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