In 2024, ********* on Instagram generated more engagement from users than single images, Reels, and Reels from the feed. Overall, the engagement rate of Carousels stood at ***** percent in 2024, compared to **** percent for images.

This statistic presents information on the U.S. consumer social media brand engagement rate. During the August 2016 survey, 58.6 percent of respondents stated that they interacted about one to three times per day with brand posts on social media.

*****Documentation Process***** 1. Data Preparation: - Upload the data into Power Query to assess quality and identify duplicate values, if any. - Verify data quality and types for each column, addressing any miswriting or inconsistencies. 2. Data Management: - Duplicate the original data sheet for future reference and label the new sheet as the "Working File" to preserve the integrity of the original dataset. 3. Understanding Metrics: - Clarify the meaning of column headers, particularly distinguishing between Impressions and Reach, and comprehend how Engagement Rate is calculated. - Engagement Rate formula: Total likes, comments, and shares divided by Reach. 4. Data Integrity Assurance: - Recognize that Impressions should outnumber Reach, reflecting total views versus unique audience size. - Investigate discrepancies between Reach and Impressions to ensure data integrity, identifying and resolving root causes for accurate reporting and analysis. 5. Data Correction: - Collaborate with the relevant team to rectify data inaccuracies, specifically addressing the discrepancy between Impressions and Reach. - Engage with the concerned team to understand the root cause of discrepancies between Impressions and Reach. - Identify instances where Impressions surpass Reach, potentially attributable to data transformation errors. - Following the rectification process, meticulously adjust the dataset to reflect the corrected Impressions and Reach values accurately. - Ensure diligent implementation of the corrections to maintain the integrity and reliability of the data. - Conduct a thorough recalculation of the Engagement Rate post-correction, adhering to rigorous data integrity standards to uphold the credibility of the analysis. 6. Data Enhancement: - Categorize Audience Age into three groups: "Senior Adults" (45+ years), "Mature Adults" (31-45 years), and "Adolescent Adults" (<30 years) within a new column named "Age Group." - Split date and time into separate columns using the text-to-columns option for improved analysis. 7. Temporal Analysis: - Introduce a new column for "Weekend and Weekday," renamed as "Weekday Type," to discern patterns and trends in engagement. - Define time periods by categorizing into "Morning," "Afternoon," "Evening," and "Night" based on time intervals. 8. Sentiment Analysis: - Populate blank cells in the Sentiment column with "Mixed Sentiment," denoting content containing both positive and negative sentiments or ambiguity. 9. Geographical Analysis: - Group countries and obtain additional continent data from an online source (e.g., https://statisticstimes.com/geography/countries-by-continents.php). - Add a new column for "Audience Continent" and utilize XLOOKUP function to retrieve corresponding continent data.

*****Drawing Conclusions and Providing a Summary*****

• The data is equally distributed across different categories, platforms, and over the years.
• Most of our audience comprises senior adults (aged 45 and above).
• Most of our audience exhibit mixed sentiments about our posts. However, an equal portion expresses consistent sentiments.
• The majority of our posts were located in Africa.
• The number of posts increased from the first year to the second year and remained relatively consistent for the third year.
• The optimal time for posting is during the night on weekdays.
• The highest engagement rates were observed in Croatia then Malawi.
• The number of posts targeting senior adults is significantly higher than the other two categories. However, the engagement rates for mature and adolescent adults are also noteworthy, based on the number of targeted posts.

This dataset provides granular, hourly influencer engagement metrics across major social media platforms, including detailed audience demographics and campaign associations. Brands and agencies can leverage this data for AI-powered analysis of peak activity times, audience response profiles, and campaign effectiveness, enabling data-driven marketing strategies and influencer selection.

According to a survey conducted among Gen Z consumers in the United States in May 2022, ** percent of respondents said they were motivated to engage with a new brand on social media when the brand appeared trustworthy and transparent. An ad's relevance or personalization was cited as a motivating factor by ** percent of respondents, while ** percent cited a brand's values and mission.

Introduction

Influencer Marketing Statistics: Influencer marketing has become a vital component of modern advertising strategies, largely due to the rapid growth of social media platforms and the shift toward digital content consumption. Brands are increasingly partnering with influencers to reach their target audiences more authentically, as influencers are seen as trusted voices within their communities.

This form of marketing is particularly effective in driving brand awareness, engagement, and consumer loyalty. With influencers leveraging their credibility and direct connections with followers, they can influence purchasing decisions, shape perceptions, and foster stronger relationships between brands and their audiences.

The growing popularity of micro and nano influencers, who engage with niche communities, has further amplified the impact of influencer marketing. As the landscape continues to evolve, influencer marketing is poised to remain a vital tool for brands aiming to connect with consumers in a more personal and engaging way.

In 2024, the average engagement rate of higher education posts on Instagram was *** percent, which is almost *** times the overall average of **** percent. Sports teams' posts also achieved higher than average interaction on Instagram, with a *** percent engagement rate. Fashion and Health and Beauty brands received lower than average engagement on Instagram. On average, brands on Facebook had an engagement rate of ***** percent.

This dataset contains 195 high-quality .jpg images curated for developing and evaluating augmented reality (AR) experiences in branding and marketing. Each image represents various branded products, packaging designs, or interactive graphic elements used in immersive AR scenarios.

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?

This dataset provides monthly engagement statistics for gaming influencers across major social and streaming platforms, including follower growth, content activity, and detailed interaction metrics. It supports brand partnership tracking and ROI calculation, enabling marketers and agencies to assess influencer effectiveness and optimize campaign strategies.

This study investigates how social media usage, engagement behavior, and emotional connection collectively influence consumer–brand relationships within the digital fashion industry. Using a quantitative, correlational research design, survey data from 414 active social media users across India were analyzed through descriptive statistics, correlation analysis, and Principal Component Analysis (PCA). The results reveal three distinct yet interrelated dimensions—Social Media Usage, Social Media Engagement, and Brand Intimacy—explaining over 54% of total variance. Findings demonstrate that engagement significantly mediates the relationship between social media usage and brand intimacy, indicating that consistent, interactive, and personalized content enhances emotional trust and loyalty. The study contributes theoretically by affirming engagement as a multidimensional construct encompassing behavioral and emotional elements, and by extending relationship marketing and social exchange theory in digital contexts. From an industry perspective, it emphasizes that authenticity, personalization, and transparency are key to fostering sustained engagement and emotional attachment. These insights provide a strategic framework for fashion brands to design human-centered, data-informed, and emotionally resonant digital marketing strategies that transform visibility into long-term brand intimacy and consumer loyalty.

Gain valuable insights with our comprehensive Social Media Dataset, designed to help businesses, marketers, and analysts track trends, monitor engagement, and optimize strategies. This dataset provides structured and reliable social media data from multiple platforms.

Dataset Features

User Profiles: Access public social media profiles, including usernames, bios, follower counts, engagement metrics, and more. Ideal for audience analysis, influencer marketing, and competitive research. Posts & Content: Extract posts, captions, hashtags, media (images/videos), timestamps, and engagement metrics such as likes, shares, and comments. Useful for trend analysis, sentiment tracking, and content strategy optimization. Comments & Interactions: Analyze user interactions, including replies, mentions, and discussions. This data helps brands understand audience sentiment and engagement patterns. Hashtag & Trend Tracking: Monitor trending hashtags, topics, and viral content across platforms to stay ahead of industry trends and consumer interests.

Customizable Subsets for Specific Needs Our Social Media Dataset is fully customizable, allowing you to filter data based on platform, region, keywords, engagement levels, or specific user profiles. Whether you need a broad dataset for market research or a focused subset for brand monitoring, we tailor the dataset to your needs.

Popular Use Cases

Brand Monitoring & Reputation Management: Track brand mentions, customer feedback, and sentiment analysis to manage online reputation effectively. Influencer Marketing & Audience Analysis: Identify key influencers, analyze engagement metrics, and optimize influencer partnerships. Competitive Intelligence: Monitor competitor activity, content performance, and audience engagement to refine marketing strategies. Market Research & Consumer Insights: Analyze social media trends, customer preferences, and emerging topics to inform business decisions. AI & Predictive Analytics: Leverage structured social media data for AI-driven trend forecasting, sentiment analysis, and automated content recommendations.

Whether you're tracking brand sentiment, analyzing audience engagement, or monitoring industry trends, our Social Media Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

This research explores the language features used by leading consumer brands with successful marketing in their promotional messages. Coca-Cola, McDonald’s, PepsiCo, Mondelez, and Unilever were selected because they appear in the Effie’s Most Effective Marketers’ Index and are active on a range of media platforms.

This dataset provides detailed, platform-specific metrics for social media influencers, including follower growth, engagement rates, post frequency, and brand partnerships. It enables marketers and analysts to identify trends, benchmark influencer performance, and optimize campaign strategies across various content categories and regions.

When Emma launched her direct-to-consumer skincare brand, she thought a strong website would do the trick. But soon, her traffic stagnated, and sales didn’t budge. Then she tried something different: mobile marketing. Within six months, mobile-driven traffic doubled, and over 60% of conversions came from phones. Her story isn’t unique....

This dataset provides detailed, structured records of social media influencer posts, including campaign associations, audience reach, and granular engagement metrics. Brands and agencies can use it to measure influencer effectiveness, optimize campaign strategies, and analyze audience lift across platforms and regions.

The Digital Brand Engagement market has emerged as a critical component in the broader advertising landscape, fundamentally changing how brands connect with consumers in an increasingly digital world. This dynamic market focuses on leveraging innovative strategies and platforms to foster meaningful interactions that

In 2024, the average engagement rate for sports teams' posts on X (formerly Twitter) was ***** percent, the highest engagement rate of all selected industries. Overall, fashion, food and beverage, health and beauty, and home decor brand posts achieved the lowest engagement rates of all selected industries. Moreover, the average engagement rate of brand posts on X/Twitter was ***** percent. Brands posting on Facebook achieved an average engagement rate of ***** percent.

This dataset provides granular engagement metrics for top Twitter influencers, including detailed tweet-level statistics, sentiment scores, and network mapping data such as hashtags and mentions. It is optimized for AI/ML applications, predictive modeling, and targeted marketing analytics, making it ideal for brand strategy, influencer analysis, and social network research.

Description:

The "Daily Social Media Active Users" dataset provides a comprehensive and dynamic look into the digital presence and activity of global users across major social media platforms. The data was generated to simulate real-world usage patterns for 13 popular platforms, including Facebook, YouTube, WhatsApp, Instagram, WeChat, TikTok, Telegram, Snapchat, X (formerly Twitter), Pinterest, Reddit, Threads, LinkedIn, and Quora. This dataset contains 10,000 rows and includes several key fields that offer insights into user demographics, engagement, and usage habits.

Dataset Breakdown:

• Platform: The name of the social media platform where the user activity is tracked. It includes globally recognized platforms, such as Facebook, YouTube, and TikTok, that are known for their large, active user bases.

• Owner: The company or entity that owns and operates the platform. Examples include Meta for Facebook, Instagram, and WhatsApp, Google for YouTube, and ByteDance for TikTok.

• Primary Usage: This category identifies the primary function of each platform. Social media platforms differ in their primary usage, whether it's for social networking, messaging, multimedia sharing, professional networking, or more.

• Country: The geographical region where the user is located. The dataset simulates global coverage, showcasing users from diverse locations and regions. It helps in understanding how user behavior varies across different countries.

• Daily Time Spent (min): This field tracks how much time a user spends on a given platform on a daily basis, expressed in minutes. Time spent data is critical for understanding user engagement levels and the popularity of specific platforms.

• Verified Account: Indicates whether the user has a verified account. This feature mimics real-world patterns where verified users (often public figures, businesses, or influencers) have enhanced status on social media platforms.

• Date Joined: The date when the user registered or started using the platform. This data simulates user account history and can provide insights into user retention trends or platform growth over time.

Context and Use Cases:

• This synthetic dataset is designed to offer a privacy-friendly alternative for analytics, research, and machine learning purposes. Given the complexities and privacy concerns around using real user data, especially in the context of social media, this dataset offers a clean and secure way to develop, test, and fine-tune applications, models, and algorithms without the risks of handling sensitive or personal information.

Researchers, data scientists, and developers can use this dataset to:

• Model User Behavior: By analyzing patterns in daily time spent, verified status, and country of origin, users can model and predict social media engagement behavior.

• Test Analytics Tools: Social media monitoring and analytics platforms can use this dataset to simulate user activity and optimize their tools for engagement tracking, reporting, and visualization.

• Train Machine Learning Algorithms: The dataset can be used to train models for various tasks like user segmentation, recommendation systems, or churn prediction based on engagement metrics.

• Create Dashboards: This dataset can serve as the foundation for creating user-friendly dashboards that visualize user trends, platform comparisons, and engagement patterns across the globe.

• Conduct Market Research: Business intelligence teams can use the data to understand how various demographics use social media, offering valuable insights into the most engaged regions, platform preferences, and usage behaviors.

• Sources of Inspiration: This dataset is inspired by public data from industry reports, such as those from Statista, DataReportal, and other market research platforms. These sources provide insights into the global user base and usage statistics of popular social media platforms. The synthetic nature of this dataset allows for the use of realistic engagement metrics without violating any privacy concerns, making it an ideal tool for educational, analytical, and research purposes.

The structure and design of the dataset are based on real-world usage patterns and aim to represent a variety of users from different backgrounds, countries, and activity levels. This diversity makes it an ideal candidate for testing data-driven solutions and exploring social media trends.

Future Considerations:

As the social media landscape continues to evolve, this dataset can be updated or extended to include new platforms, engagement metrics, or user behaviors. Future iterations may incorporate features like post frequency, follower counts, engagement rates (likes, comments, shares), or even sentiment analysis from user-generated content.

By leveraging this dataset, analysts and data scientists can create better, more effective strategies ...