Approaching an Analytical Project

Tuba Islam, Analytics CoE, SAS UK

Approaching an Analytical Project

Starting with questions..

• What is the problem you would like to solve?

• Why do you need analytics?

• Which methods you need to apply?

• How do you turn the outcomes into business decisions?

A Variety of Needs

• Trying to solve a known problem

• High churn rate, low campaign response, high default

• Detecting the unknown

• Fraud analysis, cyber attack

• Understanding the customer preferences

• Behavioral segmentation, affinity analysis

• Searching for the future trends and change in time

• Demand forecasting, churn rate forecasting

• A new data source to gain insight from

• Smart metering data, social media, call centre records

Market Basket Analysis

Cross and Up Selling

Customer Link Analytics

Credit Scoring Social Media Analytics

Customer Segmentation

Fraud Detection

KPI Forecasting

Analytical Methods

Supervised

Classification

Prediction

Time-Series Analysis

UnsupervisedClustering

Affinity Analysis

Social Network AnalysisSemi Supervised

A Variety of Methods

Designing an End-to-End Process to Increase

the Value Gained from Analytics

Approaching an Analytical Project

Roles & Life Cycle

IDENTIFY

BUSINESS

PROBLEM

ONE QUESTION CAN SPLIT INTO MANY..

How can I improve the profitability of my organisation?

1. Who are the most profitable customers?

2. Who is more likely to churn within the high-value customer segment?

3. What would be the best offer to retain these customers?

DATA

PREPARATION

COLLECT RELEVANT INFORMATION

The relevant data source would be different for each business question

CREATE AN ANALYTICAL DATA MART

• Training data mart

• For a predictive model, a reference date to take the snapshot of the historical data will be

chosen and the prediction window will be excluded from the datamart

• The data will be aggregated to summarise the information in the entity level (customer

ID, account ID etc.)

• Different analytical transformations will be applied for different types of models

• The input variables will be have dynamic names to avoid the dependency on time (eg.

Balance_M1: last month’s balance, Number_of_Calls_W1: last week’s calls)

• A target variable will be created (eg. churner, fraudulent) based on the prediction window

• Scoring data mart

• A scoring dataset from the up-to-date source data will be created which includes the

input variables that are used in the production model and no target.

MX1M3 M2 M1

Observation Window Prediction Window

Action Window

DATA

PREPARATION

• Apply the selection criteria for the population of the model

• Eligibility rules (eg. no credit risk history, no purchase of the campaign offer)

• Follow the SEMMA methodology (Sample, Explore, Modify, Model, Assess) to

create the analytical model

• Partition the data as train and test

• Take a stratified sample of the data if the event rate is rare (e.g. change 5/100 to 20/100)

• Transform the data to remove outliers, impute missings, maximise normality etc.

• Try different techniques to build models and select the best one to deploy after comparison

• Save/register the model

BUILDING THE MODELLING PROCESSMODEL

DEVELOPMENT

MODEL MONITORING AND PERFORMANCE

REPORTING

• Manage all analytical models from a centralised model management

environment.

• Create performance reports to monitor the changes in the output and also the

input variables.

• Retrain or retire the models if the performance decreases beyond a pre-

defined threshold. Models can also be published in-database to eliminate

data movements from the data source to the analytics server.

MODEL

MANAGEMENT

EXECUTE MODELS AND TAKE ACTIONSMODEL

DEPLOYMENT

• Extract the scoring code and run on production data to score new customers

• Real-time, near real-time or batch execution (e.g application scoring in real-time, churn scoring

weekly)

• If there are some constraints, such as the number of offers per campaign, the contact

policy of the organisation etc, then the model scores would be used as input variables

for the optimisation engine and the optimum outcome will be chosen to take actions.

• After the deployment of the models, collect the actuals from the operational system and

store them in the analytical data mart for monitoring performance and retraining models.

Below a certain value of the performance metric, the model gets retired or retrained.

• Turn the scores into actions to support the business decisions by integrating with the

existing systems such as the call center, risk management, campaign management.

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Analytics

Journey

A CASE STUDY

Identify Business Problem

Marketing Need: Finding the customer target group to

communicate who are high-likely to churn next month

Copyr i g ht © 2013, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

Data Preparation

Data Model Design and Data Mart Development

Analytics

Journey

Designing and creating the data mart for the business problem

Copyr i g ht © 2013, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

Model Development

Exploration and Modelling

Starting with Exploration and Visualisation of data on

customer demographics, products, transactions and

purchases

Analysing relationships

between factors

Analysing Interactions e.g.

campaign response vs account type

DATA MINER / STATISTICIAN

Analysing different

customer groups and using clustering methods

Building interactive models for each customer segment and finding the key factors influencing churn

DATA MINER / STATISTICIAN

Building production models for deployment and automation

Using unstructured data to improve

the accuracy

DATA MINER / STATISTICIAN

Creating a SAS model package for deployment and registering in repository

Copyr i g ht © 2013, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

Model Management and Model Deployment

Executing models and taking actions

Analytics

Journey

Designing the hierarchy for the centralised repository of the enterprise models (department

level, topic level etc.)

DATA MINER / DATA MANAGEMENT

Performance Monitoring

reports:Lift Chart, KS Graph, input distributions,

stability graphs

Model can be retrained and the parameters are updated automatically

DATA MINER / STATISTICIAN

DATA MANAGEMENT

Publishing models into the database for scoring. No data movement.

DATA MANAGEMENT

Creating scoring jobs to execute models in production and feed the business decisions

Approaching an Analytical Project

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