Browsing by Author "Aalto, Henrik"
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- Competition-Based Dynamic Pricing in E-Commerce
Perustieteiden korkeakoulu | Master's thesis(2019-12-17) Aalto, HenrikE-commerce is a fascinating field of retail. It offers numerous advantages for retailers starting from widely reachable customers, easily changeable assortments, and reduced fixed costs. On the other hand, e-commerce accelerates competition. For a consumer it is merely a matter of some clicks to switch from one online store to another offering the same item with faster shipping or lower price. The goal of this study is to find and evaluate a method to dynamically reprice durable goods over an infinite horizon in an online store based on competitor prices for the same products. To provide support for the method, we first model the daily sales quantities with a zero-inflated binomial regression. Then, we build a myopically optimal pricing method on top of the demand model. The performance of the model is assessed by simulations with two different scenarios, one assuming competitors would not react and another assuming one of them starts a price war. In both the scenarios, our pricing method is able to perform well with up to 20 percent increase in gross margin compared to current pricing. Last, we conduct a case study by repricing a group of products in an online store for a month with our method. Due to large variances in sales and a small test group, no statistically sound conclusions can be made. However, the performance of our method is roughly equal to the current pricing policies. Thus, one could conduct a larger and longer test to gain more information. The key findings in our study are as follows. First, even rather simple pricing methods should outperform any static pricing rules. Second, having competitor price information available in e-commerce is crucial as the difference to competitors matters more than the price point itself. Third, detecting changes from the sales of slowly moving goods would require huge sample sizes and thus performances of pricing methods should be assessed with products with higher demands. - Efficiency of Algorithms for Computing Influence and Information Spreading on Social Networks
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2022-08) Kuikka, Vesa; Aalto, Henrik; Ijäs, Matias; Kaski, Kimmo K.Modelling interactions on complex networks needs efficient algorithms for describing processes on a detailed level in the network structure. This kind of modelling enables more realistic applications of spreading processes, network metrics, and analyses of communities. However, different real-world processes may impose requirements for implementations and their efficiency. We discuss different transmission and spreading processes and their interrelations. Two pseudo-algorithms are presented, one for the complex contagion spreading mechanism using non-self-avoiding paths in the modelling, and one for simple contagion processes using self-avoiding paths in the modelling. The first algorithm is an efficient implementation that can be used for describing social interaction in a social network structure. The second algorithm is a less efficient implementation for describing specific forms of information transmission and epidemic spreading. - Immateriaalioikeudet ja tuotekonsepti. Tuotekonseptoinnin tulosten suojaamisesta.
School of Business | Master's thesis(2008) Aalto, Henrik - Influence spreading model for partial breakthrough effects on complex networks
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2023-11-15) Almiala, Into; Aalto, Henrik; Kuikka, VesaBreakthrough effects are a crucial part of many kinds of influence spreading, such as social or infectious contagion. We introduce a novel model that can accurately simulate influence spreading on complex networks with partial breakthrough happening at a given probability. The novel model unifies our earlier analytical and simulation versions of the model that are only applicable to a fixed-breakthrough scenario. A wide range of applications in, for example, social influence and epidemic spreading analysis are enabled by the ability to consider partial breakthrough effects. The breakthrough effects of the new model are controlled by an arbitrary breakthrough probability that determines how likely it is for a node to get reinfluenced. We demonstrate our model on real-world social network structures and provide an example application in the study of epidemic spreading. - Jäännösjännitysten mittausmenetelmät
Insinööritieteiden korkeakoulu | Bachelor's thesis(2016-12-09) Aalto, Henrik