Does Online Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance

User-Generated Content in online platforms or chatter for short provides a valuable source of consumer feedback on market performance of firms. This study examines whether chatter can predict stock market performance, which metric of chatter has the strongest relationship, and what the dynamics of the relationship are. The authors aggregate chatter (in the form of product reviews) from multiple websites over a four year period across six markets and fifteen firms. They derive multiple metrics of chatter (volume, positive chatter, negative chatter, and 5-start ratings) and use multivariate time series models to assess the short and long term relationship between chatter and stock market performance. They use three measures of stock market performance: abnormal returns, risk, and trading volume. The findings reveal that two metrics of chatter can predict abnormal returns with a lead of a few days. Of four metrics of chatter, volume shows the strongest relationship with returns and trading volume, followed by negative chatter. Whereas negative chatter has a strong effect on returns and trading volume with a short “wearin” and long “wearout,” positive chatter has no effect on these metrics. Negative chatter also increases volatility (risk) in returns. A portfolio analysis of trading stocks based on their chatter provides a return of 8% over and above normal market returns. In addition to the investing opportunities, the results show managers that chatter is an important metric to follow to gauge the performance of their brands and products. Because chatter is available daily and hourly, it 2 can provide an immediate pulse of performance that is not possible with infrequent sales and earnings reports. The fact that negative chatter is more important than positive, indicates that negatives are more diagnostic than positives. The negatives suggest what aspects of the products managers should focus on

Managing positive and negative trends in sales call outcomes : the role of momentum

Existing research treats sales performance as a series of discrete, independent events rather than as a series of sales attempts with intertemporal spillover across these attempts. This research examines whether there are systematic short-term trends (“momentum”) in sales performance. To do so, the authors use the clumpiness approach to examine the existence of sales momentum in a high-frequency call-level data set obtained from two call centers of a large European firm. They further investigate the effect of positive (negative) momentum, or the positive (negative) deviation from the long-term expected performance on subsequent sales performance. Exploiting the differences in the social environment of the call centers, the authors find that the social working environment mitigates the harmful effect of negative momentum and sustains positive momentum. Further, they demonstrate that calls made midday, early-week and late-week boost performance by mitigating the adverse effects of negative momentum. The findings suggest that monitoring sales performance can help managers detect momentum and use timely interventions to enhance sales productivity. Managers can also leverage momentum by creating a more social working environment to optimize overall salesperson performance

Big data marketing during the period 2012–2019: a bibliometric review

The present study identifies the most significant trends in production of high impact scientific papers related to the Big Data Marketing variable during the period between the years 2012 and 2019 through a revision of the Scopus database, which manages to highlight the relevance of 113 indexed papers. For this purpose, the following descriptive bibliometric indicators are implemented: production volume, type of document, number of citations, and country of application. In the studied time period, the evidence suggests an annual growth in the production volume of papers related to the variable, but with a significant drop in 2017. The knowledge areas that showcases more researches about the Big Data Marketing variable are computer science, mathematics, decision-making, and engineering domain

Managing brands in the social media environment

The dynamic, ubiquitous, and often real-time interaction enabled by social media significantly changes the landscape for brand management. A deep understanding of this change is critical since it may affect a brand's performance substantially. Literature about social media's impact on brands is evolving, but lacks a systematic identification of key challenges related to managing brands in this new environment. This paper reviews existing research and introduces a framework of social media's impact on brand management. It argues that consumers are becoming pivotal authors of brand stories due to new dynamic networks of consumers and brands formed through social media and the easy sharing of brand experiences in such networks. Firms need to pay attention to such consumer-generated brand stories to ensure a brand's success in the marketplace. The authors identify key research questions related to the phenomenon and the challenges in coordinating consumer- and firm-generated brand stories

PERENCANAAN FDD-LTE MENGGUNAKAN FREKUENSI 1800MHZ PADA PERANCANGAN INDOOR BUILDING COVERAGE DI YOGYA KEPATIHAN BANDUNG

Material sebuah gedung merupakan salah satu penyebab dari terjadinya fading sehingga menghambat sinyal masuk ke dalam gedung yang mengakibatkan sinyal didalam gedung tersebut lemah. Pada gedung Yogya Kepatihan memiliki masalah terhadap kualitas jaringan didalamnya sehingga butuh dilakukannya perencanaan Indoor untuk mengatasi masalah tersebut. Berdasarkan hasil analisis dilakukannya walk test diperoleh nilai rata-rata dari RSRP sebesar -96 dBm dan SINR sebesar 8 dB, sedangkan drivetest sekitaran Gedung memperoleh hasil RSRP sebesar -72 dBm dan SINR sebesar 5 dB. Penerapan Indoor Building Coverage (IBC) ini menggunakan sistem Distributed Antenna System (DAS) dengan teknik FDD-LTE pita frekuensi 1800 MHz, untuk simulasinya menggunakan Radiowave Propagation Software (RPS) dengan model propagasi Cost-231 Multi-Wall Indoor. Operator Telkomsel menjadi kasus dalam penerapan ini. Walktest di dalam gedung menggunakan Nemo handy, sedangkan drivetest menggunakan GnetTrack Pro. Setelah mendapatkan data lalu dilanjutkan perhitungan capacity planning dan coverage planning sehingga mendapatkan perhitungan untuk jumlah antena yang akan di simulasikan ke dalam software RPS untuk mendapatkan hasil parameter yang sesuai dengan standar Key Performance Indicator (KPI) Operator Telkomsel yaitu RSRP > -85 dBm dan SINR > 10 dB. Dari hasil simulasi ini, diperoleh peningkatan nilai rata-rata RSRP > -76 dBm sebanyak 81,95% dan rata-rata nilai SINR > 26 sebanyak 71,56%. Kata Kunci: LTE, FDD, IBC, DAS, RPS

Customer Experience Management

Dieser Beitrag leistet einen Beitrag zur Marketingforschung, da er den jungen aber von zunehmender Relevanz geprägten Forschungsstrang zum Themenkomplex CEM grundlegend entwickelt. Zum einen zeigt das identifizierte Rahmenkonzept auf, dass CEM über einzelne unternehmerische Fähigkeiten wie dem Design von Serviceerlebnissen, das die bisherige CEM-Forschung bestimmt hat, hinausgeht. Zum anderen leistet das Konzept einen Beitrag zur Synthese fragmentierter, aber miteinander zusammenhängender Literaturströmungen in der Marketingforschung ..

In Search of New Product Ideas: Identifying Ideas in Online Communities by Machine Learning and Text Mining

Online communities are attractive sources of ideas relevant for new product development and innovation. However, making sense of the ‘big data’ in these communities is a complex analytical task. A systematic way of dealing with these data is needed to exploit their potential for boosting companies' innovation performance. We propose a method for analysing online community data with a special focus on identifying ideas. We employ a research design where two human raters classified 3,000 texts extracted from an online community, according to whether the text contained an idea. Among the 3,000, 137 idea texts and 2,666 non-idea texts were identified. The human raters could not agree on the remaining 197 texts. These texts were omitted from the analysis. The remaining 2,803 texts were processed by using text mining techniques and used to train a classification model. We describe how to tune the model and which text mining steps to perform. We conclude that machine learning and text mining can be useful for detecting ideas in online communities. The method can help researchers and firms identify ideas hidden in large amounts of texts. Also, it is interesting in its own right that machine learning can be used to detect ideas.submittedVersio