Customer Lifetime Value under Complex Contract Structures Christoph Heitz, Andreas Ruckstuhl, Marcel Dettling

1. Customer Lifetime Value under Complex
Contract Structures
Christoph Heitz, Andreas Ruckstuhl, Marcel Dettling
Swiss Institute of Service Science
Institute of Data Analysis and Process Design
Zurich University of Applied Sciences, School of Engineering

2. Context
 B2B services with contracts
– Telecom
– Newspaper/video/… subscriptions
– other service subscriptions
 Research question: how to interact with the different
customers?
– Marketing, more specifically direct marketing
 Ultimate goal: generate maximum future revenue at
minimum interaction costs
IESS 1.0, Geneva, Feb 17-19, 2010

3. A typical offer and some customers
The offer
High-speed internet, 20 Mbit/s, $29/month
Incentive: first six months free, plus free router (value $200)
Kevin, 17 years Maria, 70 years John, 38 years
Student retired Manager
IESS 1.0, Geneva, Feb 17-19, 2010

4. Research project
 Forecast of behavior of individual customers under
marketing activities
– Customers are intrinsic part of a service system
– B2C: Marketing shapes the interaction between
provider and customer
– Knowledge about customers contained in CRM
systems
 Motivation: decision support for direct marketing
(micro-marketing)
 Project partners
– SAS Institute
– Swisscom (Schweiz) AG
– AFO Marketing AG
IESS 1.0, Geneva, Feb 17-19, 2010

5. Metrics: Customer lifetime value
 Concept of customer lifetime value (CLV)
– sum of future revenue
– discounting  net present value
– well known concept in marketing
 Properties:
– CLV is an individual measure
– CLV depends on future behavior
• Autonomous dynamics by cancelling, subscribing, ….
• Formal contract restrictions
– CLV is influenced by marketing activities
IESS 1.0, Geneva, Feb 17-19, 2010

6. Research questions and results
 How to describe the autonomous contract dynamics of
customers?
– Stochastic state space model: Semi-Markov model  paper
– Model parameters estimated individually
 How to derive the CLV from the individual dynamics?
– Part of Semi-Markov model
 How to estimate the model parameters?
– Statistical methods (survival analysis, generalized linear models)
 How to predict change in CLV by specific marketing action?
– Coupling model with acceptance model for marketing action
 How to optimize a marketing strategy based on micro-
marketing?
– Currently being developed
IESS 1.0, Geneva, Feb 17-19, 2010

7. Some results
 Classical models used in marketing are of restricted
value under complex contract structures
– Often errors in CLV in the range of 50% and more
 Semi-Markov models are well suited in this context
 Estimation of individual model parameters is not
trivial - but possible with standard procedures
– Implementation in SAS completed
 Case studies (Telecom, Newspaper)
– 15-100% increase of ROI for marketing by optimum
selection of customers, compared with classical direct
marketing approaches
IESS 1.0, Geneva, Feb 17-19, 2010

8. Thank you for your
attention!
IESS 1.0, Geneva, Feb 17-19, 2010