle-goldengate-realtime-access-2031152.pdf
2 http://www.osfi-bsif.gc.ca/eng/docs/data_maint_ja06.pdf

10. Maintaining data
 Backing up data1
 Deleting data based on established retention1
 Performance optimization2
1 http://www.osfi-bsif.gc.ca/eng/docs/data_maint_ja06.pdf
2 http://www.oracle.com/technetwork/database/bi-datawarehousing/twp-bp-for-stats-gather-12c-1967354.pdf

11. Accessing
 Access via graphical user interfaces (GUIs) through
applications for
 Structured data or unstructured data
 Real time or ex-post data
 Access for
 System administration and maintenance
 Validation, editing and estimating (VEE)
 Analyzing and modelling processed data (Ultimate Goal)
 Audit access controls1
1 http://www.osfi-bsif.gc.ca/eng/docs/data_maint_ja06.pdf

12. Criteria
 Describe the systems and methods used to:
 Acquire data
 Store data
 Maintain data
 Access data
 Describe the infrastructure.
 Describe how these systems and methods work together.

13. Acquire data
 Python and R languages will be used for data acquisition.
 Lightweight and flexible.
 Mature - industry standards.
 Multiple libraries available for data handling and analytics.
 Data acquisition
 Facebook1 and Twitter2 provide APIs for access
 Python supported
 Returns JSON format
 Company data
 Years of company credit card history available.
1 https://developers.facebook.com/docs/graph-api/using-graph-api/v2.5
2 https://dev.twitter.com/overview/api

14. Storing, Maintaining and Accessing
 Oracle DBMS for internal data
 PostgreSQL for analytics
 Supports structured and unstructured data.
 Better performance than MongoDB
 Purchase history tied to customers maintained for 90 days.
 Data used for analytic models maintained for 90 days.
 Data anonymized and maintained for 2 years.
 Used for general trending and analysis.
 Sold to third parties.
 Views will be created in the PostgreSQL environment to allow access for the
mobile application.

15. Infrastructure and How it works.

16. Data Preparation
 Text Analysis
 Cleanse the data for keywords
 GPS Data
 Broad scale demographic segmentation
 Contact Networks
 Identifiable features of people and groups

17. Data Issues
Problems
 Application Permissions
 Consumer Participation
 Inaccurate Text Analysis
 Data Storage and Processing
Solutions
 Provide benefits for permissions
 Entice consumers with offers
 Segmented Analysis
 Commodity Clustered Servers

18. Data Quality
 Outliers & Incomplete data
 Used where applicable, likely excluded
 Bootstrapping
 Estimation of additional sampling distributions
 Influential observations
 Used to further define possible segmentations

19. In Conclusion…
Opportunities
 DERAK is well positioned to create key analytical insights to our credit card
customer, which will benefit its services by:
 Improving its market access
 Gaining new insights on its credit card customers through mobile app
 Utilizing the collected data for predictive modeling (spending habits, etc.)
 Exploring uses of GPS data
 Selling anonymized customer data to third party companies

20. In Conclusion…
Full Services

21. In Conclusion…
Data Sources

22. In Conclusion…
Systems and Methods

23. In Conclusion…
Data Issues and Preparation

24. In Conclusion…
Direction and future state
 Refining data collection and
analytics
 Improving data mining methods
 Expanding our services and market
reach to other industries

25.  Team and Presenters (in order of appearance)
 Aaron Matthews
 Ketan Lakhani
 Eric Gero
 Daniel Anderson
 Raphael Brykman

26. References
 Royal Bank of Scotland Case Study (2013)
 Mobile Banking Overview (January 2009)
 10 ways to use GIS data in Retail Banking (2012)
 A Case for Enterprise Data Management in Banking (2012)
 Data Maintenance at IRB Institutions (2006)
 Best Practices for Gathering Optimizer Statistics with Oracle Database 12c