Predictive analytics are increasingly a must-have competitive tool. A well-defined workflow and effective decision modeling approach ensures that the right predictive analytic models get built and deployed.
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The Value of Predictive Analytics and Decision Modeling
1. Customer
Retention in the
Airline IndustryJamesTaylor,
Decision Management Solutions
Matthew Kitching,
Apption
The Value of Predictive
Analytics and How
Using Decision
Modeling Helps You
Succeed
2. Your Presenters
James Taylor
We work with clients to
improve their business by
identifying and modeling
decisions, and applying
business rules and analytic
technology to automate &
improve these decisions.
Spent 12 years championing
Decision Management.
DMN Submitter
Matthew Kitching
We help our clients leverage
their data for better
decision-making – analyzing
large data sets allows us to
build robust predictive
models which you can embed
in your operations.
Developers of analytics
solutions since 2007.
Senior Data Scientist
4. Customer Retention in the Airline Industry
Goal
Retain valued customers to
maximize profit
Candidate Predictions
Likelihood of churn
Customer lifetime value
Customer response
Based on an Apption engagement
with a major airline
5. Apption Big Data Analytics Workflow
Data
Exploration
Predictive
Models
Reporting
and
Evaluation
Data
Preparation
Design Implementation Delivery
Deliverables
Requirements
Gathering and
Design
13. Apption Big Data Analytics
Data
Exploration
Predictive
Models
Reporting
and
Evaluation
Data
Preparation
Requirements
Gathering and
Design
Deliverables
Design Implementation Delivery
14. Step 2 - Data Preparation
•Hadoop Infrastructure Setup
•Data assessment and consolidation
•Cleanse data
de-duplication
de-identification
unstructured text processing
At this point, the data is ready to be analyzed
15. Airline Case Study – Data Preparation
What we learned from the data:
• Shorter than expected timeline
for survey data
• Impact of omitting customer
survey results can be visualized
• Decision Model can be updated
with information about the data
sources
We can adjust our assumptions after analyzing the data
16. Apption Big Data Analytics
Data
Exploration
Predictive
Models
Reporting
and
Evaluation
Data
Preparation
Requirements
Gathering and
Design
Deliverables
Design Implementation Delivery
17. Step 3 - Data Exploration
•Identify actionable insights from data:
• statistics about data features
• correlations between features
• aggregation of data
• creation of new features
•Convert into a visual or tabular format
•Data Requirements Models focus data
scientists on most relevant data
18. Airline Case Study - Data Preparation
Results found provided interesting and surprising insights:
• Useful positive or negative indicators for predicting churn
• Surprisingly not useful indicators for predicting churn
19. Apption Big Data Analytics
Data
Exploration
Predictive
Models
Reporting
and
Evaluation
Data
Preparation
Requirements
Gathering and
Design
Deliverables
Design Implementation Delivery
20. Step 4 - Predictive Models
Data science and technology at work:
• Algorithms: Segmentation, classification, clustering,
regression…
• Technologies: Hadoop, Spark, Python, R, SAS…
A data asset is created that can be reused over time
21. Airline Case Study - Predictive Models
Update the Decision Model based on the results:
• Original definition of churn did not lead to a stable model
• Many passengers who churned in year 1 did not in year 2
• No correlation between lost baggage claims and churn
The Decision Requirements Model to be updated
22. Airline Case Study – Churn Model results
98%
2%
Customers identified as low risk of
churn based on year 1 data
Customers did not churn in year 2 Customers did churn in year 2
49%51%
Customers identified as high risk of
churn based on year 1 data
Customers did not churn in year 2 Customers did churn in year 2
Low-churn group shows model accuracy
High-churn group identifies target market
23. Step 5 - Reporting and Evaluation
Data Exploration
• statistics
• correlations
Predictive Models
• performance versus
success criteria
Further iterations of the Implementation cycle
based on the results obtained
Report on the results obtained in the previous steps:
Data
Exploration
Predictive
Models
Reporting
and
Evaluation
Data
Preparation
Deliverables
Implementation Delivery
25. Apption Big Data Analytics
Data
Exploration
Predictive
Models
Reporting
and
Evaluation
Data
Preparation
Requirements
Gathering and
Design
Deliverables
Design Implementation Delivery
26. step 6 - Finalize•New reusable software asset is deployed
•Knowledge transfer allows the business to integrate
this asset in their enterprise processes
•Potential roadmap for evolution of the asset
•Reports
Visualizations
Actionable insights
Predictive model results
Decision Requirements Model
Step 6 - Deliverables
30. Big Data Lessons Learned
Strong Case for Big Data Analytics
Big Data Analytics extracts actionable insights
from unstructured and often messy data
Meaningful actionable insights can be
achieved within a reasonable amount of time
Big Data Analytics assets created allow
ongoing insights as data changes
33. Apption
Data Science, Data Management and Analytics Software
Development and Consulting Experts
Founded in 2004
Full Stack Big Data Analytics Services
Data Engineering
Data Science and Analytics experience
Data Visualization
Custom Software Development
Focus on Security Analytics and Customer Intelligence
Website: http://www.apption.com
Email: info@apption.com