A resource and community space for modern marketers, sellers, and builders using customer voice to grow — together.
This hub is built for anyone who wants to do more with the voices of their customers. Whether you're scaling advocacy, building trust with proof, or rethinking how to go to market — you're in the right place.
How-to guides and playbooks for building with customer voice
Campaign-ready templates and swipe files
Benchmark reports and reference best practices
Event recordings, expert sessions, and community spotlights
Ask questions. Share ideas. Trade wins. This is your space.
You don’t have to figure this out alone. The Deeto community connects you with other leaders using customer voice to build better GTM motions, faster-growing brands, and smarter strategies. If you are interested in joining when it launches, sign up below.
Automate advocacy management workflows
Dynamically generate customer stories and social proof
Eliminate manual reference management
Track and report advocacy impact on revenue
Discover practical guides, templates, and tools to help your team close more deals, faster.
Three years ago, if you could produce a polished PDF report faster than your competitor, you won a deal.
Two years ago, a better dashboard gave you an edge.
Four years ago, a thoughtful, personalized email template shifted perception.
Today, all of that is background noise.
AI LLMs have made output generation trivial. A competent prompt engineer can build what used to take a product team six months to ship. Not the underlying workflow. Not the infrastructure. The output. The thing your customer actually sees.
A dashboard that took engineering effort to design? Claude can generate a similar layout in seconds.
A case study template that felt special? Prompt engineers have dozens of variations ready.
An email that seemed to resonate? Run it through an LLM and you'll get something functionally equivalent in structure, tone, and persuasiveness.
This is happening in your category right now. The question is whether you've noticed it already.
For the last decade, SaaS moats looked like this: build software that produces better output than competitors. Charge for that output. Win.
That output layer has become commoditized. Fast.
If your product's defensibility depends on generating dashboards, reports, emails, templates, or insights faster or prettier than an LLM, you're playing a game you've already lost. You can't outrun commodity pricing on productivity tools.
Some companies know this already. They've started moving upmarket, adding compliance features, or bolting on horizontal AI capabilities to stay relevant. Those moves buy time. They don't fix the core problem.
The thing that's hard to replicate isn't what you output. It's what you know.
Specific knowledge. Proprietary data. Information that an LLM can't generate because it doesn't exist in training data and can't be hallucinated because it requires ground truth.
The companies that will survive the next five years aren't necessarily the ones with the fanciest output. They're the ones sitting on structured, hard-to-replicate information.
Data collection has always been boring. It's why most SaaS companies avoid it. But boring data is becoming the only data worth owning.
You probably already have this data. You're just not treating it like your moat. You're treating it like a side effect.
If you're in SaaS, you have three choices.
You can keep competing on output. Watch your margins compress. Commoditize faster. Try to be the cheapest or the most efficient, which is a race nobody wins long-term.
You can add compliance, security, or horizontal AI features to stay relevant.
That extends runway by 18 to 24 months. The problem doesn't go away.
Or you can ask:
what do we actually know that no one else knows?
What data do we have that matters?
What information is sitting in our system that, if structured and connected, would be impossible to replicate?
That third option is where the moat lives. But it requires rethinking what you're actually building. Treating data collection as a first-class problem, not a side effect. Building infrastructure to make that data usable, not just stored. Understanding that the future isn't about smarter output, it's about better input.
Most SaaS companies aren't ready for that shift. They're too invested in the output game.
The window is now.
Dashboards, reports, templates. AI does all of it now. Here's where the real advantage lives.
Here's the problem most customer-facing teams walk into budget season with: they know their program is working, but they can't prove it.
ROI for a voice of the customer program is the measurable return generated by capturing, organizing, and activating customer insights, expressed as revenue impact, cost savings, and efficiency gains relative to program investment. The teams that can calculate it clearly protect their budget. The teams that can't are always the first to get cut.
This post walks through the full ROI calculation, the inputs that matter, and how to turn the output into a business case that lands with a CFO or CRO.
The difficulty isn't that the value isn't there. It's that it's fragmented.
A reference shortens a deal. A testimonial lifts a landing page. Win-loss research sharpens the pitch. Churn interviews surface a product gap before renewals are at risk. Each of those moments drives real revenue, but none of them show up in a single report, and most teams don't have a system that connects them.
The result: customer-facing teams operate on anecdotes. Leaders know it works but they can't quantify it. That's a budget problem, a headcount problem, and eventually a program-survival problem.
The fix isn't working harder. It's building the right measurement architecture from the start.
The math is simple, but the inputs are where most teams get stuck.
ROI = (Total Value Generated - Program Cost) / Program Cost x 100
Total Value Generated has two components:
The sections below break each one down with the inputs to use and the benchmarks to anchor them.
Peer proof has a direct effect on competitive deals. When buyers are evaluating multiple vendors, a credible reference or customer story shifts the conversation from "do we trust this vendor" to "does this solution fit our needs," and that's a fundamentally different place to be in. Deeto's ROI model benchmarks a 15% win rate improvement as the baseline expectation for programs with active references and customer evidence in play, with the range running up to 35% depending on how deeply proof is integrated into the sales motion.
Track which closed-won deals had a reference, a customer story, or a peer review involved in CRM. That data turns a benchmark into your number.
Buyers who arrive via peer recommendation have already done a significant portion of their own validation before a rep is involved. The awareness stage, the credibility check, the "is this vendor worth talking to" question; much of that is resolved before the first call. Deeto's model uses a 20% cycle reduction as its baseline assumption, with programs that embed references and social proof more systematically seeing reductions toward the 40% end of the range.
Model it by taking your current average cycle length, applying a conservative reduction, and calculating how many additional deals per rep that unlocks per year.
Leads that have engaged with customer proof before reaching sales convert at higher rates. The reason is straightforward: a buyer who has already read a relevant case study or seen a peer review has self-qualified in a way that a cold MQL hasn't. Deeto's model applies a 25% MQL-to-SQL conversion lift as its default, reflecting the difference between leads that have encountered customer evidence and those that haven't.
Track conversion rates for influenced versus uninfluenced leads separately. The gap is your attribution.
Referred leads are a different category of prospect entirely. They arrive with pre-existing trust, a warmer disposition toward the product, and a shorter path to a buying conversation. Deeto's model sets a 30% referral conversion rate premium as the baseline, reflecting the well-established pattern that referred leads close faster and at higher rates than leads from other channels. A structured referral program is one of the highest-return activities in customer-facing teams precisely because the hard work of building credibility has already been done before the lead enters the funnel.
Systematic win-loss and churn research surfaces competitive gaps and messaging problems before they cost deals. The reason most teams don't act on this sooner is a data quality problem: salespeople only get the complete and honest truth about why they win or lose 40% of the time, meaning 60% of the deal outcome data in a CRM is incomplete or inaccurate. Deeto's model applies an 8% win rate lift as the baseline for teams running structured research programs, with upside to 20% for programs that feed insights back into messaging and sales enablement consistently. This input is often left out of ROI models entirely, which means most teams are undervaluing their research function significantly.
Revenue impact is the bigger number, but cost savings are often faster to prove internally and easier to tie to headcount.
Manual reference coordination including spreadsheets, Slack routing, and back-and-forth matching is expensive. Teams that handle it manually typically spend 20–40 hours per month on tasks a centralized system handles in minutes. Calculate: monthly hours x loaded hourly cost x 12. Then model a 40–60% reduction.
If your team buys case studies or VoC research from agencies, centralizing that work reduces per-piece cost significantly. Calculate: annual assets produced x agency cost per piece, then model the reduction from bringing production in-house at scale.
The retention effect of an active advocacy program is well documented. The Wharton study on referred customers found they churned at an 18% lower rate than non-referred customers, and unlike other value metrics, that retention advantage did not erode over time. The mechanism is straightforward: customers who are actively engaged with your program have more touchpoints, surface dissatisfaction earlier, and have a stronger relationship with the product than customers who go dark after onboarding. For a program with meaningful ARR at risk, this is often the largest cost savings line in the model.
Deeto's ROI calculator lets you input your real business parameters including deal volume, average deal size, win rate, sales cycle length, churn rate, and program costs, to model projected impact across every category above.
For a mid-market SaaS company running 150 deals per year at a $60,000 average deal size, with a 25% win rate and a 90-day sales cycle, the calculator typically surfaces:
The output includes a projected ROI multiple, net annual value, months-to-payback, and a 3-year cumulative view that factors in compound adoption as the program matures.
You can adjust confidence levels for each category to model conservative, expected, and optimistic scenarios. This makes the output defensible in executive conversations because it shows range, not just a single number.
Run your numbers in Deeto's ROI Calculator
The benchmark-based model above is a starting point. The teams getting the clearest ROI data are the ones using a platform that tracks it automatically by connecting customer voice activity to deal outcomes without manual reconciliation.
That's the problem Deeto is built to solve. By centralizing the capture of customer voice, organizing it into a live intelligence layer, and surfacing it in active workflows, Deeto gives teams the attribution data that most ROI models are missing.
Here's what that looks like from the teams using it.
Brendan Hong, who leads customer marketing at Agiloft, described the before state plainly: "Before Deeto, we were managing everything manually. Now we have a centralized system that scales with us."
Michele Teixeira at Datarails put it this way: "Before Deeto, our reference process was cumbersome and manual. We handled everything via Slack. Now the platform automates the entire workflow and gives us visibility we never had."
The visibility of knowing which references are being used, which content is influencing deals, and which advocates are engaged is what makes the ROI model live rather than theoretical.
Frank Provenzano at Boomi described the shift more directly: "Before Deeto, we dealt with too much information, no organization, and the amount of time that we needed to collect customer stories was overwhelming. Deeto changed all of that."
The operational savings those teams describe map directly to the cost savings inputs in your model. The deal-influence data Deeto surfaces maps to the revenue impact inputs. The model stops being an estimate and starts being a report.
A model is only as strong as the data feeding it. These are the metrics worth setting up from day one:
Deal influence rate: What percentage of closed-won deals involved customer evidence in some form. This is the single most important attribution metric in the program, and it lives in CRM.
Reference request fulfillment rate: How quickly and completely your team matches prospect requests to willing references. Slowdowns here signal advocate pool gaps before they become deal-level problems.
Content asset utilization: Which customer stories and testimonials are actually getting used in active sales cycles. Low utilization means the activation layer is breaking down somewhere.
Advocate engagement score: A composite of participation frequency, activity type, and responsiveness to outreach. Declining scores are early churn signals for your advocate base, not just your customer base.
Win-loss pattern changes: Are the competitive gaps that surface in win-loss research shrinking over time as messaging improves? This is the clearest evidence that your research function is generating ROI.
Modeling only Year 1. Voice of the customer programs compound. An advocate network built in Year 1 produces references, referrals, and content in Year 2 and Year 3 with decreasing marginal cost. A 3-year model captures this and typically makes the case two to three times stronger.
Leaving out cost savings. Teams that only model revenue impact are presenting half the picture. The combined number is always more compelling and more credible.
Not anchoring to named benchmarks. A projected number without a source is an opinion. A projected number without a source is just an opinion. Sources like Nielsen, TrustRadius, Wharton, and Sales Benchmark Index carry real weight in executive conversations. Use them.
Skipping the confidence range. Present conservative, expected, and optimistic scenarios. It signals rigor and makes the model harder to dismiss.
Most voice of the customer programs are delivering more value than their teams can prove. The gap isn't in results. It's in measurement.
Deeto is built to close that gap, from capturing authentic customer voice at scale, to organizing it for reuse, to surfacing it in the moments that move deals. The ROI calculator gives you a starting point for the business case before you even run a single campaign.
Start with the numbers: run your ROI calculation here. If you want to see how Deeto makes every input in that model trackable and automatic, book a demo.
What is a good ROI for a voice of the customer program?
A well-run voice of the customer program typically delivers a 10–30x ROI multiple in Year 1, with the figure increasing as the advocate network matures and content assets compound. The specific number depends on deal volume, average deal size, and how deeply the program is integrated into sales and marketing workflows. Programs connected to CRM and surfaced in active sales enablement tools consistently see the highest return because utilization rates are higher and attribution is cleaner.
How long does it take to see ROI from a voice of the customer program?
Most programs show measurable impact within 2–4 months in reference management efficiency and early deal influence. Win rate lift and referral conversion typically take 6–9 months to appear as deal cycles complete. Research-driven improvements from win-loss analysis often show up within 3–6 months as messaging tightens and objection handling improves. Deeto's ROI calculator models months-to-payback based on your specific inputs.
What is the easiest ROI to prove for a voice of the customer program?
Reference management cost savings are typically the fastest to prove because they are direct and immediate. If your team currently spends 20+ hours per month on manual coordination at a known hourly cost, and a platform reduces that by 50–60%, the calculation requires no assumptions. Revenue impact is larger but requires CRM tracking and a longer measurement window to attribute correctly.
How does Deeto help teams measure customer voice ROI?
Deeto centralizes customer voice capture, organization, and activation in one platform, which means deal influence, content utilization, and advocate engagement data all live in the same system rather than across spreadsheets and Slack threads. Teams using Deeto replace manual reconciliation with automatic attribution, turning the ROI model from a quarterly estimate into a live dashboard.
What data do I need to build a customer voice ROI model?
At minimum: annual deal volume, average deal size, current win rate, current sales cycle length, monthly MQL volume, MQL-to-SQL conversion rate, active advocate count, monthly reference management hours, loaded hourly cost, annual content production spend, and annual churn rate. Deeto's ROI calculator walks you through each input and contextualizes your outputs against industry research.
How does customer advocacy affect churn?
Engaged advocates churn 26% less than non-advocates, according to Influitive research. Referred customers show 37% higher retention, per Wharton data. The mechanism is consistent engagement: advocates who regularly participate in program activities surface dissatisfaction earlier, deepen their relationship with the product, and have more touchpoints with the vendor team than passive customers. For programs with meaningful ARR at risk, churn reduction is often the largest single cost savings input in the ROI model.
Learn how to calculate customer voice ROI and how Deeto customers prove real revenue and savings impact.
Overview:
The signals that could improve your win rate, protect retention, and sharpen your forecast are already inside your organization. This guide shows revenue leaders how to connect authentic customer voice to every stage of the revenue motion and put a number on what that's worth.
Spotlight:
Inside, you'll find a numbers-first playbook built for CROs, RevOps leaders, and sales teams who need to justify the investment and understand the return. See how structured win/loss programs deliver 5 to 15% higher win rates, how continuous listening drives 15 to 25% higher renewal rates, and why a conservative model for mid-market B2B teams shows a 4x to 8x return within 12 months.
What to Expect:
Why It Matters:
Your customers are generating signals in every conversation, every renewal, every deal debrief. When those signals are connected and orchestrated, they become the intelligence that improves forecasts, accelerates pipeline, and protects revenue. That's what customer orchestration delivers.
Download the guide and turn customer voice into your most predictable growth lever.
The revenue intelligence you need already exists. This guide shows you how to put it to work.
Overview:
Product marketers sit at one of the most valuable intersections in a B2B company. But when customer voice lives in five different systems and case studies take six weeks to clear legal, that advantage gets lost. This guide makes the quantitative case for treating Voice-of-Customer as a strategic infrastructure investment.
Spotlight:
Inside, you'll find a numbers-first playbook built for CROs, RevOps leaders, and sales teams who need to justify the investment and understand the return. See how structured win/loss programs deliver 5 to 15% higher win rates, how continuous listening drives 15 to 25% higher renewal rates, and why a conservative model for mid-market B2B teams shows a 4x to 8x return within 12 months.Inside, you'll find a numbers-first framework built for PMMs who need to prove revenue contribution and build the business case for orchestration. See how consolidating customer voice reduces redundant research spend by up to 75%, how activating customer evidence at the right deal stages delivers a 5 to 15% win rate lift, and why a conservative model for mid-market B2B teams shows a 3x to 8x return within 12 months.
What to Expect:
Why It Matters:
Customer voice is already your most powerful go-to-market asset. This guide shows you how to build the system that makes it work across every stage of the revenue cycle.
Download the guide and turn customer voice into your most measurable competitive advantage.
This guide shows product marketers how to turn customer voice into measurable revenue impact.
Overview:
Customer marketers own the most commercially important relationship in a B2B company: the one that already exists. But when feedback, health scores, advocacy contacts, and campaign data all live in different systems, programs get built on incomplete pictures. This guide makes the quantitative case for treating customer orchestration as infrastructure, not an activity.
Spotlight:
Inside, you'll find a numbers-first framework built for customer marketers who need to scale programs, prove impact, and build the business case for orchestration. See how consistent lifecycle engagement protects $240,000 in ARR annually, how structured referral programs generate attributable pipeline at near-zero acquisition cost, and why a conservative mid-market model shows a 3x to 6x return on platform investment within 12 months.
What to Expect:
Why It Matters:
Customer voice is already your most powerful growth asset. This guide shows you how to build the system that makes it work across retention, expansion, and advocacy at every stage of the customer lifecycle.
Download the guide and turn customer engagement into your most attributable revenue lever.
This guide shows customer marketers how to connect engagement to revenue and prove the impact.
Most companies are sitting on a mountain of customer signals they never actually use. Call transcripts, survey responses, support tickets, interview notes. The sentiment is there. The insight isn't, because no one has a system to extract it.
Sentiment analysis is the process of identifying and measuring the emotional tone expressed in customer communications, classifying it as positive, negative, or neutral to help teams understand how customers feel at any point in the relationship.
In this guide, you'll learn what sentiment analysis is, why it matters for B2B teams specifically, and the exact steps to run it in a way that produces decisions, not just dashboards.
Sentiment analysis is a method of processing text, audio, or video to detect the underlying emotional tone and classify it as positive, negative, or neutral. It is a form of natural language processing (NLP) that enables teams to move from reading individual customer comments to identifying patterns across hundreds or thousands of interactions.
In a B2B context, sentiment analysis helps customer success, product, and marketing teams answer questions that would otherwise take weeks of manual review: Are renewal conversations trending negative? Are customers satisfied with a specific feature? Are certain account segments at higher churn risk?
Sentiment analysis systems help teams move from reactive to proactive. Instead of waiting for a customer to escalate, you see the signal before the problem compounds.
Sentiment analysis includes more than just survey scores. It covers everything from call transcript tone to open-text NPS responses to product interview feedback. When those signals are organized and analyzed together, they become one of the most reliable inputs a B2B company can have.
A single NPS score tells you almost nothing on its own. A sentiment trend across 200 customer touchpoints tells you a lot.
B2B companies lose customers slowly, then all at once. The warning signs are almost always present in the language customers use weeks or months before a churn event, but most teams don't have the infrastructure to catch them. According to Bain & Company, a 5% increase in customer retention can produce a 25–95% increase in profits, yet most retention efforts are still built on lagging indicators. Deeto's data shows that teams with earlier visibility into customer sentiment see 10–15% higher renewal rates as a direct result.
Sentiment analysis bridges that gap. It gives customer success teams an early warning system, product teams a continuous feedback loop, and marketing teams proof that the language they use actually resonates with buyers.
Running sentiment analysis well requires more than a tool. It requires a clear process for collecting the right signals, analyzing them in context, and routing insights to the teams who can act on them. Here are the steps.
Start with the question, not the data. The most common mistake teams make is running sentiment analysis without a clear objective, which produces charts nobody uses.
Before collecting any data, define the specific question you're answering. Are you trying to understand how customers feel about a recent product launch? Identify which accounts are at churn risk before renewal? Measure how onboarding sentiment changes over the first 90 days?
A clear question determines which data sources you need, which time windows matter, and what a meaningful shift in sentiment actually looks like for your business.
Sentiment analysis is only as good as the signals feeding it. Most B2B teams underestimate how many customer voice channels they have available: NPS and CSAT surveys, customer success check-ins, support tickets, onboarding interviews, product feedback sessions, and sales conversations.
Deeto's Listen module is built to capture authentic customer voice continuously across all of these channels, including AI-powered interviews, surveys, and in-product microfeedback. Instead of sending a quarterly survey and hoping for responses, you build a continuous collection system that captures sentiment in context, at the right moment in the customer journey.
The goal at this step is breadth. Pull from every available channel so your analysis reflects the full picture, not just the loudest voices.
One of the richest and most underused sources of customer sentiment in B2B is the sales and customer success call. A customer can give a 9 on an NPS survey and still use language in a renewal call that signals serious dissatisfaction. Text-based surveys don't catch tone, hesitation, or the specific phrasing customers use when they're concerned.
Call recording analysis addresses this directly. By analyzing transcripts and audio from recorded calls, teams can identify sentiment patterns that never surface in structured feedback channels.
Deeto's integration with Gong makes this seamless. Gong captures and transcribes sales and CS calls automatically. Deeto pulls those transcripts into the platform, analyzes the sentiment and key themes expressed, and ties that intelligence back to the customer record. That means a CS manager can see not just what a customer said in a survey, but what tone they used in their last three calls, and whether that tone has shifted. It also means product marketing can identify recurring objections or praise across hundreds of calls without manually reviewing a single one.
Call-level sentiment analysis is especially powerful for identifying churn signals early. A customer who uses phrases like "we were hoping for more" or "we haven't really gotten there yet" in a renewal call is telling you something. A system that captures and surfaces that language gives your team a real window to respond.
Raw sentiment data isn't intelligence yet. The next step is classifying what you've collected into themes, topics, and sentiment scores that can be compared over time and across segments.
Good classification answers three questions about every piece of feedback: What is the customer talking about? How do they feel about it? How does this compare to what other customers are saying?
Deeto's Analyze module handles this layer of the process. It identifies patterns, tracks sentiment trends, and organizes customer signals into dashboards that give teams a clear view of where sentiment is strong, where it's declining, and which segments or product areas need attention. This is where raw customer voice becomes structured intelligence.
The output of this step should be a categorized view of sentiment by segment, topic, lifecycle stage, and time period, not just an aggregate score.
Once signals are classified, the analytical work begins. Look for clusters: which topics have the highest concentration of negative sentiment? Which customer segments are trending in the wrong direction? Which onboarding milestones correlate with positive long-term sentiment?
This step is where customer sentiment analysis shifts from descriptive to predictive. You're no longer just measuring how customers feel today. You're identifying which patterns precede churn, which precede expansion, and which indicate a customer who's ready to become an advocate.
Teams that build this pattern recognition into their regular workflow stop waiting for customers to tell them something is wrong. They start seeing it in the data first.
The most common failure point in sentiment analysis programs isn't the analysis itself. It's the last mile. Insights sit in a dashboard nobody checks, or they're shared in a monthly report that's two weeks out of date by the time it's read.
Effective sentiment analysis requires a routing system. A negative sentiment spike in a CS account should trigger a notification in the account owner's workflow. A cluster of negative product feedback should reach the product team in a format they can act on. A pattern of strong positive sentiment around a specific outcome should reach marketing before the message becomes stale.
Deeto's platform connects the intelligence layer to the activation layer, surfacing the right insights to the right people at the right moment, whether that's in Salesforce, Slack, or a product team's roadmap tool. That's what separates a sentiment analysis program that changes decisions from one that produces reports.
Sentiment analysis isn't just a tool for internal decision-making. It's also a way to strengthen customer relationships, but only if you close the loop.
When a customer shares negative sentiment, reaching out to address it directly is one of the most effective retention moves a company can make. When positive sentiment clusters around a specific outcome, it's an opportunity to capture a case study, a testimonial, or a referral.
Customer voice research and evidence programs work best when customers feel heard. Closing the loop, telling customers what changed because of their feedback, creates a relationship dynamic that reinforces loyalty and generates more authentic input over time.
What is sentiment analysis in simple terms?
Sentiment analysis is the process of reading customer communications, whether written or spoken, and classifying the emotional tone as positive, negative, or neutral. In a B2B context, it helps teams understand how customers feel about a product, relationship, or experience without having to manually review every interaction.
What data sources can you use for sentiment analysis?
Sentiment analysis can be applied to almost any customer communication: NPS and CSAT survey responses, call transcripts, product feedback sessions, support tickets, email threads, and interview notes. The most effective programs pull from multiple sources simultaneously, because no single channel captures the full picture of how a customer feels.
How is sentiment analysis different from NPS?
NPS is a single numeric score that measures overall customer loyalty at a point in time. Sentiment analysis goes deeper, classifying the language customers actually use across dozens of touchpoints to identify themes, emotional patterns, and signals that a score alone cannot surface. NPS tells you a customer gave you a 7. Sentiment analysis tells you why, and what they said in their last three calls that suggests they might not renew.
How does Gong integrate with sentiment analysis?
Gong captures and transcribes sales and customer success calls. When connected to a platform like Deeto, those transcripts are analyzed for sentiment, key themes, and patterns that get tied back to individual customer records. This makes it possible to track how call-level sentiment evolves over time and surface early warning signals before they become churn events.
How do you act on sentiment analysis results?
Sentiment analysis creates value only when insights are routed to the people who can act on them. That means connecting your analysis layer to the workflows your CS, sales, and product teams already use. Negative sentiment signals should trigger account reviews. Positive patterns should feed marketing and advocacy programs. The goal is making sentiment a live input to decisions, not a retrospective report.
What is the difference between structured and unstructured sentiment analysis?
Structured sentiment analysis uses predefined scales or questions, like survey rating scales. Unstructured sentiment analysis processes free-text or audio data, such as open-ended survey responses, call transcripts, or interview notes. B2B teams get the most complete picture when they analyze both. Structured data tells you where to look. Unstructured data tells you why.
Deeto is a customer orchestration platform that turns authentic customer voice into connected intelligence and action. To see how Deeto handles sentiment analysis across the full customer lifecycle, book a demo.
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What is sentiment analysis? Learn its definition, key steps, and how to turn customer signals into decisions.
Overview:
Product feedback is everywhere. The challenge is making it useful without overwhelming your customers or your team in the process.
Most product and marketing teams are sitting on more customer input than they know what to do with. The problem isn't volume. It's that feedback is scattered, hard to act on, and disconnected from the decisions that actually shape the roadmap. By the time insights reach the right people, the moment has passed.
In this session, Shawnna Sumaoang will walk through how teams are solving this today. Not through a single use case, but a practical look at the many ways you can collect, centralize, and activate product feedback across your organization.
You’ll learn:
Date: Thursday, April 23, 2026
Time: 9:00 AM PT / 12:00 PM ET
Location: Zoom virtual event (Link sent upon registration)
Speakers:
Shawnna Sumaoang, CMO, Deeto
Survey fatigue is real. Here's how product teams scale feedback that actually drives roadmap decisions.
Your reps are having great conversations. Gong is capturing every one of them. And now, with Deeto in the picture, every one of those conversations can become intelligence your entire go-to-market organization acts on.
Gong already gives revenue teams an exceptional foundation: recorded calls, transcript search, deal signals, coaching workflows. The Gong + Deeto integration builds on that foundation by connecting conversation intelligence to customer evidence, activation, and measurable pipeline impact across sales, marketing, and customer success.
Here is how it works, from first call to closed pipeline.
The Gong + Deeto integration is a bi-directional connection between Gong's conversation intelligence platform and Deeto's customer orchestration platform. It is designed to move customer insights captured during sales and success conversations through a structured workflow, from raw signal to activated evidence to measurable revenue impact.
The integration is built around four stages: Capture, Analyze, Activate, and Measure. Each stage builds on the last. Most GTM teams have the first stage covered. The challenge is building the full loop. Without a system that moves insights from capture through to measurable action, customer intelligence stays siloed in the tools that collected it instead of flowing to the people who need it.
Gong handles conversation intelligence with exceptional depth. Deeto's customer orchestration platform handles activation and orchestration.
Together, they close the loop between what customers say and what your business does about it.
Every sales call, renewal conversation, and QBR contains intelligence. Customers tell you what they care about, what they value, what competitors they are evaluating, and what they would love to see on the roadmap. Gong is purpose-built to capture all of it.
Call recordings, transcripts, deal signals, and speaker data are logged automatically. The integration pulls that rich data into Deeto's platform without requiring any manual effort from reps or CS teams, so the intelligence Gong generates flows directly into the system that organizes and activates it.
What gets captured includes call transcripts tied to specific accounts and deal stages, speaker-level data that attributes insights to specific customers, deal metadata including stage, health score, and outcome, and signals that indicate sentiment, objections, or competitive mentions.
The value of this stage is the combination of Gong's capture depth and Deeto's organizational layer. Every compelling customer moment is preserved and ready to be used, not just reviewed.
According to Gartner, revenue teams that use AI-guided conversation intelligence reduce ramp time by up to 30% and improve forecast accuracy. Deeto builds on that advantage by making those captured signals available across the full go-to-market team, not just inside the revenue org.
Gong already surfaces deal intelligence, coaching moments, and forecast signals from call data. The Deeto integration extends that intelligence into a new dimension: customer evidence that travels across the entire go-to-market organization.
Deeto's Analyze module processes incoming Gong data using AI to identify patterns, extract meaningful quotes, score sentiment, and tag insights by topic, persona, and business theme.
What comes out of the Analyze stage includes customer quotes categorized by use case and buyer persona, sentiment scores that reveal satisfaction trends across segments, competitive mentions flagged and grouped, and signals tied to specific deal outcomes, wins and losses included.
This is not a search interface where someone hunts for a needle in a haystack of transcripts. Deeto surfaces the insights automatically and connects them to the customer record, the account, and the evidence library.
For product marketing teams, this means messaging that is grounded in real customer language, not assumptions built in a conference room. For sales enablement, it means proof points that are current, specific, and tied to actual outcomes.
An important note on competitive intelligence: the integration flags competitor mentions in Gong transcripts and aggregates them in Deeto. Over time, this becomes a living view of how your competitive position is perceived by real buyers, updated after every call. That is a signal worth acting on. For teams building a more systematic approach, Deeto's competitive insights use case is built exactly for this.
Analysis is only useful if it gets to the people who need it, at the moment they need it.
Deeto's Activate module is where customer intelligence becomes action. Once insights are extracted and tagged from Gong, Deeto routes them into the workflows where your team actually operates: CRM records, sales enablement tools, Slack, and marketing campaigns.
Here is what activation looks like in practice.
The word "activation" matters here. It is not just delivery. It is contextual delivery. The right insight, to the right person, at the right moment in their workflow. That specificity is what separates a useful integration from a feed nobody opens.
Deeto customers see 15-20% faster deal cycles when reps have access to contextual customer evidence at the point of need. The Gong integration extends that advantage by making conversation intelligence the input to that evidence pipeline, automatically and continuously.
For teams managing customer advocacy and references, the integration connects directly to Deeto's reference management capabilities. When a Gong call signals a highly satisfied customer, Deeto can automatically flag that account as a potential reference and initiate the follow-up workflow, with no manual triage required.
The question every revenue leader asks is the same: is this actually moving the number?
The Gong + Deeto integration gives you a clear answer. Because Deeto connects customer intelligence to deal data from Gong, you can measure how the presence of activated insights affects pipeline outcomes.
What you can track includes win rates on deals where customer evidence was surfaced vs. deals where it was not, time-to-close differences for opportunities where reps received Deeto briefings, engagement rates on customer quotes and stories used in campaigns and deal rooms, and reference request outcomes tied to specific Gong signals.
This is the pipeline impact layer. It answers not just "what are customers saying" but "how much does acting on it change the result."
Deeto's revenue trends use case is built for this view. It connects the dots between customer intelligence inputs and revenue outputs so that the business case for investing in customer voice becomes quantifiable, not anecdotal.
For teams that want to demonstrate the full value of their customer intelligence investment, this is the layer that makes it visible. The Gong + Deeto integration connects two platforms that are each already generating value, and creates a measurable multiplier between them.
Gong and Deeto are each strong in their own right. Gong is the leader in conversation intelligence, giving revenue teams unprecedented visibility into what happens in every customer interaction. Deeto is the customer orchestration platform that turns authentic customer voice into connected intelligence and action across the full go-to-market organization.
The integration exists because the two platforms are genuinely complementary. Gong excels at capturing and analyzing the revenue conversation layer. Deeto excels at organizing, activating, and measuring what that intelligence means for the broader GTM team.
Together, Gong and Deeto form a complete intelligence loop. Authentic customer voice goes in. Connected, activated, measurable intelligence comes out across sales, marketing, customer success, and leadership. That loop is what customer orchestration is designed to power.
The integration is available now. Setup connects your Gong workspace to Deeto's platform and begins syncing call data, account metadata, and deal signals immediately. No custom engineering required.
If you want to see how the full loop works for your team, the best place to start is a demo. The Deeto platform walkthrough covers the integration directly, including how your current Gong data maps to Deeto's intelligence and activation workflows.
The conversation is already happening. Now every insight it generates can travel further than ever before.
The Gong + Deeto integration connects conversation intelligence captured in Gong with Deeto's customer orchestration platform. It automatically extracts customer insights, quotes, sentiment signals, and competitive mentions from Gong call data and routes them into Deeto's intelligence and activation workflows. The result is that sales, marketing, and customer success teams receive actionable customer evidence in the tools they already use, without manual effort.
Deeto uses AI to process Gong transcripts as they sync into the platform. The analysis identifies meaningful customer quotes, categorizes them by topic, persona, and use case, scores sentiment, and flags competitive mentions. Insights are then tied to the relevant account and customer record in Deeto's system of record, making them searchable and activatable across the go-to-market team.
Sales teams benefit from contextual evidence surfaced at the right moment in a deal cycle. Product marketing teams gain access to real customer language that can sharpen messaging and positioning. Customer success teams receive signals that indicate satisfaction, risk, or expansion readiness. Revenue operations gains a measurable view of how customer intelligence affects pipeline outcomes. The integration is designed to serve the entire revenue team, not just one function.
Yes. When Gong call data signals high satisfaction or a particularly strong customer outcome, Deeto can flag that account as a potential reference and initiate an outreach workflow automatically. This removes the manual triage step that typically delays reference program growth and ensures that satisfied customers are identified and engaged while the sentiment is still fresh.
Deeto connects deal outcome data from Gong with activation data inside its platform. This allows revenue teams to compare win rates, time-to-close, and engagement metrics across deals where customer evidence was surfaced versus deals where it was not. The revenue trends use case in Deeto is specifically designed to surface this comparison and make the business impact of customer intelligence visible to revenue leadership.
No. The integration is designed for straightforward setup that connects your Gong workspace to Deeto without requiring engineering resources. Once connected, data begins syncing automatically. Configuration options allow teams to control which call types, deal stages, and customer segments feed into Deeto's intelligence pipeline.
Learn how the Gong + Deeto integration turns conversation intelligence into pipeline impact.
Ask any sales rep what they do when a prospect asks for a customer reference, and the honest answer is usually the same: they call the one customer who always says yes.
A customer reference is a satisfied customer who agrees to speak directly with your prospects, sharing their real experience, the outcomes they've seen, and the honest tradeoffs they navigated. It's peer-to-peer validation at the moment a buyer needs it most. This article covers what makes references work, why most programs quietly fail, and what a reliable system actually looks like.
A customer reference is a verified customer who participates in direct conversations with prospective buyers on behalf of a vendor. References typically join sales calls, take one-on-one calls with prospects, or exchange emails with buyers who want unfiltered answers before making a decision.
Customer reference programs are the structured systems companies build to identify, manage, and activate these conversations at scale.
What makes a reference different from a testimonial or a case study is that it's live and two-way. A prospect can ask about the implementation headaches, the support response times, the things they'd do differently. That candor is exactly what moves a stalled deal.
The problem isn't that prospects don't trust you. It's that they trust your customers more.
According to Gartner, B2B buyers who receive helpful peer information are three times more likely to make a larger purchase with less regret. That's not a small lift. That's the difference between a deal that closes confidently and one that drags or dies.
References work because they carry something no sales deck can: lived experience. A prospect asking "did the integration actually work with Salesforce?" gets a very different answer from a peer who ran it than from a rep who's read the release notes. Specificity builds trust. Trust accelerates decisions.
For sales and sales enablement teams, references are one of the few proof assets that work at the exact moment of maximum buyer hesitation. Late stage, when a deal is close but not closed.
These three terms get used interchangeably. They shouldn't.
Testimonial: A written or recorded quote from a customer. Works best at the top of funnel — website, ads, social.
Case study: A structured narrative of a customer's results. Works best mid-funnel, when a prospect is in consideration mode.
Customer reference: A live conversation between your customer and your prospect. Works best late stage, pre-close, when a buyer needs peer validation before deciding.
Customer references are the highest-touch form of social proof. They're also the hardest to scale, which is why most companies treat them reactively instead of building a real system around them.
Not every happy customer makes a strong reference. The ones that consistently move deals forward share a few things:
The best references aren't just satisfied customers. They're customers who feel seen, valued, and invested in the relationship, which is itself a signal about how well you're running your post-sale motion.
Most reference programs aren't really programs. They're habits.
A sales rep knows one customer who always picks up. That customer gets called six times a year. They're still saying yes, but they're tired. And the prospect on the other end of that call can sometimes tell.
The problem with most customer reference programs isn't a shortage of happy customers. It's a shortage of infrastructure.
The structural failures are consistent across companies:
References are siloed with individual reps. When the relationship between a rep and a customer is the only path to a reference, that reference becomes that rep's asset, not the company's. When the rep leaves, the reference disappears.
There's no matching system. Without structured data on which customers are willing, what they're comfortable discussing, and which segments they represent, teams default to whoever they know. Relevance suffers.
Advocate fatigue goes undetected. With no visibility into how often a customer has been asked, the same handful of enthusiastic advocates get used until they stop responding. By then, the relationship has already taken a hit.
The ask is framed as a favor. Customers aren't enrolled in a program, they're asked ad hoc, with no clear value in return. That framing doesn't scale and doesn't build loyalty.
The fix isn't more outreach. It's building reference management as an actual system, one where customer willingness, segment fit, and participation history are tracked, matched, and maintained.
References aren't just a late-stage tool. Teams that get the most value from them deploy customer voice at multiple points:
Mid-funnel. A case study or short video from a customer in the prospect's industry answers objections before the prospect even raises them. It doesn't require a live call, it just requires having the right story available.
Late-stage evaluation. This is where live reference calls do the most work. A 30-minute peer conversation matched by role and use case can move a deal from stalled to signed.
Executive alignment. For enterprise deals, connecting a prospect's executive to a customer's executive creates credibility no sales motion can replicate. These conversations require the most care in matching, but they close the biggest deals.
Post-sale onboarding. References aren't only for prospects. Connecting a new customer to an established one who's been through the same implementation journey reduces anxiety and accelerates adoption.
For customer marketing teams, the goal is building a reference pool diverse enough to support all of these moments, not just the late-stage sales call.
Customer references are one part of a broader customer advocacy system. Advocacy includes reviews, event participation, community engagement, referrals. References are the highest-commitment form of advocacy, they require the most from the customer and deliver the most for the deal.
The difference matters because customers willing to do one aren't always willing to do the other. A customer who'll write a G2 review might not want to take sales calls. Conflating the two leads to over-asking, and over-asking is how you burn your best advocates.
A well-run advocacy program tracks each customer's willingness across different activity types. References, reviews, events, referrals, each is a different ask with a different level of effort. Managing them separately is what keeps customers engaged instead of exhausted.
When references are tracked, matched by segment, and activated through a system rather than a spreadsheet, sales cycles shorten and the same small group of customers stops getting worn down.
If you're starting from scratch, how to build a customer reference program is a good place to begin. If you're ready to see what a system looks like in practice, Deeto's reference management handles matching and activation automatically, so the right reference shows up for the right deal, without the scramble.
What is a customer reference in B2B sales?
A customer reference is a satisfied customer who agrees to speak directly with a prospective buyer, sharing their real experience with a product or service. Unlike a testimonial or case study, a customer reference is a live, two-way conversation, making it the most credible and interactive form of peer validation in the B2B sales process.
How is a customer reference different from a testimonial?
A testimonial is a static, pre-written or pre-recorded quote. A customer reference is a live conversation where the prospect can ask their own questions, about implementation, support, outcomes, or whatever's making them hesitate. That interactivity is what makes references more persuasive at late-stage evaluation.
What makes someone a good customer reference?
The best references have seen measurable results, match the prospect in role and industry, are genuinely willing to participate, and have recent enough experience to speak credibly to current capabilities. Willingness matters as much as satisfaction — a reluctant reference often does more harm than no reference at all.
When should customer references be used in the sales process?
References are most effective late-stage, when a prospect has narrowed their options and needs peer validation before deciding. But customer voice in the form of case studies, stories, and matched introductions can add value earlier, at mid-funnel when objections are forming, and post-sale when new customers need confidence during onboarding.
Why do most customer reference programs fail?
Most programs fail because references are treated as individual rep relationships rather than company assets. There's no system for matching prospects to relevant customers, no visibility into advocate fatigue, and no structured value exchange for participating customers. The result is over-reliance on a small group of willing customers until they stop responding.
How do you scale a customer reference program?
Scaling requires three things: a centralized system that tracks customer willingness and availability by segment, a matching process that connects prospects to the most relevant reference by role, use case, and industry, and a clear value exchange so participating customers feel recognised rather than used. Platforms like Deeto automate matching and surface the right reference for each opportunity without manual searching.
What is a customer reference? Learn what makes them work, why most programs fail, and how to build a system that scales.
See how Deeto helps you turn customer voice into a GTM advantage.
