Approaching an Analytical Project. Tuba Islam, Analytics CoE, SAS UK

Transcription

1 Approaching an Analytical Project Tuba Islam, Analytics CoE, SAS UK

2 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?

3 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 Customer Segmentation Fraud Detection Customer Link Analytics KPI Forecasting Cross and Up Selling Credit Scoring Social Media Analytics

4 A Variety of Methods Classification Supervised Prediction Analytical Methods Unsupervised Time-Series Analysis Clustering Affinity Analysis Semi Supervised Social Network Analysis

5 Designing an End-to-End Process to Increase the Value Gained from Analytics

6 Approaching an Analytical Project Roles & Life Cycle

7 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?

8 DATA PREPARATION COLLECT RELEVANT INFORMATION The relevant data source would be different for each business question

9 DATA PREPARATION 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. M3 M2 M1 Observation Window MX1 Action Window Prediction Windo

10 MODEL DEVELOPMENT BUILDING THE MODELLING PROCESS 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

11 MODEL MANAGEMENT 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 predefined threshold. Models can also be published in-database to eliminate data movements from the data source to the analytics server.

12 MODEL DEPLOYMENT EXECUTE MODELS AND TAKE ACTIONS 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.

13 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 titut e Inc. All rights res er ve d.

14 Analytics Journey Data Preparation Data Model Design and Data Mart Development Copyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d.

15 Designing and creating the data mart for the business problem

16 Model Development Exploration and Modelling Starting with Exploration and Visualisation of data on customer demographics, products, transactions and purchases Copyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d.

17 Analysing relationships between factors

18 Analysing Interactions e.g. campaign response vs account type

19 DATA MINER / STATISTICIAN Analysing different customer groups and using clustering methods

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

21 DATA MINER / STATISTICIAN Building production models for deployment and automation

22 Using unstructured data to improve the accuracy

23 DATA MINER / STATISTICIAN Creating a SAS model package for deployment and registering in repository

24 Analytics Journey Model Management and Model Deployment Executing models and taking actions Copyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d.

25 Designing the hierarchy for the centralised repository of the enterprise models (department level, topic level etc.) DATA MINER / DATA MANAGEMENT

26 Model can be retrained and the parameters are updated automatically Performance Monitoring reports: Lift Chart, KS Graph, input distributions, stability graphs DATA MINER / STATISTICIAN

27 DATA MANAGEMENT Publishing models into the database for scoring. No data movement.

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

29 Approaching an Analytical Project

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