Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media

Consumer spending is a vital macroeconomic indicator. In this paper we present a novel method for predicting future consumer spending from social media data. In contrast to previous work that largely relied on sentiment analysis, the proposed method models consumer spending from purchase intentions found on social media. Our experiments with time series analysis models and machine-learning regression models reveal utility of this data for making short-term forecasts of consumer spending: for three- and seven-day horizons, prediction variables derived from social media help to improve forecast accuracy by 11% to 18% for all the three models, in comparison to models that used only autoregressive predictors

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

Does Consumer Confidence Forecast Household Spending?

The traditional consumption function based on the life cycle permanent income hypothesis (LC-PIH) considers that consumer spending is based on households’ expectations of their future income. However, in short-term forecasting, the traditional economic determinants of consumption do not perform accurately. In addition to these macroeconomic variables, a measure of uncertainty is needed to better assess the short-term dynamics of the consumption function. Such a measure of uncertainty may be given by households’ expectations about their personal financial situation and general economic situation. A measure of these expectations is provided by consumer confidence (measured by the Consumer Confidence Index - CCI). In addition, consumer confidence seems to contain both a forecasting and independent explicative ability to predict consumption. Economic variables do not fully explain confidence, suggesting that its independent explicative power stems from its idiosyncratic features. We discuss in detail these features thanks to a review of the theoretical and empirical literature by discussing the consistency of consumer confidence with the standard consumption theory, analysing the determinants of the CCI and studying the predictive and causal power of the CCI.Consumer confidence; consumption function; forecasting

A little bluebird told me : social media conversation effects on business outcomes-evidence from the movie industry

textIn this dissertation, I examine how online conversations as electronic word-of-mouth (eWOM) information via social media networks affect business outcomes. Using data from the movie industry, my goal is to show how conversation quantity and quality, defined here as volumes and valence, on social network sites affect important business outcomes such as sales. Using a dynamic simultaneous equation system, I find that social media conversations can be a precursor to and an outcome of sales. Aggregated data from multiple sources show how social media variables and other key variables—volume, valence, and other information related to movies such as YouTube views, ratings, advertising, production budget, number of screens—contribute to box-office and home video sales through eWOM via social media. Findings highlight that eWOM volume correlates with box-office performance and home video sales: the more positive and strong the conversation, the higher the box office and home video sales. The study extends prior research on WOM and offers insight into how film studios can strategically manage social media to enhance box office and home video revenue.Advertisin

Exploration of Brand Satisfaction on Purchase Decision: Theory of Planned Behavior Perspective

Brand satisfaction is prominent to bridge purchasing decisions, and it is often linked with perceived value, social media marketing, and brand trust. This study explores the mediating role of brand satisfaction in the relationship between perceived value, social media marketing, brand trust, and purchase decision. The convenience sampling technique was used to collect the data. A self-administered survey from 226 consumers who were sampled in Semarang was involved in this study, and further data was processed using PLS-SEM software. The findings show that perceived value, social media marketing, brand trust, and satisfaction simultaneously have a positive and significant impact on consumer purchasing decisions. The role of brand satisfaction is proven to bridge the relationship between perceived value and the role of social media in purchasing decisions. However, there are negative results related directly or indirectly through brand satisfaction, from brand trust to purchasing decisions. In addition, social media marketing has no impact on brand satisfaction. The findings indicate that brand decisions have yet to become a benchmark for consumers to determine purchasing decisions for shampoo products. The usefulness of this research can trigger product business owners to observe consumer behavior continuously amid fast-moving product innovation competition.Keywords: Perceived value, Purchase decision, Social media marketing, Brand satisfaction, Brand trus

Examining Retailer And Consumer Perceptions In Determining Economic Expectations: A Demonstration With The 2008 Holiday Season

Two stratified probability samples were conducted in the southeastern United States, one of consumers and one of retailers, to measure economic perceptions in the fall of 2008. Direct comparisons were made between consumers’ and retailers’ perceptions in the areas of the economy, financial soundness, cost of living, and planned consumer holiday spending.  The results suggested that retailers and consumers held similar negative perceptions in terms of the state of the economy.  Consumers though felt less financially sound than retailers did.  Additionally, consumers felt the cost of living has increased to a greater degree than retailers felt their cost of operating had.  Finally, there was a considerable difference in consumers’ planned holiday spending and retailers’ expectations of holiday spending as retailers better predicted that consumers were planning to spend less. The managerial implications for retailers based on these predictions are presented

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

Exploring the Relationship Between Revenue Management and Hotel Loyalty Programs

Loyalty programs are a staple of the hospitality industry. As time progressed, there has been a shift among the structure of loyalty programs to not only reward the large spend of casino players, but also to compensate other frequent travelers of the hotel. As hotels continue to offer increasing benefits and compensation while reevaluating the tier structure of loyalty programs, research was necessary to discover if these loyalty programs are extracting the maximum revenue per guest and creating overall revenue for the hotel. The purpose of the study was to uncover the relationship between revenue management and hotel loyalty programs. While some research has been conducted on the relationship between customer relationship management and revenue management (Shoemaker & Lewis, 1999; Wang, 2011), further research was necessary to bridge the gap between hotel loyalty programs and revenue management (Wilco, Shanshan & Eric, 2011). Little evidence existed on whether revenue management and hotel loyalty programs work cohesively, or even if they should. A pilot study of a focus group was conducted to assess the general relationship between revenue management and hotel loyalty programs, followed by thirteen in-depth interviews. After the interviews were transcribed, content analysis was performed, followed by the use of Atlas.ti to further analyze the data. Participants’ were asked questions regarding the interaction of revenue management and hotel loyalty programs. Overall, the goal was to understand consumer behavior to drive repeat business; if a hotel can generate repeat business, then an emotional connection may develop between the hotel/brand and the guest. Revenue management used a loyalty program as a tool to track and gather data on the customer