Introduction

Word of Mouth Statistics: WOM, or word-of-mouth advertising, is one of the oldest forms of marketing introduced in the history of humanity. It is defined as customers talking to each other regarding their experience or consumption of a given product or service. Such word of mouth is more likely to be accepted as it is based on experience and is hence more likely to be effective than the traditional advertising method.

In the year 2024, that is not the case, as word-of-mouth statistics marketing is still very much alive and reaching its peak at the age and time when most people enjoy making their views known through the internet in social networks, Reviews, and discussion boards. Here are some interesting facts and figures regarding word-of-mouth marketing in 2025.

This statistic shows data on the type of word of mouth product information received by consumers in the United States in 2015. During the survey period it was found that ***** percent of US respondents obtained product recommendation from someone they knew through social media.

This statistic shows a ranking of the leading brands among Millennials in Denmark in 2019, by Word of Mouth (WMO) score. Netflix ranked first with a WMO score index of ****, followed by MobilePay, reaching a score of ****. Rema 1000 ranked third and had a score of **** in Denmark.

Financial overview and grant giving statistics of Word of Mouth Ministries Inc.

This statistic shows data on the type of word of mouth product information received by consumers in the United Kingdom (UK) in 2015. During the survey period it was found that ** percent of UK respondents obtained product recommendation from someone they knew through social media.

Dataset comprising of 794 respondents that we have utilized in our calculations and analyses.

Post-purchase online word of mouth (WOM) has emerged as a major research area in marketing and information systems literature as it functions both as a predictor and driver of sales revenue. While the relationship between WOM and sales is now quite well understood, it is less clear what drives WOM itself. Of specific interest is the question of whether a population’s propensity to contribute to post-consumption WOM is dependent on various indicators of that product’s availability. The study of these product-level effects is complicated by simultaneous social dynamic-level effects which might also differ for products of varying availability and popularity. Furthermore, the bi-modal distribution commonly found in review ratings complicates the use of established summary statistics such as arithmetic mean and standard deviation. In this paper we study a dataset of almost 280,000 consumer reviews for 433 movies over three years to disentangle these product-level effects and social dynamic-level effects to better understand their effect on a population’s propensity to contribute to post-consumption WOM. We first show how the use of a simple arithmetic mean can lead to estimation problems. To resolve these problems and allow the study of social dynamics, we introduce a novel measure to capture the disagreement contained in non-normally distributed review ratings based on an expectation-maximization algorithm for finite mixture models. Using this measure, we find that consumers are more likely to post reviews for products that are perceived to be less available in the market but no effect for those perceived to be less successful in the market. Investigating the social dynamic-level effects, we find a positive effect for products that have already accumulated a larger volume of prior reviews and a positive effect of disagreement on consumers’ propensity to engage in post-consumption WOM. Investigating the interaction between the movie-level effects and social dynamic-level effects, we find that while high disagreement helps gen erate WOM for less popular (niche) products, it hampers that of highly popular (hit) products.

In the contemporary world everything has been digitalized and marketing sector has also seen significant changes. Before buying a product, people used to consult their peers, friends, or relatives, but this has altered now. We need not spend much time to know about the new product or brand. We instantly get to know about the products through social media platforms, websites, search engines, independent reviewing sites. This enables the consumer to get access to the newly introduced product as well as already existing products by electronic word of mouth in the form of online reviews. Therefore big salute to the internet and technology which made the things easier. Generally, consumers have the confusions while choosing the products. Certain questions arise when they decide to buy. The questions such as whether it is of good quality? whether the price of the product is reasonable or whether they are paying more penny? Before making an online purchase, customers typically read the reviews that are provided underneath the product in order to get answers to all of these questions and also look for reviews on social media platforms like YouTube and Instagram. Therefore this study tries to find out the consumer perception about online reviews which is considered to be the electronic word of mouth among the consumers. This study uses a descriptive approach to gather information from consumers by using a structured questionnaire. For the purpose of the study, 200 samples are collected from the Chennai city. The information gathered will be then analyzed to determine the expected outcomes of the study. The findings of the study will be beneficial to the consumers, producers, retailers and marketers. Keywords: Online reviews, Electronic word of mouth, Social Media sites, Review sites, Video platform, consumer perception

ABSTRACT The digital age has transformed how brands communicate and interact with their customers. One consequence is that the effect of negative word of mouth on a brand’s reputation has intensified. This study investigates various response strategies employed to protect organizations’ reputations in the online environment. Accordingly, it collected data through two methods. First, 10 semi-structured interviews were conducted with brand managers to identify the strategies that they used to minimize negative word of mouth in social media. Second, the social media interventions of different brands under management by agencies were collected to complement the interviews, and determine whether any additional strategies could be identified. The results showed that, when negative word of mouth events occurred, companies preferred to either apologize, hide the original message, respond in private rather than in public, or simply ignore the negative comments from customers.

Financial overview and grant giving statistics of Word of Mouth Stories N 3d Inc.

During a survey conducted among internet users in the United States in the no third quarter of 2024, word-of-mouth emerged as the most common source of new brand, product, and service discovery, mentioned by approximately **** percent of the participants.TV ads and search engines followed, cited by roughly ** and ** percent of the respondents, respectively.

Research data used in the paper entitled "Negative online word-of-mouth and consumers’ product attitude: A nonlinear relationship"

This statistic shows data on the type of word of mouth product information received by consumers in New Zealand in 2015. During the survey period it was found that **** percent of New Zealand respondents obtained product recommendation from someone they knew through social media.

The buzz agent is any consumer perceived by others as a source of product referral. Previous literature in word of mouth (WOM) has looked into characteristics of individuals who successfully persuade others to choose a brand. While there have been studies in this field, the literature is still scattered and little has been done to profile the consumer playing the buzz-agent role. We aim to deepen our understanding about the consumer who must be recruited as a buzz agent by a firm in a WOM marketing (WOMM) initiative. The proposed profile is comprised of three key characteristics: the consumer's position in the social community, nature of ties in the community and brand attachment. We tested our hypotheses with a survey of 542 consumers from a controlled population. Rather than relying on self-reported questions about referral behavior, we asked respondents in the population to name the individuals to whom the respondents go to obtain information to help pick a brand. This accurately pinpoints which individuals fit the profile of a buzz agent. Results show that buzz agents are popular in their social community (friends and tech experts), carry dissimilar brands as target consumers and are product experts. Our study identifies a profile of consumers that helps firms select buzz agents for WOMM initiatives.

Research data used in the paper entitled "Negative online word-of-mouth and consumers’ product attitudes: A nonlinear relationship" published in Revista Brasileira de Gestão de Negócios (RBGN) V26, n1 (2024) Acess: https://rbgn.fecap.br/RBGN

This statistic shows data on the type of word of mouth product information received by consumers in Mexico in 2015. During the survey period it was found that ** percent of Mexican respondents obtained product recommendation from someone they knew through social media.

On the basis of the cognitive–affective–behavioral model, this study investigated the effects of narrative transportation in movies on audience emotion and positive word-of-mouth.

This statistic shows data on the type of word of mouth product information received by consumers in France in 2015. During the survey period it was found that ***** percent of French respondents obtained product recommendation from someone they knew through social media.

FC and WOM - Study 1 - Correlational Past BehaviorFour hundred adults located in the US were recruited through IPSOS, a leading market research panel provider, to complete this correlational survey.FC and WOM - Study 2 - Correlational ChatroomFour hundred and five adults on MTurk completed this study, which examined the association between financial constraints and choice of chatroom (purchases vs. local/state parks).FC and WOM - Study 2 - PretestForty three adults on MTurk responded to questions about potential conversation topics. This data was used to identify a chatroom topic for study 2.FC and WOM - Study 3a - MediationTwo hundred and fifty three adults, recruited through the IPSOS market research panel organization, completed a two condition (financial constraint vs. control) between-subjects design. This study examined the causal relationship between financial constraints and word-of-mouth and identified the role of reduced anticipated pleasure in driving the results.FC and WOM -...

The proliferation of internet technology and social media platforms has revolutionized consumer-brand interactions, enabling widespread participation in online brand conversations and significantly amplifying the impact of word-of-mouth communication. Therefore, they actively post and increasingly rely on online product reviews, particularly when the reviews are negative. These product reviews carry cognitive and affective information that can be found in comparative and non-comparative formats. In this study, we focused on cognitive information available online in comparative and non-comparative formats. This study examines the impact of negative word-of-mouth communication on consumer attitudes and the moderating role of attribute-based advertising under comparative and non-comparative cognitive involvement conditions. A two cognitive negative word-of-mouth and two cognitive attribute-based advertisement mixed designs were used to empirically test the proposed research model. Data were analyzed using a two-way ANOVA in SPSS to examine the interaction effects between NWOM and advertising formats on consumer attitudes. The findings reveal that attribute-based advertising communication moderates the negative impact of NWOM communication on consumer attitudes under comparative and non-comparative cognitive involvement conditions. The theoretical implications, practical implications, and recommendations are discussed for further research in the field of online media communication.