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.