Do Customers Speak Their Minds? Using Forums and Search for Predicting Sales

A wide body of research uses data from social media websites to predict offline economic outcomes such as sales. However, in practice, such data are costly to collect and process. Additionally, sales forecasts based on social media data may be hampered by people’s tendency to restrict the topics they publicly discuss. Recently, a new source of predictive information—search engine logs—has become available. Interestingly, the relationship between these two important data sources has not been studied. Specifically, do they contain complementary information? Or does the information conveyed by one source render the information conveyed by the other source redundant? This study uses Google’s comprehensive index of internet discussion forums, in addition to Google search trend data. Predictive models based on search trend data are shown to outperform and complement forum-data-based models. Furthermore, the two sources display substantially different patterns of predictive capacity over time

The Dynamic Impact of Web Search Volume on Product Sales — An Empirical Study Based on Box Office Revenues

In order to explore how Web search volume dynamically influences product sales during the whole product life cycle, this paper collects Web search volume and sales data of movies and does an empirical analysis using econometric models. The empirical results show that Web search volume before the launch of a new product has a positive impact on the product sales in the initial period of introduction stage. During the whole product life cycle, Web search volume has a positive and significant impact on product sales, but the impact declines gradually across the life cycle. The impact of Web search volume on sales is larger in the early stage of the product life cycle than in the late stage of the product life cycle

Purchase Intentions on Social Media as Predictors of Consumer Spending

The paper addresses the problem of forecasting consumer expenditure from social media data. Previous research of the topic exploited the intuition that search engine traffic reflects purchase intentions and constructed predictive models of consumer behaviour from search query volumes. In contrast, we derive predictors from explicit expressions of purchase intentions found in social media posts. Two types of predictors created from these expressions are explored: those based on word embeddings and those based on topical word clusters. We introduce a new clustering method, which takes into account temporal co-occurrence of words, in addition to their semantic similarity, in order to create predictors relevant to the forecasting problem. The predictors are evaluated against baselines that use only macroeconomic variables, and against models trained on search traffic data. Conducting experiments with three different regression methods on Facebook and Twitter data, we find that both word embeddings and word clusters help to reduce forecasting errors in comparison to purely macroeconomic models. In most experimental settings, the error reduction is statistically significant, and is comparable to error reduction achieved with search traffic variables

THE IMPACT OF COUNTRY IMAGE AND CELEBRITY ENDORSER STRATEGY ON PURCHASE INTENTION

The global market competition is getting intense, as well as the automotive industry in Indonesia, that has been predicted will grow positively in 2019. Today, Chinese car manufacturers flock to market their products in Indonesia. Previously, consumer’s perception about Chinese automotive products were considered as low quality inexpensive products, but now the trend is shifting. Country image become one of the important things for consumer to evaluate the quality of products. In the middle of 2018 DFSK (DongFengSokon), one of Chinese car manufacturer launched their SUV product, DFSK Glory 580. In conjunction with the launch of their new product, DFSK also took Agnez Monica as the celebrity endorser to promote their products. The purpose of this study is to investigate the impact of country image (cognitive and affective) toward product image and purchase intention. And also investigate the success driver of marketing strategy by using celebrity endorser on purchase intention of DFSK Glory 580. There are 211 respondents lived in Jakarta and surround areas that participated in this survey. The data processed with Structural Equation Modeling through AMOS 22 software. The study shows both two components of country image; cognitive and affective has significant impact to product image, and also product image to purchase intention. And there are three out of four endorser’s success driver that has significant impact to purchase intention of DFSK Glory 580, they are; attitude toward brand, familiarity of the brand, and brand fit of the endorser and the brand.Keywords: Country image, cognitive country image, affective country image, celebrity endorser, purchase intentio

Are Generational Attitudes Toward Digital Marketing Technology Exhibited in Automobile Purchase Behaviors?

This dissertation was focused on the current digital purchasing trend in the used automotive industry in order to understand which factors impacted the growth of this trend through the lens of generational cohort theory. The growth of consumer informedness in the automotive sector has created drastic changes in how consumers are able, and willing, to purchase vehicles. Used car dealerships who adopt successful internet marketing techniques can capture and engage potential customers and then convert that engagement into sales. Companies like Carvana, Vroom, and CarMax have seized this opportunity and created a digital marketing phenomenon with major impacts on consumer purchasing behavior throughout the durable goods sector. As consumer behavior trends toward an increase in digital shopping and purchasing, this research shows that the generations considered digital natives are mostly driving that trend, which has significant implications for the sales and marketing efforts of automobile dealers. While there was ample literature available regarding generational cohort theory and its impact on consumer behavior, there remained a noticeable gap in the academic body of knowledge examining this behavior in relation to large online purchases, such as automobiles. The research question under review was, to what extent do trust, social factors, and sales strategies impact online automobile purchase behaviors, and are the relationships among the constructs moderated by generational cohort theory? For this dissertation, a survey simulation of 1361 respondents was conducted to understand which key factors impact a consumer’s willingness to purchase an automobile online. The statistical testing revealed three variables that can help predict this behavior. Further, one of the hypotheses was rejected after testing, and the others were confirmed, but only with the moderation of certain generational cohorts. Future research should be considered that follows the trend of these cohorts in their high-involvement purchase decisions, particularly in the wake of Covid-19 and the impact from brick-and-mortar stores closing during the pandemic

Consumption values, consumer attitude, brand preference and intention to purchase hybrid car among Malaysian consumers

This study focuses on the determinants of hybrid car purchase intention in the Malaysian automotive industry. This study conceptualizes consumption values as a multi-dimensional construct which consists of five dimensions of values, i.e. functional value, symbolic value, emotional value, novelty value, and conditional value. This study examines the relationships between consumption values, consumers’ attitudes toward the hybrid car, brand preference, and intention to purchase the hybrid car. This study also examines the role of attitudes toward the hybrid car as a mediator and brand preference as a moderator of intention to purchase the hybrid car. Including both the mediating and the moderating factors in this study allows a more precise description of the relationships between all the variables mentioned and the outcome of the study. This study involves 306 respondents from the Klang Valley. Out of the 17 hypotheses tested, nine are supported. The analyses reveal positive relationships between functional value, emotional value, and consumers’ attitudes toward the hybrid car and the intention to purchase it. Besides, a significantly positive relationship is found among functional value, emotional value and conditional value, and the consumers’ attitudes toward the hybrid car. Consumers’ attitudes toward the hybrid car mediate the relationship between functional value, emotional value and conditional value and the intention to purchase the hybrid car. On the other hand, brand preference does not moderate the relationship between consumers’ attitudes toward the hybrid car and the intention to purchase it. The study also highlights the implications and limitations of the study as well as the suggestions for future research

Predicting online product sales via online reviews, sentiments, and promotion strategies

Purpose – The purpose of this paper is to investigate if online reviews (e.g. valence and volume), online promotional strategies (e.g. free delivery and discounts) and sentiments from user reviews can help predict product sales. Design/methodology/approach – The authors designed a big data architecture and deployed Node.js agents for scraping the Amazon.com pages using asynchronous input/output calls. The completed web crawling and scraping data sets were then preprocessed for sentimental and neural network analysis. The neural network was employed to examine which variables in the study are important predictors of product sales. Findings – This study found that although online reviews, online promotional strategies and online sentiments can all predict product sales, some variables are more important predictors than others. The authors found that the interplay effects of these variables become more important variables than the individual variables themselves. For example, online volume interactions with sentiments and discounts are more important than the individual predictors of discounts, sentiments or online volume. Originality/value – This study designed big data architecture, in combination with sentimental and neural network analysis that can facilitate future business research for predicting product sales in an online environment. This study also employed a predictive analytic approach (e.g. neural network) to examine the variables, and this approach is useful for future data analysis in a big data environment where prediction can have more practical implications than significance testing. This study also examined the interplay between online reviews, sentiments and promotional strategies, which up to now have mostly been examined individually in previous studies