rosaria silipo and phil winters predictive analytics world...
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Predictive Analytics World, London 2011
Rosaria Silipo and Phil Winters
Identify Differentiate Interact Customize
Customer Intelligence…
“… is the process of creating and applying new fact-based insight about individuals.“ Phil Winters
Customer Intelligence Goals
Customer Value
Valuable Customers Marketing Spend Promotion Candidates Campaign Evaluation
Customer Behaviour
Current Products Customer Journey Touchpoints Loyal Customers Demographics
Future Products Sentiment Analysis New Product Features Potential Customers …
Customer Needs
Past and Current Projects
The Challenge
Very Large Amounts of Data Definition of New Complex Input Features Input Data Sets with Very High Dimensionality
Many Data Analysis Models suitable for different goals and different data
Selection of the best Input Feature Subset
Selection of the most appropriate Data Analysis Model
Results Integration from more than one model
Interpretation and
Communication
We want to parallelize the search for the best input
feature subset.
Key Data Categories
Worth
Products
Demographics
Survey Data
Loyalty / History
Sentiment
The Best Input Feature Subset
Option A. We can progressively eliminate input features till the optimal subset is found.
Option B. We can define input feature sets based on the key data categories and train models on them. The best input feature set produces the model with the highest accuracy.
Takes long and might not bring
additional information
Faster and might tell us if it was
worth it to make that survey.
The Input Feature Subset Selection
Input Feature Subset 1
Input Feature Subset 2
Input Feature Subset …
Input Feature Subset n
… and the best input subset is …
Accuracy
Accuracy
Accuracy
Accuracy
The Data Analysis Model
Depending on the data
Missing Values
Large Amount of Data
Gaussian Distributions
Nominal Values
Text
Depending on the problem:
To classify what we already know
To discover the unknown
The Workflow Architecture
Selection of the Best Input Feature Subset
Selection of the Best Input Feature Subset
Selection of the Best Input Feature Subset
Results Integration
Interpretation
Customer Worth Analysis Workflow
Generate a report
Summary
For some projects we used the decision tree model, when a classification was available and rules were required
For other projects we used clustering algorithms, when we had no idea about the analysis goal.
In some projects we have used more than one analysis model: one supervised to produce rules and one unsupervised to discover new facts.
The results from different data analysis models do not always overlap.
Next Steps: New Data Sources
Social Media and Text
Next Steps: Methods
Recommendation / Next best / etc.
http://knime.org/node/52703
Next Steps: Realtime
Predictive Analytics World, London 2011