The document outlines the implementation of predictive analytics within operational workflows, detailing its purpose in improving business performance through customer churn prediction and marketing response strategies. It elaborates on a predictive analytics framework that includes preparation, investigation, modeling, evaluation, and deployment phases while discussing the importance of data quality and modeling accuracy. Key considerations for both business users and data scientists are highlighted to ensure effective utilization of predictive models in decision-making processes.

1. www.globalbigdataconference.com
Twitter : @bigdataconf

2. Implementing Predictive
Analytics into
Operational Workflows
Derek Wilson
CDO Advisors
02/03/18 1:40 - 2:20

3. Agenda
• What is Predictive Analytics
• Predictive Analytics Framework
• Customer Churn
• Cross Selling
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4. What is Predictive Analytics?
Predictive Analytics is used to improve the performance of your business by
focusing on:
• Improving Revenue
• Decreasing Expenses
• Improving Service for Internal or External Customers
Predictive Analytics uncovers patterns and relationships in data by leveraging
historical data to apply to current information.
Predictive Analytics is forward looking and attempts to anticipate outcomes
and behaviors based on mathematical models not assumptions or gut
feeling.
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5. Predictive Analytics Framework
Preparation - Initial phase that focuses on partnering with the
business to determine what is to be
accomplished.
Investigation - Exploring the organizations data assets to see
what data is available
Modeling - Is where the data experimentation takes place
Evaluation - Reviewing the best performing models with the
business users
Deployment - Model is loaded with production data to creation
actionable insights
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6. Customer Churn Questions
• Can we predict which customers have the highest risk of churn?
• Do we have data that can be useful to understand patterns in why
customers have churned?
• Can we access and process the data?
• Is the data of sufficient quality?
• What kind of models can we build?
• How accurate are the models?
• How do we make the model’s outcome actionable?
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7. Customer Churn - Preparation
Business Users – Problem Statement
• Define the business problem that you want to better
understand
• How will you track Experimental and Control group to track the
impact of the analytics
• Establish the current Churn Rate baseline
• Define the parameters of the project
• Line of Business, Product, Segmentation
• Amount of time to spend on the problem
• Determine where model output will be used in operations
Data Scientists – Questions
• How will you use the output from the model?
• What prior analysis has been done?
• What has been done successfully to reduce churn in the past?
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8. Telco Churn Sample Data Elements
• Spouse
• Dependents
• Internet Service
• Online Security
• Tech Support
• Contract Type
• Paperless Billing
• Payment Method
• Lifetime Months Tenure
• Monthly Charges
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9. Customer Churn - Investigation
Business Users – Process Clarification
• Ensure the process for customer lifecycle is well defined
• What applications have data that would be beneficial to use?
• Are there known external data that could be used?
Data Scientists – Data and Process Questions
• What data is available?
• Leverage a variety of tools and methods to understand the
data?
• Visualization tools
• Statistical analytics
• What is the quality of the data?
• Distribution of data
• Quality of data
• Volume of data
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10. Customer Churn - Modeling
Business Users – Engagement on Modeling
• Review output of trial models as requested
• Check assumptions that are made on the data
• Ensure output will satisfy business problem
Data Scientists – Experimentation of Models
• Create and build models
• Trial and error on the output of the models
• Experimentation on different data elements
• Model accuracy
• Different algorithms
• Optimization of models
• Testing of the model
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11. Data Mining – Decision Tree (Partial)
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12. Customer Churn - Evaluation
Business Users
• Review model source data, modifications and process
• Review output of best performing models
• Ensure output can be used in business operations
Data Scientists
• Present models to the business users
• Share discovers in the data:
• What patterns were observed
• Review data chosen, modifications, quality issues
addressed
• Review how the model output will be shared with the business
• Excel output
• Written to a database
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13. Predictive Analytics - Evaluation
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14. Customer Churn - Deployment
Business Users – Operational Actions
• Implement changes to operational processes based on the new data
from the model
Data Scientists – Monitoring and Progress of Actions
• Monitor the progress of the control and experimental group
• Review results of the operational changes monthly with business
users
• If the model is reducing churn, expand to other areas and start the
process from the preparation stage
• Determine when to re-evaluate the model, when does the current
models accuracy no longer work
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15. Applying the Model
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16. Marketing Response Questions
• Can we predict which people are most likely to respond to our campaign?
• What kind of campaign are we doing? (email, mail, sms)
• Do we have data that can be useful to understand patterns prior
campaigns?
• Can we access and process the data?
• Is the data of sufficient quality?
• What kind of models can we build?
• How accurate are the models?
• How do we make the model’s outcome actionable?
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17. Marketing Response Sample Data Elements
• Age
• Gender
• Job
• Marital Status
• Education
• Housing Loan
• Personal Loan
• Automobile Ownership
• Postal Code
• Census Block
• Contact (method)
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18. Marketing Response - Flow
Preparation - Initial phase that focuses on partnering with the
business to determine what is to be accomplished.
Investigation - Exploring the organizations data assets to see what data
is available
Modeling - Is where the data experimentation takes place
Evaluation - Reviewing the best performing models with the
business users
Deployment - Model is loaded with production data to creation
actionable insights
copyright 2018 CDO Advisors - www.cdoadvisors.com 18

19. Applying the Model
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20. Getting started with Predictive Analytics?
• Start with the data you have
• Identify a core business problem that you want to solve
• Determine the required data elements and quality of data
• Build models….Evaluate models…test models….
• Implement the model
• Limited scope to build confidence
• A/B Testing to show actual performance of the model versus control group
• Monitor and measure the results
• How is the model performing versus normal business operations?
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21. Questions or Follow Up
Derek Wilson, CEO – CDO Advisors
dwilson@cdoadvisors.com
www.cdoadvisors.com
Office : 832-819-5744
Connect with me on LinkedIn –
https://www.linkedin.com/in/derekewilson/
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