Gong turns real sales conversations into original research on pricing, discovery, buyer behavior, and sales performance. These findings then power a wider content library that competitors cannot easily reproduce.

Most sales articles begin with someone’s opinion.

A sales leader explains what worked for their team. A consultant shares a framework. A writer collects several recommendations and turns them into a guide.

Gong built a different kind of content engine.

The company develops software that captures and analyzes interactions between sales teams and potential customers. These interactions include calls, online meetings, emails, and other activities connected with a sales opportunity.

That access gives Gong an unusual source of editorial material. Instead of only asking experienced sellers what they believe works, Gong can study what happened across thousands or millions of real sales interactions.

It can examine how much successful sellers speak, when pricing appears in a conversation, how many people participate in a deal, or which email patterns receive more replies. It can then compare those behaviors with commercial outcomes.

Gong turns the findings into articles, reports, guides, benchmarks, webinars, and practical sales advice through its research program, Gong Labs.

The articles are the visible part of the system. The larger advantage comes from the connection between the product, the data it collects, the questions the research team asks, and the content Gong publishes.

Gong began with a sales problem that existing data could not explain

Gong was founded in 2015 by Amit Bendov and Eilon Reshef.

Bendov had spent more than 20 years working in enterprise software. Before Gong, he served as CEO of the business intelligence company Sisense and held a senior marketing role at the software company Panaya. Reshef was an experienced software entrepreneur who had co-founded the ecommerce technology company Webcollage.

The idea for Gong came from a problem Bendov experienced while leading another software company.

The company had recorded its worst quarter. The leadership team could see that sales performance had fallen, but it could not confidently explain why. Managers had pipeline reports, forecasts, and updates from sales representatives. Those records showed which opportunities were open and which had been lost. They did not show everything customers had said during the conversations that shaped those outcomes.

Bendov concluded that business decisions were being made with an incomplete picture. Sales leaders were hearing summaries of customer conversations rather than the conversations themselves. He contacted his friend Eilon Reshef to explore whether AI could help companies capture and understand what customers were actually saying.

The two founders saw a gap between the information companies recorded and the information they needed.

A sales representative might update an opportunity by selecting a deal stage, entering an estimated value, and adding a short note. The note might say that the buyer was interested, the timing was uncertain, or the price was too high.

Those updates were useful, but they were also subjective. Two representatives could interpret the same customer response differently. Important details could be forgotten, shortened, or softened before reaching a manager.

The real evidence remained inside the original call or email. Gong was built to capture that evidence.

How Gong connects customer conversations with sales outcomes

Gong initially became known as conversation intelligence software.

The product recorded online sales calls, created transcripts, separated the speakers, and used AI to identify subjects and patterns inside the conversation. Gong could show when pricing was discussed, when a competitor was mentioned, which questions were asked, how long each person spoke, and what next steps were agreed upon.

It also connected those conversations with the company’s existing sales records.

Most B2B sales teams use customer relationship management software (CRM). A CRM stores information about potential customers and tracks sales opportunities as they move through the pipeline. It may show when an opportunity was created, its estimated value, the people involved, the expected closing date, and whether the deal was eventually won or lost.

The CRM supplied the outcome. Gong supplied the behavior that led to that outcome.

For example, Gong could identify a group of completed deals and separate them into won and lost opportunities. It could then compare the conversations in both groups.

• Did customers speak more during the won deals?
• Did the seller introduce pricing earlier or later?
• Did successful opportunities involve more people from the buyer’s company?
• Were certain questions more common in calls that progressed?

This connection allowed Gong to study sales performance at a much larger scale than a manager listening to a few recordings.

It also created the foundation for the company’s content strategy.

Once Gong could identify patterns across many interactions, those patterns could be turned into useful research for the wider sales community.

How Gong Labs turns product data into original research

Gong introduced its research ambition publicly in November 2016 through the Sales Conversation Science Manifesto.

The company argued that sales were still guided heavily by personal theories, anecdotes, and intuition. Sales books and training programs offered many different explanations of what successful sellers did. Gong believed its data could test some of those assumptions against real conversations.

The goal was not to create a fixed formula for selling. Human conversations are too varied for that. Gong wanted to identify recurring behaviors that appeared more frequently in successful or unsuccessful opportunities.

Gong does not need to guess what to publish next. Its customer data gives the research team a steady supply of real sales questions to investigate.

Its early research process followed a clear sequence.

First, Gong collected anonymized conversations recorded through its product.

Each call was separated by speaker and converted into a transcript. The product identified topics, key moments, and behaviors within the conversation.

Gong then matched the call with the relevant CRM opportunity. This allowed the research team to see whether the opportunity was won, lost, delayed, or still active.

Finally, the team compared behaviors across groups of calls and looked for patterns linked to the outcomes.

The first large analysis used 25,537 B2B sales conversations from 17 customer organizations. Most of those customers were mid-market software companies. The average call lasted 43 minutes.

That dataset gave Gong enough material for far more than one article.

What Gong learned from its first 25,537 sales calls

Gong’s early analysis examined several parts of a sales conversation.

One of the best-known findings concerned the amount of time sellers and customers spent speaking.

Gong reported that the strongest-performing calls followed an approximate 43% speaking and 57% listening ratio for the seller. By comparison, most representatives spoke during 65% to 75% of their calls.

The finding gave a numerical shape to familiar advice.

Sales trainers had long told representatives to listen more. Gong gave managers a benchmark they could examine during call reviews.

The same study also investigated pricing.

Gong found that three or four pricing questions or mentions from the buyer correlated with higher win rates. It also observed that stronger performers tended to discuss pricing later in the conversation rather than introducing it evenly throughout the call.

Other findings examined customer talking streaks, timeline language, risk-reversal phrases, and the amount of time sellers spent explaining their own company.

Gong could have placed every result into a single long report.

Instead, it separated the findings into individual content topics.

That decision became one of the most important parts of the research engine.

One research study became an entire series of sales articles

The 25,537-call analysis became a source for several focused articles.

Gong published separate pieces about the talk-to-listen ratio, pricing conversations, competitor mentions, customer talking streaks, risk-reversal language, forecast signals, and company introductions. Each article examined one specific behavior rather than repeating the entire study.

Gong turned one study of 25,537 sales calls into content on pricing, competitors, buyer behavior, talk ratios, and more. One dataset became several useful articles without repeating the same story.

This allowed Gong to organize the research around questions that salespeople already had.

• How much should a salesperson speak during a call?
• When should pricing be introduced?
• Does mentioning a competitor help or hurt a deal?
• What language suggests that a buyer may move forward?
• How long should a seller spend explaining their company?

Each question could become a separate entry point into the Gong website.

A sales manager searching for coaching advice might discover the talk-to-listen article. A representative preparing for a pricing call might find the pricing analysis. A revenue leader concerned about forecasting might read the article about buyer language.

The underlying dataset remained the same, but the content addressed different moments in the sales process.

This made the research more productive. It also made the articles more useful.

A reader did not have to search through a large report to find one relevant answer. Gong brought each answer into a focused article with a clear conclusion.

Gong built its content calendar around real sales questions

The research worked because Gong understood the audience it wanted to reach.

Devin Reed, who later led content at Gong, has explained that the team initially avoided writing heavily about the product. It focused on helping sales professionals with the problems they experienced before they were ready to buy software.

The team mapped the sales process and divided it into smaller challenges.

A representative might need help with cold outreach, discovery calls, product demonstrations, pricing conversations, negotiation, deal progression, or executive communication.

Those challenges gave Gong a broad content roadmap.

Gong Labs then made many of those topics more defensible.

An ordinary publisher could write an article about cold calling by interviewing successful representatives or summarizing popular sales books. Gong could analyze recorded cold calls and see how different opening lines performed.

In one analysis, Gong examined more than 90,000 cold calls. It found that calls beginning with “How have you been?” recorded a 10.01% success rate in that dataset, which was 6.6 times higher than the baseline. Opening with “Did I catch you at a bad time?” was associated with a much lower success rate.

Whether every company should copy the exact wording is open to debate. The finding still gave Gong something specific to discuss, test, and explain.

The same approach appeared in its discovery call content.

Gong analyzed 519,000 recorded discovery calls to study how successful representatives uncovered customer problems and structured the conversation. The analysis found that stronger calls often explored three or four business problems in depth rather than covering a long list superficially.

This gave the company a consistent editorial method.

The content calendar did not depend entirely on brainstorming new headlines. The questions came from the work the audience was already doing.

Storytelling made Gong’s research easier to understand

Proprietary data gave Gong authority. It did not automatically make the articles readable.

A research article can become difficult when it begins with methodology, technical terms, and a long description of the dataset. Gong often began with a familiar sales situation instead.

Its articles about company introductions described a representative spending too much time explaining the history and achievements of the seller’s organization. The competitor article began with buyers asking how Gong compared with rival products. The reader first saw a recognizable situation and then learned what the data showed about it.

Reed later described this approach as using the storytelling technique in medias res. The reader is placed inside the situation before receiving the analysis.

The structure helped Gong avoid producing content that felt like a formal research paper.

A typical article followed a practical sequence.

The data answered the question raised at the beginning.

This connection between evidence and storytelling became an important part of Gong’s content advantage. The research gave the article substance. The writing helped the reader remember and use it.

Research findings strengthened Gong’s wider content library

Gong Labs articles did not remain isolated from the rest of the website.

The findings were brought into larger guides about cold calling, prospecting, discovery, email, sales processes, and closing deals.

For example, Gong’s wider guide to the sales process was based on an analysis of more than one million conversations connected with 384,923 deals. It combined findings from different stages of the buyer journey into one broader educational resource.

Its cold-calling guide brought several separate observations together into a list of 17 techniques. The article included research on call length, opening lines, speaking patterns, interaction levels, and meeting-booking language.

Its collection of sales statistics turned individual Gong findings into a highly scannable reference page. Its sales-techniques article organized research-backed advice according to prospecting, meetings, discovery, pitching, objections, and closing.

This created a layered content library.

• A focused Gong Labs article could explain the original study.
• A larger guide could place the findings inside a complete workflow.
• A statistics page could make the result easy to find and cite.
• A downloadable checklist or cheat sheet could help the reader apply it.
• A later article could revisit the same question with new data.
• The value of the research, therefore, extended beyond the first publication.

Each finding could improve several related pages across the site.

The research demonstrated the product without becoming a sales pitch

Gong’s research also helped potential customers understand the product.

Consider the capabilities needed to calculate a talk-to-listen ratio across thousands of calls.

The system must record conversations, identify individual speakers, measure how long each person speaks, create usable transcripts, connect the conversation with an opportunity, and compare the results across won and lost deals.

The research shows what Gong’s product can uncover without constantly talking about the product. Readers get useful sales insights while seeing the value of Gong in practice.

A reader may visit the article because they want to improve their sales calls. While reading, they also learn that Gong can measure these behaviors across an entire team.

Thus, the research becomes a practical demonstration of the product’s capabilities.

The same pattern appears in newer studies.

In 2025, Gong analyzed 1.8 million new business opportunities. It found that 77% involved more than one buyer contact. Won deals had twice as many buyer contacts as lost deals. For opportunities worth more than $50,000, multi-threading was associated with a 130% improvement in win rates. This includes building relationships with several people inside the buyer’s organization rather than relying on one contact.

The article teaches sales teams why wider stakeholder engagement can strengthen an opportunity. It also creates a natural reason to use technology that can identify which people are involved and where important relationships are missing.

The product does not need to appear in every paragraph.

The research makes the problem visible. The reader can then understand why a tool that tracks the problem could be useful.

Gong revisited successful studies as its dataset grew

The first talk-to-listen study was published almost a decade ago. Sales technology, buyer behavior, remote work, and Gong’s own dataset have changed considerably since then.

In March 2025, Gong returned to the question with an analysis of 326,000 calls that lasted at least ten minutes.

The updated research found that representatives in the won deals spoke during an average of 57% of the conversation. Representatives in lost deals averaged 62%. The broader difference was smaller than the original 43:57 benchmark might suggest.

The new study also added a more detailed conclusion. Strong performers kept their speaking patterns relatively consistent across won and lost deals. Weaker performers showed larger variations.

That changed the article from a simple rule about talking less into a more useful discussion about consistency, question quality, and buyer participation.

Gong preserved the recognition attached to the original study while improving it with a larger dataset.

The updated finding then appeared in Gong’s roundup of its strongest 2025 sales research. It was placed alongside newer work on multi-threading, team selling, cold email, and AI adoption.

This creates a research maintenance loop.

• Older questions can be reanalyzed.
• Popular statistics can be tested again.
• New datasets can reveal whether the original pattern still holds.
• The revised result can update the original page and support several new pieces of content.

The content library becomes stronger as the product collects more information.

Major studies expanded into reports, press coverage, and executive content

Gong Labs has gradually expanded beyond articles about individual sales techniques.

The State of Revenue AI 2026 research combined a survey of 3,048 revenue leaders with an analysis of 7.1 million sales opportunities from 3,613 companies. The study examined how companies were adopting AI and how deeper use related to revenue performance, productivity, and decision-making.

Research at this scale can support a wider content campaign.

The complete report gives executives a detailed view of the findings.

• A press release presents the most newsworthy statistics in a format that journalists and industry publications can reference.
• Webinars allow Gong researchers and external experts to interpret what the findings mean.
• Blog articles can explore individual conclusions without asking readers to consume the complete report.
• Year-end roundups can reconnect the research with other studies.
• Product and solution pages can use selected findings where the evidence is relevant.

This is more useful than simply copying one article into several formats.

Each asset serves a different reading need.

Gong Labs is expanding from sales research into market intelligence

Gong currently describes Gong Labs as an original research engine that turns billions of sales conversations, deals, and workflows into insights.

Its newer Gong Labs Trends initiative broadens the purpose of the research.

Instead of only asking which sales behaviors produce stronger results, Trends examines what buyers and sellers are discussing across millions of customer interactions.

The June 2026 dataset covered 33.5 million deals worked since February 2024. Gong found that the share of deals containing AI discussions had increased by 85%. Discussions related to hiring remained broadly flat during the same period.

This type of research can reveal changes in buyer priorities.

It may show when AI agents begin appearing more frequently in commercial conversations, when budget pressure increases, when security becomes a stronger concern, or when businesses start discussing new pricing models.

The audience also becomes wider.

Research about discovery questions mainly serves sales teams. Research about shifting buyer priorities can interest founders, chief revenue officers, marketers, product leaders, investors, analysts, and business journalists.

Therefore, Gong is using the same underlying data to move into a larger field of business research.

The product still captures customer interactions. The research question has expanded from “How should a seller handle this call?” to “What are businesses discussing across the market?”

Why proprietary research remains valuable in AI search

Generative search creates a new challenge for every publisher.

A reader can ask an AI assistant how much a salesperson should speak during a discovery call. The answer may include Gong’s findings without requiring the reader to visit Gong’s website.

That can reduce traffic to the original article.

However, the AI system still needs a source for the statistics. It cannot independently analyze Gong’s private customer interactions or compare those conversations with commercial outcomes.

It can reproduce the published finding. It cannot create the underlying evidence.

This gives proprietary research a stronger position than content built entirely from public information.

A general explanation can be summarized and recreated easily. A newly observed data point must originate somewhere.

The opportunity for Gong is to remain clearly associated with that origin.

The company can strengthen this association by publishing transparent methodologies, using consistent research branding, updating source pages, creating distinctive charts, and ensuring that every reused statistic points towards the original study.

The objective may gradually shift from winning every click to becoming the source behind the answer.

What founders can learn from Gong’s content engine

Gong’s real advantage is the connection between its product, its customer data, and its publishing. The software gives the company access to patterns inside billions of sales conversations, deals, and workflows. Gong Labs turns those patterns into research that helps revenue teams understand buyer behavior and compare their performance.

Founders should begin by asking what their own business can observe more clearly than the wider market. That advantage may come from product usage, transactions, customer support, implementation work, audits, or repeated client projects.

Access to data is only the starting point. Useful research begins with a decision the customer is already trying to make. Gong studies practical questions such as how much a seller should speak, which stakeholders should join a deal, and which behaviors appear more often in successful opportunities.

It also returns to important questions as its dataset grows. In 2025, Gong re-examined its well-known talk-to-listen research using 326,000 sales calls rather than continuing to rely on its original benchmark. This keeps the content current and shows that the company is willing to refine earlier conclusions.

The research should then strengthen the entire content library. One substantial study can support a detailed source article, several focused explanations, an executive report, a practical guide, and later updates. Each asset should answer a different question rather than repeat the same statistic. Over time, the company becomes associated with a specific field of knowledge because its findings continue to appear in relevant articles, training material, industry discussions, and external citations.

The wider lesson is to treat content as an output of the business rather than a separate marketing activity. Gong’s customer interactions generate data. Its research team turns that data into evidence. Its editorial team makes the evidence understandable.

The resulting content earns attention before many readers are ready to buy and quietly demonstrates what the product can uncover. That system is harder to reproduce than a large publishing calendar because competitors can copy the topic, but they cannot easily recreate the data, methodology, and accumulated authority behind it.

Turn the data your business already owns into insights people can use, remember, and come back to.

First Round built the Review around questions founders could not easily answer elsewhere. Its editors went beyond executive opinions to document how experienced operators handled hiring, product-market fit, management, customer feedback, and scaling. Those interviews became detailed articles, memorable frameworks, and eventually a startup knowledge library that readers could return to years later.

Most venture capital firms publish opinions about markets, technology, and fundraising.

First Round Capital went directly to the people building companies and asked them to explain how the work was actually done.

How did Slack collect and use customer feedback during its launch? How should managers hand over responsibilities as a startup grows? How can a company measure product-market fit before the answer becomes obvious? How should a founder find the right co-founder?

These questions helped First Round create a growing library of startup operating knowledge.

Their publication, First Round Review, launched in June 2013 with an ambitious goal: to build the “Harvard Business Review for startups.” Within its first few months, the site had published more than 30 articles, increased visitors to First Round’s website fivefold, and regularly generated more than 1,000 tweets for individual stories. Its first article, about Etsy increasing the number of female engineers, received tens of thousands of views and led to follow-up coverage from several major publications.

By 2015, articles were between 2,000 and 5,000 words. The site was generating around 480,000 monthly page views, while an average article was shared approximately 2,000 times.

A decade after launch, First Round reported nearly 600 articles, millions of page views, hundreds of thousands of newsletter subscribers, and more than 100 episodes of its In Depth podcast. Its current homepage promotes an archive of more than 600 pieces and a subscriber base of over 200,000.

The impressive part is that First Round found a way to convert private experience into public intellectual property. Every strong interview added another framework, phrase, process, or case study to its archive. Over time, those individual articles became a recognizable body of knowledge about building startups.

That is the real First Round Review content engine.

The Review Began With a Gap in Startup Media

When First Round introduced the Review, startup media was already crowded.

Funding announcements, product launches, valuations, acquisitions, and technology trends received constant coverage. Founders could easily learn what another company had announced. They had far fewer places to learn how that company had solved the problems behind the announcement.

First Round saw that gap clearly, and its launch article argued that most startup coverage explained what companies were doing while very little explained how they were doing it. The firm believed much of the useful knowledge remained inside the heads of experienced operators, engineers, product managers, marketers, and founders.

This distinction shaped the publication.

A conventional interview might ask a founder about their vision, leadership philosophy, or predictions for an industry. Those answers can be interesting, but they are rarely useful enough to change how another company operates.

First Round wanted instructions.

The publication would cover topics such as interviewing product managers, building high-performing teams, hiring executives, setting prices, handling customer feedback, finding product-market fit, and navigating management problems.

That gave every article a clear test. A reader should be able to use at least part of the advice immediately.

The Review still describes its value in similar terms. Its current homepage promises tactical company-building advice, detailed steps, and articles that can reach roughly 5,000 words when the topic requires that depth.

The format has evolved. The editorial promise has remained remarkably stable.

First Round Already Had the Raw Material

First Round did not begin with a large newsroom because it already had access.

By the time the publication was formally introduced, the firm had worked with more than 600 entrepreneurs. It was also running more than 70 events a year for its community. Founders and operators were already sharing advice inside meetings, summits, online groups, and private conversations.

The publication gave that knowledge a permanent form.

First Round had access to 600+ entrepreneurs and 70+ community events a year. The Review gave those private founder conversations a much bigger audience and a much longer life.

An answer shared at a private CEO event could help the people in the room once. An edited article could help thousands of founders for years.

This was an important operational advantage. First Round did not have to compete with technology publications for every source. Its investing and community work naturally placed the firm close to people who had built notable companies or solved difficult operating problems.

The network also helped the editors find valuable experts who were not famous.

First Round has said its strongest stories can come from a well-known CEO or from a vice president most readers have never heard of. The determining factor is whether that person is willing to explain the work in depth and share how it was done.

That approach expanded the possible source pool.

A publication driven by celebrity needs a continuous supply of recognizable names. A publication driven by expertise can interview the person who designed the hiring process, built the first sales team, repaired a failing onboarding system, or managed the company through hypergrowth.

For founders, the second person may be far more useful.

First Round also looked beyond its own portfolio. Camille Ricketts, the editor who shaped the early Review, explained that the publication was not intended simply to promote First Round companies. It wanted to feature outstanding people across the technology ecosystem and make their advice widely available.

That decision made the Review feel larger than the fund behind it.

Readers did not need to care which companies First Round had financed. They could visit because they needed to hire their first marketer, improve customer discovery, manage a growing team, or make decisions faster.

The business benefited because the publication was useful beyond its own boundaries.

The Interview Was Treated as Editorial Research

Expert-led content often fails because the interview is treated as a recording session.

Someone schedules a call, asks broad questions, receives broad answers, and turns the transcript into a lightly edited article. The final piece sounds credible because an expert participated, but it rarely contains anything that the expert has not said elsewhere.

First Round developed a more deliberate process.

Before an interview, the editorial team worked with the subject to identify the right topic. The strongest topic sat between three things: what the person understood deeply, what they were genuinely willing to discuss, and what could produce actionable advice for startup readers.

The team then sent questions in advance. An interview lasted around an hour and was transcribed before the article was developed. The preparation changed the conversation.

An executive who receives “What are your leadership principles?” can respond with familiar ideas.

An executive who receives questions about the exact moment a management system failed, what they changed the following week, how they communicated the decision, and what they would measure differently has to retrieve real experience.

The value, therefore, came from the specificity of the inquiry.

The transcript was raw material. It still required an editor to locate the strongest idea, remove repetition, find supporting examples, create a logical sequence, name the framework, and turn the experience into something another leader could apply.

Ricketts originally managed the interviews, ideation, and writing herself. Because attaching the same writer’s name to every story felt distracting, she often removed the editorial byline. She wanted readers to feel that they were hearing directly from the person being profiled.

This was more than a stylistic choice.

It placed the expert at the center of the article. The editor became the person shaping the knowledge rather than the personality competing with it.

Ricketts also treated subjects as partners in the production process. Sources could review what was planned for publication. She found that this made people more forthcoming about mistakes, weaknesses, and personal experiences because the purpose was educational, and they were not worried about being surprised by the final story.

That arrangement created a useful exchange.

The expert received a carefully produced account of their ideas, the audience received uncommon detail, and First Round received original material that could not be gathered from public quotes and existing articles.

Trust became an editorial input.

First Round Turned Experience Into Memorable Frameworks

A transcript preserves a conversation. A strong article improves its usefulness.

The Review repeatedly converted complex experiences into ideas that readers could remember and repeat.

Molly Graham‘s advice about handing over responsibilities during hypergrowth became “Give Away Your Legos.” The metaphor explained a difficult emotional problem. As new employees arrive, early team members can feel that important parts of their work are being taken away. Graham argued that people who want to grow with the company must repeatedly give away parts of their existing job and find a larger problem to own.

Dave Girouard‘s experience at Google and Upstart became “Speed as a Habit.” The article moved beyond the general instruction to work faster. It divided organizational speed into decision-making and execution. It then gave leaders concrete practices, such as deciding when a decision will be made, questioning delivery dates, removing dependencies, and asking why important work cannot be completed sooner.

Kim Scott‘s management experience became “Radical Candor.” The Review article explained the framework through stories, a two-axis model, contrasting management behaviors, and an acronym that made the advice easier to use. The piece helped turn a broad idea about honest feedback into a practical management language.

First Round had access to 600+ entrepreneurs and 70+ community events a year. The Review gave those private founder conversations a much bigger audience and a much longer life.

These concepts, including Radical Candor and Superhuman’s product-market-fit framework, gained early attention through its pages. Phrases such as “Give Away Your Legos” and “Speed as a Habit” became part of startup vocabulary.

These examples show what the editor adds.

That structure might be a sequence, diagnostic question, maturity model, score, operating rule, metaphor, decision tree, or set of warning signs.

Once the idea has a clear form, it becomes easier to remember. Once it is easier to remember, it is easier to share. Once it is shared widely, the article can influence how an entire professional community talks about the problem.

First Round Wrote for Founders, But Built an Audience Much Larger Than That

When First Round Review launched, the audience was much narrower. The editorial team wanted to help founders build companies. Every article started with a practical problem: hiring early employees, finding product-market fit, managing a growing team, launching a product, or making faster decisions.

That focus influenced the way the articles were written.

Instead of asking founders what they believed about leadership, the Review asked how a particular decision had been made. Instead of summarizing success, it unpacked the sequence of events that produced it.

The interview with Slack co-founder Stewart Butterfield is a good example.

Rather than simply saying Slack listened closely to customers, the article explains how the company invited its first external teams onto the platform, what changed when a 120-person organization started using the product, how support requests were categorized, how Twitter conversations were monitored, and how customer feedback reached product teams. Readers can follow the operating system behind Slack’s early growth instead of seeing only the outcome.

The same pattern appears in Rahul Vohra‘s product-market-fit article.

Instead of encouraging founders to “talk to users,” Vohra walks through the survey methodology Superhuman used, explains how responses were segmented, shows how those responses influenced product priorities, and even links to a tool that lets readers apply the framework themselves.

This level of detail gave the articles a much longer lifespan.

News loses value quickly. A funding announcement is interesting for a few days. A product launch matters until the next launch arrives.

Operating decisions are different.

Every generation of founders has to hire managers, improve onboarding, understand customers, build product teams, and decide when a company is ready to scale. The context changes, but the underlying questions stay remarkably similar.

That helps explain why many of the Review’s best-known articles are still among its most-read years after publication. They attract readers because they solve recurring problems rather than documenting one moment in startup history.

Interestingly, this focus also expanded the publication’s audience.

Although the Review was written with founders in mind, product managers, recruiters, engineering leaders, marketers, designers, executives, and even investors found themselves reading the same articles. Once an article genuinely helps people do their jobs better, the audience naturally grows beyond the original target.

That wider readership wasn’t created by covering more topics. It happened because the publication stayed disciplined about the problems it wanted to solve.

Readers returned because they knew what to expect. Whatever company appeared in the next story, the article would explain how experienced operators approached a difficult business problem and provide enough detail for someone else to adapt the same thinking inside their own organization.

The Archive Became More Valuable as It Grew

A publication can become harder to navigate as its archive expands.

First Round responded by turning individual articles into structured collections.

In 2015, the Review reorganized its work into nine digital magazines covering areas such as management, fundraising, product, design, and company culture. The goal was to help a sales leader, designer, founder, or manager discover more material relevant to their work instead of scrolling through a general feed.

The current site uses topic pages, curated collections, classics, newsletters, series, and podcast formats.

Its collections now gather advice around clear reader situations. Examples include becoming a better manager, joining an early-stage startup, building a team, improving customer conversations, and navigating career decisions.

This makes the archive behave more like a knowledge library.

An article may first reach readers through a newsletter or recommendation. Years later, it can resurface inside a management collection, a list of classic articles, a related story, a podcast compilation, or a new thematic series.

The Review demonstrated this compounding model when it marked its tenth anniversary by selecting 100 pieces of advice from its archive. It has also published collections of lessons from podcast episodes and product-market-fit interviews.

Older reporting became material for new editorial products.

That helps because original interviews are expensive. They require access, preparation, calls, transcripts, editing, fact-checking, source coordination, design, and distribution.

A strong archive improves the return on that investment. One interview can remain discoverable for years. A group of related interviews can later support a collection. A mature collection can become a series, learning program, newsletter theme, or podcast discussion.

The asset continues producing value after the original publication date.

The Review Built Trust Before the First Founder Meeting

A founder choosing an investor has limited information.

Capital is visible. The working relationship is harder to judge.

Every venture firm can say that it understands founders, has a strong network, and provides valuable support. The Review allowed First Round to demonstrate those claims publicly.

Its articles showed that the firm could reach experienced operators. They showed that it understood the problems founders faced. They showed that it was willing to invest time and resources into making useful knowledge available.

Ricketts described brand identity as one of the publication’s main goals. First Round wanted to be seen as a constructive participant in the startup ecosystem. She also acknowledged the commercial effect. Wider content reach could encourage more founders to approach the firm, giving First Round a stronger pool of potential investments.

She made the relationship even clearer in another interview. When founders see high-quality content, deep knowledge, and strong connections coming from a firm, they may become more likely to want that firm involved in their company.

This does not mean every reader became a portfolio founder.

First Round gave founders years to understand the firm before ever discussing an investment. More than 200,000 subscribers now have regular access to the people, ideas, and operating knowledge connected to its network.

First Round had not established a direct statistical connection between Review traffic and new investments when Fralic discussed the publication in 2016. The primary goal remained broad reach and practical usefulness.

That lack of direct attribution does not remove the business value.

A founder might read the Review for years before raising capital. By the time First Round appears in a financing conversation, the firm is familiar. The founder already knows its voice, network, areas of expertise, and attitude toward company building.

The content has completed part of the trust-building process before a partner joins the meeting.

Why the Model Is Difficult to Copy Quickly

A competitor can copy the article length.

It can create similar category pages, interview founders, launch a newsletter, and publish titles containing words such as “framework,” “playbook,” and “tactical guide.”

The underlying system is much harder to reproduce.

First Round’s advantage came from several connected assets:

• Access to founders and operators with useful experience
• Enough trust for those people to discuss mistakes
• Editors who could find a narrow and valuable topic
• Interview questions that extracted operating details
• Editorial judgment that turned conversation into structure
• A recognized publication that experts wanted to appear in
• An archive that increased the value of every new article
• A distribution network of founders, executives, and investors

Each asset reinforced the others.

A stronger publication attracted better experts. Better experts produced better articles. Better articles expanded the audience. A larger and more credible audience made participation more attractive to the next expert.

The loop took years to build.

This is why interview-led thought leadership should not be reduced to “book experts and write articles.” Access without editorial skill produces ordinary interviews. Editorial skill without access produces secondhand summaries. Distribution without substance creates a temporary traffic spike.

The engine works when all three are present.

Expert-Led Content Has a Different Role in the AI Era

AI systems can summarize a First Round Review article.

They can extract the main framework, shorten the advice, compare it with other management models, or turn it into a checklist.

That does not remove the need for the original work.

The distinctive information existed because an editor interviewed someone with direct experience. The source remembered the event, explained the decision, disclosed the mistake, and provided the example. The editor then selected and organized the material.

An AI system working only from public information cannot independently remember what happened inside Slack’s early customer-support operation. It cannot recreate a private performance conversation at Google. It cannot know how a founder interpreted a failed product launch unless someone records that experience.

It can process the knowledge after publication. The interview creates the knowledge before publication.

Current search guidance is moving in the same direction. Google advises publishers to demonstrate first-hand expertise and depth. It warns against pages that mainly summarize what others have already said. Its guidance for generative search specifically recommends unique, expert-led, non-commodity content that adds value beyond common knowledge.

Google also states that generative AI can help research and structure original material, while large-scale generation without added value may violate its spam policies.

OpenAI describes ChatGPT search as a way to connect users with original, high-quality web sources through citations and links.

None of this guarantees that an interview-based article will receive rankings, traffic, or AI citations.

It does show why original source material remains strategically valuable. Every answer system needs information to retrieve, compare, summarize, and cite. Publications that create new information have a stronger foundation than those repeating the same public consensus.

First Round’s defensibility lies upstream of the article.

The moat is the relationship with the expert, the quality of the questions, the willingness to reveal specifics, and the editorial ability to make those specifics useful.

First Round Is Expanding the Same Editorial Principle

The Review has added new formats without abandoning its original idea.

Its Paths to Product-Market Fit series examines the early years of companies, including false starts, difficult pivots, customer discoveries, and the decisions that eventually unlocked demand. First Round partner Todd Jackson conducts many of the interviews. The series includes detailed company histories from businesses such as Linear, Clay, Vanta, Vercel, and others.

Its Zero to $5M series focuses on founders and early sales hires navigating the first few million dollars in revenue. The In Depth podcast applies the publication’s interview style to long conversations with founders and operators.

In early 2026, First Round introduced another podcast called Executive Function. It focuses on senior executives at fast-growing companies and aims to examine how exceptional leaders hire, make decisions, build systems, and keep pace with companies that may change fivefold or tenfold within a year.

The publication has also begun featuring more personal, narrative work. Recent homepage stories include firsthand accounts from early employees and deeper profiles of influential technology figures.

These formats may look different, but the operating logic is consistent.

Find knowledge that is difficult to access. Ask the person closest to it. Go deeper than the public story. Give the reader enough detail to use what was learned.

What Can Founders and Content Leaders Learn from First Round?

First Round Review worked because it built its content around an advantage that competitors could not easily copy. The firm already had access to experienced founders and operators. Instead of using that access for promotional interviews, it turned real company-building experience into practical editorial assets.

The publication also stayed disciplined about the problems it covered. Rather than chasing startup news or broad leadership advice, it focused on questions that founders repeatedly face, from hiring and product-market fit to sales, management, and scaling. Those problems rarely disappear, which is why many of its articles continue to attract readers years after publication.

The editorial process deserves just as much credit as the interviews themselves. Every conversation was shaped into a clear framework with examples, practical steps, and memorable ideas. The result was content that readers could immediately apply, not simply admire. As the archive expanded, First Round organized it through collections, series, newsletters, and podcasts, allowing older articles to keep creating value instead of disappearing into a blog archive.

Perhaps the biggest lesson is that thought leadership begins long before the writing starts. Original ideas come from original conversations. The interview uncovers the experience, the editor turns that experience into something others can use, and the publication gradually builds a knowledge library that becomes more valuable with every article. That is why First Round is remembered not just for publishing excellent startup content, but for documenting how great companies are actually built.

A founder’s experience becomes far more useful when the reader can see the decisions behind the outcome.

ClickUp matches each content format to a specific buyer intent, from templates for task-seekers to comparisons for buyers evaluating tools. Together, these assets guide users from discovery and evaluation to product use.

How This Teardown Was Researched

This analysis draws only on public sources: ClickUp’s website, template directory, comparison hub, team pages, funding and growth announcements, and a published partner case study. Company- and vendor-reported figures are labeled as such throughout. No private analytics, conversion, or revenue-attribution data was used or assumed.

We reviewed six areas:

1. ClickUp’s template directory and individual template pages
2. Its competitor comparison hub and individual comparison pages
3. Team and department landing pages
4. Editorial blog content and software roundups
5. Public company announcements and founder statements
6. A published case study on ClickUp’s content-production workflow

We did not use, and this analysis does not assume access to: Google Analytics or Search Console data, content conversion rates, product-qualified lead data, CAC, revenue attribution, retention data, internal briefs, or editorial guidelines. Any statement about acquisition, activation, or pipeline is our external read of a public content structure, not a confirmed internal metric.

The Core System: Matching Content Format to Buyer Intent

ClickUp doesn’t use one blog format for every query. It routes search demand into distinct buckets: informational, functional, evaluative, and transactional, and it builds a purpose-specific page type and CTA for each one. A “template” searcher and a “vs” searcher get completely different experiences because they’re at completely different points in the buying journey.

A visitor searching “what is an SOP” and a visitor searching “ClickUp vs Asana” are both in ClickUp’s target market. They are not, however, the same buyer. One is learning a concept. The other has a shortlist and a decision to make. ClickUp’s content architecture treats that difference as the organizing principle for the entire library.

Reader Need	ClickUp Content Type	Likely Next Step
Learn how a process works	Educational article	Explore a related method or template
Start a process quickly	Individual template	Add the template / create a workspace
Compare software categories	Software roundup	Review ClickUp alongside other platforms
Evaluate named vendors	Comparison page	Sign up, compare features, consider switching
Assess departmental fit	Team / department page	Explore relevant workflows
Implement a feature	Help / product content	Use the product more effectively

ClickUp uses content to present one platform through many different customer needs and evaluation stages, rather than forcing every visitor through the same funnel.

What Growth Actually Coincided With This Strategy

ClickUp’s public milestones show strong company growth running alongside heavy investment in organic, product-led content. The available evidence does not isolate how much revenue the content specifically drove. What it does show is a model where specific content assets bring in a first use case, and a broad product expands usage from there.

In October 2021, alongside a $400 million raise at a $4 billion valuation, ClickUp’s revenue had tripled year-over-year. Its customer base had grown from 200,000 to 800,000 teams, and more than 90% of customers were already using at least three ClickUp products.

In March 2024, it served more than 10 million users across 2 million teams. In a February 2026 article, ClickUp stated revenue had grown tenfold since it reported nearly $20 million in ARR in 2020.

The available figures show ClickUp’s overall growth, not the specific impact of its content strategy. Growth also came from product quality, freemium distribution, sales, paid marketing, partnerships, and retention. What the pattern does support is a two-stage read of the funnel:

1. A specific piece of content, such as a template, a comparison, or a team page, gives a visitor one relevant reason to try ClickUp.
2. Once inside, ClickUp’s product breadth (90%+ multi-product adoption, per its own 2021 disclosure) does the work of expanding that single use case into a full workspace.

A template built for a marketing team can be the entry point for an account that later adopts docs, dashboards, and automations. A comparison page aimed at Asana switchers can be the entry point for a company that eventually replaces three or four other tools. Content’s job isn’t to close the deal on its own; it’s to win the first, narrow reason to log in.

Templates: The Fastest Route From Search to Product Use

A template page does something a normal landing page can’t: it lets someone use the product before they’ve signed up. That’s why it converts better than a typical blog post or feature page.

Here’s the logic. Someone searching “SOP template” isn’t looking to learn what an SOP is; they already know. They want a ready-made structure they can start filling in today. ClickUp built its 1,000+ template library specifically for this kind of searcher, across different categories:

A new user setting up a workflow from scratch normally has to decide on structure, build the workspace, configure fields, choose views, assign owners, and set deadlines before doing any real work. A template page compresses that into a shortcut. So, they are not only introduced to your product, they are already using it.

Thus, template pages don’t just attract traffic; they collapse the gap between “learning about the product” and “using the product” into a single click. That’s the entire reason this format outperforms a standard landing page.

Comparison Pages: Meeting Buyers Who Already Have a Shortlist

ClickUp’s comparison hub covers 24+ named competitors, targeting readers who’ve already picked a category and narrowed it to specific vendors. This is lower-volume, higher-intent traffic than a broad informational query, and it’s traffic that ClickUp would otherwise have to buy at high CPCs on Google Ads if it didn’t own the organic result.

ClickUp even targets comparisons that don’t include its name, such as “Trello vs Jira.” This puts ClickUp in front of buyers while they are still deciding which tools to consider.

Bidding on terms like “Asana alternatives” or “Monday vs Jira” in paid search is expensive precisely because the buyer intent is so strong; advertisers know these searchers are close to a decision. ClickUp’s comparison hub, which covers Monday.com, Notion, Asana, Jira, Trello, Airtable, Smartsheet, Wrike, and roughly 15 others, earns that same high-intent placement organically.

What’s notable is that some of these comparisons don’t mention ClickUp in the query at all. “Trello vs Jira” is two competitors’ traffic, captured by a third party who shows up with a balanced breakdown and then earns the right to pitch. Competitors rarely defend paid budget against searches that name two other companies, which makes this some of the cheapest high-intent traffic available.

Thus, a vendor writing about its own comparison has an obvious conflict of interest, and buyers know it. ClickUp’s main Asana comparison leans into product breadth as the differentiator, which is reasonable, but it’s also exactly what you’d expect a vendor to say.

Its help-center comparison is more convincing, because it explicitly concedes that Asana may suit teams that want more structure with less configuration. That kind of acknowledged trade-off is what separates a comparison page that gets cited from one that gets dismissed as marketing.

Team Pages: Making One Broad Platform Feel Built for You

Next comes the Team Pages. ClickUp solves the “does everything, means nothing” problem of broad platforms by splitting its messaging along department lines. Marketing, sales, and engineering each get a page that speaks their specific workflow language instead of a generic feature list, which matters both for first conversion and for internal expansion once one team is already inside.

A general project management pitch rarely lands with a software engineer the same way it lands with a marketing coordinator. However, the vocabulary, the pain points, and the proof points are different.

ClickUp content topics by audience

Audience	Topics Emphasized
Marketing	Briefs, campaigns, calendars, approvals, reporting
Development	Product requirements, sprints, backlogs, bugs, releases
Sales	Lead handoffs, deal tracking, customer onboarding
Design	Creative production, feedback, asset delivery
Product Management	Roadmaps, priorities, execution

The underappreciated role of these pages is internal expansion, not just first-touch acquisition. A single project manager who onboards ClickUp for one team has a ready-made page to forward to marketing, design, or sales colleagues, one that explains, in their language, why the same tool fits their workflow too. That’s a meaningfully cheaper expansion motion than a second cold acquisition effort.

The Blog Loop: Turning Educational Traffic Into Product Trials

ClickUp’s blog is structured to catch readers who aren’t searching for ClickUp at all, they’re searching for how to do something. The strongest articles don’t stop at explaining the process; they embed the exact template or tool needed to execute it, so the “next step” is inside the product rather than a separate search.

Most B2B blogs treat educational content and product features as separate systems. ClickUp’s process guides are built to close that gap directly:

The product reference works because it’s positioned as the easiest way to do the thing the article already taught, not as an interruption. Its task-management software roundup, ranking ClickUp alongside Todoist, Asana, Monday.com, Trello, TickTick, and Notion, does the same job for category-level, non-branded search. It puts ClickUp inside the comparison set before the reader has even decided to compare vendors directly.

Scaling Production Without Losing the Plot

ClickUp includes about 150+ new articles and 130+ optimizations of existing pages over 12 months, correlated with an 85% increase in non-branded organic traffic. These are vendor-reported figures, not independently audited ones, but the operating model they describe is instructive regardless of exact attribution.

ClickUp published 150+ new articles and optimized 130+ existing pages in 12 months. Almost half of its 280+ content actions were updates, not new content.

At roughly 280 combined publish and optimize actions over a year, that’s about one significant editorial action per working day. The real detail here is that updating existing content was treated as equal in priority to publishing new content.

Articles moved into an optimization queue when rankings slipped, with results tracked on a four-to-six-week cycle. That’s a maintenance discipline most content teams skip in favor of always chasing new posts, and it’s usually the difference between a library that compounds and one that slowly decays.

What a Library This Size Costs You

Scaling to hundreds of comparison pages, templates, and guides creates real operational challenges. Near-duplicate pages can begin competing for the same search query, making it harder to maintain clear search intent.

The workload also grows over time. Competitor pricing and feature information can become outdated, while some pages continue attracting traffic without contributing to signups, product adoption, or pipeline.

None of this is a reason to avoid scale. It’s a reason to budget for maintenance from day one, not as an afterthought.

Four risks show up predictably at this scale:

• Search-intent overlap. A single topic can spawn a definition article, a template roundup, an individual template, a software list, and a department page, all technically about the same subject. Without a clear content map defining each page’s distinct job, these pages cannibalize each other’s rankings.
• Repeated editorial formats. Templated structures (“explain the topic → list options → place ClickUp prominently → CTA”) produce content faster. But it also produces content that starts to look interchangeable, which is exactly the kind of content generative search engines summarize instead of citing.
• Stale product claims. Competitor pricing, feature limits, and integrations change multiple times a year. A comparison page with 2024 pricing data is unhelpful and also actively damages trust with a buyer who’s already checked the competitor’s current site.
• Traffic without business value. Visits are not the same as a qualified pipeline. Every page type should have its own success metric. Template pages should measure template use, comparison pages should measure trial or demo starts, and educational content should measure qualified click-throughs.

Which of These Assets Survives the Shift to AI Search

Generative search engines can now answer basic definitional queries directly, with zero clicks to the source. What survives that shift is content that offers something an AI answer can’t: a usable artifact, a transparent test, or genuinely proprietary data. ClickUp’s templates and interactive tools sit in a much stronger position here than its definitional and list-style content.

AI search vulnerability by content asset

Content Asset	AI Search Vulnerability	Strategic Response
Basic definition guides	High — summarized instantly, low click-through	Rebuild as interactive hubs, not static explainers
Competitor comparison pages	Medium — AI can extract feature lists but needs verified sourcing	Add original screenshots, transparent testing methodology
Templates & workspace assets	Low — a functional structure can’t be reproduced in a text summary	Double down; this is the most defensible format ClickUp has
Proprietary research & data	Very low — AI search must cite the original source of new statistics	Publish first-party data other publishers can’t replicate

Google’s own guidance for AI Overviews and AI Mode reinforces this: it recommends content that is unique, useful, and based on information or experience that adds value beyond what’s already summarizable. It should not be a different technical playbook, just a higher bar for originality. A template clears that bar automatically, because the value only exists inside the product. A definition article does not, no matter how well it’s written.

The Replicable Framework

You don’t need ClickUp’s budget to run this playbook. You need the same discipline: map real customer intent to the right format, connect every piece of content to a specific next action inside your product, and measure each format against the metric that actually matches its job.

Match the asset to the task

Reader Intent	Right Format
Understand a process	Educational guide
Start a task	Template, checklist, or tool
Research a category	Software evaluation
Compare vendors	Competitor comparison
Assess team fit	Department or use-case page
Build internal approval	ROI model or buying guide

Measure by format, not by traffic:

• Educational content → qualified click-through to product pages
• Comparison pages → trial starts, demo requests, influenced pipeline
• Template pages → workspace activation and template-use rate

And build maintenance into the plan from the start: fact-checking cadence, internal-link reviews, and a clear removal or consolidation policy. Don’t treat your content library as something you only ever add to.

Final Takeaway

ClickUp’s content strength comes from assigning a clear commercial purpose to every format in its library.

Templates reduce the time between search and product use. Comparison pages help buyers evaluate software at the moment they are making a decision. Team pages explain the same platform in language that different departments understand. Behind all of these, you find a disciplined optimization process that keeps the library useful as it grows.

As AI search changes how people discover information, simply publishing more articles is unlikely to create a lasting advantage. Practical assets, first-hand evidence, transparent comparisons, and continuously maintained content are harder to replace with an AI-generated summary and more valuable to both readers and search engines.

That’s the principle behind how we think about modern content strategy. AI is excellent at accelerating research, drafting, and maintenance. But deciding which customer problems deserve content, which format best serves the reader, and how each asset supports product adoption or revenue still requires human judgment. A high-performing content engine is built on that combination, not on volume alone.

Want to build a content engine like this? Every teardown on this site reverse-engineers a public growth system to uncover the strategic decisions behind it. If you’re building a B2B content engine that prioritizes qualified pipeline over pageviews, we’d love to talk.

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The real advantage is not publishing more content, but building the right asset for each buyer need and keeping it useful over time.