Slides of the presentation at SAS Forum Belux 2016, Brussels As today's energy market is strictly regulated but very competitive, customers will typically not remain equally valuable over time. To better understand this reality and get an accurate view on future customer value, Eni Gas & Power has built a Long Term Value model. In this presentation, we explain how Predictive Analytics were used to construct this model, share the insights gained through the process and show how the outcome helps Eni Gas & Power to better steer future strategies.

1. www.eni.it
Predicting Customers' Long Term Value
Elke Coremans - Wouter Buckinx
eni Python Predictions

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Eni’s activities
 Active in 66 countries, more than 29 000 employees.
 As integrated energy company and reliable partner we offer our customers
across the world energy solutions.
 Eni is working to build a future where everyone can access energy
resources efficiently and sustainably
Eni worldwide

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POWER
What does Eni do in Belgium?
Eni in Belgium
 Active on the Belgian market since 2008.
 Active in all markets
(Power and gas)
and in all segments
(retail, sme and industry).
 In the top 3 of Belgian energy suppliers.
 Retail: >700 000 connections
B2B: >50.000 connections
GAS

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Every customer matters. Really?

5. Long Term Value
The net present value of the profits
linked to a specific customer
once the customer has been acquired,
after subtracting incremental costs associated
with marketing, selling, production and servicing
over a predefined period of time
Source: Database Marketing (2008), Blattberg, Kim & Neslin

6. Long Term Value
The net present value of the profits
linked to a specific customer
once the customer has been acquired,
after subtracting incremental costs associated
with marketing, selling, production and servicing
over a predefined period of time
Source: Database Marketing (2008), Blattberg, Kim & Neslin
Estimation over a period of 2 years

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Benefits
Customer value
How much worth?
Acquisition spend
Most and less profitable
Marketing investments

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Benefits
Company value
Focus on long term profitability
Focus on customer relationship
Business plan

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Benefits
Impact
Compare strategies
What-if scenarios
Sensitivity analysis

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Benefits
Monitor
Set objectives
Analyze KPI’s
Traffic lights

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Benefits

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Components

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Components
Revenues Retention
Credit
Losses
Service
Costs
How long will
the customer
stay?
Will he
experience
payment
issues?
How much will
the customer
spend?
What are
the costs
to serve?

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Components
RevenuesRetention
Credit
Losses
Service
Costs

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Components
Expected
Duration
Credit Losses
Expected
Volume
Product
Margin
Activity
Costs
Invoicing
Costs
RevenuesRetention
Credit
Losses
Service
Costs
survival
segments
predictive
Model
individual
extrapol.
predictive
Model
Input Input

16. Predictive models

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Survival Segments
Components
Used to compute 2-year survival probabilities
Computed for different customer segments: product type, channel,...
0 1 2 3 4 5 6 7 8
100%
50%
0%
Lifetime
Survival
Proportion

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Accuracy
PredictedLTV
Real LTV
Real vs Predicted LTV
Does it work?
Perfect Prediction
Previous Estimation
Predicted LTV

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Usage

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Usage
Revenues Retention
Credit
Losses
Service
Costs

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Usage
All
Household
Business
Existing
New
Existing
New
Electricity
Gas
Electricity
Gas
Electricity
Gas
Electricity
Gas
Product A
Product B
Product C
Product A
Product B
Product C
Product A
Product B
Product C
Product A
Product B
Product C
Product A
Product B
Product C
Product A
Product B
Product C
Product A
Product B
Product C
Product A
Product B
Product C
Channel 1
Channel 2
Channel ..
Channel 1
Channel 2
Channel ..
Channel 1
Channel 2
Channel ..
Channel 1
Channel 2
Channel ..
Channel 1
Channel 2
Channel ..
Channel 1
Channel 2
Channel ..
Channel 1
Channel 2
Channel ..
Channel 1
Channel 2
Channel ..
Channel 1
Channel 2
Channel ..
Channel 1
Channel 2
Channel ..

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Usage
Products

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Usage
Revenues Retention
Credit
Losses
Service
Costs

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Usage
Monthly Dashboard
Jan-16 Feb-16 Mar-16 Apr-16 May-16 Jun-16
LTV evolution
Low Medium High
Segments evolution

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Thanks
Elke Coremans - Wouter Buckinx
eni Python Predictions