Simulative and Game-Theoretical Approaches for Strategic Behavior in Name-Your-Own-Price Markets

Name-Your-Own-Price is a popular interactive pricing mechanism in Electronic Commerce that lets both, buyer and seller, influence the price of a product. At the outset, a seller defines a secret threshold price indicating the minimum price he is willing to sell the product for. Subsequently, a buyer is asked to place a bid indicating her willingness-to-pay for the product offered. If the bid value is equal or above the seller’s threshold price, the transaction is initiated for the price denoted by the buyer’s bid. In this paper we show how buyer and seller strategically behave in such markets and derive from the results what product classes seem suitable for sale in a Name-Your-Own-Price-channel. To address this question, we apply two different approaches - an agent-based simulation and a game theoretical approach - and illustrate thereby the advantages and disadvantages of both methods

Measuring Frictional Costs in E-Commerce: The Case of Name-Your-Own-Price Auctions

Frictional costs are defined as the disutility related to the conduct of an online transaction. Thus,frictional costs can accrue through the consumer‘s decision-making process prior to an onlinetransaction, e.g., bidding in interactive pricing mechanisms like auctions. We present two models forthe measurement of frictional costs in Name-Your-Own-Price auctions where these costs can either bemeasured through a discount factor or in absolute values. We compare the fit and estimation results ofthese models by analyzing bidding data from a German NYOP seller. Our results show that bothmodels are equally parsimonious, explain a comparable fraction of variance and both models yieldrobust and reasonable parameter estimates

Making Digital Freemium Business Models a Success: Predicting Customers’ Lifetime Value via Initial Purchase Information

In digital freemium business models such as those of online games or social apps, a large share of overall revenue derives from a small portion of the user base. Companies operating in these and similar businesses are increasingly constructing forecasting models with which to identify potential heavy users as early as possible and create special retention measures to suit those users’ needs. In our study, we observe three digital freemium companies that sell virtual credits and investigate to what extent initial purchase information can be used to determine a given customer’s lifetime value. We find that customers represent higher future lifetime values if they (a) make a purchase early after registration, (b) spend a significant amount on their initial purchase, and (c) use credit cards to purchase credits. In addition, we see that users tend to spend increasing amounts on subsequent purchases

MARKET ANOMALIES ON TWO-SIDED AUCTION PLATFORMS

A market anomaly (or market inefficiency) is a price distortion typically on a financial market that seems to contradict the efficient-market hypothesis. Such anomalies could be calendar, technical or fundamental related and have been shown empirically in a number of settings for financial markets. This paper extends this stream of research to two-sided auction platforms in Electronic Commerce and empirically analyzes whether calendar anomalies are persistent on such markets. Our empirical study analyzes 78,068 transactions completed between buyers and sellers on a German auction platform and covers the period between April 2005 and May 2009. We observe a persistent turn-of-the-month effect and a day-of-the-week effect that would allow buyers to realize small additional surpluses (0.3% price discount). Prices are also persistently lower in the highly competitive Christmas trade period while sellers benefit from higher prices at the beginning of every year. Overall our results support the common notion that two-sided auction platforms are rather efficient markets on which we however can observe some marginal market inefficiencies

Using Twitter to Predict the Stock Market - Where is the Mood Effect?

Behavioral finance researchers have shown that the stock market can be driven by emotions of market participants. In a number of recent studies mood levels have been extracted from Social Media applications in order to predict stock returns. The paper tries to replicate these findings by measuring the mood states on Twitter. The sample consists of roughly 100 million tweets that were published in Germany between January, 2011 and November, 2013. In a first analysis, a significant relationship between aggregate Twitter mood states and the stock market is not found. However, further analyses also consider mood contagion by integrating the number of Twitter followers into the analysis. The results show that it is necessary to take into account the spread of mood states among Internet users. Based on the results in the training period, a trading strategy for the German stock market is created. The portfolio increases by up to 36 % within a six-month period after the consideration of transaction costs

On the Design of Sales Support Systems for Online Apparel Stores

Many online stores apply several sales support systems, e.g., recommender systems, sorting and filtering tools, to support buyers during the shopping process. Although, the research highlights the positive effect of such systems, the current study questions its applicability in online stores for products which serve users\u27 needs to be unique like apparel or luxury products. We analyze female users\u27 buying behavior of apparel products in a laboratory setting and find that users with high trendiness undertake in general more search steps. Further, we find that most users rely during their search process on different sorting and filtering as well as on keyword search tools while personalized and non-personalized recommendations play a minor role for users in this industry. Further, we find that users with high trendiness avoid following top seller lists and wear with it -recommendations. Moreover, the provision of top seller rankings does not influence the consumers\u27 product choice

Return on IT Investments in Two-Sided Markets

In two-sided markets an intermediary brings together two distinct customer populations, such as buyers and sellers on an e-commerce platform. In these markets the growth process of customer populations depends on network effects both within and between buyers and sellers. Thus, assigning IT investments to customer populations and quantifying the monetary value of these investments is complex. We show that measuring the intermediary’ s platform value may provide a remedy, and make IT investments in two-sided markets accountable. Thereby, we develop a model for the platform value and the growth process of customer populations accounting for network effects in two-sided market. We apply our model to an e-commerce platform. Our results highlight a significant contribution of buyers to the platform value. Analysing former IT investments we find further evidence to rather invest in buyers than sellers, and to promote investments that increase buyers’ trust in products, intermediary and trading partners (sellers)