Search Results

This is the transcript of our latest live session “Global developer population trends 2025 - How many developers are there?” which you can watch in the following video. Join our newsletter  and keep up to date with new webinars . You can find all past sessions on our webinars page .  Getting started & quick developer population estimation methodology overview Moschoula Hi everyone. Welcome back to SlashData's webinar series for 2025. For those who aren't familiar with us and are joining for the first time, SlashData is a market research firm active in the technology community for nearly 20 years. We serve the technology community, helping companies make data-backed, high-impact decisions with confidence. We help you understand your customers, your users, your decision-makers, and guide everything from product design to marketing strategies. We will continue this series throughout the year, so stay tuned and join our newsletter to get invited to the next sessions. Without further ado, we have our Director of Research here today, Kostas Korakitis. He will end the long-winded debate on software developer populations. There has been a ton of discussion on the number of developers and whether that's been declining over the last couple of years—and even faster day by day. With the introduction of a massive wave of AI-assisted coding tools, this has become a big issue. So we want to talk about it as well and end that debate. I don't think we need anything else; I'll hand it over to Kostas. Thanks a lot, see you later. Kostas Korakitis Thank you, Moschoula, and hi everyone. Welcome to today's webinar on global developer population trends. As Moschoula said, I'm Kostas Korakitis, Director of Research at SlashData. Over the next half-hour or so, we are going to explore how the global developer population has evolved over the past three years: what's driving this growth, where it's happening, and how its composition, focus, and geography are changing. Let me first quickly walk you through what we will cover today. I'll start with a brief description of our developer population sizing methodology—essentially, how we derive the population estimates that I will present today. Then we'll look at how the developer population has grown over the past three years and how the balance between professionals and amateur developers has shifted. We'll then present several breakdowns of the global developer population by important dimensions, such as region, types of software development projects, programming language communities, industry verticals, and company size. First, on our population sizing methodology—how we arrive at our estimates. We calculate the number of active software developers globally using our own independent bottom-up methodology, firmly rooted in reliable measurement through our Global Developer Survey. We're not just using available third-party population estimates; we derive our own estimates independently. Our methodology is based on two main pillars. First, we make use of reliable sources of developer numbers or direct indicators of their activity. This includes the number of GitHub accounts, Stack Overflow accounts, and employment statistics from the USA and the European Union. Second, we rely on our Global Developer Survey data, where we directly measure developer activity. So far, we've run 29 waves of this large survey, and in each, we reach more than 10,000 developers globally. We combine these two main sources to derive our estimates. One important point is that we avoid making assumptions about similarities between geographies or other subsets of the developer population. For example, while we use employment statistics from the EU and USA, we do not extrapolate to other regions. Instead, we rely on measurements from our surveys about the geographic distributions of developers to estimate numbers by region. There are 47.2 million developers in the world in 2025 With that, let's begin with one of the most important questions: How many developers are there globally? According to our data, at the beginning of this year, we estimate the global developer population at just over 47 million. That's a striking increase of about 50% from Q1 2022, when the number was just over 31 million. Such growth is impressive in any sector, but particularly for developers—it shows how pervasive software development has become in shaping the global economy. How the developer economy and population is growing in 2025 However, while the three-year growth curve looks impressive, it's even more revealing to see how growth rates have changed over time. Between 2022 and 2023, we saw an increase of 15%. Then, from 2023 to 2024, there was a sharp spike—21% in just one year—likely fueled by post-pandemic investments, startup funding booms, and surging demand for digital services. However, in the last 12 months, the growth rate has decelerated to just 10%. This slowdown may mark the beginning of a new phase: a plateau. A cooling global economy, saturation in mature markets, or diminishing returns on digital investments could all be contributors. This doesn't necessarily mean the developer economy is in decline, but it does suggest that we should temper expectations for continued exponential growth. The immediate implication is that opportunities may shift from quantity to quality. That is, how companies train, retain, and support developers will become more important than simply how many are entering the field. This slowing of growth becomes even more meaningful when we dig into who is driving the expansion. What we see is a clear divergence: the professional segment is expanding, while the amateur segment has begun to contract. From early 2022 to early 2025, the number of professional developers grew significantly—by 70%—from 21.8 million to 36.5 million. By contrast, the population of amateur developers only grew moderately and actually declined by over 1 million in the last year. This is a telling sign: professional developers are staying longer and growing in numbers, while the traditional feeder population—amateurs—is shrinking. There are many forces at play. In the past decade, software development has become a stable career path. More people are attending university or boot camps with the express goal of becoming software developers, building long-term careers around it. Is the developer economy reaching maturity levels? At the same time, there's been a cultural shift. Coding is less about exploration and more associated with work, startups, and monetisation. Younger audiences now have many more ways to express their creativity—through game mods or design tools—than simply by coding. This shift could pose a risk to the long-term pipeline. If fewer people explore programming today, fewer professionals may enter the field tomorrow. While the professional population is growing now, declining engagement at the entry level is a red flag. Unless companies invest in developer education and make entry points more accessible, we may face stagnation or even decline in future years. This brings us to another related trend: the ageing of the developer population. At the beginning of 2022, developers aged 18–24 made up close to a third of the global population. By early 2025, they represent only 23%—a drop of eight percentage points in just three years. Meanwhile, the share of developers aged 35–44 has steadily climbed from 22% to 26%. The overall picture is clear: fewer young people are entering development, and more experienced developers are staying. The population is gradually aging, growing more seasoned, and becoming more professionalised. There are benefits—deeper experience, more institutional knowledge, more stable career paths. These developers are leading teams, driving architectural decisions, and mentoring others. However, the concern again is about sustainability. If fewer younger developers replace retirements and transitions, we may face long-term shortages. This also impacts companies offering developer tools and platforms. As developers grow older and more experienced, their learning and tooling preferences shift. There's less focus on quick experimentation and more on robust, reliable tooling and long-term support, including high-quality documentation. Companies that want to stay close to this evolving community need to account for these shifts in priorities. Geographical distribution of developers When we look at how developers are distributed geographically, we see a rich and evolving map of the global developer population. Western Europe and North America remain the largest communities, with about 9.5 million developers each. These markets have long been centres of software innovation and continue to be deeply influential. Western Europe and North America remain the largest communities, with about 9.5 million developers each. However, growth is happening in other regions too. South Asia, for example, has nearly doubled in size—from 4 million developers in 2022 to 7.5 million today—largely driven by India’s massive and increasingly sophisticated tech workforce. The region combines a young, dynamic workforce, strong STEM education, and a growing ecosystem of startups and tech giants. Greater China has seen explosive growth as well, nearly tripling its developer population since 2022—from 2.4 million to 5.8 million developers. This reflects China's investment in developer education, homegrown platforms, and government-backed initiatives. South America has also grown steadily, from 1.7 million developers to 3.4 million over three years. Countries like Brazil, Argentina, and Colombia are emerging as tech outsourcing hubs, with strong local demand in industries like FinTech and mobile solutions. The takeaway is clear: while Western markets remain dominant in size, the fastest growth and most dynamic momentum are coming from Asia and Latin America. What developers are working on in 2025 Now let's turn our attention and focus on what developers are actually working on—the types of projects they're involved in. First of all, it's perhaps unsurprising that the most popular application area is web. Over 23 million developers are working on front-end and back-end applications. Over 23 million developers are working on front-end and back-end applications. Right behind them are backend services and data science and ML/AI applications. However, what's perhaps a bit more interesting is that the top sectors—the most popular sectors—are the ones that are rising fast and declining. So let's talk about the fast risers first. Here we see that the development of applications and extensions for third-party ecosystems has seen steady growth since 2022. This sector includes things like extensions for commerce platforms like Shopify, extensions for IDEs, browser add-ons, and so on. Developers are really finding value in building on top of existing platforms where user bases are already established. This is a model that offers distribution, monetization, and low go-to-market friction. Another fast-growing area is embedded software, where the population involved in these types of projects has more than doubled since 2022. This growth reflects the rise of connected devices, automotive systems, and custom hardware. From consumer electronics to industrial sensors, embedded development is moving into the mainstream. Now the flip side: some projects have seen a decline. For example, mobile app development has slipped slightly in the last year. In previous years the growth was modest, but recently we've seen a downturn. This might be due to market saturation, app store consolidation, or even the rising costs of user acquisition. Similarly, desktop apps have seen a decline in the number of developers involved. This reflects the long-term trend away from native desktop software applications and toward web-based or cross-platform solutions. The message here is that developer interest, although still strong in traditional application areas, is slowly shifting toward other paradigms—especially those offering integration, automation, and ecosystem leverage. The most used programming languages Now, another question we often get is to size specific language communities. JavaScript continues to hold the top spot in terms of number of users, with 20 to 28 million users and healthy growth over the last three years. Java and Python have been in the top three for a while now. Both show steady and healthy growth since 2022, and each now has around 23 million users. These languages have wide applicability, strong communities, and very mature ecosystems. If we focus on growth rates, we find some interesting insights. Rust stands out as the fastest-growing of the major languages—those with over 5 million users—more than doubling in size since 2022. That growth is driven by Rust’s focus on safety, performance, and concurrency. It’s becoming the go-to choice for systems programming, embedded development, and blockchain infrastructure. C++ is another fast-growing language. Although often seen as a legacy language, it remains relevant. It has grown from 9.4 million developers in 2022 to 16.3 million in 2025. This reflects its continued importance in high-performance applications, gaming, and modern embedded systems where performance and efficiency are critical. Developer preferences are diversifying. It's not just about picking the most popular language—it's about choosing the right tool for the right job. So the key takeaway is that developer preferences are diversifying. It's not just about picking the most popular language—it's about choosing the right tool for the right job. Developers often use multiple languages at once, depending on the projects they work on. The most popular industries that attract developers Moving our attention to the industry verticals developers are active in, we see that software products and services is by far the largest vertical, with nearly 14 million developers. That’s expected, as it's the core of the tech economy. Software products and services is by far the largest vertical, with nearly 14 million developers. But beyond that, we see important growth in other verticals too. For example, manufacturing has nearly doubled its developer population in just three years—from just over 2 million developers to nearly 4 million in 2025. This is largely driven by Industry 4.0, where connected factories, automation, and robotics make software a central pillar of production. Telecommunication and networks have also seen strong growth. As telcos embrace 5G, edge computing, and software-defined infrastructure, they’re hiring developers to manage increasingly complex systems. Data analytics and BI is another fast riser. The number of developers in this sector has grown from 4 million to 5.8 million in three years. As every company becomes a data company, demand for people who can extract insights continues to rise. In short, software is no longer confined to the tech sector. Every industry is becoming digital, and every digital strategy needs developers to bring it to life. The size of companies developers work for Finally, let’s look at where developers are working. Medium-sized businesses—those with 51 to 1,000 employees—are the fastest-growing employers of developers. This group has expanded to 14.5 million developers at the beginning of 2025. Large enterprises also continue to grow, employing 7.5 million developers today. Together, these two segments account for over 60% of all professional developers globally. Small businesses and freelancers have remained relatively stable. This may point to consolidation in the industry, higher costs for independent developers, or more structured employment paths. This tells us that innovation is no longer just the domain of startups or tech giants. Mid-sized businesses are becoming innovation hubs. They’re growing fast, hiring aggressively, and often have the flexibility to explore new technologies without the red tape of large enterprise environments. For companies building software tools and platforms, this middle tier is a sweet spot. They’re large enough to have impact but nimble enough to adopt and scale quickly. Key takeaways and summary for software developer trends in 2025 This brings me to the end of the presentation. We've covered a lot. We started with the explosive growth of the developer population, then looked at signs of potential slowdown and stagnation. We saw that this growth is primarily driven by professionals, while the amateur segment is shrinking, and the developer community is gradually becoming older. We also looked at regional shifts—where the strongest growth is happening. We explored how developers are expanding into new types of software development, how language preferences are evolving, which industries are growing fast, and how medium-sized businesses are becoming centres of innovation. One of the main takeaways is that the developer economy isn’t just growing—it’s transforming. The era of rapid expansion is giving way to maturity, specialisation, and deep integration with all facets of industry and society. So understanding developers—what drives them, where they work, what projects they're involved in, and their technology choices—is more critical than ever. Thank you. I’d love to open the floor for questions now. Q&A with the expert Moschoula Thank you so much, Kostas, for that presentation. Indeed, it’s extremely insightful, especially at this time. I want to revisit something as well. You gave a clear view that while there is still growth, it’s declining. But not necessarily in population yet—we’re not there. Is there any forecast for when growth is estimated to stagnate? Our view is different from some others who are claiming the developer population is decreasing by the millions. We don’t see that in the data. From what we’ve seen and what your team has found, there's a slowdown, but what do you see for the next few years? Kostas Korakitis Yes, that’s a good point. This is something people talk about a lot—that the developer population is shrinking. But this is not what we see in the numbers. A slowdown is evident—we've seen it over the past year. It’s always hard to predict exactly what will happen, but if the slowdown continues at the same rate, then in a year or two, we may reach the point where the population is no longer growing. That doesn’t necessarily mean it will decline, but stagnation is a likely scenario. Moschoula Thank you for that. I also want to go back to the topic of programming languages. Understanding the size of programming language communities is probably the most popular data point that we—and our community—look at regularly. You mentioned Rust being the fastest growing, and touched on what’s next. But what about visual tools and C++—how do you justify their growth? Kostas Korakitis Yes, one thing I didn’t cover in detail is visual tools, which have seen really fast growth over the last three years. In 2022, they had 5 million users, and now we’re close to 9 million. That’s very impressive growth. This is proof that more people are using no-code or low-code platforms. More than anything, it expands the definition of who a developer is. In our survey, we use “developer” in a broad sense—anyone involved in software development projects in any capacity. The number of people using visual tools, who may not be traditional coders, is increasing. They're using no-code platforms to build business apps, automate tasks, and contribute to digital workflows. This is a real shift. We’ve been hearing about the rise of no-code tools, and now we’re seeing the data to back it up. Moschoula Really interesting and impactful. This is extremely useful information and very helpful in validating some of those numbers for us. Okay, I don’t see any more questions today, so we can close the webinar. We hope you all got a lot out of it. This will be available as a recording after the live session. Join our newsletter  and keep up to date with new webinars —we’ve got a few exciting ones coming up with our partners. You'll hear more soon. Thank you all, and have a great day. Bye for now. About the expert Kostas Korakitis, Director of Research at SlashData Konstantinos heads the Research Product team at SlashData and is responsible for all syndicated research products and custom research projects. With more than 10 years of experience as an engineer, consultant and manager, he oversees research planning, survey design, data analysis, insights generation and research operations.

AI is transforming industries at an incredible pace, but with its power comes significant security risks. From adversarial attacks to data breaches, companies must be prepared to protect the AI-powered applications they build. Yet, how do technology managers approach security and risk management in AI? Which practices are becoming standard, and who is leading the charge? Our recent research sheds light on how organisations secure their AI systems according to technology professionals in leadership positions, revealing some notable gaps. This blog post is based on a bigger report  that talks about trust, risk, and security management in AI overall. The blog narrows down the focus, diving deeper into data collected from 569 professionals in management positions within tech companies, namely tech/engineering team leads, CIOs / CTOs / IT managers, and CEO/management. They answered questions about trust, risk, and security management in AI in the 27th edition of our global Developer Nation Survey , which was fielded in Q3 2024. How are organisations protecting their AI-powered applications? AI security risks range from adversarial attacks and data breaches to model manipulation. To mitigate these threats, organisations deploy various protective measures. Companies are mainly investing in AI-specific security tools and technologies (33%) and encryption tailored for AI data (31%) to stay ahead of potential threats. Regular AI security audits (29%), staff training on AI security risks (29%), and data privacy management for AI (28%) are also common practices among organisations. However, not every organisation has made AI security a priority. While 82% of technology professionals report their company uses at least one mitigation strategy, 10% admit they have no AI-specific risk management in place, and another 8% simply don’t know what their company is doing to address security risks. Nearly one in five technology leaders either have no AI risk strategy or don’t know if their organisation has one Who is driving AI security efforts within organisations? Security is no longer just the responsibility of IT teams. CIOs, CTOs, IT managers, and senior executives (including CEOs) report equal adoption rates of AI-specific security practices, 86% and 85%, respectively. This suggests that AI security is recognised as both a technical challenge and a business priority at the leadership level. However, tech and engineering team leads lag behind, with only 72% reporting the implementation of AI security practices within their organisations. This gap indicates a disconnect between leadership’s security policies and awareness at the development level rather than differing priorities. Team leads may have less visibility into company-wide AI security strategies, which could explain the lower reported adoption. Among technology professionals, tech and engineering team leads report lower awareness of AI security practices within their organisations Company size matters: Are smaller firms falling behind in AI security? Company size plays a significant role in how AI security is handled. Managers in large enterprises (i.e., companies of more than 1,000 employees), with their expansive resources and dedicated security teams, report the highest adoption rate of AI security practices, with 90% implementing such measures. Managers in medium-sized businesses (51-1,000 employees) follow closely at 86%, but those in small businesses (up to 50 employees) lag far behind at just 64%. This gap isn’t just about awareness - it’s about priorities and resources. Large enterprises are far more likely than small businesses to conduct AI-specific penetration testing (32% vs. 10%), regular security audits (34% vs. 18%), and threat intelligence and risk assessments (28% vs. 14%). With tighter budgets and fewer specialised security personnel, smaller companies often struggle to allocate resources for AI-specific protections, relying instead on broader cybersecurity measures that may not fully address AI-related risks. However, medium-sized companies take the lead over both small and large companies when it comes to employing certain AI security practices. They lead in the adoption of AI-specific security tools, with 40% using them compared to 33% of large companies and just 20% of small businesses. Similarly, 35% of medium-sized businesses have data privacy management solutions tailored for AI, surpassing large enterprises at 27% and small businesses at 16%. This suggests that medium-sized companies, while not having the vast resources of large corporations, may be more agile in adopting emerging security technologies, striking a balance between strategy and execution. While large enterprises have the budgets and teams to prioritise AI-specific protections, small businesses struggle to keep up, leaving them more vulnerable to AI-related threats How does AI security vary across development types? AI security is far from uniform across industries. Each sector faces unique challenges shaped by the nature of its AI applications, the volume of data it processes, and the potential risks associated with AI-driven automation. While some industries have embraced AI security as a fundamental requirement, others are lagging, either due to a lack of awareness, lower perceived risks, or resource constraints. At the forefront of AI security adoption are managers involved in consumer electronics (96%), augmented reality (95%), and industrial IoT (95%) projects. Managers in these industries prioritise security not just because of regulatory pressures but also due to the inherent risks associated with their AI-driven operations. Consumer electronics and IoT devices, which process vast amounts of real-time personal and behavioural data, place a heavy focus on robust encryption and access control. In fact, 45% of managers in this sector report implementing these protective measures to prevent data breaches and adversarial attacks. Augmented reality (including mixed reality applications) goes even further, with 59% of managers reporting using encryption measures tailored specifically for AI data. This emphasis likely stems from the fact that AR systems often involve real-time spatial data processing, biometrics, and interactive user engagement, making them highly sensitive to security threats. However, not all sectors demonstrate the same level of urgency when it comes to AI security. Backend services fall significantly behind, with only 69% of managers working in backend reporting that their organisation has AI-specific security measures in place. Of the rest, 16% are unsure whether their company has any AI security practices at all, and a notable 15% confirm that their organisation has no such measures in place. This lack of adoption suggests that backend service providers may still be relying on traditional cybersecurity approaches, underestimating the distinct vulnerabilities that AI-powered applications introduce. Industries handling sensitive consumer data, like consumer electronics and IoT, lead in AI security adoption. However, backend services may be underestimating AI-specific risks by relying on traditional cybersecurity measures. How does experience impact AI security awareness? One of the more unexpected findings in AI security management is that less-experienced managers are more likely to implement AI security measures in their companies than their seasoned counterparts. Managers with less than two years of experience in software development report the highest adoption rate within their organisations, at 90%, while those with over a decade of experience drop to 74%.  This decline could indicate that organisations with more experienced managers rely more on traditional cybersecurity approaches rather than AI-specific frameworks. While awareness levels remain consistent across experience groups, companies led by seasoned professionals may be slower to adapt their security strategies to evolving AI threats. As AI risks become more sophisticated, ensuring that security measures keep pace will require continuous evaluation and adaptation at the organisational level. Organisations with more experienced managers may be slower to adopt AI-specific security frameworks, potentially relying more on traditional cybersecurity approaches Want to dig deeper? This post only scratches the surface of how tech leaders approach AI security and risk management. For a more comprehensive view, check out our full report on  Trust, Risk, and Security Management in AI . You'll find deeper insights into how organisations build trustworthy AI systems and where critical gaps still exist. You can also explore AI and related topics more on SlashData’s blog . Questions or feedback? We’d love to hear from you. Whether you're looking to collaborate, dig into our data, or simply want to chat, feel free to contact us. About the author Bleona Bicaj, Senior Market Research Analyst Bleona Bicaj is a behavioral specialist, enthusiastic about data and behavioral science. She holds a Master's degree from Leiden University in Economic and Consumer Psychology. She has more than 6 years of professional experience as an analyst in the data analysis and market research industry.

Artificial intelligence (AI) is transforming the world of modern software development, with use cases that range from data processing to generating code. In this blog post, we explore the future of AI in software development from the perspective of professionals involved in software development who hold leadership positions [1] . We first consider how their opinions differ from their counterparts in non-leadership positions and then move on to breaking down their beliefs by company size and region. These insights provide a window into how the adoption of AI is evolving across the industry and where beliefs may diverge depending on organisational and geographical contexts. This blog post is based on data collected from over 4,500 technology professionals who answered questions about AI in the 28th edition of SlashData’s global Developer Nation Survey, which was fielded in Q4 2024. Looking for a broader business perspective? Discover how Sales & Marketing use Generative AI in our free report . Most important future use cases for AI in software development according to technology leaders When looking at the opinions of technology professionals in leadership roles and comparing them to those who work in non-leadership roles, we see a lot of broad similarities but also a selection of distinct differences. For instance, both groups show strong recognition of intelligent development assistants  (30% vs 29%) and data processing, analytics and visualisation  (26% vs 26%) amongst the most important future use cases of AI in software development. In terms of differences, we find that technology leaders are significantly more likely than their counterparts to emphasise the importance of AI in the future of cybersecurity  (25% vs 20%). While this is likely due to the differences between these two groups in terms of the scope of their responsibilities, the popularity of this particular use case points to AI playing a critical role in enhancing security against an ever-growing landscape of threats. Technology leaders are also more likely to believe in the future importance of areas such as AI for DevOps  (22% vs 18%) and predictive project management  (16% vs 13%), highlighting their focus on optimising workflows and managing their teams. However, they are less likely than those in non-leadership roles to consider code generation  (25% vs 29%) as important. This suggests that those who work closer to the code are more likely to see immediate benefits from automating coding tasks. Most important future uses cases of AI in software development by company size On looking closer at what technology leaders think, we find an interesting set of patterns when segmenting their beliefs by company size. While certain trends remain the same, our findings also show that the future importance levels of some use cases are perceived very differently across different company sizes. In terms of similarities, there is little variation in the perception of use cases, such as intelligent development assistants,  when we consider company size. This suggests that technology leaders expect future AI tools targeting such use cases to be just as useful for developers who work for small businesses as those who work for larger firms. This also points to a potential shift in the dynamics of the developer workforce as a whole, where developers can take on more strategic roles and focus on the bigger picture while leaving routine coding tasks to AI. We find that leaders who work for large companies are significantly more likely than average to place emphasis on code generation  (35%) when considering the future of AI in software development. This suggests that larger companies see greater potential in using AI-generated code in their applications. This may be because these companies often have extensive codebases that require a lot of developer resources. Large companies may be the most likely to use AI-generated code in the next three to five years Similarly, we see that the perceived importance of AI for cybersecurity  is strongly linked to company size. As businesses grow, they also increase the attack surface of their systems and require more complex security measures. This is reflected in our data, with technology professionals in leadership roles at large companies being the most likely to mention cybersecurity  (31%) in their beliefs of the most important use cases. This drops to 27% amongst technology leaders at midsize companies and further down to 20% at small companies. This suggests that smaller businesses may be less likely to prioritise advanced cybersecurity solutions when considering AI. Most important future uses cases of AI in software development by region Regional differences in culture, regulations and socio-economic circumstances often play important roles in technology. As such, it is no surprise that these differences extend to the opinions of technology leaders about which use cases for AI in software development will be most important in the next three to five years. As with the case of company sizes, some use cases receive similar favourability from technology leaders across Europe, North America and the Rest of the World. This suggests that certain use cases, like intelligent development assistants  and performance monitoring and optimisation , show universal promise of addressing challenges and opportunities in the landscape of modern software development. The benefits of intelligent development assistants are perceived to be not only company-size agnostic but also region-independent. Technology leaders working in Europe are the most likely to perceive cybersecurity  as one of the top use cases for AI in the near future of software development (30% vs 26% in North America and 21% in the Rest of the World). While this is partly due to Europe having an above-average concentration of large companies, it also highlights the greater emphasis placed on topics such as data protection in this region due to regulations. Despite this, these technology leaders also recognise the potential that AI has to bring to their future applications. In fact, technology professionals in leadership roles working in Europe are significantly more likely than their counterparts in other regions to believe that adding AI functionality to applications  will be amongst the most important future use cases of AI (25% vs 19%). Furthermore, we also see that they are disproportionately more likely to consider bug detection and fixing  as important than their counterparts in other regions (27% vs 20%). Key takeaways Technology leaders foresee AI playing a crucial role in software development, with strong recognition of intelligent development assistants and data processing. They also emphasise cybersecurity, AI for DevOps, and predictive project management more than those in non-leadership roles. Leaders in larger companies are more likely to believe in the growing importance of code generation and cybersecurity in the future. We also see some interesting regional differences, with European technology leaders placing a higher emphasis on cybersecurity, adding AI functionality to applications, and bug detection/fixing. These insights highlight the evolving adoption of AI in the industry and varying favour based on organisational and geographical contexts. What type of AI data are you looking for? Maybe we already have what you need. Get in touch with us. [1] We consider those who self-identify to be in at least one of the following roles as technology leaders: “tech/engineering team lead”, “CIO / CTO / IT manager”, “CEO/management”. About the author Nikita Solodkov, Market Research and Statistics Consultant Nikita is a multidisciplinary researcher with a particular interest in using data-driven insights to solve real-world problems. He holds a PhD in Physics and has over five years of experience in data analytics and research design.

The Internet of Things (IoT) is transforming how we interact with technology and the world around us. This blog post explores the key players driving this transformation - companies building in the IoT space. We will examine the regional distribution of IoT professionals, analyse how organisations participate in the IoT supply chain, and conclude by focusing on Industrial IoT and the markets these companies are focusing on. The data in this blog post comes from the 27th edition of SlashData’s global Developer Nation survey, fielded in Q3 2024. This survey gathered responses from over 7,500 technology professionals, including more than 900 individuals professionally involved in IoT projects - over 50% of whom are decision-makers - spanning both Industrial IoT and Consumer IoT. If you want to look at the general IoT developer population, you can have a look at our full report .  Where IoT professionals are located Before exploring how organisations engage in the IoT supply chain, we’ll first look at the regional distribution of IoT professionals to provide a general context that can help understand IoT industry dynamics. The professional IoT ecosystem is heavily concentrated in North America and Western Europe, which together account for 55% of the world’s IoT professionals. This concentration is likely due to the presence of mature ecosystems, advanced infrastructure, numerous market leaders, and strong business networks in these regions. North America leads the pack, hosting nearly one-third (30%) of the global IoT workforce. North America and Western Europe together account for 55% of the world’s IoT professionals When compared to the broader technology landscape, North America and Greater China stand out as hotspots for the IoT industry. The concentration of IoT professionals in these regions surpasses the concentration of all technology professionals by at least five percentage points, indicating a stronger interest and focus on IoT. In contrast, South Asia presents a different scenario. Despite being home to 17% of the world’s technology workforce, the region accounts for only 9% of IoT professionals. This disparity could be attributed to infrastructure limitations, skill gaps, or market dynamics that prioritise other technology sectors over IoT. Deep dive into the dynamics of the IoT supply chain The digital backbone Supplying software solutions and operating IoT services are the most common ways organisations participate in the IoT supply chain, with 28% and 26% of professional IoT developers engaging in these activities, respectively. Both activities form the backbone of the digital side of the IoT ecosystem and are deeply interconnected. Software suppliers create the platforms and tools that enable IoT systems to function, while IoT service operators leverage these platforms to deliver solutions such as connectivity management and device monitoring directly to customers. Likely due to this close collaboration and interdependence, we observe that many organisations involved in these activities are leveraging their synergies to expand their value propositions. According to our data, approximately one-third of software suppliers are also operating IoT services and vice versa. Completing the digital side of the IoT chain are network operators, engaging 19% of IoT professionals, who provide the connectivity infrastructure that enables IoT devices to communicate and exchange data, acting as bridges between physical devices and digital platforms. Similarly, we also observe that many network operators are diversifying their offerings within the digital IoT space, extending beyond infrastructure services. According to our data, at least one-third of IoT professionals working for network operators are also engaged in providing software solutions (40%) or operating IoT services (33%). The device side On the physical side of the IoT supply chain, Original Equipment Manufacturers (OEMs) lead the pack. 19% of professional IoT developers work for organisations that design, develop, and market products under their own brands. Other manufacturing-related activities –those performed by EMSs, CEMs, OCMs, and ODMs– each account for 15% or less of organisations in the IoT ecosystem. However, when accounting for overlaps, we find that about half (52%) of IoT professionals are engaged in manufacturing or design activities, closely mirroring the 54% involved in the digital side of the IoT chain. This near-parity highlights how hardware remains just as integral as software and services in shaping the IoT ecosystem. 19% of IoT professionals work for OEMs, which design, develop, and market products under their own brands. Similar to the digital side, we find strong synergies between different manufacturing activities. Many companies engage in multiple activities to capitalise on operational efficiencies and expertise. For example, 31% of Original Design Manufacturers (ODMs) are leveraging their design capabilities to produce their own branded products, effectively becoming OEMs, while continuing to create custom designs for other customers. Similarly, 30% of Contract Electronics Manufacturers (CEMs) are combining their contract manufacturing capabilities with in-house product development. Despite the overlaps and synergies observed across both the digital and physical sides of IoT, fully integrated organisations (those managing all aspects of design, manufacturing, software development, and service delivery) remain relatively rare, with only 11% of IoT professionals working for fully-integrated businesses. This suggests that most companies prefer to leverage synergies within closely related areas rather than taking on the complexity of full vertical integration. By focusing on adjacent activities, companies can diversify revenue streams and reduce reliance on a single business function while maintaining operational focus and avoiding the challenges associated with managing end-to-end operations. The services side  Beyond the core building blocks of the IoT ecosystem (services and devices), technical consultancies (22%) hold a notable presence in the IoT supply chain. This likely reflects the complexity of IoT deployments, where organisations rely on external expertise for solution design, system integration, and implementation strategies. Lastly, at the bottom of the chart, we find value-added resellers and distributors, accounting for only 10% of IoT professionals. These entities play a crucial role in bridging gaps between hardware manufacturers, software providers, and end-users by customising solutions to meet specific needs. Markets targeted by Industrial IoT professionals Now that we understand how IoT professionals participate in the IoT supply chain, let’s examine the markets they are currently targeting. For the purpose of this blog post, the analysis focuses exclusively on Industrial IoT (ΙΙοΤ). If you want to explore more insights on consumer IoT, get in touch. We observe a strong inclination towards industrial and infrastructure-related markets, likely driven by IoT’s ability to enhance operational efficiencies and reduce costs in these areas. Manufacturing is, by far, the most commonly targeted sector, with 35% of IIoT professionals focusing on it, likely driven by the shift towards smart factories under the Industry 4.0 movement . The second most targeted market is smart cities and infrastructure, attracting 24% of IIoT professionals. This highlights the growing role of IoT in urban development, supporting applications such as traffic management, waste management, and public safety systems. Following closely behind is a diverse set of markets, each targeted by 16% to 20% of IIoT professionals, highlighting the versatility of these solutions. Environmental monitoring (20%) leads this group, likely driven by sustainability initiatives and increasing regulatory requirements. Lastly, the least targeted markets include hospitality and tourism (13%), retail (12%), and defence (7%). While these sectors leverage IIoT for specific applications such as customer experience enhancement or security, they remain less attractive to IoT professionals, likely due to lower overall demand or fewer opportunities to effectively leverage IoT in these markets compared to others. Are you involved in IoT? Or simply curious about IoT market analytics? This blog post is just a glimpse into the demographic and firmographic insights of IoT professionals that we can offer. For a deeper dive into the world of IoT, we have a wealth of additional data and insights waiting to be explored. Get in touch , and we can talk about the details. About the author Álvaro Ruiz, Research Manager Álvaro is a market research analyst with a background in strategy and operations consulting. He holds a Master’s in Business Management and believes in the power of data-driven decision-making. Álvaro is passionate about helping businesses tackle complex strategic business challenges and make strategic decisions that are backed by thorough research and analysis.

Right between the first pumpkin spice beverages and gift exchanging, now is the season when we all sit down and set our strategy for the year to come.  What are your goals for 2025:  Increasing conversions from free to paid?  Driving engagement or  Boosting adoption?  No matter what you strive for, you need data to ensure all your decisions are based on concrete data. To help with that direction, in this article, we will touch the surface of 2 of the 6 State of Developer Nation 27th Edition industry reports, just made publicly available and open to all to access and download . You can follow the links to access the full free report to dive deeper into the technology industry and use clean data to drive your 2025 successes. On February 25, we ran a webinar on AI chatbots and Network APIs. Watch it now or keep reading for the report insights. Let’s explore together what is new in programming language communities and AI chatbot usage. Developer Research Report: Sizing programming language communities Always on time for our biannual check-in with programming language communities, we see in Q3 2024 that JavaScript reigns supreme again, with a thriving community of 27.4M developers. JavaScript’s dominance has been unchallenged for a while now, but it’s always exciting to see how these numbers evolve and what they reveal about shifts in developer interests. The choice of programming language shapes the kinds of projects developers work on, the communities they engage with, and even the career paths they follow. A language isn’t just a tool; it’s often a gateway to specialised fields and opportunities. Python and Java continue to battle closely to be the second-largest language community, but over the last year, Python has begun to solidify a leading position. At the same time, Go and Rust are the second and third fastest-growing languages. In this report, we estimate the global developer population using each of these languages and examine how coding experience and emerging tech trends shape language adoption across different fields. The JavaScript community grew by 4.8M users in the last 12 months. Here is a full breakdown of the size of programming languages communities: Explore the full report  in the SlashData Research Space to discover which types of developers are driving the growth of Go and Rust, and how the sizes of these language communities have evolved over time. Do you need data that is more tailored to your needs? Get in touch  or explore our case studies  on how we helped clients answer their questions.  Developer Research Report: The rise of AI chatbots for problem-solving Are AI chatbots the new go-to solution for problem-solving? Well, for 45% of technology professionals, hobbyists, and students, they are! From guiding users through complex troubleshooting to empowering businesses with 24/7 assistance, AI chatbots are now essential tools in our fast-paced world, reshaping problem-solving one query at a time. 45% of technology professionals, hobbyists and students use AI chatbots for problem-solving This report explores the use of AI chatbots for problem-solving by professionals, hobbyists, and students involved in technology projects. It also looks into how much the adoption of AI chatbots changed between Q1 and Q3 of 2024 across different role types, experience levels, and geographic regions. To understand adoption, we asked developers about the extent to which they rely on AI chatbots for different purposes - problem-solving, learning and research. While adoption rates for learning and research remained stable in the last six months, the percentage of those using AI chatbots for problem-solving increased from 40% in Q1 2024 to 45% in Q3 2024. To learn more about how the use of AI chatbots changes based on developers’ professional status, role, years of experience in software development, and regions, you can access the full report here . More developer insights are coming soon As I mentioned in the introduction, this is only a first taste of the first 2 of the 6-part series. Stay tuned for 2 additional previews of the reports coming in December:  Network APIs: The New Oil In The 5G Economy How developers build AI-enabled applications  What developers think about their teams  Comparing startups to established technology companies You can access everything (no strings attached) in the SlashData Research Space . The Developer Nation survey & State of Developer Nation reports Hopefully, this is not the first time you hear of SlashData, a market research firm with a passion for clean data and actionable insights. Every quarter, SlashData runs its Developer Nation survey, a global independent survey that measures the pulse of the technology ecosystem and how software developers feel about new technologies, tools, platforms, support from developer programs and more. Our expert analysts work on identifying key trends and translate raw data into actionable insights that professionals and companies addressing a developer audience can utilise to fine-tune their strategy and address developers’ needs and wants. The 27th edition of the Developer Nation survey reached more than 9,000 respondents from 130+ countries worldwide. The State of the Developer Nation report series highlights the key trends to look out for the beginning of 2025 and beyond.  About the author Stathis Georgakopoulos, Product Marketing Manager Always keen to see what’s next in the industry, Stathis is the Product Marketing Manager for SlashData, setting the table and running the marketing activities. He's our go-to guy for all things marketing and does not hide his love for content marketing and creating helpful content.

This is our last batch of insights for the year! Expect us to return stronger in 2025, with everything you need for a much better understanding of the industry and its needs. This blog post is the second installment of the free insights we bring to the world through the State of the Developer Nation report series, currently on our 27th edition. The series delivers six reports that look into technology trends and share data and key insights on what matters to software developers. You can find links to all of these at the end of this blog post. If you are interested in programming language communities and AI chatbots, make sure to check out part 1: “ 27.4M developers use JavaScript  & AI chatbots are used by 45% of developers for problem-solving ” which also includes more information about our Developer Nation surveys and insights on the 2 topics.  For now, we will focus on two trending topics: Network APIs and 5G and how developers build AI applications. On February 25, we ran a webinar on AI chatbots and Network APIs. Watch it now or keep reading for the report insights. Developer Research Report: Network APIs: The new oil in the 5G economy We dug into how developers around the world are adopting network APIs, and the results are pretty interesting!  Network APIs are essential for enabling communication, data transfer, and seamless connectivity across a wide range of applications. So, in what development projects are developers using them the most? Leading the charge is Industrial IoT, with 14% of developers integrating network APIs to enable real-time communication and data exchange across connected systems. Following closely are consumer electronics devices at 13% and apps/extensions for third-party ecosystems at 12%, reflecting the need for seamless integration in smart devices and platforms. Industrial IoT leads network API adoption, proving their vital role in powering connected and intelligent systems. What does this mean? Developers are prioritising network APIs for projects where real-time data flow, device communication, and smart integration are critical. For industries like IoT, consumer tech, and AI, these APIs are becoming essential building blocks for innovation and scalability. Want to know more about the profile of developers using network APIs and which are the most common types of APIs? We break it all down in the full report . Developer Research Report: How developers build AI-enabled applications AI is everywhere these days, and developers are paving the way in bringing smarter applications to life. Our latest research reveals how developers are integrating AI functionality into their apps and how different factors like professional status and company size play a major role in adoption rates. According to our data, open-source AI models dominate the landscape, with 66% of developers, who build AI-powered applications, choosing them to add AI functionality to their applications. This preference is consistent across professionals (67%) and amateurs (65%), underlining the universal appeal of open models for innovation and rapid implementation. On the other hand, proprietary or closed-source models see a significant gap: while 43% of professionals integrate them, only 30% of amateurs do the same. This suggests professionals are more likely to leverage closed models for advanced or enterprise-level applications requiring reliability and support. Open-source AI models dominate adoption, but proprietary models find favor with professional developers for specialised use cases. Interested in seeing AI functionality adoption rates and the types of AI models used, broken down by company size? Dive into the full report  for all the insights. Enjoy the full report series.  As I mentioned in the introduction, the State of Developer Nation series shares six reports. The topics covered?  Sizing programming language communities The rise of AI chatbots for problem-solving Network APIs: The New Oil In The 5G Economy How developers build AI-enabled applications  What developers think about their teams  Profiling of professionals working at startups All reports are offered freely to our community and anyone interested in the technology space. Access everything with a single sign-in in our SlashData Research Space . Do you need to look deeper into technology topics? Explore our tailored solutions. Get in touch  with us.  About the authors Stathis Georgakopoulos, Product Marketing Manager Always keen to see what’s next in the industry, Stathis is the Product Marketing Manager for SlashData, setting the table and running the marketing activities. He's our go-to guy for all things marketing and does not hide his love for content marketing and creating helpful content. Bleona Bicaj, Senior Market Research Analyst Bleona is a behavioral specialist, enthusiastic about data and behavioral science. She holds a Master's degree from Leiden University in Economic and Consumer Psychology. She has more than 6 years of professional experience as an analyst in the data analysis and market research industry.

This article is the last and final instalment of our blog trilogy presenting the highlights of the State of Developer Nation 27th report series.   You can quickly find links to the reports at the end of this article. Read the first parts of our blogs here: 27.4M developers use JavaScript  & AI chatbots are used by 45% of developers for problem-solving 66% of developers use open-source AI models, and Industrial IoT leads network API adoption The topics we will focus on today are profiling the developers working on startups and how they are different from those in established companies, and exploring teamwork and how developers feel about their teams.  25 February 2025 | LIVE webinar with Q&A | Inside Technology Trends: AI chatbots & Network APIs Developer Research Report: Profiling of technology professionals working at startups We explored how startup workers differ from those working in non-startup or “established” firms, and the findings are quite revealing! Startups take a diverse approach to revenue generation. Topping the list is contracted development, research, and consulting at 24%, emphasising the demand for specialised expertise. Close behind, selling software through portals (23%) and subscriptions (21%) showcases the growing importance of digital products and recurring revenue. Startups are more likely to rely on direct software sales, subscriptions, and contracted services, while established firms have a more balanced revenue mix. What does this tell us? Unlike established firms, startups lean more on in-app purchases, online commerce, and even physical product sales - showing their ability to experiment with new monetisation approaches. Curious about how startups stack up against larger companies in software development experience, revenue, and areas of interest? We break it all down in the full report ! Developer Research Report: What developers think about their teams Strong teams are the foundation of great software development, but how do developers really feel about their teams? We took a deep dive into their perceptions, and the results are revealing! Developers overwhelmingly value teamwork and adaptability - 78% report a strong sense of camaraderie, and 77% say communication within their team is efficient and transparent. These high levels of collaboration make it easier to tackle complex challenges and work across multiple technologies, which 78% of developers say they do regularly. Developers overwhelmingly value teamwork and adaptability - 78% report a strong sense of camaraderie However, while 76% believe their team is quick to adapt to changing priorities, innovation isn’t always a given. Slightly fewer developers (72%) agree their team actively explores new technologies, indicating that while many teams are agile, they may not always be pushing the boundaries of what’s possible. What does this mean? While teamwork and flexibility are strong, hierarchy is less clear - only 58% agree that their team has well-defined roles and boundaries. This suggests that many development teams function with loose or flat structures, which can be both a strength and a challenge. Developers work in collaborative and adaptable environments, but innovation and hierarchy remain mixed experiences Interested to learn more about how developers view their teams, leadership, and collaboration and how this differs based on software experience and region? Check out the full report ! 6 openly available reports for you to enjoy in full  As we mentioned in the introduction, the 27th State of Developer Nation series opens up a world of insights on 6 key, trending topics. You can access each industry report for free* here:  Sizing programming language communities The rise of AI chatbots for problem-solving Network APIs: The New Oil In The 5G Economy How developers build AI-enabled applications  What developers think about their teams  Profiling of professionals working at startups *No purchase is required, but you need to sign in to our Research Space All reports are offered freely to our community and anyone interested in the technology space. Access everything with a single sign-in in our   SlashData Research Space . Do you need something more tailored to your needs? We do a lot of custom work.   Get in touch  with us.  About the authors Stathis Georgakopoulos, Product Marketing Manager Always keen to see what’s next in the industry, Stathis is the Product Marketing Manager for SlashData, setting the table and running the marketing activities. He's our go-to guy for all things marketing and does not hide his love for content marketing and creating helpful content. Bleona Bicaj, Senior Market Research Analyst Bleona is a behavioral specialist, enthusiastic about data and behavioral science. She holds a Master's degree from Leiden University in Economic and Consumer Psychology. She has more than 6 years of professional experience as an analyst in the data analysis and market research industry.

In today’s hyper-competitive technology landscape, the role of a product marketer is more critical and more complex than ever. With countless software solutions, hardware innovations, and services vying for attention, success hinges not just on creative campaigns but on decisions backed by solid, actionable data. Gone are the days when intuition alone could guide strategy. Whether launching a new SaaS product, refining the positioning of a device, or gaining an edge in the enterprise space, data has become the foundation of effective product marketing. But what kind of data is essential, and why? For product marketers, the answer lies in understanding the unique needs of your customers, the position of your brand, and the rapidly shifting dynamics of your market. From pinpointing high-value customer segments to tracking shifts in brand perception or measuring campaign effectiveness, the right data can be the difference between a product that dominates its space and one that struggles to find its audience. As the technology landscape evolves, the ability to leverage insights effectively is what separates leaders from followers.  By prioritising key data, product marketers can refine strategies, enhance positioning, and deliver value that resonates with modern audiences. Why product marketing needs data  In the fast-paced technology sector, decisions need to be informed, agile, and precise. Data serves as the backbone for product marketers, enabling them to move beyond assumptions and build strategies rooted in real-world insights. The stakes are high: without accurate data, even the most innovative products may fail to meet marketing demands. While creativity remains a cornerstone of marketing success, the margin for error has shrunk dramatically. With increasing competition in software, hardware, and tech services, the ability to draw from real-world data - be it market trends, brand awareness metrics, or competitive analysis - ensures that product marketing decisions are responsive, targeted, and rooted in reality. Data for every stage of the product lifecycle From pre-launch research to post-launch optimisation, data is a compass for every stage of the product marketing lifecycle. During the development phase, market and customer data help identify unmet needs and shape messaging. At launch, segmentation and competitive insights dictate messaging and channel strategy. Market and customer data obtained during the development phase can help identify unmet needs and guide communication. At launch, communication and channel strategy are dictated by segmentation and competitive insights. Post-launch, data on customer sentiment and market trends allows product marketers to adapt their strategies, ensuring sustained growth and market fit. Technology markets are crowded, with multiple players competing for limited mindshare. Data provides the clarity needed to stand out. It reveals who your audience is, what they care about, and how your product delivers unique value. Whether it’s identifying a niche audience for a new enterprise tool or highlighting a standout feature in consumer hardware, data helps craft narratives that resonate. The better product marketers can collect, analyse, and act on data, the more effectively they can build compelling strategies that connect with their audience and outperform the competition. Data isn’t just a tool; it’s your competitive advantage Which data do Product Marketers need? To craft strategies that resonate and deliver results, product marketers rely on two key types of insights: user-focused data and market-focused data. These complementary data sets provide the clarity needed to meet customer needs, refine product offerings, and navigate competitive landscapes. Understanding the user At the heart of every great product marketing strategy is a deep understanding of the user. Whether your audience includes B2B decision-makers, enterprise IT buyers, or individual consumers, user-focused data helps product marketers answer critical questions: Who is the target audience?  Demographic and behavioural segmentation identifies which groups will benefit most from your product, enabling you to tailor your messaging and positioning. How aware are users of your brand?  Brand awareness data reveals your current visibility in the market and uncovers opportunities to build trust and visibility in underserved segments. What do users associate your brand with? Brand perception data reveals the attributes and values users link to your brand. This includes both emotional and practical links, helping you understand if your brand is seen as trustworthy, innovative, or aligned with your intended positioning. Are your marketing efforts resonating with users?  Brand tracking measures how your brand’s performance evolves over time, monitoring changes in awareness, favourability, and loyalty. Meanwhile, message testing evaluates whether your key messages resonate with your audience, ensuring they’re compelling, relevant, and drive action. What challenges are users facing?  Insight into user pain points, priorities, and decision-making criteria allows you to position your product as the solution to their most pressing needs. You can find the answers to these questions by working with us. Explore all our services here .  This user-centric data is particularly important in the technology sector, where decision cycles can be complex, and customer needs vary across roles, sectors, and regions. For example, technical decision-makers and executive-level stakeholders place different importance and priorities on different forms of technical and customer support. Understanding these differences allows product marketers to align their strategies with the unique concerns of each audience segment. Navigating the market Equally important is a comprehensive understanding of the broader market. Market-focused data enables product marketers to identify opportunities, assess risks, and position their offerings strategically. Key aspects include: Market Trends:  Insights into how consumers view trends like AI integration or the future of 5G connectivity can shape your messaging and highlight key differentiators. Competitive Landscape:  Competitive intelligence highlights who your competitors are, their strengths and weaknesses, and how they’re positioning their products. For example, in a crowded SaaS market, knowing how a rival’s product underperforms in customer support can help you differentiate your positioning and messaging. Product Landscape Radars: Technology landscape radars offer invaluable insights, revealing how products are viewed in terms of usability, feature completeness, and other key attributes. Understanding which solutions stand out in these areas helps product marketers position their offerings more effectively within competitive fields. Arming yourself with this data ensures that your product doesn’t just fit the market - it stands out in it By identifying whitespace opportunities or adapting to competitive moves, product marketers can carve out a distinct space that resonates with their audience. Linking data to outcomes At SlashData, we excel in delivering full-service market research tailored to the needs of product marketers in the technology sector. From survey design and fielding to data cleansing, analysis, and actionable intelligence, we handle every step of the research process. Our expertise spans both quantitative and qualitative research, ensuring that no insight is overlooked and every data point contributes to a clear, actionable narrative. Our deep understanding of the market allows us to bridge the gap between raw data and strategic decisions, and for product marketers, this means solving many of the most pressing challenges. We help marketers understand shifting audiences through segmentation analysis and persona development, ensuring their products and messaging stay ahead of evolving customer needs.  At the same time, we provide clarity in navigating competitive pressures, from identifying whitespace opportunities to crafting data-driven responses to new competitors. Finally, our insights support critical strategic decisions around brand positioning, messaging, and resonance, enabling marketers to align their efforts with customer expectations and market demands. Today, data is no longer a luxury for product marketers; it’s a necessity. The ability to understand your audience, position your brand effectively, and navigate competitive markets hinges on having access to the right insights at the right time. For product marketers striving to differentiate their products, anticipate market trends, and meet customer needs, data isn’t just about numbers; it’s the foundation for impactful decision-making. Whether you’re refining a product launch, exploring new segments, or navigating a crowded market, our team is here to help you succeed. Contact us today to learn how we can support your product marketing efforts and drive your success in a competitive, data-driven world. About the author Liam Dodd, Senior Market Research Analyst Liam is a former experimental antimatter physicist, and he obtained a PhD in Physics while working at CERN. He is interested in the changing landscape of cloud development, cybersecurity, and the relationship between technological developments and their impact on society.

The challenge The Product Strategy and Pricing team of a leading identity management provider was looking to improve their product strategy in a way that included a price structure to attract wider audiences, increase competitiveness, and grow conversions from free to paid tiers.  The approach SlashData worked closely with the Client to understand their research objectives, and designed a 25-question survey that best answered the Client’s business questions. Then, SlashData gathered responses from 300+ developers with experience in utilising authentication APIs in their applications. The respondents were sourced through SlashData’s own developer community, as well as through vetted third-party survey panels. The sample collected covered the geographies and specific developer profiles requested by the Client.  The result The data collected from SlashData’s survey answered the Client's key questions:  What use cases are developers targeting with competitive authentication services? What features led them to choose their primary solution? What would make them move to another offering? How important are free plans to authentication service users? What would make them switch from a free to a paid authentication API tier? As a result, the Client was able to identify key features vs competitors and gained a better understanding of how developers interact with authentication services.  Why SlashData? SlashData, acting as a full-service market research partner, helped the Client go from a high-level research brief to actual data that provided answers to critical business questions in just 30 days. Tapping into its own developer community and selected third-party sample providers, SlashData distributed an online survey that reached respondents who precisely matched the Client’s target audience. The survey was hosted on SlashData’s proprietary survey platform, which enables in-depth data cleansing through the advanced metadata it collects. Related services Audience Insights Product development & improvement Are you focused on increasing conversions from free to paid? Our data can help. Contact us .

The challenge Our client, a top cloud-based CRM platform, came to us with the challenge of better understanding the developers in their ecosystem in terms of challenges, tool use, and satisfaction. Specifically, the client was interested in whether there were significant differences in satisfaction levels between developers using certain tools or making use of select in-house environments. Further, the client was also posed more exploratory questions such as: “ Are there certain roles or profiles for developers in our ecosystem that are more or less satisfied? ” or “ When we account for regional differences and varying experience levels, who are the developers that are most likely to use AI-based tools? ”  The approach To address the various hypotheses, we needed to collect representative data and analyse it in a rigorous, reliable manner that would empower their team to make decisions. We crafted a questionnaire and designed an outreach strategy that allowed us to field a tailored, large survey that collected data from a representative sample of developers within our clients' community. We analysed the data in a statistically rigorous manner using various regression models and inferential statistics to test if there was evidence to support hypothesised trends and differences.  The result We provided data-backed answers to each of the questions our client posed. By identifying which groups or profiles had significantly higher or lower satisfaction, we enabled our client to prioritise resources and investments. Further, using the various regression models we estimated, the client could also project a holistic profile of developers who are more likely to be satisfied or use select tools (i.e., AI-based tools) in their workflows within their ecosystem.    Why SlashData Our survey design expertise and panel access enabled us to efficiently collect relevant data from a representative sample of developers working in our clients’ ecosystem. When used in conjunction with our quantitative research capacities, this enables us to analyse the data in depth and take into consideration the uncertainty or variability in our work.   Related services Product configuration & optimisation - Conjoint Analysis Quantitative research  Are you trying to get a deeper understanding of software developers in your ecosystem? We can help. Let’s talk.

The challenge The Developer Relations team of a cloud communication company was looking to deepen their understanding of the evolving needs of professional developers who integrate third-party APIs. They aimed to refine program content, optimise engagement strategies, and assess the potential role of no-code/low-code and AI-assisted tools in meeting developer expectations. The approach In close collaboration with the client, SlashData utilised its extensive collection of existing data from the 26th edition of the Developer Nation survey, which closed in Q1 of 2024. This survey provided fast access to insights by utilising a globally representative sample of over 5,000 professional developers who use third-party APIs. The sample covered a range of experience levels and API types, satisfying the client’s target audience. The result The data gathered from SlashData’s survey provided the client with valuable insights into the preferences and behaviors of developers using third-party APIs. The analysis answered the client’s core questions, including: What program resources are most valuable to API users, and how does that vary by region and experience level? What problem-solving resources are favoured by developers, and how does this preference shift with experience? How do professional developers stay informed on software development trends, and how is that different across regions? How prevalent is the usage of no-code/low-code tools among API users? What functions do AI-assisted tools serve, and what are the main challenges developers encounter when using these tools? With these insights, the client gained a deeper understanding of API usage patterns and developer program needs, enabling them to tailor their offerings to meet regional and experience-based preferences and improve developer engagement across key resources and tools. Following this success, the client requested further research to benchmark their developer program, aiming to assess their market position more accurately and identify strategic areas for improvement. Why SlashData With a deep understanding of the developer landscape, SlashData leveraged its extensive developer community and a curated network of third-party providers to reach precisely the right audience for the client’s research objectives. Our proprietary survey platform not only hosted the survey but also enabled robust data integrity through advanced metadata analysis, ensuring the responses accurately represented the client’s target professional developers. By tapping into SlashData’s developer community and verified third-party panels, comprehensive insights were delivered more quickly and efficiently than would have been possible with a fully custom survey. Related services Audience Insights Developer Insights Are you trying to get a deeper understanding of how developers integrate APIs? Let’s talk.

The challenge The developer marketing team from a leading cloud service provider wanted to better understand their users' satisfaction with various support features, such as documentation and sample code, as well as benchmarking themselves against their largest competitors.  The approach SlashData worked closely with the Client to understand the research objectives, and designed a 30-question survey that best answered the Client’s business questions and need. Then, SlashData gathered responses from 750+ developers currently working at an organisation with more than 1,000 employees, and were current users of the leading cloud service providers. The respondents were sourced through SlashData’s own developer community, as well as through vetted third-party survey panels. The sample collected covered the geographies and specific developer profiles requested by the Client.  The result The data collected from SlashData’s survey was able to answer the Client’s business questions, as well as allow them to understand how their users differed from their competitors: What types of projects and development focuses does their userbase do more or less than their competitors? How satisfied are developers with the Client’s support features? How frequently do the Client’s users access support features, compared to their competitors? How do the languages developers work in impact their satisfaction with the Client’s resources? How does the developer’s involvement in tool or technology purchases and discovery impact their satisfaction with the Client’s resources? How does a developer’s level of experience impact their satisfaction with the Client’s resources, and in comparison with their competitors? Why SlashData SlashData, acting as a full-service market research partner, helped the Client go from their research intention to actual data. SlashData was able to leverage its experience with cloud developers and the wider market to turn the data into actionable insights and easier-to-understand visualisations. SlashData was able to tap into its own developer community, as well as select third-party sample providers, to distribute an online survey that reached respondents who precisely matched the Client’s target audience. The survey was hosted on SlashData’s proprietary survey platform, which enables in-depth data cleansing through the advanced metadata it collects. Related services Quantitative Research Developer Research Are you delivering on your users' needs and wants? Contact us , and we will explore their needs together.

The challenge The localisation team at a leading global technology company was using machine translations to make developer content accessible without the heavy demands of traditional translation workflows. The challenge was to determine if machine translation was sufficient or if additional localisation efforts were needed to ensure an optimal user experience. The approach SlashData worked closely with the client to understand their research objectives, and designed a methodology and a survey to measure the effectiveness of the client’s machine translations. SlashData gathered responses from around 700 developers who shared their experience using the client’s developer resources in their native language. The respondents were sourced through SlashData’s own developer community, as well as through vetted third-party survey panels. The sample collected covered the geographies, locales, and specific developer profiles requested by the client. The result This project provided the client with valuable insights into the effectiveness of machine translations across various locales, highlighting one locale that required additional localisation efforts, enabling the client to optimise resource allocation. Additionally, we analysed the key components of machine translations, offering a detailed understanding of their strengths and areas for improvement. We also conducted profiling analysis to identify developers who were more likely to face challenges with machine translations, considering factors such as their experience in software development. Why SlashData SlashData, serving as a comprehensive market research partner, guided the client from an initial high-level research brief to actionable data that addressed key business questions. Leveraging our expertise in survey design and our access to a diverse pool of developers, we efficiently gathered relevant data from a representative sample of developers using the client’s resources in their native language. Our advanced quantitative research capabilities allowed us to conduct an in-depth analysis and deliver an actionable, insights-driven report to the client. Related services Audience insights Product development and improvement Customer segmentation and profiling Is your strategy global, or do you segment your audience based on their location? Our surveys get responses from 130+ countries. Do you need data? Unlock the true power of insights.

The challenge Sentry came to us with a central tenet they wished to explore: Happier developers are more productive. They wanted to measure happiness in a reliable manner that allowed us to estimate its effect on productivity and efficiency as well as examine how various obstacles developers commonly encounter impact their workflows. Sentry sought to publish these results publicly to support both marketing and several products they were working on.  The approach Drawing inspiration from validated scientific work, we adopted questions from the “Oxford Happiness Questionnaire” and designed a survey that appropriately targeted professional developers working for firms with live software applications. Upon receiving the data, we worked with the client to estimate the various relationships of interest and model how happiness impacts developer productivity.   Why SlashData Relying on our extensive survey experience, we were able to construct a survey that was relevant to the audience of interest and used scientifically validated metrics to collect reliable data that could be used publicly with confidence.  The result The survey delivered clear, actionable insights: happier developers are more productive. We additionally found that larger companies faced unique productivity hurdles such as internal processes and communication issues. In identifying specific hurdles and relationships, Sentry received a reliable piece of research they could publish and take to their clients to offer solutions with their products.  You can dive into developer happiness in the free report  or get in touch  for more insights.  Related services Developers Research  Quantitative research