Different Prices for Different Customers – Optimising Individualised Prices in Online Stores by Artificial Intelligence

Today’s information tracking technology and Big Data open up new opportunities for e-commerce. Online stores can collect personal information to estimate customers’ willingness-to-pay. This enables the application of price differentiation where different customers are charged different prices for the same product. Lower prices offered to customers who share the word have an advertisement effect, while higher prices have adverse effects. In this paper we develop a decision model for individualised prices in online stores that considers the sharing of prices by word of mouth which is mostly neglected by current literature. Complex decision models in e-commerce are caught between the need of adequately representing the reality and the demand of being solvable within reasonable time limits. We use various artificial intelligence solution methods to solve the decision model for numerical examples. Our results indicate that despite word of mouth differential pricing can be financially worthwhile

Pengembangan Teknologi Tata Kelola Keuangan dan E-Koperasi untuk Peningkatan Kapasitas Petani Perhutanan Sosial

Perhutanan sosial merupakan skema kebijakan presiden yang memberikan hak pengelolaan hutan negara kepada rakyat. Presiden membentuk tim percepatan penyelesaian konflik agraria dan pengelolaan perhutanan sosial. Pengelola selama ini mengalami kesulitan dalam hal operasional, keuangan, distribusi dan penjualan komoditas pertanian hutan. Di Jawa, pengelolaan dilakukan oleh Gerakan Masyarakat Perhutanan Sosial (GEMA PS) yang beranggotakan sekitar 60.000 petani hutan dan dengan kurang lebih 32.000 hektar lahan perhutanan sosial. Namun, GEMA PS tidak memiliki informasi akurat mengenai jenis dan jumlah komoditas yang bisa dijual. Selain itu, akses penjualan selama ini masih terbatas pada tengkulak, sehingga manfaat ekonomi belum dapat dirasakan oleh petani hutan. Pengelola dan petani tidak bisa menghitung berapa keuntungan dari penjualan komoditas dan tidak memiliki akses kepada pasar karena informasi jalur pasokan tidak bisa diidentifikasi dengan jelas. Selain masalah rantai pasokan produksi, GEMA PS tidak dapat mengetahui hasil penjualan hasil pertanian dengan cepat, karena tidak terdapat sistem pelaporan keuangan yang memadai. Pencatatan keuangan yang terintegrasi akan memudahkan pengelola dalam melakukan analisis dan penyusunan strategi untuk memperoleh manfaat ekonomi yang lebih besar bagi petani perhutanan sosial. Selain itu, komunitas petani yang kuat dan terorganisasi dengan baik akan semakin meningkatkan taraf hidup petani. Oleh karena itu, salah satu hal penting yang perlu dilakukan oleh pengelola adalah mengembangkan tata kelola dan sistem e-koperasi untuk petani perhutanan sosial. Penelitian ini bertujuan untuk mengembangkan teknologi tata kelola berbasis web, mulai dari tata kelola operasional (e-supply chain), keuangan dan e-koperasi untuk komunitas petani perhutanan sosial. Teknologi ini didukung dengan sistem pengambilan keputusan, penyusunan strategi dan marketpace untuk peningkatan kapasitas petani melalui ekonomi digital. Tahapan penelitian adalah sebagai berikut; Tahun pertama, a) identifikasi detail kebutuhan, b) pengembangan prototype teknologi tata kelola keuangan dan marketplace serta integrasi dengan modul e-supply chain, c) uji coba sistem pada skala kecil dan evaluasi, d) implementasi sistem. Tahun kedua, a) peningkatan kapasitas koperasi GEMA PS b) pengembangan teknologi dan integrasi e-koperasi, b) uji coba sistem e-koperasi pada skala kecil c) implementasi sistem e- koperasi. Tahun ketiga, a) integrasi sistem keuangan dan e koperasi b) uji coba sistem dalam skala besar, c) implementasi sistem secara penuh

Graph-based Event Extraction from Twitter

International audienceEvent detection on Twitter has become an attractive and challenging research field due to the popularity and the peculiarities of tweets. Detecting which tweets describe a specific event and clustering them is one of the main challenging tasks related to Social Media currently addressed in the NLP community. Existing approaches have mainly focused on detecting spikes in clusters around specific keywords or Named Entities (NE). However, one of the main drawbacks of such approaches is the difficulty in understanding when the same keywords describe different events. In this paper, we propose a novel approach that exploits NE mentions in tweets and their entity context to create a temporal event graph. Then, using simple graph theory techniques and a PageRank-like algorithm, we process the event graphs to detect clusters of tweets describing the same events. Experiments on two gold standard datasets show that our approach achieves state-of-the-art results both in terms of evaluation performances and the quality of the detected events

The Quicker One is the Better One? − How to Fight Negative Word of Mouth

With the rising popularity of online social networks it became much easier for firms to spread viral messages for marketing purposes. But on the reverse side, negative messages can also spread much more easily and may harm the reputation of a firm severely. Therefore, we investigate how firms can react on negative word of mouth in online social networks with the help of positive word of mouth. For this, we develop a novel diffusion model that incorporates several new aspects: the aging of a message, the content of a message, the change of opinion, the delay between the negative message and the firm\u27s reaction, and different kinds of markets. Results show that a firm is better off when reacting with a carefully designed message even if it takes some time instead of reacting quickly or with multiple seeds but with a message of lower quality

Electronic word of mouth in online social networks: strategies for coping with opportunities and challenges

In today's world, the widespread success of the Internet, social media, and online social networks (OSN) provide the basis for electronic word of mouth (EWOM). EWOM can be seen as a digital enhancement of traditional word of mouth that makes communication more efficient and involves less effort by its users. The resulting speed of diffusion and information transparency have caused transformative changes in consumer behaviour in all types of markets, which requires the development of new business strategies for adequately dealing with the new circumstances. This doctoral dissertation is divided into three overall subject areas that concern the investigation of capable strategies for coping with the emerged opportunities and challenges of EWOM in OSN. The first subject area concerns negative electronic word of mouth in OSN and investigates capable countermeasure strategies for firms to adequately address claims of unsatisfied customers. For this, three simulation studies are conducted in which the propagation of a negative message and its countering by a positive message published by the firm are numerically analysed. The results reveal that, in general, the persuasiveness of a firm's response is more important than a quick response with a less persuasive counter-message. To some extent, this also holds if the number of OSN members who initially disseminate the counter-message on behalf of the firm is increased. In the second subject area, an optimisation model for individualised pricing is developed for an online store whose customers are interconnected in an OSN and can share price information via EWOM. The model is solved numerically by artificial intelligence solution methods. The results indicate that personalised prices can be financially worthwhile even under price transparency. The third subject area investigates market entry strategies for social media apps and services that are advertised in an OSN for acquiring new users and examines the role of EWOM in this context. A diffusion model is developed and analysed numerically by simulation. Three different targeting approaches are compared to each other regarding their ability to reach a high share of active users in the OSN: (1) a random marketing strategy, where randomly chosen members in the OSN are presented the advertisement, (2) cluster marketing, where whole clusters of members who are densely connected to each other are simultaneously shown the advertisement, and (3) influencer marketing, where the most influential users in the OSN are selected to share sponsored posts about the app in the OSN. The results suggest that EWOM can have detrimental effects if OSN members are too early informed about the app or service. If the information about the app reaches clusters in the OSN prematurely where a sufficient level of activity is not present yet, it can deplete the excitement of the users. The lack of excitement, in turn, can significantly reduce the effect of subsequent marketing campaigns. However, if applied appropriately, a higher level of EWOM about the app or service can increase the performance of the random marketing strategy to the extent that it outperforms cluster and influencer marketing

Time-sensitive topic derivation in twitter

Much research has been concerned with deriving topics from Twitter and applying the outcomes in a variety of real life applications such as emergency management, business advertisements and corporate/ government communication. These activities have used mostly Twitter content to derive topics. More recently, tweet interactions have also been considered, leading to better topics. Given the dynamic aspect of Twitter, we hypothesize that temporal features could further improve topic derivation on a Twitter collection. In this paper, we first perform experiments to characterize the temporal features of the interactions in Twitter. We then propose a time-sensitive topic derivation method. The proposed method incorporates temporal features when it clusters the tweets and identifies the representative terms for each topic. Our experimental results show that the inclusion of temporal features into topic derivation results in a significant improvement for both topic clustering accuracy and topic coherence comparing to existing baseline methods.15 page(s