The Instagram Reach Forecasting dataset contains data on the reach of Instagram posts for a period of 100 days. The data includes the following features:

Post ID: The unique identifier for each post. Date: The date the post was published. Reach: The number of people who saw the post. Engagement: The number of likes, comments, and shares the post received. Hashtags: The hashtags used in the post. User Count: The number of users who follow the account that published the post. Category: The category of the post (e.g., food, travel, fashion). This dataset can be used to train machine learning models to forecast the reach of Instagram posts. This information can be used by businesses and individuals to optimize their Instagram marketing campaigns.

Here are some of the ways this dataset can be used:

Businesses: Businesses can use this dataset to forecast the reach of their Instagram posts and optimize their marketing campaigns. For example, a business could use this data to determine which hashtags are most effective at reaching their target audience. Individuals: Individuals can use this dataset to track their own Instagram reach and improve their social media strategy. For example, an individual could use this data to determine which times of day they should post to get the most reach.

During a global 2024 survey among fashion, lifestyle and beauty professionals, around 75 percent of respondents indicated their main objective with influencer collaborations was to increase sales. Moreover, 70 percent had a similar objective with celebrity collaborations. Roughly 50 percent stated their aim was to build their following with influencer or celebrity collaborations.

In early 2025, Emily, a small business owner from Denver, ran her first Instagram ad to promote her sustainable skincare line. Within 48 hours, she saw a 34% jump in profile visits and a 12% boost in website clicks. She wasn’t alone. From local brands to global corporations, businesses across...

Cristiano Ronaldo has one of the most popular Instagram accounts as of April 2024.

              The Portuguese footballer is the most-followed person on the photo sharing app platform with 628 million followers. Instagram's own account was ranked first with roughly 672 million followers.

              How popular is Instagram?

              Instagram is a photo-sharing social networking service that enables users to take pictures and edit them with filters. The platform allows users to post and share their images online and directly with their friends and followers on the social network. The cross-platform app reached one billion monthly active users in mid-2018. In 2020, there were over 114 million Instagram users in the United States and experts project this figure to surpass 127 million users in 2023.

              Who uses Instagram?

              Instagram audiences are predominantly young – recent data states that almost 60 percent of U.S. Instagram users are aged 34 years or younger. Fall 2020 data reveals that Instagram is also one of the most popular social media for teens and one of the social networks with the biggest reach among teens in the United States.

              Celebrity influencers on Instagram
              Many celebrities and athletes are brand spokespeople and generate additional income with social media advertising and sponsored content. Unsurprisingly, Ronaldo ranked first again, as the average media value of one of his Instagram posts was 985,441 U.S. dollars.

Access detailed insights with our Instagram datasets, featuring follower counts, verified status, account types, and engagement scores. Explore post information including URLs, descriptions, hashtags, comments, likes, media, posting dates, locations, and reel URLs. Perfect for understanding user engagement and content trends to drive informed decisions and optimize your social media strategies. Over 750M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Account Fbid Id Followers Posts Count Is Business Account Is Professional Account Is Verified Avg Engagement External Url Biography Business Category Name Category Name Post Hashtags Following Posts Profile Image Link Profile URL Profile Name Highlights Count Highlights Full Name Is Private Bio Hashtags URL Is Joined Recently And much more

As of July 2025, almost 33.3 percent of global Instagram audiences were aged between 25 and 34 years, and 29.7 percent of users were aged between 18 and 24 years. Overall, 17.4 percent of users belonged to the 35 to 44-year-old age group. Instagram users With roughly one billion monthly active users, Instagram belongs to the most popular social networks worldwide. The social photo-sharing app is especially popular in India and in the United States, which have about 480 million and 181 million Instagram users each. Instagram features One of the most popular features of Instagram is Stories. Users can post photos and videos to their Stories stream, and the content is live for others to view for 24 hours before it disappears. In January 2019, the company reported that there were 500 million daily active Instagram Stories users. Instagram Stories directly competes with Snapchat, another photo-sharing app that initially became famous due to its “vanishing photos” feature. As of the third quarter of 2025, Snapchat had 477 million daily active users.

Discover high-performing influencers with our comprehensive Instagram Influencers dataset. Access critical metrics including follower counts, engagement rates, verified status, business categories, and bio information. Analyze top posts, profile details, related accounts, and contact information to identify the perfect influencers for your brand partnerships and marketing campaigns. Millions of influencer records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Account Fbid Id Followers Posts Count Is Business Account Is Professional Account Is Verified Avg Engagement External Url Biography Business Category Name Category Name Following Posts (Top Posts Data) Profile Image Link Profile URL Profile Name Highlights Count Full Name Is Private Bio Hashtags URL Is Joined Recently Has Channel Partner ID Business Address Related Accounts Email Address And much more

data Source - https://statso.io/instagram-reach-analysis-case-study/

Certainly! Let's conduct a case study on Instagram reach analysis. To make the case study more specific, let's imagine a scenario where a fashion brand called "Fashionista" wants to analyze the reach of their Instagram account over the past six months.

Objective: Analyze the reach of Fashionista's Instagram account and identify trends, patterns, and insights that can help improve their reach and engagement.

Steps for the Instagram Reach Analysis:

1. Data Collection:

   • Gather data from Fashionista's Instagram account for the past six months.
   • Collect metrics such as follower count, post reach, impressions, likes, comments, and engagement rate.
   • Use Instagram's built-in analytics or third-party tools like Iconosquare or Sprout Social to retrieve the necessary data.

2. Define Key Metrics:

   • Identify the key metrics that will help assess the reach of Fashionista's Instagram account.
   • Key metrics may include follower growth rate, average reach per post, total impressions, engagement rate, and engagement per post.

3. Analyze Follower Growth:

   • Plot the follower count over the past six months to observe any trends.
   • Calculate the follower growth rate to understand the rate at which the account is gaining or losing followers.
   • Look for any significant changes in follower count and investigate potential reasons behind those changes.

4. Evaluate Post Reach and Impressions:

   • Analyze the average reach per post and total impressions to understand the reach of Fashionista's content.
   • Identify posts with the highest and lowest reach and compare their characteristics.
   • Look for patterns or themes that resonate well with the audience and those that underperform.

5. Assess Engagement:

   • Calculate the average engagement rate and compare it across different types of content (e.g., images, videos, stories, reels).
   • Identify posts with the highest engagement rate and analyze their content, captions, and hashtags.
   • Look for patterns or elements that encourage higher engagement from the audience.

6. Identify Optimal Posting Times:

   • Analyze the data to identify the days and times when Fashionista's posts receive the highest reach and engagement.
   • Experiment with posting at different times and measure the impact on reach and engagement.

7. Monitor Competitors:

   • Analyze the reach and engagement of Fashionista's competitors' Instagram accounts.
   • Identify strategies or content types that work well for competitors and consider adopting similar approaches if relevant.

8. Generate Insights and Recommendations:

   • Summarize the findings from the analysis and identify key insights and trends.
   • Recommend strategies to improve Fashionista's Instagram reach based on the insights obtained.
   • Provide actionable recommendations such as optimizing content, using relevant hashtags, collaborating with influencers, or running Instagram ads.

By conducting a thorough analysis of Fashionista's Instagram reach, you'll gain valuable insights into their audience's behavior, content performance, and engagement patterns. These insights can help guide future content strategies and optimize reach and engagement on Instagram.

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 ...

Access Instagram company profiles from 48 European countries, enriched with global B2B firmographic data. This dataset offers a detailed view of businesses worldwide, including company name, industry classification, employee size, estimated revenue, headquarters location, and Instagram public URLs.

Designed for B2B sales teams, martech providers, recruiters, and market analysts, this dataset empowers precise targeting, segmentation, data enrichment, and research across any industry or region.

The dataset represents a sample of users and their engagement on the Instagram platform. The dataset contains 7 CSV files with different attributes. This requires a user analysis process to track how users engage and interact with our digital product (software or mobile application) in an attempt to derive business insights for the marketing, product, and development teams. Scenario: Work with the product team and derive insights from the questions asked by the product manager.

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.

Back in 2010, few could have predicted that a simple photo-sharing app would evolve into one of the world’s most influential platforms. Fast forward to 2025, and Instagram has become a digital cornerstone for creators, businesses, and consumers alike. From driving brand awareness to creating viral trends overnight, followers are...

Introduction

Social Media Marketing Statistics: Social media marketing has become a vital tool for businesses seeking to connect with their audience and accelerate brand growth. With platforms such as Instagram, TikTok, and LinkedIn taking the lead, companies are harnessing the potential of social media to build meaningful relationships with their customers.

By using a mix of organic content and paid advertising, brands can effectively target specific audiences, strengthen connections, and boost brand awareness. Beyond just promoting messages, social media marketing also enables businesses to track campaign performance and make real-time adjustments.

As digital advertising continues to evolve, understanding key trends and statistics is crucial for businesses looking to stay ahead of the competition. This introduction delves into the influence of social media marketing, focusing on the strategies that drive success in today’s digital landscape.

Use our Instagram Hashtags dataset (public data) to extract insights by filtering hashtags, follower counts, account type, or engagement metrics. Depending on your needs, you can purchase the full dataset or a customized subset. Popular use cases include trend analysis, brand monitoring, hashtag optimization, and influencer marketing. The dataset includes key data points such as hashtags, engagement scores, associated posts, locations, account types (business/non-business), and much more.

In this research, we aim to understand the sociotechnical role of Instagram in the modest fashion market in Brazil, proposing a theoretical-empirical model for the analysis of this social media as a market-thinking infrastructure. Therefore, we used two techniques for data collection: (a) semi-structured and in-depth interviews with 27 agents of the modest fashion market and (b) non-participant observation on Instagram. The data collected were analyzed by thematic content analysis. The results demonstrate that Instagram offers an infrastructure that mediates and standardizes interactions between market actors, allowing the construction of the modest fashion market, once seen as stigmatized. Instagram's infrastructure establishes rules that will dictate the success (or exercised power) of users (market actors) in the network and, consequently, in the market. // Steps to reproduce: The data for this research were collected using in-depth semi-structured interviews and non-participant observation on the Instagram social network. The interviews took place with 27 agents from the modest fashion market, divided into four groups of actors: digital influencers (9); Christian female singers (4); shopkeepers (5); and consumers (9). The choice of subjects participating in the interview was based on the peculiarities of each group of actors. Christian influencers and singers were chosen for their representation in the market, which meant having more than 5,000 followers on Instagram. Based on these requirements, the authors sent direct messages from Instagram to the influencers inviting them to participate in this research. Consumers were chosen through the snowball technique, that is, the authors had contact with two evangelical women in Minas Gerais and São Paulo who participated in the research and indicated others to participate. And, finally, the tenants were chosen based on their time in the market – defined as over 6 months. The scripts of the interviews covered three themes central: (a) cultural-religious influence; (b) social media; (c) perspectives on the modest fashion market. Because they took place during the Covid-19 Pandemic period (October/2020 to March/2021), all interviews were carried out in online format through Google Meet, Zoom, or WhatsApp applications, the choice being an option for the interviewees. Only four interviews followed each other asynchronously. The interviews were carried out asynchronously via WhatsApp, where separate audios were sent for each question in the script. Three interviewees responded via audio and only one responded in text format. On the same day, they were once the questions were sent, the answers were obtained – no dialogues took place on other days. The non-participant observation on Instagram co-occurred during the period of the interviews and exclusively on the profiles of the participants who agreed to participate in the research. In the non-participant observation, the sociotechnical aspects that involved actors in the modest fashion market stood out. To record the observations, a field diary with 32 pages was developed. For interpretation and analysis of the collected data, we used the content analysis. The categories were defined after the study, as they emerged from the data. The operationalization of the analysis took place through the steps proposed by Bardin (1977). First, a floating reading of the data was carried out, in which possible categories were identified. Subsequently, there were cuts and allocations of data in the types defined in the previous phase, and (re)organization of the categories. And, finally, the treatment of data and interpretation based on the theoretical connections proposed in the literature review were carried out. The entire analysis process was organized using spreadsheets in Microsoft Excel.

This horizontal bar chart displays revenues ($) by Instagram link using the aggregation sum in Deerfield. The data is about companies.

This horizontal bar chart displays CEO approval (%) by Instagram link using the aggregation count. The data is filtered where the industry is Leisure Products. The data is about companies.

Survey of 250 businesses on how much they spend on Instagram advertising and their Instagram advertising costs

Zomato Social Media Engagement Dataset – 30 Days (Synthetic)

This dataset contains 30 days of synthetic social media performance data for a fictional version of Zomato’s Instagram content. It is created purely for academic, analytical, and practice purposes. It includes key engagement metrics such as likes, comments, shares, reel views, and metadata like post type, format, and date.

⚠️ Disclaimer: This dataset is fully synthetic and does not represent real Zomato data. It is artificially generated to help students and analysts practice social media analytics, data visualization, content performance evaluation, and engagement modeling.