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What’s up everyone today we have the pleasure of sitting down with Jane Menyo, Sr. Director, Solutions & Customer Marketing at Gong.

Summary: Jane built her marketing practice around listening. At Gong, she turned raw customer conversations into a live feedback system that connects sales calls, product strategy, and messaging in real time. Her team uses AI to surface patterns from the field and feed them back into content that actually reflects how people buy. She runs on curiosity and recovery, finding her best ideas mid-run. In a world obsessed with producing more, Jane’s work reminds marketers to listen better. The smartest strategies start in the quiet moments when someone finally hears what the customer’s been saying all along.

In this Episode…

• How Solutions Marketing Turns Customer Insights Into Strategy
• Using AI to Mine Real Customer Intelligence from Conversations
• Why Stitching Research Sequences Works in Customer Marketing
• Using AI Trackers to Uncover Buyer Behavior in Sales Conversations
• How Standardized Prompts Improve Sales Enablement Systems
• How Gong’s Research Assistant Slack Bot Delivers Instant Customer Proof
• Avoiding Mediocre AI Marketing Research
• Why Customer Proof Outperforms AI-Generated Marketing

About Jane

Jane Menyo leads Solutions and Customer Marketing at Gong, where she’s known for fusing strategy with storytelling to turn customers into true advocates. She built Gong’s customer marketing engine from the ground up, scaling programs that drive adoption, retention, and community impact across the company’s revenue intelligence ecosystem.

Before Gong, Jane led customer and solutions marketing at ON24, where she developed go-to-market playbooks and launched large-scale advocacy initiatives that connected customer voice to product innovation. Earlier in her career, she helped shape demand generation and brand strategy at Comprehend Systems (a Y Combinator and Sequoia-backed life sciences startup) laying the operational groundwork that fueled growth.

A former NCAA All-American and U.S. Olympic Trials contender, Jane brings a rare blend of discipline, creativity, and competitive energy to her leadership. Her approach to marketing is grounded in empathy and powered by data; a balance that turns customer stories into growth engines.

How Solutions Marketing Turns Customer Insights Into Strategy

Jane’s role at Gong evolved from building customer advocacy programs to leading both customer and solutions marketing. What began as storytelling and adoption work expanded into shaping how Gong positions its products for different personas and industries. The shift moved her from celebrating customer wins to architecting how those wins inform the company’s broader go-to-market strategy.

Persona marketing only works when it goes beyond demographics and titles. Jane treats it as an operational system that connects customer understanding with product truth. Her team studies how real people use Gong, where they get stuck, what outcomes they care about, and how their teams actually make buying decisions. Those details guide every message Gong sends into the market. It is a constant feedback loop that keeps the company close to how customers think and work.

Her solutions marketing team functions like a mirror to product marketing. Product marketers focus on what the product can do, while Jane’s team translates that into why it matters to specific audiences. They do not write from feature lists. They write from the field. When a sales manager spends half her day in Gong but still struggles to coach reps efficiently, Jane’s team crafts stories and materials that speak directly to that pain. The goal is to make every communication feel like it was written from inside the customer’s daily workflow.

“Our work is about meeting customers where they are and helping them get to outcomes faster,” Jane said.

That perspective only works when every team in the company has equal access to the customer’s voice. Gong’s own technology makes that possible. Conversations, feedback, and usage patterns are captured and shared automatically, so customer knowledge is no longer limited to those on the front lines. Jane’s group uses that visibility to deepen persona profiles, test new positioning, and identify emerging trends before they reach scale. It makes the company more responsive and keeps messaging grounded in real behavior instead of assumption.

For anyone building customer marketing systems, the lesson is practical. Treat persona development as a live system, not a static report. Use customer data to update your understanding regularly. Create tools that let everyone in your company hear what customers say in their own words. That way you can write content, sales materials, and product messaging that actually aligns with how people buy, not how you wish they did.

Key takeaway: Persona marketing works when it functions as an always-on loop between customer data and company action. Map real behaviors, refresh those insights often, and share them widely. When everyone in your company hears the customer directly, you can shape messaging that feels relevant, personal, and authentic. That way you can scale customer understanding instead of guessing at it.

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Using AI to Mine Real Customer Intelligence from Conversations

AI is reshaping how teams understand their customers. Jane uses it as a force multiplier for customer research, not a replacement for human interpretation. Her process starts inside Gong’s platform, where every call, email, and deal interaction holds untapped evidence of what customers actually think. Instead of relying on small surveys or intuition, her team digs into those real conversations to extract patterns that explain why deals move forward or stall.

When the team explores a new persona or market, they begin with what customers have already said. They gather every interaction tied to that persona and run it through a standardized set of research questions. In one project focused on CIOs, Jane’s team analyzed hundreds of calls to understand how these executives engage in deals. They wanted to know what information CIOs request, what they challenge, and how their questions differ from other buyers.

“We were able to run a series of questions across hundreds of calls and get standardized insights in a couple of days,” Jane said. “That changed the tempo of how we learn.”

Once they finish mining internal conversations, they widen their view to external data. They use AI tools like ChatGPT to scan analyst reports, trade publications, and articles that mention the same personas. That process identifies what topics are rising in the market and how those trends align with what Gong’s customers are discussing in their calls. The result is a dual-layered map of reality: what customers say in private conversations and what the market signals in public forums.

This kind of research produces better decisions because it pairs scale with nuance. AI speeds up analysis across thousands of data points, but empathy gives meaning to those patterns. That way you can identify where customer perception shifts are happening and adjust messaging, enablement, or product focus before the market catches up.

Key takeaway: Use AI to process the noise, not to replace your judgment. Start with the data you already have; call recordings, customer emails, and deal transcripts, and create a structured framework for what you want to learn. Then validate those findings with external signals. Combine automation for reach with human sense-making for relevance. That combination produces customer intelligence that actually drives better marketing decisions.

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Why Stitching Research Sequences Works in Customer Marketing

Jane treats research like a product pipeline. Her team at Gong builds structured sequences that turn raw customer conversations into repeatable learning systems. Each sequence connects data, feedback, and external validation into one continuous loop. The goal is to keep marketing, product, and sales decisions grounded in what real buyers say and do, not in what teams assume.

“We kind of keep revisiting insights that we have through Gong and then go back and do these cycles of validation,” Jane says. “It’s a sequence of events, not a one-and-done project.”

The process starts with Gong’s internal data. Jane’s team combs through conversations to find recurring signals; patterns in objections, themes in decision drivers, or language that correlates with wins. Those patterns form a hypothesis, which they then test against external data sets. This keeps the loop fast and practical. Instead of waiting for quarterly agency reports, the team runs lean validation cycles that match the speed of real go-to-market decisions.

When a hypothesis holds up, the team deepens its analysis with closed-lost data. They use a platform called Clozd to automate customer interviews and categorize reasons for loss. That automation frees up time while still delivering nuance. Each cycle feeds sharper insights back into Gong’s positioning, sales enablement, and product narrative. Over time, the system compounds, producing a shared language between marketing and product that is rooted in live market behavior.

Jane still makes room for direct human research. She encourages her team to interview prospects who never made it into the funnel. That external layer fills a blind spot in most organizations. Internal data reflects engagement, but it rarely shows how awareness begins. Those early conversations reveal what captures attention in the first place and how buyers think before sales ever reaches them. Connecting those two phases (awareness and engagement) creates a complete view of the buyer journey.

Jane’s research model operates like a perpetual feedback engine. It links data with dialogue, internal truth with external perspective, and tactical iteration with strategic learning. It keeps customer marketing alive instead of frozen in PowerPoint decks.

Key takeaway: Build your research as a continuous loop instead of a quarterly deliverable. Start with internal data to identify recurring patterns, validate them through external signals, and reinforce your findings with structured feedback from lost deals. Add direct interviews to capture pre-funnel behavior. That way you can build a system that continuously refines your messaging, product strategy, and customer understanding in sync with how your market actually evolves.

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Using AI Trackers to Uncover Buyer Behavior in Sales Conversations

Marketers have always wanted to know what really happens inside a deal, but most companies still rely on outdated detective work. Jane sees this every day. Teams chase sales reps for insights, beg for ten minutes to “pick their brain,” and send endless surveys to customers who already told them everything during the buying process. The truth is that the answers are already there, hiding in plain sight inside thousands of recorded sales calls. The problem is that no one’s been listening closely enough.

Jane’s team at Gong uses AI trackers to change that. Instead of pulling reps away from their quota or pestering customers with follow-up questions, marketers can mine real conversations for patterns. You can search for calls with CIOs or CFOs and hear how those roles think about risk, value, or change management. You can listen to how they push back, what words they repeat, and where hesitation creeps in. That way you can stop guessing what your personas care about and start hearing it in their own voices.

“We already know these things,” Jane said. “We just aren’t very good at collecting them as marketers.”

The technology does more than keyword scanning. Gong’s AI trackers understand the intent behind words. If a customer says, “There’s a delay with the pilot,” that matters. If they say, “Sorry I was late, there was a delay in traffic,” it doesn’t. The system knows the difference, filtering noise from signal. That means marketers can trust what they’re seeing instead of wasting time validating false positives. You can track objections, stalled deals, or recurring frustrations with precision that spreadsheets never deliver.

For Jane, this capability reshapes how marketing, sales, and product teams work together. Marketers can walk into strategy meetings with data-backed narratives instead of anecdotes. They already know what messages land and what drives confusion. Sales teams stay focused on selling, and customers avoid yet another round of “What made you buy?” surveys. The insights flow directly from reality—unedited, authentic, and scalable.

Key takeaway: Use conversation intelligence to replace opinion with evidence. Start by building AI trackers around moments that matter, such as objections, pricing discussions, or deal delays. Then, listen for patterns across roles and industries. That way you can craft campaigns rooted in the language your buyers actually use, free your sales team from endless debriefs, and design messaging that mirrors how decisions really happen.

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How Standardized Prompts Improve Sales Enablement Systems

Standardized prompts turn chaos into rhythm. Jane uses them to translate the unpredictable world of sales conversations into structured, repeatable learning loops. Her team studies real buyer language at scale and uses that data to build enablement systems that stay grounded in reality. Instead of collecting opinions from the field, they collect patterns, and those patterns become tools the entire revenue team can use.

“We built custom GPTs that take the insights from discovery and objection data, then feed them directly into assets like discovery guides or objection-handling templates,” Jane said. “That gives us a solid first draft that reflects what actually works in those conversations.”

The process begins with tagging and categorizing discovery calls. Every call contributes to a growing dataset that identifies which questions spark productive conversations and which ones stall deals. Those findings fuel the next generation of discovery guides and objection-handling templates. When Gong enters a new vertical, like financial services, the sellers already have tested frameworks based on actual buyer interactions instead of guesses or recycled decks.

The real impact comes from speed. Jane’s team can refresh materials weekly, even daily, when needed. They can see if objections have changed, spot new deal blockers, and adapt faster than competitors still relying on sales anecdotes. That speed creates alignment across marketing and sales because everyone operates from the same living library of what works. It also keeps the company ahead of false narratives that form when a single bad deal becomes “proof” of a larger issue.

Gong’s enablement engine operates like a research lab that never sleeps. The team pulls signals from the market, converts them into usable content, and returns those assets to sellers in near real time. It is a constant feedback loop that keeps strategy tied to what customers actually say, not what internal teams think they say.

Key takeaway: Standardized prompts turn raw conversation data into systems of shared intelligence. That way you can build repeatable enablement programs, refresh them automatically with every new dataset, and keep your sales teams aligned around proven buyer behavior instead of internal opinions.

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Building Messaging Systems That Scale Across Industries

Most companies struggle to turn customer understanding into consistent messaging. They collect insights, file them away, and move on to the next campaign. Jane treats those insights as raw material. She and her team at Gong build what she calls a “bill of materials,” a detailed system that defines how every message should connect to both sales and marketing outcomes. It works like a manufacturing blueprint, outlining every asset, message pillar, and talking point that will help close deals and grow the funnel.

“One of the first deliverables we create as part of our BOM is a foundational messaging house,” Jane explained. “We take Gong’s corporate messaging and reapply it to each industry or persona with differentiated language that actually sounds like something they’d say.”

That messaging house keeps structure consistent but adjusts tone and focus for each audience. Health tech buyers care about compliance. Fintech buyers focus on risk and regulation. Sales leaders want coaching outcomes. Each variation uses familiar concepts but translates them into words that match how those audiences think and talk. The result is a single framework that feels unified but adaptable, giving every marketing team a clear starting point. That way you can scale messaging without losing authenticity.

Jane’s team then turns to performance data. They review which assets drive the most qualified leads, then use those findings to refine messaging across verticals. Gong’s research reports are a top performer. They already bring in strong inbound traffic, but Jane’s team slices the data by industry to uncover unique trends. For example, a report might highlight adoption maturity in healthcare or compliance readiness in finance. These cuts make the content feel built for the reader, which lifts engagement and conversion rates.

Jane measures success by movement through the funnel, not sheer volume. Some audiences are too small to justify large campaigns, but if they convert faster or close at higher rates, they earn priority. Her philosophy is simple: scale what works, refine what converts, and focus on the precision of messaging rather than the quantity of assets.

Key takeaway: Build a repeatable messaging framework that transforms audience understanding into a scalable system. Define your message pillars, customize language for each vertical, and measure impact by how efficiently content moves buyers forward. That way you can equip every team to write with clarity, confidence, and relevance.

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How Gong’s Research Assistant Slack Bot Delivers Instant Customer Proof

Marketers spend endless hours creating decks, videos, and case studies that end up buried in shared drives. Jane fixed that. Her team built a Slack bot that acts like a research assistant, surfacing every customer proof asset inside Gong’s ecosystem in seconds. Anyone can type a query, like “FinTech ROI” or “APAC renewals,” and the bot instantly delivers customer stories, verified G2 reviews, or approved references.

“We wanted to make it effortless for sales to find proof,” Jane said. “They shouldn’t have to search five tools or ping us in Slack just to get a customer story.”

The system runs on PeerBound, a platform designed for customer marketing teams that need automation without extra layers of process. It connects to Gong’s public story library, pulls in third-party review data, and syncs with their CRM to confirm which accounts can be referenced. Everything happens inside Slack, where reps already spend most of their day. That way you can eliminate the manual back-and-forth that slows deals down.

Within six months of rollout, usage exploded. The team tracked thousands of searches that showed exactly what sales wanted. Industry proof topped every category by a wide margin, outpacing product and competitive requests by nearly four times. Sellers wanted content that mirrored their buyers’ world. That insight shifted Gong’s strategy toward deeper vertical content that reflected how people actually sell.

Jane’s favorite part is the loop it created. Every search becomes feedback. If sellers keep asking for stories in manufacturing or EMEA and get no results, that gap shapes the next round of content. The Slack bot doesn’t just store assets; it measures demand in real time and informs what marketing builds next.

Key takeaway: Build enablement systems that collect data while they serve it. Gong’s Slack bot proved that automation can replace guesswork with clear signals from the field. When your internal tools double as listening systems, you gain speed, precision, and a roadmap for what to create next.

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Avoiding Mediocre AI Marketing Research

AI makes it easy to generate more marketing faster, but Jane warns that ease often comes at the cost of originality. After experimenting with every new tool that promised to speed up research or messaging, she noticed a pattern. Everything started sounding the same. She realized that most AI tools pull from the same recycled thinking already circulating online, which guarantees average results. The content feels polished but hollow, like it was written by a machine trained to please an algorithm instead of a human audience.

“When I leaned too heavily on AI to generate ideas, I started seeing my own work mirror what was already out there,” Jane said. “That was my signal that efficiency had quietly turned into mediocrity.”

Jane believes that strong marketing requires a clear position, a sharp perspective, and real expertise. Those qualities cannot be automated. They come from the research process itself: talking to customers, studying behaviors, and listening for patterns that reveal what people actually care about. She encourages marketers to treat AI as a partner in scaling research, not as a substitute for curiosity or critical thinking. AI can summarize what exists, but only you can interpret what matters.

Algorithms are already rewarding this kind of depth. Search engines are prioritizing originality, authority, and credibility over keyword-stuffed volume. Jane sees this as validation for marketers who invest in substance. When your content reflects real experience, expertise, and empathy, it earns more trust and performs better over time. Those who chase speed for its own sake end up producing content that fades into the background noise.

“AI turns conversation into data,” Jane explained. “The marketers who win will be the ones who feed it stories and context that nobody else has.”

Key takeaway: AI can accelerate your research, but it cannot define your perspective. Use it to process language, organize data, and surface connections, then inject human judgment, customer empathy, and firsthand experience before hitting publish. That way you can produce work that feels sharp, informed, and unmistakably your own.

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Why Customer Proof Outperforms AI-Generated Marketing

AI has already swallowed most of marketing’s low-effort output. The endless sea of listicles, templated case studies, and “thought leadership” posts now sound nearly identical. Jane sees the future of marketing through a sharper lens. She believes product and customer marketers (the ones shaping positioning and empathy-driven storytelling) will outlast the wave of machine-generated sameness because their craft depends on human synthesis, not production speed.

In her view, product marketing is only getting more complex. The job demands faster judgment, not faster writing. Marketers must synthesize market signals, customer sentiment, and competitive shifts in real time, all while keeping brand narratives coherent. That level of pattern recognition cannot be automated. It requires someone who can interpret nuance, understand organizational context, and translate chaos into clarity.

Jane calls customer proof “the antidote to mediocre content.” 

AI can summarize data, but it cannot carry conviction. A real customer story can. When a practitioner shares what worked and what failed in their own words, the message lands with credibility that no algorithm can replicate. That authenticity builds more than trust, it becomes strategic armor in markets saturated with synthetic noise.

As AI floods the zone with cheap content, the differentiator will not be volume but verification. Brands that invest in customer proof (actual humans validating real outcomes) will dominate awareness and loyalty metrics alike. Those who continue to chase scale through automation will blend into the background noise of the feed.

Key takeaway: Customer proof anchors credibility in an AI-saturated market. Marketers who pair strategic positioning with authentic customer stories will outpace those chasing automation, because truth told through real experience still cuts through faster than any machine.

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Why Rest Strengthens Creative Output in Marketing

High-performing marketers tend to treat exhaustion as proof of commitment. The late nights, the packed calendars, the constant sprinting between projects, all of it signals ambition. Jane learned early that this mindset backfires. Her years as a track and field athlete drilled one truth into her routine: growth depends on recovery. The same principle applies to creative work. Brains, like muscles, need recovery cycles to adapt and strengthen.

“When you’re maxing out and weightlifting, you can’t do that day after day,” she said. “Your body literally needs to heal those muscles to grow stronger. It’s the same with your brain.”

Jane credits her best ideas to moments when she steps away from work. Morning runs often bring sudden clarity. One project connects to another, forming a bigger idea she could not see at her desk. That space between effort and reflection becomes the birthplace of new strategy. Creative breakthroughs are not the product of more hours; they come from creating space for connections to form.

Her philosophy extends beyond personal productivity to team culture. She encourages her team to slow down and prune aggressively. Like a gardener shaping roses, she believes teams need to trim projects that drain energy so others can thrive. Pruning means choosing focus over volume. It means creating systems that reward rest, clarity, and depth over raw activity. She reminds her team that slowing down to decide what matters most is not wasted time. It’s a strategy in motion.

Happiness, for Jane, lives inside that rhythm of effort and recovery. Ambitious marketers can always add more to their plate, but fulfillment depends on knowing when to stop and reflect. She sees balance not as a state of calm, but as a discipline of choosing what fuels you and what depletes you. That perspective keeps her work grounded, her creativity intact, and her team motivated through long cycles of growth.

Key takeaway: Rest is a performance multiplier. Recovery creates the mental space where ideas connect and strategy takes shape. Protect time away from work, prune the projects that dilute focus, and give your mind the same recovery you would give your body. That way you can grow stronger, sharper, and more creative over time.

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Episode Recap

Jane Menyo’s story at Gong starts with listening. She built her career on paying attention to what customers actually say, not what marketing teams assume they mean. Her team treats every recorded call, every objection, and every win as data. When they spot a pattern (like CIOs hesitating over integrations or sales managers struggling with adoption) they use it to reshape how the company positions itself.

Over time, that process turned into a living system. Instead of waiting for reports or chasing opinions, Jane’s team built continuous research loops. They use AI to sift through thousands of conversations, connect them to market trends, and surface what buyers care about before it hits the mainstream. Sales and marketing then feed those insights back into the field through updated messaging, refined playbooks, and even a Slack bot that pulls real customer proof on demand. Gong moves at the pace of its customers because it listens in real time.

Jane also knows that scale can kill originality. When she leaned too heavily on AI-generated ideas, everything started to sound the same. That moment forced her to draw a line: automation should speed up learning, not replace it. Real customer stories still carry the conviction that machines can’t fake. They make people believe you because they’re grounded in truth, not pattern recognition.

She balances that discipline with recovery. Her background in track and field taught her that growth only happens between sprints. The same rule applies to creative work. She steps away to reset, often during a morning run when ideas start clicking together naturally. That rhythm of curiosity, structure, and rest keeps her team creative without burning out.

In a world obsessed with producing more, Jane’s work reminds marketers to listen better. The smartest strategies start in the quiet moments when someone finally hears what the customer’s been saying all along.

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Intro music by Wowa via Unminus
Cover art created with Midjourney (check out how)

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