In this blog, we will look at the latest statistics about Fiverr users and see what the future will likely look like for the popular marketplace.

Here is a breakdown of their revenue year over year.

Here’s a breakdown of how active buyers Fiverr has had since 2017:

Here’s the full breakdown of average users’ spend since 2012:

Traffic analytics, rankings, and competitive metrics for fiverr.com as of August 2025

Dataset

This dataset was created by X1 REXORDS

Contents

Non-traditional data signals from social media and employment platforms for FVRR stock analysis

The gig economy, encompassing freelance work and on-demand services, is experiencing robust growth, driven by technological advancements, evolving work preferences, and a desire for flexible employment options. The market, estimated at $300 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $1 trillion by 2033. This expansion is fueled by several key factors: the increasing adoption of digital platforms connecting businesses with independent contractors; a growing preference among workers for flexible schedules and autonomy; and the scalability these platforms offer businesses needing temporary or project-based assistance. Furthermore, the gig economy's diverse sectors, including transportation (DoorDash, Favor Delivery, Turo), home services (TaskRabbit, BellHops), professional services (Guru.com, Upwork, Fiverr), and pet care (Rover), contribute to its overall market strength.
However, challenges remain. Regulatory uncertainties surrounding worker classification and employment benefits pose significant hurdles. Competition among gig platforms is fierce, requiring constant innovation and adaptation to maintain market share. Fluctuations in the broader economy can also impact demand for gig services. Despite these restraints, the overall trajectory suggests a continued expansion of the gig economy, driven by ongoing technological advancements, evolving workforce demographics, and the increasing reliance of businesses on flexible talent pools. The major players, including TaskRabbit, Upwork, and Fiverr, are well-positioned to capitalize on this growth, provided they navigate the regulatory and competitive landscapes effectively. Successful strategies will likely involve investments in technology, focus on user experience, and proactive engagement with regulatory bodies.

Weekly statistics showing how many Fiverr coupon codes were verified by the CouponBirds team. This dataset reflects real-time coupon validation activity to ensure coupon accuracy and reliability.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Fiverr Future Forecast: Where's the Market Heading? (FVRR)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The freelance platforms market, valued at $6.56 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 16.66% from 2025 to 2033. This surge is driven by several key factors. The increasing preference for flexible work arrangements among both employers and freelancers is a primary driver. Businesses are increasingly outsourcing projects to reduce overhead costs and access specialized skills on demand. Simultaneously, freelancers are seeking greater autonomy and diverse work opportunities, leading to a substantial increase in platform usage. Technological advancements, including improved communication tools and project management software integrated within these platforms, are further streamlining workflows and enhancing user experiences. The expanding gig economy, coupled with the rise of remote work, fuels the demand for efficient platforms connecting freelancers with clients across diverse industries. Market segmentation reveals significant opportunities within various application sectors. Project management, sales and marketing, IT, web and graphic design, and other specialized applications all contribute substantially to the market's overall value. The end-user segment is broadly divided between employers seeking cost-effective solutions and freelancers aiming for lucrative and manageable work. North America currently holds a significant market share, but regions like Asia and Europe are expected to witness rapid growth, driven by increasing internet penetration and the adoption of digital work practices. Competitive analysis shows a landscape populated by established players like Upwork and Fiverr, alongside emerging platforms vying for market share. The sustained growth trajectory indicates a promising future for this market, with continued innovation and expansion across geographical and application segments. Freelance Platforms Market: A Comprehensive Analysis (2019-2033) This in-depth report provides a comprehensive analysis of the global freelance platforms market, projecting a robust growth trajectory fueled by technological advancements and evolving work dynamics. The study period spans from 2019 to 2033, with 2025 serving as the base and estimated year. The report offers invaluable insights for businesses, investors, and researchers seeking to understand this dynamic market valued at billions. Recent developments include: November 2023: Upwork launched a new set of AI apps and offers, along with new educational content, so that independent talent on Upwork can utilize the overall potential of generative AI to enhance their productivity as well as improve the overall quality of their work. The launch mainly includes partnerships with industry-leading providers of tools that include generative AI, involving Amazon, Adobe, ClickUp, and Miro, as well as training resources from Jasper, Coursera, and Udemy., August 2023: Fiverr International Ltd launched a brand-new business solutions suite for mid- and large-size businesses, the all-new Fiverr Pro, and the debut of its neural network-powered Fiverr Neo with an aim to tackle the complex task of matching talent with customers.. Key drivers for this market are: Growing Need for Flexible Workforce, Increasing Demand for Specialized Skills. Potential restraints include: Slower Response Time of Underfloor Heating Systems than Radiator Systems. Notable trends are: Services Component to Witness Major Growth.

The global freelance platforms market size is expected to see substantial growth, increasing from USD 127.04 million in 2024 to USD 541.4 million by 2034, at a CAGR of over 15.6%. Leading industry players include Fiverr, Skyword, Upwork, Designcrowd, Freelancer.com, 99Designs, Catalant, Bark.com, CrowdSpring, and Guru.com.

Fiverr's stock may experience moderate growth due to expanding remote work and freelancing trends. However, increased competition from established platforms, uncertainty in the broader tech sector, and macroeconomic headwinds pose potential risks to its performance.

Context

I always wanted to access a data set that was related to the world’s population (Country wise). But I could not find a properly documented data set. Rather, I just created one manually.

Content

Now I knew I wanted to create a dataset but I did not know how to do so. So, I started to search for the content (Population of countries) on the internet. Obviously, Wikipedia was my first search. But I don't know why the results were not acceptable. And also there were only I think 190 or more countries. So then I surfed the internet for quite some time until then I stumbled upon a great website. I think you probably have heard about this. The name of the website is Worldometer. This is exactly the website I was looking for. This website had more details than Wikipedia. Also, this website had more rows I mean more countries with their population.

Once I got the data, now my next hard task was to download it. Of course, I could not get the raw form of data. I did not mail them regarding the data. Now I learned a new skill which is very important for a data scientist. I read somewhere that to obtain the data from websites you need to use this technique. Any guesses, keep reading you will come to know in the next paragraph.

https://fiverr-res.cloudinary.com/images/t_main1,q_auto,f_auto/gigs/119580480/original/68088c5f588ec32a6b3a3a67ec0d1b5a8a70648d/do-web-scraping-and-data-mining-with-python.png" alt="alt text">

You are right its, Web Scraping. Now I learned this so that I could convert the data into a CSV format. Now I will give you the scraper code that I wrote and also I somehow found a way to directly convert the pandas data frame to a CSV(Comma-separated fo format) and store it on my computer. Now just go through my code and you will know what I'm talking about.

Below is the code that I used to scrape the code from the website

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3200273%2Fe814c2739b99d221de328c72a0b2571e%2FCapture.PNG?generation=1581314967227445&alt=media" alt="">

Acknowledgements

Now I couldn't have got the data without Worldometer. So special thanks to the website. It is because of them I was able to get the data.

Inspiration

As far as I know, I don't have any questions to ask. You guys can let me know by finding your ways to use the data and let me know via kernel if you find something interesting

Analysis of ‘Population by Country - 2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/tanuprabhu/population-by-country-2020 on 21 November 2021.

--- Dataset description provided by original source is as follows ---

Context

I always wanted to access a data set that was related to the world’s population (Country wise). But I could not find a properly documented data set. Rather, I just created one manually.

Content

Now I knew I wanted to create a dataset but I did not know how to do so. So, I started to search for the content (Population of countries) on the internet. Obviously, Wikipedia was my first search. But I don't know why the results were not acceptable. And also there were only I think 190 or more countries. So then I surfed the internet for quite some time until then I stumbled upon a great website. I think you probably have heard about this. The name of the website is Worldometer. This is exactly the website I was looking for. This website had more details than Wikipedia. Also, this website had more rows I mean more countries with their population.

Once I got the data, now my next hard task was to download it. Of course, I could not get the raw form of data. I did not mail them regarding the data. Now I learned a new skill which is very important for a data scientist. I read somewhere that to obtain the data from websites you need to use this technique. Any guesses, keep reading you will come to know in the next paragraph.

https://fiverr-res.cloudinary.com/images/t_main1,q_auto,f_auto/gigs/119580480/original/68088c5f588ec32a6b3a3a67ec0d1b5a8a70648d/do-web-scraping-and-data-mining-with-python.png" alt="alt text">

You are right its, Web Scraping. Now I learned this so that I could convert the data into a CSV format. Now I will give you the scraper code that I wrote and also I somehow found a way to directly convert the pandas data frame to a CSV(Comma-separated fo format) and store it on my computer. Now just go through my code and you will know what I'm talking about.

Below is the code that I used to scrape the code from the website

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3200273%2Fe814c2739b99d221de328c72a0b2571e%2FCapture.PNG?generation=1581314967227445&alt=media" alt="">

Acknowledgements

Now I couldn't have got the data without Worldometer. So special thanks to the website. It is because of them I was able to get the data.

Inspiration

As far as I know, I don't have any questions to ask. You guys can let me know by finding your ways to use the data and let me know via kernel if you find something interesting

--- Original source retains full ownership of the source dataset ---

Four brands - Toyota, Jeep, Brass Pro Shops, and M&Ms - received an effectiveness score of *** for their Super Bowl 2021 advertising campaigns. Other brands to land a top 10 spot included Doritos, Door Dash, Budweiser and Fiverr, while Super Bowl fixtures Amazon and Bud Light did not make the top 10. Super Bowl 2021 ads ended up with an average score of ***, higher than the United States average of *.