Christian Schlereth

Optimal Product-Sampling Strategies in Social Networks: How Many and Whom to Target?

Using an agent-based model to study the success of product-sampling campaigns that rely on information about social networks, this paper investigates the essential decisions of which consumers and how many of them to target with free product samples. With an unweighted and a weighted real-world personal communication network, we show that the decision of which consumers to target is more important than that of how many consumers to target. Use of social network information increases profits by at least 32 percent. Companies should use a high-degree targeting heuristic to identify the most influential consumers. Use of social network information increases profit for single-purchase products mainly because it supports targeting more influential consumers and therefore speeds up diffusion throughout the network. For repeat-purchase products, social network information decreases the optimal number of samples and thus the cost of the campaign.

DISE: dynamic intelligent survey engine

Knowledge about consumers’ preferences is of utmost importance for many marketing decisions, but transactional data are frequently unavailable. Therefore, marketing researchers have developed ground-breaking methods that build upon stated preference data to measure consumers’ preferences; these methods include self-explicated methods, ratingbased conjoint analysis, and choice-based conjoint analysis. This article describes DISE (Dynamic Intelligent Survey Engine), which aims to enhance research involving the measurement of consumer preferences. DISE is an extendable, web-based survey engine that supports the construction of technically sophisticated surveys and that limits the effort that researchers must invest to develop new preference methods. We discuss the overall architecture of DISE, discuss how to implement and include new data collection methods, and finally outline how these new methods can be employed in surveys, using an illustrative example. We conclude this article with an invitation to researchers to join in the development of DISE.

Measurement of Preferences with Self-Explicated Approaches: A Classification and Merge of Trade- off- and Non-trade-off-based Evaluation Types

Self-explicated approaches are popular preference measurement approaches for products with many attributes. This article classifies previous self-explicated approaches according to their evaluation types, i.e. trade-off- versus non-trade-off-based, and outlines their advantages and disadvantages. In addition, it proposes a new method, the presorted adaptive self-explicated approach that is based on Netzer and Srinivasan’s (2011) adaptive self-explicated approach and that combines trade-off- and non-trade-off-based evaluation types. Two empirical studies compare this new method with the most popular existing self-explicated approaches, including the adaptive self-explicated approach and paired comparison preference measurement. The new method overcomes the insufficient discrimination between importance weights, as usually found in non-trade-off-based evaluation types; discourages respondents’ simplification strategies, as are frequently encountered in trade-off evaluation types; is easy to implement; and yields high predictive validity compared with other popular self-explicated approaches.

Pricing plans for a financial advisory service

Purpose – In cooperation with a German online retail bank, the aim of this paper is to investigate how the bank should price a new fee-only financial advisory service. Two types of pricing plans differ in terms of their strategies for determining monthly prices: a fixed monthly price that is identical for all clients (i.e. a flat pricing plan) or a monthly price that varies as a function of each clients assets under management (i.e. a volume pricing plan). Design/methodology/approach – With a discrete choice experiment, this article studies client preferences for the two types of plans. To ensure that the respondents understood the financial consequences of their decisions, a price calculator was embedded into the discrete choice experiment to enable the respondents to determine their individual monthly prices based on their assets under management. Findings – Methodologically, the price calculator is useful for simplifying mathematically complex decisions, and it provides additional valuable information...

Schätzung von Zahlungsbereitschaftsintervallen mit der Choice-Based Conjoint-Analyse

ZusammenfassungFür eine sinnvolle Preisgestaltung ist die Kenntnis über die Zahlungsbereitschaften von Konsumenten unerlässlich. Dabei wurde die Zahlungsbereitschaft immer als Punktschätzung aufgefasst, typischerweise als der Preis, der den Konsumenten indifferent zwischen Kauf und Nicht-Kauf des Produkts macht. Dieser Beitrag entwickelt ein Modell für die sehr populäre Choice-Based Conjoint-Analyse, das sehr leicht und ohne zusätzlichen Erhebungsaufwand Zahlungsbereitschaftsintervalle schätzt. Anhand einer empirischen Studie wird verdeutlicht, dass die Kenntnis von Zahlungsbereitschaftsintervallen vor allem interessante Einsichten über die Preissensitivität der Konsumenten sowie deren Kaufwahrscheinlichkeit bietet. Die Ergebnisse der Studie verdeutlichen zudem, dass die Schwankungen innerhalb des Zahlungsbereitschaftsintervalls für Produktkategorien groß sein können und dass das Ignorieren dieser zu nicht optimalen Preisentscheidungen führt.AbstractKnowledge about the willingness-to-pay of consumers is essential for the pricing of products. In this regard, willingness-to-pay has always been understood as a point-estimator, which is typically the price at which a consumer is indifferent between buying and not buying. In this work, we develop a model to estimate willingness-to-pay intervals for the very popular choice-based conjoint analysis. We demonstrate in an empirical study the validity of our model and show that willingness-to-pay intervals provide information about the price-sensitivity and the purchase probabilities of consumers and that enable to more appropriately set prices. The results from the empirical study also demonstrate that the distribution of these willingness-to-pay intervals is fairly symmetric.

Schnelle und einfache Messung von Bedeutungsgewichten mit der Restricted-Click-Stream Analyse: Ein Vergleich mit etablierten Präferenzmessmethoden

ZusammenfassungZur Messung von Bedeutungsgewichten nutzen Marktforscher etablierte Präferenzmessmethoden wie die Choice-Based Conjoint Analyse. Parallel hierzu nutzen Verhaltensforscher eigene Methoden zur Aufzeichnung des Informationssuchprozesses. Ziel dieses Beitrages ist, zu zeigen, dass die Beobachtung der Aufmerksamkeit, welche Konsumenten den Produkteigenschaften in solchen Experimenten widmen, bereits zur Bestimmung von Bedeutungsgewichten ausreicht. Hierzu wird eine neue, leicht einzusetzende Methode entwickelt, die Restricted-Click-Stream Analyse, und in einer empirischen Studie mit etablierten Präferenzmessmethoden verglichen. Die Ergebnisse zeigen, dass die Restricted-Click-Stream Analyse eine ähnliche Validität aufweist, zugleich aber die kognitive und emotionale Belastung der Probanden geringer ist als bei etablierten Methoden. Daher bietet die Restricted-Click-Stream Analyse für Anwender dann eine praktikable Alternative, wenn Erkenntnisse zu Bedeutungsgewichte in einer möglichst kurzen Befragung gewonnen werden sollen.AbstractMarket researchers typically apply established preference measurement methods like choice-based conjoint analysis to determine attribute importance weights. Behavioral researchers established their own methods to analyze the information search process in product decisions. Goal of this paper is to demonstrate that the information on how much attention has been devoted to an attribute in product choice is sufficient to determine attribute importance weights. The authors develop a new preference measurement method, the restricted-click-stream analysis, and compare it with established methods. The results show that the validity of the new method is comparable with conjoint methods; however, the cognitive and emotional load for respondents is lower. Thus, the restrictedclick- stream analysis offers a valid alternative for researchers hoping to gain insights on top of product preferences within a survey.

Why do consumers prefer static instead of dynamic pricing plans? An empirical study for a better understanding of the low preferences for time-variant pricing plans

Abstract Time-variant pricing plans in electricity markets aim to mitigate mismatches between demand and supply by incentivizing consumers to shift their demand from costly peak to cheaper off-peak times. Their implementation can be manifold; they could depend statically on the time of the day (i.e., time-of-use pricing) or adjust prices dynamically in nearly real time (real-time pricing). If consumers reduced demand in peak times, then they would realize lower prices and providers would operate at lower costs. Still, consumers frequently refuse time-variant pricing plans. The authors develop a new conceptual framework to study and explain this behavior. It supports the optimal choice of time-variant pricing plans by jointly considering price fairness and economic antecedents. In a discrete choice experiment, the authors use a hierarchical Bayes covariate extended logit estimation to measure respondents’ probability of switching from a time-invariant pricing plan to a time-variant pricing plan. The results show that economic antecedents, such as price consciousness and flexibility, have a stronger effect on the choice of a time-variant pricing plan than price fairness considerations; cost insurance is a promising instrument for increasing acceptance of dynamic pricing plans. The results also suggest new ways to target prospective customers.