Predicting Customers' Long Term Value

Transcription

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

2 Eni s activities Eni worldwide Active in 66 countries, more than 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 2

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

4 Every customer matters. Really? 4

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

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

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

9 Benefits Impact Compare strategies What-if scenarios Sensitivity analysis

10 Benefits Monitor Set objectives Analyze KPI s Traffic lights

11 Benefits

12 marketing risk operations segmentation acquisition development retention credit risk fraud detection debt collection process mining demand forecasting Predictive maintenance hr absenteeism flight risk

13 Components

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

15 Components Revenues Retention Credit Losses Service Costs

16 Components Retention Revenues Credit Losses Service Costs

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

18 Predictive models

19 Components Survival Segments 100% Survival Proportion 50% 0% Lifetime Used to compute 2-year survival probabilities Computed for different customer segments: product type, channel,...

20 Accuracy

21 Predicted LTV Accuracy Does it work? Real vs Predicted LTV Predicted LTV Perfect Prediction Used Estimation Real LTV

22 Usage

23 Usage Revenues Retention Credit Losses Service Costs

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

25 Usage Products

26 Usage Monthly Dashboard LTV evolution Segments evolution jan/16 feb/16 mrt/16 apr/16 mei/16 jun/16 jan/16 feb/16 mrt/16 apr/16 mei/16 jun/16 Low Medium High

27 Thanks Elke Coremans - Wouter Buckinx eni Python Predictions