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Xpanse Analytics Platform
www.xpanseanalytics.com
Predictive Modelling is used to understand and predict:
xpanse analytics - confidential materials
?
It requires a rare set of skills and a lot of experience
It takes a lot of manual data prep and the software might be very expensive
How it works
xpanse analytics - confidential materials
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800 ‘non-Targets’
(80% of the population)
200 Targets: future
churners, defaulters on
a loan, buyers, etc.
(20% of the population)
Without any prioritising mechanism,
we will get 20% conversion on each batch of customers
How it works
xpanse analytics - confidential materials
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Predictive Model is able to sort the customers by certain propensity (to buy/churn, etc)
High Scores
(high probability of something happening)
Low Scores
(low probability of something happening)
xpanse analytics - confidential materials
1st decile
The 10% of population with highest scores
60% of them will ‘click, buy or die’
depending on what is our modelling target
10th decile
The 10% of population with lowest scores
None of them will ‘click, buy or die’
2nd decile
The second 10% of population with highest scores
40% of them will ‘click, buy or die’
How it works
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The Lift Curve
xpanse analytics - confidential materials
4
3
2
1
0
Lift
% of the targeted population
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
The Lift chart shows ‘how many times’ more customers
we will get in each bucket (decile) comparing to random targeting
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The CRISP-DM process
xpanse analytics - confidential materials
Business
Understanding
Data
Understanding
Modelling
Model
Validation
Deployment
Data
Preparation
xpanse analytics - confidential materials

Project duration - 2 months
Small - Cross Sell Model of Household Insurance for General Insurance
Total effort – 44 days


6 daysProject Manager
Data Scientist
Data Analyst
18 days
20 days
Some real-life examples from the past (1/3)
xpanse analytics - confidential materials

Project duration - 3 months
Medium - Cross Sell Model of Savings Product for Bank
Total effort – 64 days


7 daysProject Manager
Data Scientist
Data Analyst
28 days
29 days
Some real-life examples from the past (2/3)
xpanse analytics - confidential materials

Project duration - 9 months
Large – Churn Model for Telco
Total effort – 340 days


31 daysProject Manager
Data Scientist
Data Analyst
80 days
229 days


Some real-life examples from the past (3/3)
xpanse analytics - confidential materials
What exactly are these high skilled resources doing
Steps semi-automated by data mining tools, such as
SAS Enterprise Miner, IBM Modeller or Alteryx
xpanse analytics - confidential materials
What Xpanse automates
Steps semi-automated by data mining tools, such as
SAS Enterprise Miner, IBM Modeller or Alteryx
xpanse analytics - confidential materials
Demo Time

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Xpanse Analytics Platform

  • 2. Predictive Modelling is used to understand and predict: xpanse analytics - confidential materials ? It requires a rare set of skills and a lot of experience It takes a lot of manual data prep and the software might be very expensive
  • 3. How it works xpanse analytics - confidential materials                                                                                                     800 ‘non-Targets’ (80% of the population) 200 Targets: future churners, defaulters on a loan, buyers, etc. (20% of the population) Without any prioritising mechanism, we will get 20% conversion on each batch of customers
  • 4. How it works xpanse analytics - confidential materials                                                                                                     Predictive Model is able to sort the customers by certain propensity (to buy/churn, etc) High Scores (high probability of something happening) Low Scores (low probability of something happening)
  • 5. xpanse analytics - confidential materials 1st decile The 10% of population with highest scores 60% of them will ‘click, buy or die’ depending on what is our modelling target 10th decile The 10% of population with lowest scores None of them will ‘click, buy or die’ 2nd decile The second 10% of population with highest scores 40% of them will ‘click, buy or die’ How it works                                                                                                    
  • 6. The Lift Curve xpanse analytics - confidential materials 4 3 2 1 0 Lift % of the targeted population 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% The Lift chart shows ‘how many times’ more customers we will get in each bucket (decile) comparing to random targeting                                                                                                    
  • 7. The CRISP-DM process xpanse analytics - confidential materials Business Understanding Data Understanding Modelling Model Validation Deployment Data Preparation
  • 8.
  • 9. xpanse analytics - confidential materials  Project duration - 2 months Small - Cross Sell Model of Household Insurance for General Insurance Total effort – 44 days   6 daysProject Manager Data Scientist Data Analyst 18 days 20 days Some real-life examples from the past (1/3)
  • 10. xpanse analytics - confidential materials  Project duration - 3 months Medium - Cross Sell Model of Savings Product for Bank Total effort – 64 days   7 daysProject Manager Data Scientist Data Analyst 28 days 29 days Some real-life examples from the past (2/3)
  • 11. xpanse analytics - confidential materials  Project duration - 9 months Large – Churn Model for Telco Total effort – 340 days   31 daysProject Manager Data Scientist Data Analyst 80 days 229 days   Some real-life examples from the past (3/3)
  • 12. xpanse analytics - confidential materials What exactly are these high skilled resources doing Steps semi-automated by data mining tools, such as SAS Enterprise Miner, IBM Modeller or Alteryx
  • 13. xpanse analytics - confidential materials What Xpanse automates Steps semi-automated by data mining tools, such as SAS Enterprise Miner, IBM Modeller or Alteryx
  • 14.
  • 15. xpanse analytics - confidential materials Demo Time