Big Tech vs Startups: How Do Research Functions Differ?
A guide to finding your research calling in tech
User research isn’t just a big tech thing. While established products are testing tweaks and new features to find fresh opportunities for growth, startups are looking for signals to help them reliably establish their core product in the market. In both cases, user-centric product development requires a good, early understanding of how people perceive and react to new ideas (here’s my previous post on early product testing).
There’s a surprising amount of overlap between how small and big companies execute on research and interpret the insights, but there are also some important differences in how those insights ladder up through a company and ultimately impact products.
Here’s a high-level overview of that landscape that I hope will explain how different companies derive value from research.
Research Process
If you ask a researcher at a startup and a researcher at FAANG why they’re doing research, you’re likely to get a very similar answer: to support well-informed product decision-making and direction. This is the bread and butter of good research.
Companies make bad decisions all the time in building and launching new products and that’s part of what makes life in tech exciting. If you make no mistakes, you’re probably not moving quickly enough. At the same time, nobody wants to make easily avoidable or negligent mistakes that waste company resources and slow down progress. When it comes to identifying those potential mistakes, research is key.
So at a high level, the processes and motivations for doing research are consistent across the industry. If you drill down further into the details though, you can find some nuances. In my experience, there are three important differences between big tech and small tech culture that translate into differences in research processes:
Lower risk tolerance in big tech: Launching a bad feature, making an incorrect business bet, or generally getting something wrong can come with severe PR, product, and even legal risks for big tech. This is a less serious concern at startups.
Greater willingness to move quickly at startups: It’s fashionable for big companies to want to maintain a “startup culture” and expect rapid product development. But in reality, their existing market dominance, management layers, and numbers of stakeholders create many bottlenecks.
Smaller teams at startups: The most obvious difference is that startups have fewer employees than big tech companies. This means researchers have less guidance and support from a talented research network, but more room for flexibility in how their role evolves and interacts with other roles. Researchers don’t need to worry about stepping on other researchers’ toes.
How do these translate into differences in the research process? Here are two of the biggest differences from my own experience:
At startups, research is more dynamic, often happening after a single conversation with a direct manager or product manager. Researchers in big tech often need buy-in from multiple stakeholders and perhaps also formal review before they can launch research. They also need to be more concerned about risk assessments or potential metric regressions from new features, which can be complicated given the scale of the products. Startups are conscious of these problems too, but there’s less risk in moving quickly.
Startup researchers often have less predictable roles, and they define and evolve their responsibilities as needed. They generally need to be comfortable with job descriptions flying out of the window. It’s rare for a startup researcher to exclusively do qualitative research for example, even if that’s what they originally signed up for. Instead, they might bounce day-to-day between user interviews, checking scientific claims in marketing emails, experimenting with new technologies, representing the research team in front of the media, creating new data pipelines with data scientists, etc. Both junior and senior researchers need to be more adaptive with their skillset to succeed at a startup.
Methodology
The strongest overlap between big and small tech research is in methodology. All companies I’ve worked at—ranging from 10 to 60,000+ employees—tend to cluster around a few key methods to deliver insights: qualitative user interviews, surveys, and A/B tests. Other methods are important too (e.g. diary studies, card sorting, lit reviews, etc), but the top three overlap quite consistently.
Naturally, the scale of these research methods might differ dramatically. For example, big tech surveys and A/B tests might use samples that are orders of magnitude larger than what startups use, and that opens up opportunities for more sophisticated data modeling. But when it comes to the bones of the research methods themselves and the research skills required to execute them, things look similar.
The larger scale of research is also driven by one of the major advantages of working in big tech: enormous resources. At a startup, a researcher will have a tightly limited budget and a frequent inability to do ambitious research with their ideal parameters. There’s an ongoing process of paring down research designs and finding efficiencies to make a plan workable. This happens to some degree in big tech too, especially during times of economic strain, but nowhere near the same extent. You generally have a much larger playground to operate in and immediate access to unrivaled research opportunities, support services, and training programs.
Teamwork
The most marked differences between big tech and startup research emerge when you look at team structures. In big tech, there are firm org charts and hierarchies in place. The company is often split into product teams, each responsible for a particular app feature, function, or company responsibility. Each of those teams will usually have a product manager, engineers, designers, and researchers.
At startups, things aren’t so branched. After all, the size of a single product team in big tech often equates to the size of an entire startup company. For that reason, there are fewer simultaneous product or research efforts happening at a startup and a majority of the company might be involved in all of those efforts. Startup structures and priorities are more fluid, and teams are built as needed.
Startup researchers enjoy manageable communication, collaboration, and team-building across their company. A researcher at FAANG might never meet the CEO of their company in person, even if they work at the company for many years. On the other hand, a researcher at a tech startup might be sitting next to the CEO every day at the office. For that reason, as soon as a startup researcher has a good idea, they can find and talk to all of the key decision-makers within an hour. If a researcher in big tech has an idea for their own product team that doesn’t require higher-level approvals, this can work at a similarly quick pace. But if they have a bigger idea that extends beyond their own product teams, it might take days to find who the relevant stakeholders are before sending meeting invites.
It’s worth noting though that the gigantic network of people in big tech comes with obvious strengths. Whenever you want to talk to a person with specialized expertise of machine learning, or widespread experience with survey analysis, or extensive knowledge of card sorting methods, or a deep understanding of zero-to-one product development, or quite literally anything relevant to tech or research, you can contact them through internal tools and they’ll happily support you with their talent.
Product Impact
When you work at a tech startup—especially a very early startup—it’s natural to occasionally feel your research isn’t making enough of a mark. If you have a small user base, you might never see a person on the train using the app feature you just improved. And shock, horror, it’s also possible nobody recognizes the logos on your company swag. This feeling of your work mattering is rarely a problem if you work at FAANG. You can literally walk out of the office and see how people interact with everything you’ve worked so hard on. Your work might even appear on the front page of the NY Times (hopefully for good reasons).
Knowing that many people interact with and value your products isn’t just a vanity metric; it relates to our sense of purpose, which is a major driver of workplace happiness and motivational drive. Researchers in big tech love that their efforts can benefit such a large number of people around the world.
At the same time, product impact differs in many other important ways that can favor startups too:
It takes a lot more time for impact to flourish in big tech: your work may need to ladder up through multiple decision-makers, and your recommendations may be rejected at the top after weeks of review. At startups, impact is more immediate and you can speak 1:1 with everyone who is invested in your work.
Research impact at startups is more visible: Whereas your research may be lost in the weeds at a large company, you can share your insights with literally everyone at a startup company with a single presentation. It’s impossible for most researchers to talk about why their work matters with executive leadership at a big tech company, but those discussions are ongoing at a small company.
Impact priorities are clearer at startups: At a startup, it’s easy for a researcher to learn about all product efforts currently happening at the company and why. They can prioritize more freely, effectively, and confidently by knowing where their insights will best support company progress. Prioritization is more obscure at a large company and there’s less flexibility in what kinds of problems are relevant for your specific product role. Every product team believes their work is a super high priority for executive leadership, but surely they can’t all be right…
Many people will tell you that you have “more impact” as a researcher in big tech than at a startup, but this depends on your perspective. A researcher in big tech might deliver insights that change the product experience overnight for a billion users. However, a startup researcher typically has more influence in steering the company ship and driving home big bets. The question of which of these is “more impact” comes down to whichever one is more likely to get you out of bed in the morning.
Top Takeaways
There’s a lot of overlap in the research methods that dominate big tech and startup companies—research skills translate well between the two worlds.
The role of a researcher is more fluid, flexible, and self-defined at startups compared to big tech. You’ll likely take on unexpected responsibilities and will need to work with a high degree of independence, especially if you’re the only researcher at the company!
Researchers in big tech have significantly more resources and a bigger community of support, expertise, and diversity to tap into when needed.
Teamwork and impact materialize differently for researchers across big tech vs startups. Your preference will depend on your individual personality/motivation.
“I am not concerned that I have no place; I am concerned how I may fit myself for one”
~ Confucius
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