Stories with Tag Market Research

• Show HN: I scrape Steam data every month and it's yours to download for free

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  Posted: 2025-02-24 11:43:42

  Verbose tl;dr

  GGInsights offers free monthly dumps of scraped Steam data, including game details, pricing, reviews, and tags. This data is available in various formats like CSV, JSON, and Parquet, designed for easy analysis and use in personal projects, market research, or academic studies. The project aims to provide accessible and up-to-date Steam information to a broad audience.

  A data enthusiast and software engineer, operating under the moniker "GG Insights," has undertaken a significant project involving the monthly scraping and public release of data from the Steam gaming platform. This freely available dataset, accessible via the website gginsights.io, offers a wealth of information regarding games available on Steam, providing potential value to a wide array of individuals, from game developers and market analysts to researchers and curious gamers. The project aims to empower others with comprehensive and up-to-date Steam data, removing the technical hurdles associated with acquiring and processing such information on their own.

  The provided data encompasses various facets of each game listed on Steam, including but not limited to, the game's title, associated tags or genres, pricing details, release date, and the number of reviews it has garnered. This allows for diverse analyses, such as tracking trends in game development, examining the correlation between pricing and popularity, and understanding the overall landscape of the Steam marketplace. The data is meticulously collected on a monthly basis, ensuring a relatively contemporary snapshot of the platform's offerings and mitigating the risk of utilizing outdated information. This regular update cycle facilitates the observation of dynamic changes in the Steam ecosystem, permitting the identification of emerging trends and shifts in consumer preferences.

  The website, gginsights.io, acts as the central repository for this curated data, presenting it in a structured and downloadable format. This simplifies the process of accessing and integrating the information into personal projects, research initiatives, or market analyses. By eliminating the need for individual scraping efforts, GG Insights empowers others to focus on utilizing the data for their specific purposes, be it academic exploration, market research, or personal projects. This initiative effectively democratizes access to valuable Steam data, placing a powerful tool in the hands of anyone interested in exploring the complexities of the digital gaming market.

  • Steam
  • data scraping
  • Data Analysis
  • game data
  • Video Games
  • Dataset
  • Free Data
  • Open Data
  • CSV
  • Download
  • Monthly Updates
  • Gaming Industry
  • Market Research

  Summary of Comments ( 36 )
  https://news.ycombinator.com/item?id=43158425

  Verbose tl;dr

  HN users generally praised the project for its transparency, usefulness, and the public accessibility of the data. Several commenters suggested potential applications for the data, including market analysis, game recommendation systems, and tracking the rise and fall of game popularity. Some offered constructive criticism, suggesting the inclusion of additional data points like regional pricing or historical player counts. One commenter pointed out a minor discrepancy in the reported total number of games. A few users expressed interest in using the data for personal projects. The overall sentiment was positive, with many thanking the creator for sharing their work.

  The Hacker News post "Show HN: I scrape Steam data every month and it's yours to download for free" generated a fair number of comments, mostly focusing on the legality and ethics of scraping, the potential usefulness of the data, and suggestions for the project.

  Several commenters raised concerns about the legality of scraping Steam data, particularly given Steam's terms of service. They pointed out the potential for Steam to take action against the scraping activity or even against users of the data. One commenter suggested checking the robots.txt and respecting rate limits to mitigate some of these risks. Another pointed out the potential legal grey area, noting that court cases regarding scraping have had mixed outcomes.

  The usefulness of the provided data was also a topic of discussion. Some users questioned the value of monthly snapshots, suggesting that more frequent updates would be more beneficial for certain types of analysis, such as tracking game popularity or pricing changes. Others suggested potential use cases, such as identifying trending games or analyzing the effectiveness of marketing strategies. One commenter even proposed integrating the data with existing game discovery tools.

  Many commenters offered constructive feedback and suggestions for the project. These included:

  • Providing more granular data: Suggestions included details on player counts, playtime, and reviews.
  • Offering different data formats: Commenters mentioned the preference for formats like CSV or JSON over the provided Parquet format due to its broader accessibility and ease of use for analysis.
  • Improving data documentation: Users requested clearer documentation on the data schema and included variables.
  • Exploring alternative data sources: One commenter suggested using the publicly available Steam API, though acknowledging its limitations compared to comprehensive scraping.
  • Adding data visualizations: Visualizations of key trends and insights were suggested to enhance the data's usability and appeal.
  • Monetization strategies: While the data is currently offered for free, some commenters offered potential monetization strategies, such as premium tiers with more frequent updates or additional features.

  A few comments expressed appreciation for the project and the free availability of the data, while others questioned the motivation behind the project and the long-term sustainability of providing the data for free. Overall, the discussion highlighted the complex issues surrounding web scraping, the diverse potential applications of readily available data, and the importance of community feedback in shaping data-driven projects.

• Analysis of Product Hunt products from 2014 to 2021

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  Posted: 2025-01-26 14:59:26

  Verbose tl;dr

  An analysis of Product Hunt launches from 2014 to 2021 revealed interesting trends in product naming and descriptions. Shorter names, especially single-word names, became increasingly popular. Product descriptions shifted from technical details to focusing on benefits and value propositions. The analysis also highlighted the prevalence of trendy keywords like "AI," "Web3," and "No-Code," reflecting evolving technological landscapes. Overall, the data suggests a move towards simpler, more user-centric communication in product marketing on Product Hunt over the years.

  A comprehensive analysis, spanning from 2014 to 2021, delves into the evolving landscape of products launched on Product Hunt, a prominent platform for showcasing new technological innovations. The analysis meticulously examines the shifting trends in product categories, meticulously categorizing them into distinct clusters like "Gamer," "Nihilist," "Hustler," and "Builder," reflecting the underlying motivations and target audiences of these products.

  The "Gamer" category, representing a significant portion of the showcased products, focuses on entertainment and leisure, encompassing games, streaming services, and other forms of digital amusement. This category demonstrates a consistent presence throughout the analyzed period, highlighting the enduring appeal of entertainment-focused products. In contrast, the "Nihilist" category, characterized by products addressing themes of escapism, anonymity, and mental well-being, experienced a notable surge in prominence, particularly in the later years of the study. This rise arguably reflects a growing societal awareness and concern surrounding mental health and digital privacy.

  The study further explores the "Hustler" category, encompassing products designed to enhance productivity, facilitate online business ventures, and capitalize on the gig economy. This category demonstrates a steady growth trajectory, mirroring the increasing prevalence of remote work and entrepreneurial pursuits in the digital age. Finally, the "Builder" category, representing tools and platforms aimed at software developers and tech enthusiasts, maintains a consistent presence, reflecting the ongoing demand for innovative development resources.

  The analysis meticulously charts the rise and fall of these distinct product categories over time, providing valuable insights into the evolving interests and needs of the Product Hunt community. It leverages visualizations and statistical data to illustrate the shifting trends, offering a comprehensive overview of the changing landscape of product innovation within the tech industry. Furthermore, the study considers the potential impact of external factors, such as societal shifts and technological advancements, on the observed trends, providing a nuanced understanding of the complex dynamics shaping the Product Hunt ecosystem. Ultimately, the analysis offers a valuable perspective on the evolution of product development and the prevailing trends within the technology sector over the specified timeframe.

  • Product Hunt
  • Product Analysis
  • Startup Analysis
  • Tech Trends
  • Product Launches
  • Time Series Analysis
  • Data Analysis
  • Market Research
  • 2014
  • 2015
  • 2016
  • 2017
  • 2018
  • 2019
  • 2020
  • 2021

  Summary of Comments ( 45 )
  https://news.ycombinator.com/item?id=42830478

  Verbose tl;dr

  HN commenters largely discussed the methodology and conclusions of the analysis. Several pointed out flaws, such as the author's apparent misunderstanding of "nihilism" and the oversimplification of trends. Some suggested alternative explanations for the perceived decline in "gamer" products, like market saturation and the rise of mobile gaming. Others questioned the value of Product Hunt as a representative sample of the broader tech landscape. A few commenters appreciated the data visualization and the attempt to analyze trends, even while criticizing the interpretation. The overall sentiment leans towards skepticism of the author's conclusions, with many finding the analysis superficial.

  The Hacker News post discussing the analysis of Product Hunt products from 2014-2021 has several comments that delve into different aspects of the original analysis and the Product Hunt platform itself.

  A recurring theme in the comments is the perceived shift in the type of products launched on Product Hunt. Several users note the increase in AI-related tools and no-code/low-code platforms, reflecting the broader trends in the tech industry. Some commenters express a sense of nostalgia for earlier days of Product Hunt, suggesting that the platform used to feature more genuinely innovative and unique projects, while now it seems dominated by iterative improvements or trendy, less impactful tools. This sentiment is captured in comments lamenting the prevalence of "yet another AI tool" or the feeling that Product Hunt has become less about groundbreaking products and more about marketing and hype.

  Another thread of discussion revolves around the methodology of the original analysis. Some users question the chosen metrics and the interpretation of the data. For example, the use of "gamer" and "nihilist" as classifications is challenged, with commenters suggesting these labels are overly simplistic and don't adequately capture the nuances of product development and market positioning. Some propose alternative metrics or analytical frameworks that might provide a more comprehensive understanding of the trends on Product Hunt.

  There's also a discussion about the role and impact of Product Hunt itself. Some argue that its influence has waned over the years, while others maintain that it remains a valuable platform for discovering new tools and technologies. The discussion touches upon the challenges faced by indie developers in getting visibility and the increasing difficulty of standing out in a crowded marketplace.

  Several comments focus on specific examples of products mentioned in the original analysis, offering personal anecdotes and opinions about their usefulness and market fit. These examples serve to illustrate the broader points about the evolution of Product Hunt and the changing landscape of the tech industry.

  Finally, some commenters offer alternative explanations for the observed trends. For example, one user suggests that the apparent rise in "nihilist" products might simply reflect a greater focus on solving practical problems rather than pursuing grand visions. This perspective challenges the negative connotations associated with the "nihilist" label and offers a more nuanced interpretation of the data.

  Overall, the comments on Hacker News provide a rich and multifaceted discussion about the evolution of Product Hunt, the trends in product development, and the challenges of navigating the modern tech landscape. They reflect a mix of nostalgia for the past, skepticism about the present, and cautious optimism for the future.