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Predictive Analytics
Business Needs
Reduce Churn
Improve Conversion Rates
Minimise Risk
Profile Customers
Optimise Pricing
CRISP-DM Methodology
Additionally we provide an insight into key predictive variables - 'drivers' behind the Target behaviour
Automatic Model build
Xpanse takes in:
Modelling Table - if you have them
Super fast in-Ram processing in the cloud
Automated feature selection
Thousands of variables created
Deep Feature Generation
...aggregated data
And testing
...no aggregated transactional data (e.g. Current Account history, online sales history, CRM logs)
Linking separate
tables
...even as detailed as CDRs (Call Detail Records) or Weblogs
Design of
Modelling Tablle
This is the process of building the code (usually in SQL) that creates 'single view aggregates from source data
Coding of
Modelling Table
D
Data
Understanding
a
Automated
Modelling
Data Preparation
t
Automated
Automated
a
Model Evaluation
What data should we use for Modelling?
How do we define the Target?
..yes the coding and debugging is by far the most time-consuming task in the whole project
Data
Understanding
Deployment
P
Some more
Coding & Debugging
Business Understanding
Xpanse
Typically 95% of
analyst's time
r
What are we trying to achieve here?
How the Predictive Modelling can help?
e
Business Understanding
Typically
60-80% of
project cost
p
a
Deployment
Coding
&
Debugging
r
a
t
i
Model
Evaluation
o
n
Modelling
Coding
&
Debugging
This is where Machine Learning is actually used - Decision Trees, Logistic Regression, Neural Nets, etc.
This the fun part of the project.. and it's rather short..
Yey, the
Modelling
Table delivered