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Analytics – Infrastructure, Platforms and Methods.

Feyzi Bagirov26 Jan 2015

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Only about half of financial services organizations have a single system to comply with anti-money laundering directives (Accenture)

http://www.forbes.com/sites/jasonbloomberg/2014/07/29/three-way-big-data-banking-battle-brewing/

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Data Mining ◦ Retail Use cases◦ Data Mining Process

Data Mining Methodologies

Data◦ Data Training◦ Types of Business Information Systems◦ Data Warehouses◦ Data Mining Tools◦ Data Visualization Tools◦ Big Data

Data Mining

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Machine Learning is a scientific discipline that explores the construction and study of algorithms that can learn from data (Ron Kovahi; Foster Provost (1998). “Glossary of terms”.

Data Mining is the process of achieving Machine Learning.

What is Data Mining?

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Response modeling for direct marketing Uplift modeling for direct marketing Customer retention with churn modeling Churn uplift modeling

Retail Use Cases

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Use Case 1 – Response Modeling For Direct Marketing

Lifeline Screening: Response up 38%, cost down 20%, 62K more customers annually

PREMIER Bankcard: Direct mail response up 3-5%

Sun Microsystems: Doubled the number of leads per phone call

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Based on the past experience, who will respond tomorrow?

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Use Case 2- Uplift Modeling for Direct Marketing

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Use Case 2- Uplift Modeling for Direct Marketing

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Leading financial institution: incremental conversion up 0.02% to 0.43%; Revenue per contact up by over 20 times

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Use Case 3 – Customer Retention With Churn Modeling

Reed Elsevier’s Caterer & Hotelkeeper: Reduced churn by 16%; Retention ROI up by 10%

PREMIER Bankcard: $8 million est. retained

Leading North American Telecom: Identified customers with a 600% increased risk of churn with social network analysis.

Optus (Australian telecom): Doubled churn model performance with social data

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Use Case 4 – Churn Uplift Modeling

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Telenor: Reduced churn 36%; Cost-of-contact down 40%; Campaign ROI up 11-fold

US Bank: Costs down 40%, lift up 2 times, and cross-sell ROI up 5 times

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Data Mining ProcessCRISP-DM

(Cross Industry Standard Process for Data Mining)

SEMMA(Sample, Explore, Modify, Model, Assess)

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Business Task

Data Set

Data Preparation

Data cleaning

ModelingEvaluation

and validation

Use of DM results/deplo

yment

Results of action based

on DM results

Development

Data Mining Process

Strategic Objectives

Operational

Objectives

Marketing Objectives

Other Objectives

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Supervised (data training) and unsupervised methods

Age: 25-35Gender: MaleMarital Status: MarriedEducation: Graduate

Historically

Historically

Training Data

Unknown DataPrediction

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ised

Unknown Data

Historically

Unsu

perv

ised

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Transactional vs. Analysis-Based Systems

Transactional Information Systems

Analysis-Based Information Systems

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Data Warehouses

Data Warehouse 4 main features:• Topical Orientation (customer, product, etc.)• Logical integration and homogenization (relational integration)• Presence of a reference period (vs operational)• Low volatility (should not change often)

3 components of Data Warehouses:• DBMS (Database Management System)• DB (Database)• DBCS (Database Communication System)

Snowflake Star

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Data Marts

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Data Mining Tools

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Data Visualization Tools

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What is Big Data?

1. Velocity2. Variety3. Volume

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Q&A?

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