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.

According to the findings of a global survey, personal recommendations from friends and family were the most trusted advertising channel among consumers in 2021. Nearly ** percent of respondents trusted word-of-mouth recommendations, while brand websites and brand sponsorships were also considered highly reliable sources. Traditional media states its claim When it comes to advertising perception, old media formats can still be gold. Various surveys conducted in 2021 have indicated that television, radio, and print still boast the highest trust among all advertising media in the United States, United Kingdom, and several other markets. But despite their seemingly trustworthy reputation, not all traditional media outlets have managed to draw equally impressive advertising investments from brands and companies in recent years, as the decline in print advertising activity has demonstrated. To trust or not to trust in social media Despite being one of the most popular pastimes among millions of online users, social media has been voted the least credible form of advertising by consumers across many regions. Survey findings showed that a mere ** percent of internet users in the UK trusted ads they saw on social networks, which might be surprising to read considering that these platforms have become a vital driver of e-commerce growth nationwide. One reason for the lack of trust might be the comparatively low level of oversight and regulation on these platforms paired with people’s wariness of advertising scams. Interestingly, trust levels in social media advertising vary significantly depending on the region: As of 2021, online users in India displayed the highest trust, whereas respondents from Denmark, Sweden, and the UK were the most skeptical.

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

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.

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.

Discover the booming referral marketing software market! Explore a $2.5B (2025) industry with a 15% CAGR, analyze key trends, leading companies (Influitive, Ambassador, ReferralCandy), and regional insights. Boost your marketing strategy with this comprehensive market analysis.

TechCorner Mobile Purchase & Engagement Data (2024-2025)

Context

TechCorner Mobile Sales & Customer Insights is a real-world dataset capturing 10 months of mobile phone sales transactions from a retail shop in Bangladesh. This dataset was designed to analyze customer location, buying behavior, and the impact of Facebook marketing efforts.

The primary goal was to identify whether customers are from the local area (Rangamati Sadar, Inside Rangamati) or completely outside Rangamati. Since TechCorner operates a Facebook page, the dataset also includes insights into whether Facebook marketing is effectively reaching potential buyers.

Additionally, the dataset helps in determining: ✔ How many customers are new vs. returning buyers ✔ If customers are followers of the shop’s Facebook page ✔ Whether a customer was recommended by an existing buyer

This dataset is valuable for:

Retail sales analysis to understand product demand fluctuations.

Marketing impact measurement (Facebook engagement vs. actual purchase behavior).

Customer segmentation (local vs. non-local buyers, social media influence, word-of-mouth impact).

Sales trend analysis based on preferred phone models and price ranges.

With a realistic, non-uniform distribution of daily sales and some intentional missing values, this dataset reflects actual retail business conditions rather than artificially smooth AI-generated data.

Marketing & Customer Queries

Does he/she Come from Facebook Page? → Whether the customer came from a Facebook page (Yes/No). Used to analyze Facebook marketing reach.

Does he/she Followed Our Page? → Whether the customer is already a follower of the shop’s Facebook page (Yes/No). Helps measure brand loyalty and organic engagement.

Did he/she buy any mobile before? → Whether the customer is a repeat buyer (Yes/No). Determines the percentage of returning customers.

Did he/she hear of our shop before? → Whether the customer knew about the shop before purchasing (Yes/No). Identifies the impact of referrals or previous marketing efforts.

Was this customer recommended by an old customer? → Whether an existing customer referred them to the shop (Yes/No). Helps evaluate the effectiveness of word-of-mouth marketing.

Acknowledgements

This dataset is derived from real-world mobile sales transactions recorded at TechCorner, a retail shop in Bangladesh. It accurately reflects customer purchasing behavior, pricing trends, and the effectiveness of Facebook marketing in driving sales. Special appreciation to TechCorner for providing comprehensive insights into daily sales patterns, customer demographics, and market dynamics.

This dataset can be used for:

📊 Predictive modeling of sales trends based on customer demographics and marketing channels. 📈 Marketing effectiveness analysis (impact of Facebook promotions vs. organic sales). 🔍 Clustering customers based on purchasing habits (new vs. returning buyers, Facebook users vs. walk-ins). 📌 Understanding demand for different smartphone brands in a local retail market. 🚀 Analyzing how word-of-mouth recommendations influence new customer acquisition.

💡 Can you build a model to predict if a customer is likely to return? 💬 How effective is Facebook in driving actual sales compared to walk-ins? 🔍 Can we cluster customers based on behavior and brand preferences?

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.

During a survey conducted among internet users in Canada in the 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. Search engines and TV ads followed, cited by almost ** and ** percent of the respondents, respectively.

Unlock explosive growth with referral marketing! Discover the booming $2 billion market for referral marketing tools, projected to reach $6 billion by 2033. Explore key trends, leading companies, and regional insights in this comprehensive market analysis.

According to a 2024 survey on the digital usage among Chinese internet users, about **** percent of respondents said they discovered new brands through word-of-mouth recommendations from friends or family. Search engines, social media comments, and brand websites were other major channels for gathering new information about products and services.

The purpose of this dataset is to investigate a comprehensive model of user satisfaction and electronic word-of-mouth (e-WOM). The data was collected through a self-administered questionnaire at Girne American University in the Turkish Republic of North Cyprus (TRNC). The dataset was empirically evaluated using a survey of 500 respondents about their perceptions of the service provided by the mobile telecom operator.

Social Sampling Platform Market Outlook

According to our latest research, the global Social Sampling Platform market size reached USD 1.28 billion in 2024, reflecting robust demand from brands and agencies seeking innovative consumer engagement strategies. The market is projected to expand at a CAGR of 15.7% from 2025 to 2033, reaching a forecasted value of USD 4.41 billion by 2033. This strong growth is primarily driven by the increasing adoption of digital and social media marketing channels, the rising focus on personalized consumer experiences, and the growing need for data-driven sampling campaigns to optimize product launches and brand visibility.

The growth trajectory of the Social Sampling Platform market is underpinned by the evolving dynamics of consumer engagement and the rapid proliferation of digital marketing strategies. Brands are increasingly leveraging social sampling platforms to distribute product samples directly to targeted consumer segments, fostering authentic feedback and driving organic word-of-mouth marketing. The integration of advanced analytics and artificial intelligence within these platforms enables precise targeting, real-time campaign optimization, and in-depth consumer insights, enhancing the overall effectiveness of sampling initiatives. The shift towards experiential marketing, where consumers are encouraged to share their product experiences on social media, further amplifies brand reach and trust, propelling market expansion.

Another significant growth factor is the surge in partnerships between brands, influencers, and social sampling platform providers. Influencer marketing has become an indispensable component of modern brand strategies, and social sampling platforms serve as a bridge connecting brands with micro and macro influencers who can authentically promote products to their engaged audiences. The scalability and cost-effectiveness of these platforms, combined with their ability to generate measurable results, have led to widespread adoption across industries such as beauty & personal care, food & beverage, and healthcare. Additionally, the increasing penetration of smartphones and high-speed internet, especially in emerging markets, is expanding the addressable consumer base for social sampling campaigns.

The market is also benefiting from the rising demand for data-driven marketing solutions. Social sampling platforms collect valuable first-party data on consumer preferences, behaviors, and feedback, which is instrumental for brands in refining their product offerings and marketing strategies. The integration of customer relationship management (CRM) tools, machine learning algorithms, and real-time analytics allows brands to track campaign performance, segment audiences more effectively, and personalize sampling experiences. As data privacy regulations become more stringent, platforms that offer secure and compliant data management solutions are gaining a competitive edge, further fueling market growth.

From a regional perspective, North America continues to dominate the Social Sampling Platform market, accounting for the largest revenue share in 2024, followed closely by Europe and the Asia Pacific. The mature digital advertising ecosystem, high social media penetration, and presence of leading global brands in North America contribute to its market leadership. Meanwhile, the Asia Pacific region is witnessing the fastest growth, driven by rapid urbanization, increasing digital literacy, and the expansion of e-commerce and social commerce platforms. Europe’s market is characterized by a strong emphasis on data privacy and consumer rights, influencing the adoption of compliant and transparent sampling solutions.

Component Analysis

The Social Sampling Platform market is segmented by component into platforms and services, each playing a pivotal role in shaping the industry landscape. The platform segment encompasses the core technological infrastructure that enables brands and agencies to design, execute, and monitor social sampling campaigns. These platforms offer a suite of features, including campaign management, audience segmentation, analytics dashboards, and integration with popular social media channels. The continuous evolution of platform capabilities, such as the inclusion of artificial intelligence for predictive targeting and real-time campaign optimization, is significantly enhancing the value proposition for en

A variety of approaches to reducing the environmental impact of food production and consumption are being explored including technological solutions, such as food produced via biotechnological processes. However, the development of these technologies requires significant upfront investment and consumer acceptance is not guaranteed. The purpose of this research is to develop a system dynamics model to forecast demand, under multiple marketing and quality scenarios, for foods produced via novel technologies, using cellular agriculture as a case study. The model considers consumer heterogeneity, product awareness, word of mouth marketing (WOM), in-store marketing options, pricing options and product utility to estimate diffusion rates and market penetration. To our knowledge, there is no demand forecasting model available for food produced via novel technologies which relies on purchase intention data and incorporates all these factors. Therefore, this research closes a critical gap for that industry. Ultimately, the model shows that price and the consumers’ utility for the product drives the final demand regardless of marketing scenario. Further, the rate of diffusion was highest when product samples are provided in store for all scenarios except when product utility is low and the product price is high. Model results suggest that market saturation was reached within the 32-week trial period when the price of the cellular agriculture product was the same as a traditional product but not when the price was double that of traditional meat. Given the lack of available trial data, the model scenarios should be considered a prior probability which should be refined as more data becomes available.

During a global survey conducted in the third quarter of 2024, approximately **** percent of responding internet users in Colombia reported discovering new brands via advertisements on social media. Another **** percent of interviewees came upon new brands through word-of-mouth (WOM) recommendations.

Jamaica whatsapp number list is easy to use and helps you reach people who have an interest in your services. It makes communication simple and quick, giving you a chance to build relationships with your B2B and B2C customers. Again, having access to reliable whatsapp data guarantees that you can send your messages to real users. Jamaica whatsapp data will help your business find real customer contacts for promotions. This can help increase your sales and brand awareness, making your marketing efforts more successful. Our up-to-date contact data will help your business grow by giving you trustworthy information for successful marketing. Jamaica whatsapp phone number data can help you create effective marketing campaigns by reaching the people who matter most. A total of 1.6 million people use whatsapp in your country, so it’s a great tool for connecting with customers. You can use our valuable data to share updates, special offers, and important messages directly with your audience. As a result, this will support you develop your business faster by staying in touch with customers. So, List to Data gives you trusted contacts, helping you easily connect with real users and quickly grow your business. This word-of-mouth marketing can help your business grow even more.

The Referral Tracking Software market is rapidly evolving, becoming an essential tool for businesses aiming to optimize their customer acquisition strategies. This software enables companies to monitor, manage, and analyze referrals effectively, transforming word-of-mouth marketing into a quantifiable asset. By prov

During a 2021 survey, ** percent of responding consumers from Ireland stated they always took action because of recommendations from friends (word of mouth) they received. Another ** percent sometimes took action on such recommendations.

Convenience Store Loyalty Platform Market Outlook

According to our latest research, the global Convenience Store Loyalty Platform market size reached USD 1.43 billion in 2024, reflecting robust adoption across retail environments. The market is expected to grow at a CAGR of 12.1% from 2025 to 2033, reaching a projected value of USD 3.98 billion by 2033. This growth is primarily driven by increasing digital transformation, the rising need for customer retention strategies, and the expanding use of data analytics in retail. The market’s upward trajectory is reinforced by the proliferation of mobile payments and personalized engagement, as convenience stores worldwide seek to differentiate themselves in a highly competitive landscape.

One of the primary growth factors for the Convenience Store Loyalty Platform market is the escalating demand for advanced customer engagement tools. As consumer expectations for personalized experiences continue to rise, convenience stores are investing heavily in loyalty solutions that leverage big data and artificial intelligence. These platforms enable stores to gather actionable insights on purchasing behaviors, preferences, and trends, allowing for highly targeted promotions and offers. The integration of omnichannel strategies—spanning in-store, online, and mobile channels—has become a standard, enabling seamless customer journeys and fostering higher retention rates. Additionally, the increasing penetration of smartphones and digital wallets further enhances the accessibility and effectiveness of loyalty programs, making them indispensable for modern convenience stores aiming to boost repeat visits and spending.

The rapid advancement of cloud computing technologies has significantly contributed to the expansion of the Convenience Store Loyalty Platform market. Cloud-based solutions offer scalability, cost-efficiency, and ease of deployment, which are particularly appealing to both small independent stores and large convenience store chains. These platforms facilitate real-time data synchronization and remote management, enabling retailers to launch, monitor, and optimize loyalty campaigns with minimal IT overhead. Furthermore, the integration of analytics and reporting modules within these platforms empowers store managers to make data-driven decisions, refine marketing strategies, and maximize return on investment. The ability to quickly adapt to shifting consumer trends and regulatory requirements also positions cloud-based loyalty solutions as a preferred choice for future-proofing convenience store operations.

Another notable driver is the increasing focus on rewards management and gamification to enhance customer loyalty. Modern loyalty platforms are equipped with sophisticated rewards engines that allow for the creation of tiered programs, personalized discounts, and exclusive offers. The incorporation of gamified elements—such as points, badges, and challenges—encourages frequent engagement and fosters a sense of community among customers. These features not only incentivize repeat purchases but also generate valuable word-of-mouth marketing. As convenience stores strive to differentiate themselves from supermarkets and online retailers, the adoption of innovative rewards and engagement strategies is expected to remain a key growth catalyst for the Convenience Store Loyalty Platform market over the forecast period.

Regionally, North America continues to dominate the Convenience Store Loyalty Platform market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, in particular, has witnessed widespread implementation of advanced loyalty solutions, driven by a highly competitive retail environment and tech-savvy consumers. Europe is experiencing steady growth, supported by regulatory emphasis on data privacy and increasing digitalization of retail operations. Meanwhile, Asia Pacific is emerging as the fastest-growing region, fueled by rapid urbanization, rising disposable incomes, and the proliferation of mobile-first consumers. The Middle East & Africa and Latin America are also showing promising potential, albeit at a relatively nascent stage, as convenience store operators in these regions begin to recognize the strategic value of loyalty platforms in driving customer engagement and revenue growth.

Component Analysis

The Component segment of the Convenience Store Loyalty Platform mark

As of the third quarter of 2024, approximately **** percent of internet users surveyed in Germany reported discovering new brands on search engines. TV ads and word-of-mouth (WOM) followed, with reaches surpassing ** percent as well.