Customer lifetime value | EPFL Graph Search

In marketing, customer lifetime value (CLV or often CLTV), lifetime customer value (LCV), or life-time value (LTV) is a prognostication of the net profit contributed to the whole future relationship with a customer. The prediction model can have varying levels of sophistication and accuracy, ranging from a crude heuristic to the use of complex predictive analytics techniques. Customer lifetime value can also be defined as the monetary value of a customer relationship, based on the present value of the projected future cash flows from the customer relationship. Customer lifetime value is an important concept in that it encourages firms to shift their focus from quarterly profits to the long-term health of their customer relationships. Customer lifetime value is an important metric because it represents an upper limit on spending to acquire new customers. For this reason it is an important element in calculating payback of advertising spent in marketing mix modeling. One of the first accounts of the term customer lifetime value is in the 1988 book Database Marketing, which includes detailed worked examples. Early adopters of customer lifetime value models in the 1990s include Edge Consulting and BrandScience. The purpose of the customer lifetime value metric is to assess the financial value of each customer. Don Peppers and Martha Rogers are quoted as saying, “some customers are more equal than others.” Customer lifetime value differs from customer profitability or CP (the difference between the revenues and the costs associated with the customer relationship during a specified period) in that CP measures the past and CLV looks forward. As such, CLV can be more useful in shaping managers’ decisions but is much more difficult to quantify. While quantifying CP is a matter of carefully reporting and summarizing the results of past activity, quantifying CLV involves forecasting future activity. Customer lifetime value: The present value of the future cash flows attributed to the customer during his/her entire relationship with the company.

Magnetic Properties of Surface Adsorbed Metal Adatoms and Dimers

Dante Philippe Sblendorio

This thesis investigates the magnetic properties of single atoms and dimers adsorbed on graphene and oxide decoupling layers supported by single crystal metal substrates, using scanning tunneling microscopy (STM) and spin-polarized scanning tunneling microscopy (SP-STM). The goal is twofold: to use SP-STM to further advance the understanding of the interactions that determine the magnetic stability of Dy adatoms on graphene/Ir(111) and Ho adatoms on MgO/Ag(100), and to use insights from these systems to motivate the study of new systems-- mainly, IrCo heterodimers on graphene/Ir(111).For Dy adatoms on graphene/Ir(111), the characteristic lifetime of the spin system is measured as a function of temperature and tunnel bias, probing the available magnetization reversal pathways in the energy level diagram. The necessity of including the intra-atomic exchange to correctly describe spin lifetimes is demonstrated. In addition, naturally abundant Dy isotopes possess two possible nuclear spin values. Models of both nuclear spin cases of Dy are compared, and shown to produce similar behavior in the temperature and bias ranges probed by SP-STM. Furthermore, accounting for both nuclear spin cases allows for modeling of X-ray magnetic circular dichroism (XMCD) magnetization sweeps. For Ho adatoms on MgO/Ag(100), novel measurement protocols are used to determine the zero-field stability, and the correct ground state model. These protocols are also used to induce magnetic state reversal via Landau-Zener tunneling at avoided level crossings due to the hyperfine interaction. Finally, the study of the IrCo heterodimer adsorbed on graphene is motivated with stability considerations derived from the studies of Dy adatoms on graphene/Ir(111) and Ho adatoms on MgO/Ag(100), in addition to calculations based on density function theory (DFT). Strategies of engineering these heterodimers using statistical growth and atomic manipulation are successfully implemented. The heterodimer is highly mobile on graphene/Ir(111) and displays geometric instability, consistent with its predicted upstanding geometry. Despite this, measurement via STM and SP-STM is demonstrated. Observations are consistent with the robust magnetic stability expected from DFT studies, but additional investigation is needed to disentangle the measurements made thus far.

EPFL2022

Supply Chain Disruption Risk Management

Florian Christopher Matthias Lücker

It is well established that firms are exposed to the risk of supply chain disruptions. Many firms build risk mitigation strategies in order to increase the resilience of their supply chains. In this dissertation we present three essays that study operational risk mitigation levers such as inventory, reserve capacity and dual sourcing. The first essay outlines an application in the pharmaceutical industry where the risk mitigation levers inventory, dual sourcing and agility capacity are analyzed. We study the relationship between these three levers by modeling a drug manufacturing firm that is exposed to disruptions in its supply chain. The firm determines optimal inventory levels for assumed dual sourcing and agility capacity. We quantify the decrease in inventory levels in the presence of dual sourcing and agility capacity. Furthermore, using an example, we analyze inventory, dual sourcing and agility capacity decisions jointly. It turns out that inventory and agility capacity can be substitutes as long as no dual source is available. Once the dual source is available, agility capacity and dual sourcing appear to be substitutes. We further show that for long disruption times, the optimal dual source production rate may decrease in the disruption time. Within our modeling framework, we introduce an operational metric as a measure for resilience under deterministic demand. In the second essay we study the joint role of inventory and reserve capacity in mitigating supply disruptions. A reserve capacity can be used for production in a reactive fashion when a disruption occurs. We first determine optimal inventory levels and reserve capacity production rates jointly under stochastic customer demand. This allows us to fully characterize three main risk mitigation strategies: inventory strategy, reserve capacity strategy, and mixed strategy. Furthermore, we provide structural insights of optimal risk mitigation strategies. We then combine our analytical results with the well-established (Q,R) policy to perform numerical experiments using Conditional Value at Risk (CVaR) as a resilience measure. Our results suggest that the resilience measure depends on both the reorder point R and the batch size Q. Comparing the resilience measure with the service level, we find that a high service level and high resilience can be conflicting objectives. The third essay is concerned with the role of inventory in mitigating disruptions in two-echelon supply chains under stochastic demand. The research problem is to determine the optimal backup inventories for a serial, assembly and distribution supply chain respectively. For the three supply chain types, disruptions at each production site are described by an infinite-state discrete-time Markov process. We derive structural insights on the optimal inventory levels for the serial supply chain. For the assembly and distribution supply chain we show that under mild assumptions an early commitment to finished goods inventories is optimal. This finding is different from optimal safety inventory policies where often a delayed differentiation in assembly supply chains or risk pooling in distribution supply chains is optimal.

EPFL2016