Employing A-B Tests for Optimizing Prices Levels in eCommerce Applications

Price dispersion in the Internet is a well studied phenomenon. It enables companies to adjust prices to a level appropriate to their strategy. This paper deals with question how Internet retailers should do so. The discussed method optimizes short- and long-term profitability by determining the exact demand curve. The method involves the application of empirical price tests. For this purpose visitors of an Internet retailer are divided in statistically identical subgroups. Using the A-B testing method different prices are shown to each subgroup and the conversion rate as a function of price is calculated. We describe the organizational requirements, the technical approach, and the statistical analysis applied to determine the price optimizing the per-order profit and the average customer lifetime value. A field study carried out with a large Internet retailer is presented and shows that the company was able to optimize a specific price component and thus increase the contribution margin per order by about 7% while at the same time the customer lifetime value could be enhanced by 13%. We conclude that the discussed method could be applied to answer further research questions such as the temporal variation of demand curves

PREDICTING ONLINE USER BEHAVIOR BASED ON REAL-TIME ADVERTISING DATA

Generating economic value from big data is a challenge for many companies these days. On the Internet, a major source of big data is structured and unstructured data generated by users. Companies can use this data to better understand patterns of user behavior and to improve marketing decisions. In this paper, we focus on data generated in real-time advertising where billions of advertising slots are sold by auction. The auctions are triggered by user activity on websites that use this form of advertising to sell their advertising slots. During an auction, so-called bid requests are sent to advertisers who bid for the advertising slots. We develop a model that uses bid requests to predict whether a user will visit a certain website during his or her user journey. These predictions can be used by advertisers to derive user interests early in the sales funnel and, thus, to increase profits from branding campaigns. By iteratively applying a Bayesian multinomial logistic model to data from a case study, we show how to constantly improve the predictive accuracy of the model. We calculate the economic value of our model and show that it can be beneficial for advertisers in the context of cross-channel advertising

How Much Tracking Is Necessary? - The Learning Curve in Bayesian User Journey Analysis

Extracting value from big data is one of today’s business challenges. In online marketing, for instance, advertisers use high volume clickstream data to increase the efficiency of their campaigns. To prevent collecting, storing, and processing of irrelevant data, it is crucial to determine how much data to analyze to achieve acceptable model performance. We propose a general procedure that employs the learning curve sampling method to determine the optimal sample size with respect to cost/benefit considerations. Applied in two case studies, we model the users\u27 click behavior based on clickstream data and offline channel data. We observe saturation effects of the predictive accuracy when the sample size is increased and, thus, demonstrate that advertisers only have to analyze a very small subset of the full dataset to obtain an acceptable predictive accuracy and to optimize profits from advertising activities. In both case studies we observe that a random intercept logistic model outperforms a non-hierarchical model in terms of predictive accuracy. Given the high infrastructure costs and the users\u27 growing awareness for tracking activities, our results have managerial implications for companies in the online marketing field

Using Natural Language Processing Techniques to Tackle the Construct Identity Problem in Information Systems Research

The growing number of constructs in behavioral research presents a problem to theory integration, since constructs cannot clearly be discriminated from each other. Recently there have been efforts to employ natural language processing techniques to tackle the construct identity problem. This paper compares the performance of the novel word-embedding model GloVe and different document projection methods with a latent semantic analysis (LSA) used in recent literature. The results show that making use of an advantage in document projection that LSA has over GloVe, performance can be improved. Even against this advantage of LSA, GloVe reaches comparable performance, and adjusted word embedding models can make up for this advantage. The proposed approach therefore presents a promising pathway for theory integration in behavioral research

Natural killer (NK) and lymphokine-activated killer (LAK) cell functions from healthy dogs and 29 dogs with a variety of spontaneous neoplasms

To investigate natural killer (NK) and lymphokine-activated killer (LAK) cell functions from 10 healthy dogs and 29 dogs with a variety of spontaneous neoplasms, large granular lymphocytes (LGLs) from blood samples were separated by a 58.5% Percoll density gradient. LGLs were stimulated with a low dose of recombinant human interleukin 2 (rhIL-2) for 7days. Cytotoxicity of effector cells against the susceptible CTAC cell line was measured before and after stimulation. Compared with those before stimulation, the percentage of LGLs after stimulation with rhIL-2 was found to be significantly increased (P<0.01) in both dogs with tumors and controls. However, the increase was significantly higher in control animals, indicating a defect in proliferation ability of NK cells in canine tumor patients. After stimulation with rhIL-2, lymphokine-activated killer (LAK) cell activity in dogs with tumors was significantly lower (P<0.01) when compared with controls. Reduced cytotoxicity of rhIL-2-activated NK cells in dogs with tumors seems to be attributable to the presence of a diminished proliferative capacity of NK cells and a decreased ability of LAK cells to lyse target cells. Further knowledge of the precise function of IL-2-activated NK cells in dogs with tumors may help to optimize new and therapeutically beneficial treatment strategies in canine and human cancer patients. Our findings suggest that the dog could also serve as a relevant large animal model for cancer immunotherapy with IL-

Topic Embeddings – A New Approach to Classify Very Short Documents Based on Predefined Topics

Traditional unsupervised topic modeling approaches like Latent Dirichlet Allocation (LDA) lack the ability to classify documents into a predefined set of topics. On the other hand, supervised methods require significant amounts of labeled data to perform well on such tasks. We develop a new unsupervised method based on word embeddings to classify documents into predefined topics. We evaluate the predictive performance of this novel approach and compare it to seeded LDA. We use a real-world dataset from online advertising, which is comprised of markedly short documents. Our results indicate the two methods may complement one another well, leading to remarkable sensitivity and precision scores of ensemble learners trained thereupon

PREDICTING THE INDIVIDUAL MOOD LEVEL BASED ON DIARY DATA

Understanding mood changes of individuals with depressive disorders is crucial in order to guide personalized therapeutic interventions. Based on diary data, in which clients of an online depression treatment report their activities as free text, we categorize these activities and predict the mood level of clients. We apply a bag-of-words text-mining approach for activity categorization and explore recurrent neuronal networks to support this task. Using the identified activities, we develop partial ordered logit models with varying levels of heterogeneity among clients to predict their mood. We estimate the parameters of these models by employing Markov Chain Monte Carlo techniques and compare the models regarding their predictive performance. Therefore, by combining text-mining and Bayesian estimation techniques, we apply a two-stage analysis approach in order to reveal relationships between various activity categories and the individual mood level. Our findings indicate that the mood level is influenced negatively when participants report about sickness or rumination. Social activities have a positive influence on the mood. By understanding the influences of daily activities on the individual mood level, we hope to improve the efficacy of online behavior therapy, provide support in the context of clinical decision-making, and contribute to the development of personalized interventions

Datenschutzbedenken in Sozialen Netzen – ein Strukturgleichungsmodell

Nicht erst mit den Enthüllungen des ehemaligen US-Geheimdienstmitarbeiters Edward Snowden werden in Deutschland zunehmend Bedenken zum Datenschutz von onlinebasierten Sozialen Netzwerken geäußert. Aufbauend auf der Studie von Malhotra et al. (2004) zu Datenschutzbedenken im E-Commerce untersucht die vorliegende Arbeit die Auswirkungen der Datenschutzbedenken auf das faktische Nutzungsverhalten von Online Sozialen Netzwerken (OSN) mit Hilfe eines Strukturgleichungsmodells. Die durchgeführte empirische Studie mit 258 Teilnehmern belegt den direkten und indirekten Einfluss der von Malhotra et al. abgeleiteten Konstrukte (Erhebung, Kontrolle und Bewusstsein) auf das faktische Verhalten in OSN und bietet Unternehmen einen Leitfaden zur Diskussion von Gestaltungsmerkmalen und der Nutzung von OSN