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.
A rep needs a CFO reference at a fintech by Thursday, and the customer who fits best asked for a break last month.
The best customer reference management software in 2026 includes Deeto, Point of Reference (ReferenceEdge), Upland RO Innovation, UserEvidence, SlapFive, Base, Orca, and Influitive. These tools track which customers will speak for you, when, and how often, then match the right one to an open deal.
This guide includes a side-by-side comparison chart, what each tool does best, and how to choose.
Customer reference management software is a platform that stores your reference-ready customers, routes requests from sales, protects those customers from overuse, and reports on which references influenced revenue. For customer marketing teams, it replaces the spreadsheet, the Slack thread, and the one person who remembers which customer said yes last quarter.
Customer reference tools include the following core pieces:
One warning before you shop. Software directories lump very different products into this category. Capterra's customer reference management category mixes true reference platforms with loyalty programs, referral tools, and a general CRM. A referral tool rewards customers for sending you leads. A reference platform puts a real customer on the phone with a prospect who is about to sign. Those are different jobs.
Buyers want to do more of the work on their own. Gartner reports that 75% of B2B buyers prefer a rep-free sales experience. G2's 2025 research found that nearly two-thirds of software buyers prefer to engage sales only in the later stages of a purchase.
That shift puts more weight on the few moments when a buyer does talk to you. By the time a prospect asks to speak with a customer, they're close to a decision. A slow or mismatched reference at that point can stall a deal that marketing spent months creating.
The cost of getting this wrong is measurable. UserEvidence research found that 53% of B2B sellers have lost or delayed deals because they couldn't provide relevant customer evidence when it counted. At Deeto, we rarely see companies short on happy customers. The shortage is current information about who is willing to talk, about what, and how recently they were asked.
A reference is only as good as the last time you asked the customer how they feel. That's the gap a spreadsheet can't close, and it's the first thing to test in any demo.
Customer reference management software for sales reps has one job: get the right customer in front of a buyer before momentum fades. Program dashboards matter to customer marketing. Reps care about speed and fit.
When you evaluate tools from the rep's side, look for these five things:
If reps still have to email customer marketing to find out who's available, the tool has missed the point. Deeto's sales and sales enablement solution was built around that handoff, with references pre-matched to deal stage and delivered through CRM workflows.
We looked at each vendor's own product pages, recent company announcements, and public directory listings.
We compared tools on five criteria:
None of these vendors publish full pricing on their websites, except where noted. Treat any number as a starting point for a quote.
Deeto is a voice of the customer platform that treats references as one outcome of knowing your customers well. The same customer voice also feeds product teams, executive dashboards, and the rest of go-to-market, which makes Deeto the intelligence layer that references come out of, rather than one more point tool. Deeto captures willingness, comfort level, and boundaries directly from customers through AI-led interviews, then uses those signals to match references to buyers. You can see how it works on the reference management use case page.
Reps get references pre-matched to deal stage, plus buyer-ready proof like video testimonials and personalized microsites they can share without waiting on marketing. Deeto integrates with Salesforce, HubSpot, Microsoft Dynamics, Slack, Teams, Outreach, Salesloft, Highspot, Seismic, and Gainsight, and connects with Gong for call recordings.
Deeto customers report 20 to 30% faster sales cycles and 10 to 15% higher win rates. Among published customer results, Wrike reports 50 to 75% growth in its reference pool and 6sense reports 25%+ more reference call coverage.
Best for: Customer marketing and advocacy teams that want references, stories, and customer feedback in one system, with sales able to self-serve.
Point of Reference is one of the longest-running vendors in this space, with more than 20 years in customer advocacy. Its product, ReferenceEdge, runs 100% natively inside Salesforce and covers reference matching, request fulfillment, overuse tracking, and program impact reporting. Its newer ReferenceEdgeAI layer is AI-model agnostic, and the company now offers a version that works without the Salesforce interface.
The Salesforce-native design is its biggest strength and its biggest constraint. Teams that live in Salesforce get deep data alignment. Teams on another CRM should confirm how the non-Salesforce option fits their stack.
Best for: Enterprise teams running mature reference programs on Salesforce.
Upland RO Innovation focuses on helping sales find and use references quickly. Reps can search references on demand, filter customer quotes and facts with its Spotlights feature, pull approved logos for calls, and build prospect-specific collateral. It connects with Salesforce and Microsoft Dynamics, and its AI features suggest references by journey, persona, and use case and summarize won deals.
RO Innovation is part of Upland Software's larger portfolio. That can be a plus for companies already using other Upland products, and worth asking about in terms of product focus if you're not.
Best for: Sales-led organizations that want a large, searchable library of reference content alongside live references.
UserEvidence started as a customer evidence platform built on surveys and verified stories. In August 2025, it acquired Zealot, an advocacy and reference management platform, and folded those capabilities in.
Its references product lets reps request references from Salesforce or Slack, matches customers using usage, sentiment, and past participation, and lowers a customer's match priority with burnout scores after recent activity. Because the reference features came through an acquisition, ask how deeply the two products are integrated today.
Best for: Teams that want survey-based customer evidence and reference requests in one place.
SlapFive positions itself as a customer advocacy and proof system of record. It automates requesting, approving, and scheduling references, tracks advocate preferences and limits, and measures revenue influence. Its agentic AI looks for likely advocates in signals like community activity and call recordings, and it generates stories and testimonials from customer input.
SlapFive also leans into answer engine optimization for customer stories and makes advocate data available to other tools through MCP.
Best for: Customer marketing teams that want AI to find new advocates and produce proof content.
Base, formerly crowdvocate, describes itself as an AI customer growth platform. Reference management is one module among many, alongside onboarding, retention, expansion, and community. The company has grown through acquisitions, adding Laudable in February 2025 for turning customer calls into social proof and EverAfter AI in January 2026 for post-sale engagement.
That breadth suits teams looking for one platform across the post-sale lifecycle. Teams that mainly need references should check how deep the reference workflows go compared with specialists.
Best for: Companies consolidating customer success, marketing, and advocacy tools into one platform.
Orca is a native Salesforce app for managing customer advocates and reference requests. It covers request assignment, workflow management, reference usage tracking, and ROI reporting, and it connects with Slack and Google Maps for location-based selling. It's one of the few tools with a listed price, starting at $5,000 per user per year on its Capterra listing, with a free trial.
Best for: Salesforce teams that want a focused, lightweight reference app.
Influitive built its name on gamified advocate communities, where customers complete challenges, leave reviews, and earn rewards. It also supports references and referrals. In December 2023, Influitive was acquired by Jigsaw, part of private equity firm ESW Capital.
Influitive’s public product updates have been limited since the acquisition, so ask for a current roadmap and a clear view of what’s included in pricing before you sign a multi-year contract.
Best for: Programs built around community engagement and gamified advocacy, where references are a secondary need.
Reference marketing platforms and customer reference tools overlap, but they start from different places.
Customer reference tools start from the sales request. Their main job is matching, scheduling, and protecting customers who take live calls. Reference marketing platforms start from the content. They turn customer advocacy and customer voice into stories, quotes, case studies, and videos that marketing and sales reuse across campaigns.
Growing programs need both. A buyer early in research wants a short video from someone like them. A buyer in final evaluation wants a 20-minute call with a peer. If those two needs live in separate systems, your customers get asked twice and your team loses track of who said what. Deeto keeps live references and customer stories and social proof in the same place, so each customer interaction feeds both.
Start with your program's biggest bottleneck, then test vendors against it. These questions work well in demos:
If you're still designing the program itself, our guide on how to build a customer reference program covers recruiting advocates, structuring tiers, and measuring results.
What is customer reference management software?
Reference management software is a platform that helps B2B companies organize customers willing to speak with prospects, match them to open deals, and track how often each customer is asked. It typically includes a searchable reference pool, CRM-based request workflows, fatigue controls, and reporting that connects reference activity to won revenue. It replaces spreadsheets and manual coordination between sales and customer marketing.
What is the best customer reference management software?
The right choice depends on your CRM and program goals. Point of Reference (ReferenceEdge) and Orca fit teams that want Salesforce-native tools. Upland RO Innovation suits sales teams that want large reference content libraries. Deeto fits teams that want references, customer stories, and feedback in one voice of the customer platform with self-serve access for reps.
How much does reference management software cost?
Most vendors don't publish pricing and quote based on users, program size, and contract length. Orca is an exception, listing a starting price of $5,000 per user per year on Capterra. Expect enterprise reference platforms to require an annual contract. Ask vendors whether pricing is per sales user, per admin, or per customer advocate, since that changes cost a lot as you grow.
What's the difference between customer references and referrals?
A customer reference is a customer who speaks with a prospect to help them evaluate your product, usually late in a sales cycle. A referral is a customer who introduces you to a new potential buyer, often for a reward. Reference tools focus on matching and protecting advocates. Referral tools focus on tracking leads and payouts. Some platforms support both.
How do you prevent customer reference fatigue?
Prevent reference fatigue by tracking every request per customer, setting limits on how often each one can be asked, and rotating new advocates into the pool. Good customer reference tools do this automatically, lowering a customer's priority after recent calls. It also helps to offer lighter options, like a recorded video or written quote, so customers aren't always asked for live calls.
How does customer reference management software help sales reps?
Reference software helps sales reps find the right reference quickly without chasing marketing. Reps can search by industry, use case, or persona from inside their CRM, see who is available, and request a call in a few clicks. Many tools also give reps ready-to-share proof, like quotes and short videos, for buyers who aren't ready for a live conversation.
Every tool on this list can store a reference pool and route a request. The difference shows up in the moment a rep needs a specific customer for a specific deal, and whether the system knows who is willing, relevant, and rested enough to say yes.
Salesforce-native specialists work well for mature enterprise programs. Broader platforms make more sense when you want references, stories, and customer feedback to feed each other.
Deeto was built for that second path. When customers tell you directly what they're comfortable sharing, reps get the right proof faster, deals keep their momentum, and your best advocates stay happy to help. See how Deeto handles customer references.
Compare 8 customer reference tools for 2026. See features, pricing, and what sales reps need to find references fast.
Two in three research professionals (69%) now use AI in at least some of their projects, according to Maze’s Future of User Research 2026 report.
AI customer interviews are conversations with real customers that an AI moderator runs on its own. The AI asks the questions, probes with follow-ups, and summarizes what it heard. One team can run hundreds of these in parallel and get themes back in hours instead of weeks.
This guide compares six AI customer interview tools, explains how they work, and covers the question most buyers skip: what happens to the answers once the study is over.
AI customer interviews are one-on-one research conversations where software, instead of a human moderator, guides the discussion. The participant answers by voice, video, or text. The AI listens, asks a relevant follow-up, and moves through a discussion guide the way a trained researcher would.
AI-moderated interviews sit between surveys and live interviews. Surveys scale but stay shallow, while live interviews go deep but cap out at a handful per week. AI moderation keeps the follow-up questions and removes the scheduling limit.
AI customer interview tools typically cover the whole study. They draft an interview guide from your research goal, recruit participants from a panel or your own customer list, run the conversation and probe for the "why," then tag themes, pull quotes, and build highlight reels or reports from what came back.
Traditional qualitative research is slow and expensive. Frank, one of the tools below, puts the cost of a traditional interview at $500 to $1,000 each on its own website. At that price, most teams interview a few dozen customers a year and guess about the rest.
Maze's Future of User Research 2026 report found that 69% of research professionals now use AI in at least some of their projects, and 63% say it has made research turnaround faster. Transcription, synthesis, and drafting research questions are among the most common uses.
That speed changes who gets to hear from customers. A product marketer testing a new message no longer has to wait for the research team's next open slot. A customer marketing leader can ask 200 customers why they renewed, in a week.
Here's the catch. Faster interviews produce more answers, and more answers create a new bottleneck. If those answers sit in a research repository that sales, marketing, and CS never open, the company learned something and nobody acted on it.
We started with the tools buyers are likely to come across when they research this category. Outset, Listen Labs, Strella, Frank, and Conveo are among the names that show up in search results and AI-generated recommendations for AI customer interview tools.
Each tool also had to meet three requirements to make the list:
We also included Deeto, our own platform. We've kept it to the same standard, and the post is clear about where a research platform is the better choice.
We evaluated every tool against the same five criteria:
All details come from each vendor's own website, reviewed in October 2026. We didn't run paid trials of every tool, so treat this as a starting shortlist and test your top two or three before you buy. Where a vendor doesn't publish a detail, the table says so instead of guessing.
The table above reflects what each vendor publishes on its own website as of October 2026. Frank is the only tool here that publishes its pricing. The others quote pricing through a demo, so confirm current plans directly with each vendor.
Deeto is a voice of the customer platform, and AI customer interviews are how customer voice comes into it. Deeto is the layer that turns authentic customer voice into intelligence and routes it to where work already happens, from the CRM to active deals to the next campaign. It's built for B2B companies, and especially for customer marketing teams that need a steady supply of customer proof.
Deeto's AI interview agent reaches out at specific lifecycle moments, like onboarding, feature adoption, renewal, and support resolution. Each conversation adapts to the customer, and every answer is tied to the person, the account, and where they are in the lifecycle. Answers become themes and sentiment dashboards, then flow into customer stories, references, referrals, and Salesforce.
It's the strongest fit for customer marketing leaders and the marketing executives they report to, along with the product marketing and CS teams who work alongside them. It isn't built for recruiting people outside your customer base. More on how Deeto works below.
Outset is an AI-moderated research platform built for enterprise insights and UX research teams. It runs video, voice, and text interviews, and its AI can probe based on audio and visual cues during the conversation.
Outset recruits from a pool it describes as 1.1 billion+ possible participants across 85+ countries, or you can send interview links to your own lists. It also offers digital twins, simulated versions of target audiences built from real interview data, for teams that want to test ideas before fielding a live study. Analysis includes themes, quotes, highlight reels, and auto-generated slide decks.
If you run global studies and want simulated and real participants in one place, Outset is the strongest fit here. HubSpot, Microsoft, and Away are among its published case studies.
Listen Labs runs the whole study for you: it drafts the guide, recruits participants, moderates the interviews, and builds the deliverables. Interviews run 24/7, so studies can field around the clock.
Its participant network covers 50 million+ people, and it lists niche, professional, and expert audiences as a core use case. Outputs range from highlight reels to presentation-ready slides, and Listen Labs says every claim in its analysis traces back to a real interview. A shared workspace lets teams search past studies.
Listen Labs makes the most sense when you want one vendor to own the study from guide to final readout. Brand and consumer insights teams are its core buyers, along with product teams.
Strella is built for speed. Its pitch is 100 customer interviews completed overnight. The AI writes discussion guides (Strella describes them as unbiased), moderates with real-time follow-ups, and lets researchers step in and moderate live when they want to.
Strella recruits from a panel of up to 8 million participants, filtered by criteria like location and experience, or from your own customers. Analysis works at the participant, segment, and study level, and there's a chat interface for asking questions of the data.
Product and UX teams that run concept tests and usability studies every week or two will feel the speed most. Duolingo and DraftKings are among its customers.
Frank, built by Prelaunch, is designed for high-volume interviews at a lower price point. It runs AI voice interviews, WhatsApp voice and video interviews, and text-based AI interviews.
Frank is the most self-serve option here and the only one with published pricing. It offers a free plan, paid plans start under $60 a month, and it supports embedding interview links inside a product, such as a cancellation flow or a tooltip. That makes it practical for churn interviews and post-release feedback. Analysis includes summaries, thematic analysis, and short video recaps.
It suits product managers, growth teams, and founders who want to run interviews continuously without an enterprise contract.
Conveo is an AI-led qualitative platform for enterprise insights, marketing, and CX teams in industries from CPG to pharma and tech. Its AI moderator runs voice and video interviews with real people and supports live screen sharing for UX research.
Conveo goes further than most on concept and creative testing. It offers second-by-second facial coding for ad and message tests, plus AI-moderated MaxDiff. It also runs always-on programs like brand trackers and stores results in a searchable knowledge base. The company cites 400+ enterprise teams as customers, including AB InBev and Canva.
Conveo earns its place on teams that test concepts, packaging, and creative on a regular cadence.
The other five platforms above are built around the research study. You define a question, field it to a sample, and get a report. That model works well for market research, concept testing, and reaching people who aren't your customers yet.
B2B teams have a second need. They want to hear from the customers they already have, at the moments that matter: after onboarding, after a big feature launch, before renewal, after a closed deal. And they want each answer to do more than inform a deck.
A customer who says your product cut their reporting time in half is a research insight. That same answer is also a case study, a sales reference, a quote for the website, and a reason to ask for a G2 review. Research platforms handle the first use well. The others depend on someone copying the quote into another tool, and in most companies that someone is busy.
Deeto starts where the research study usually ends. The interview is the input. What Deeto adds is the system that turns that voice into intelligence and puts it in front of the people who act on it. Once an AI interview is done, Deeto handles the steps research platforms leave to you:
Deeto doesn't recruit from a consumer panel, and it isn't built to field studies with people outside your customer base. Testing packaging with 1,000 shoppers in Brazil? Use one of the research platforms above. Trying to find out why your best accounts renew, and want their words in front of a prospect next week? That's what Deeto is for.
For a customer marketing team, that means a steady flow of stories, references, and advocates without chasing customers one email at a time. For the CMO or VP of Marketing above them, it means campaigns and positioning built on what current customers actually say, with proof that sales can use. Product marketing gets the same benefit: one interview can shape the messaging and hand over the quote for the landing page.
Start with who you need to hear from, then work backward. Five questions narrow the field quickly.
First, who are the participants? If you need people who aren't customers yet, choose a tool with a strong panel, like Outset, Listen Labs, or Strella. If you're interviewing existing B2B customers, choose a tool that connects to your CRM and customer data, like Deeto.
Second, how often will you run interviews? One-off studies suit study-based platforms. If you want to listen continuously across the customer lifecycle, look for a platform that triggers interviews automatically.
Third, what formats do you need? Video matters for concept and creative testing. Busy B2B buyers often complete voice and text interviews at higher rates.
Fourth, who uses the output? If only the research team reads the results, a strong repository is enough. If sales, marketing, and CS need the answers, look for a tool that pushes them into the systems those teams work in.
Fifth, what are your compliance requirements? Check for SOC 2, GDPR, and HIPAA where relevant. Outset, Frank, and Conveo publish their compliance status on their sites, and Deeto's is on its security page.
If you want one interview to inform research and give sales something to use, weigh the fourth question most heavily. Teams building a broader voice of the customer strategy usually end up needing both kinds of tools.
AI-moderated interviews have limits, and you should know them going in. The same User Interviews report found that 41% of researchers view AI negatively, and 91% are concerned about hallucinations in AI output.
Those concerns are fair. An AI summary is only as trustworthy as its link back to the source. Before choosing a tool, check whether every theme and quote traces to a specific interview you can watch or read. Listen Labs and Outset both emphasize traceability, and it's worth testing in any trial.
AI moderators also struggle with sensitive topics and with participants who need a human to build trust first. Strategic accounts and executive sponsors may still deserve a live conversation. Use AI interviews to hear from far more customers than you could before, and keep humans on the conversations that carry the most weight.
What are AI customer interviews?
AI customer interviews are research conversations with real people that an AI moderator conducts instead of a human. The AI asks questions from a discussion guide, follows up based on each answer, and summarizes the results. You get much of the depth of a live interview at the scale of a survey, so teams can hear from hundreds of customers in days.
Are AI-moderated interviews as good as human interviews?
For many structured research goals, yes. AI-moderated interviews hold up well for concept testing, feature feedback, and understanding why customers churn or renew. They fall short on sensitive topics and with high-stakes participants who need rapport first. Most teams use AI moderation to expand coverage and keep human moderators for strategic conversations.
How much do AI customer interview tools cost?
Most AI customer interview tools use quote-based pricing tied to interview volume, recruiting needs, and features. Frank offers a free tier with starter credits. Outset, Listen Labs, Strella, Conveo, and Deeto price through a demo or sales conversation. Recruiting from a vendor's panel typically costs more than interviewing your own customers.
Which AI customer interview tool is best for B2B companies?
It depends on who you want to interview. For reaching B2B buyers who aren't customers yet, Listen Labs lists niche and professional audiences as a core use case, and Strella lets you filter its panel by criteria like location and experience. For interviewing your existing customers across the lifecycle and turning their answers into proof and references, Deeto is built for that workflow and connects to CRM data.
Can AI interviews replace surveys?
AI-moderated interviews can replace many open-ended surveys because they ask follow-up questions a survey can't. Short scored surveys like NPS and CSAT still work well for tracking trends at a glance. A common setup uses scored surveys to spot changes and AI interviews to explain why those changes happened.
AI customer interviews let you hear from hundreds of customers in the time it used to take to schedule ten. Outset, Listen Labs, Strella, Frank, and Conveo each do this well for research studies, with different strengths in recruiting, speed, self-serve access, and creative testing.
For B2B marketing teams, the bigger opportunity is the customers you already have. For a customer marketing leader, every interview with a current customer is a potential story, reference, or advocate. For the CMO, it's evidence that shapes messaging this quarter and still closes deals next year, as long as the answer reaches the people who write the messaging and run the deals.
Deeto is built for that. It interviews your customers at the right moments in the lifecycle, turns what they say into intelligence, and routes it to marketing, sales, and CS, with their consent. See how Deeto works with your customers.
What are AI customer interviews? Compare 6 tools, see what each does best, and learn how to choose one.
Gartner found that 75% of B2B buyers prefer a rep-free sales experience. So when a buyer skips the rep, the customer proof they find on their own does more of the selling.
Customer testimonial software is a platform that helps companies request, record, approve, and publish customer feedback as written quotes, video clips, and stories. The better tools also organize that proof so the right story reaches the right buyer, no matter who on your team is sending it.
Below, we compare six platforms on formats, pricing, and fit for a B2B customer marketing team. Yes, Deeto is one of them. We explain how we handled that below.
Customer testimonial software is a system for collecting and using customer proof at volume. It replaces the old routine: email a happy customer, schedule a call, transcribe it, chase legal for approval, paste the quote into a slide, and repeat.
Customer testimonial platforms typically include four parts:
B2B platforms add a fifth part. They connect testimonials to references, advocacy programs, and the CRM, so a rep working a healthcare deal can pull a healthcare story without filing a request and waiting a week.
A testimonial wall on your homepage is nice. It does nothing for the rep who needs a mid-market fintech customer to talk about switching from a competitor by Thursday.
Buyers are also talking to reps less, and later. In G2’s 2025 buyer research, nearly two in three buyers said they prefer to engage vendor salespeople only in the later stages, and 29% now start software research on tools like ChatGPT more often than on Google. By the time your team gets a meeting, the shortlist is often set.
That changes the job. Collecting a quote is easy, but programs stall on everything after that, keeping proof current, matching it to the buyer in front of you, and getting it where buyers actually look.
A testimonial nobody can find is a testimonial you didn’t collect.
Deeto is on this list, so for every other entry we stuck to what each vendor publishes about its own product and pricing. Prices come from public pricing pages as of October 2026 and can change.
We compared the platforms on:
Many of the tools on this list are built to collect a quote and put it on a page. Deeto works one layer down. It's a voice of the customer platform that turns authentic customer voice into intelligence, then routes it to the teams and tools that need it. Testimonials are one of the things that come out of that system.
It starts with AI-led interviews that follow up on what the customer actually says, instead of a static form. Every interview then runs through Deeto's five modules, Listen, Learn, Activate, Analyze, and Orchestrate. In practice, one conversation gets captured, saved to the customer record, delivered to the people who need it, mined for themes and sentiment, and kept moving through automated workflows.
That's why one interview turns into more than a quote. It can become a written story, a video testimonial, a reference profile, and a clear read on why that customer chose you. Customer stories and social proof are tagged by persona, industry, and use case, so a rep can find the right one without filing a request. The same record powers reference management, showing who's willing to take a call, how often they've been asked, and where coverage is thin. And proof shows up where teams already work, through Salesforce and other CRM integrations.
Deeto customers share results on its customer marketing solutions page. Knak saw a 75% lift in advocate conversion in 30 days. PandaDoc produced 10x more content with fully automated collection.
Deeto doesn’t have to replace your website widget. Plenty of teams keep a lightweight display tool for their homepage and use Deeto for the work that happens behind it, like running interviews, matching references to deals, and getting proof to reps.
Best for: B2B customer marketing and product marketing teams that want testimonials, references, and customer advocacy in one system.
Pricing: Custom. Book a demo for a quote.
UserEvidence is a B2B customer evidence platform for product marketing and customer marketing teams at software companies. It covers evidence collection, advocacy, AI-assisted reference matching, a private advocate community, and ROI studies.
It’s a good fit if you want research-style proof, like survey-backed stats, alongside your stories and references. UserEvidence positions itself for companies selling higher-priced products in competitive markets. Pricing isn’t published.
Best for: B2B software companies that want survey-driven evidence and reference matching.
Pricing: Not published. Demo required.
Vocal Video is built for remote video testimonials. Customers record from a browser link whenever it suits them, and the platform’s AI edits the raw footage into branded, multi-scene videos with captions, music, and motion graphics. Teams use it for employee and recruiting videos too.
The draw is finished video without a production crew. The tradeoff is that it’s a video tool first, so written case studies, references, and CRM-based reference requests will need to live somewhere else.
Best for: Marketing teams that want polished video testimonials at volume.
Pricing: Free plan with five watermarked videos. Paid plans start at $99 per month billed yearly, with Enterprise starting at $1,250 per month (pricing).
Testimonial.to collects text and video testimonials through a shareable page and displays them on an embeddable “Wall of Love,” a grid of customer quotes and videos you can drop onto any page. It can also import praise from X and LinkedIn. Higher tiers add an API, webhooks, Zapier, and Google rich snippets.
Setup is quick, and pricing is built for small teams. Testimonials are organized into project-based “spaces,” though, so B2B teams that want to tag proof by industry, persona, and deal stage will feel the limits as their library grows.
Best for: Startups and small teams that want a public testimonial wall fast.
Pricing: Free plan with 10 text and 2 video testimonials. Paid plans start at $25 per month billed annually (pricing).
Senja collects video and text testimonials through forms and imports, then displays them in widgets and Walls of Love. It also includes AI testimonial analysis, case study generation, social video reels, and thank-you rewards like e-gifts and coupons.
That’s a lot for the lower tiers, which helps explain why founders and lean marketing teams like it. Like most testimonial marketing tools here, it’s focused on collecting and displaying proof. Managing references and advocates isn’t really its job.
Best for: Lean marketing teams that want collection, display, and light case study creation in one tool.
Pricing: Free plan up to 15 testimonials. Paid plans start at $29 per month (pricing).
Boast is video testimonial software with automated email and SMS request sequences, embeddable forms, display widgets, and ad-free video hosting. It also collects online reviews and runs NPS and CSAT surveys.
Pricing is based on response volume, and multi-location support is built in, so it fits agencies and service businesses well. B2B software teams may find the reference and advocacy side thin.
Best for: Agencies and multi-location businesses that want automated video requests.
Pricing: Starts at $50 per month billed annually, with a 14-day free trial (pricing).
Figure out who’s going to use the proof before you pick the tool. A website wall and a sales reference program need very different software.
Product marketers run into this too. Positioning built on real customer language is easier to defend, which we cover in our post on product marketing strategy.
Testimonial marketing tools help you collect quotes and videos and show them on your website and social channels. Testimonial.to, Senja, Vocal Video, and Boast are in this group. They’re good at display and priced for small teams.
A customer evidence platform treats testimonials as one output of an ongoing customer program. It connects stories, references, advocacy, and customer research, then gets the right proof to sales and marketing when they need it. Deeto and UserEvidence sit closer to this end.
Video shows the gap clearly. Wyzowl’s 2026 research found that 57% of video marketers have created testimonial videos. Making the video isn’t the hard part anymore. Knowing which video helps close which kind of deal is, and that’s as much a data problem as a content problem.
There’s a bonus here too. The interviews that produce testimonials also tell you why customers picked you over the alternatives. That’s the raw material for a competitive advantage built on customer insights, and it comes from conversations you were already having.
Need a testimonial wall this week? Testimonial.to or Senja will get you there for under $30 a month. If video quality matters most, Vocal Video is built for exactly that.
B2B customer marketing teams usually outgrow display tools once sales starts asking for specific stories, specific references, and specific proof for specific deals. At that point, a widget won’t cut it. Your testimonials need to be connected to how your company listens to customers in the first place.
Picture a rep two days out from a decision who needs a fintech customer that switched from the competitor still in the deal. She finds that story in Salesforce in a couple of minutes, instead of posting in Slack and waiting. The buyer reads it in a peer’s own words, with real numbers and the details only a real customer would mention. That’s the proof that moves a deal forward. See how Deeto works with your own customer stories.
What is customer testimonial software?
Customer testimonial software is a platform that helps companies collect, approve, store, and publish customer feedback as written quotes, video testimonials, and stories. Most tools include a collection form or interview link, an approval step, a searchable library, and publishing tools like website widgets. B2B platforms also connect testimonials to references, advocacy programs, and the CRM, so sales teams can find relevant proof for each deal.
What are the best testimonial marketing tools for B2B companies?
For B2B companies that need proof across sales and marketing, Deeto and UserEvidence connect testimonials with references and advocacy. For website display and lightweight collection, Testimonial.to and Senja are popular low-cost options. Vocal Video is a strong pick for edited video testimonials, and Boast suits teams that want automated email and SMS requests for video.
How much does testimonial software cost?
Pricing varies a lot. Testimonial.to, Senja, and Vocal Video all have free plans, with paid tiers starting between $25 and $99 per month. Boast starts at $50 per month billed annually. B2B customer evidence platforms like Deeto and UserEvidence use custom pricing based on program size, integrations, and how many teams use the platform.
Are video testimonials better than written testimonials for B2B?
They do different jobs. Video builds trust fast on websites, in ads, and on social. Written quotes and stories are easier to scan, search, and drop into sales emails, proposals, and slides. Most B2B teams need both, which is why a platform that turns one interview into several formats saves so much time.
How do you get B2B customers to give testimonials?
Ask right after a clear win, like a renewal, a strong QBR, or a measurable result. Make it easy with a short recorded interview instead of a blank form, and let customers approve their final wording. Keep track of how often each customer gets asked so your best advocates don’t burn out, and thank them with rewards or recognition.
Can testimonial software help with AI search visibility?
Yes, indirectly. G2’s 2025 research found that 29% of B2B buyers start software research on AI tools like ChatGPT more often than on Google. Published customer stories with specific, named outcomes give AI answer engines credible material to cite. Testimonial software that publishes structured, searchable proof makes that content easier to find and reference.
What is customer testimonial software? Compare six platforms by price, format, and fit for B2B teams.
Your company already knows why customers buy, stay, and leave. That knowledge just lives in 40 places, and nobody can find it the week a launch brief is due.
Customer intelligence tools are software platforms that collect what customers say and do across interviews, calls, surveys, support tickets, and product usage, then turn it into patterns teams can act on. The best ones connect that intelligence to sales, marketing, product, and customer success instead of leaving it in a research folder.
This guide compares eight tools built for B2B teams, the collection methods behind them, and how to pick the right fit.
These tools gather first-party customer data, structure it, and surface the signals that matter for decisions like positioning, pricing, roadmap priorities, and renewals. They sit between raw feedback and the people who need to act on it.
In B2B, customer intelligence includes more than survey scores. It covers the reasons a deal was lost, the objections that show up in sales calls, the feature gaps that come up in renewal conversations, and the stories your happiest customers tell when someone asks them directly.
Most systems do some mix of capture, analysis, and activation. Capture means pulling in voice and behavior data from interviews, calls, tickets, reviews, and product usage. Analysis means finding themes and sentiment across thousands of data points without someone tagging them by hand. Activation means getting a finding to the team that needs it, inside the tools that team already works in.
Activation is where most stacks break. A product marketer can read a beautiful theme report and still walk into a launch without a single current customer quote to back up the message. A customer marketing lead can run a strong advocacy program and still have no view into which accounts are quietly at risk.
The terms overlap, and vendors use them interchangeably. There is a useful distinction, though.
Customer insights software usually focuses on analysis. It takes feedback you already have, such as survey responses, support tickets, or research transcripts, and helps you find themes in it. Research repositories and feedback analytics platforms fall here.
Customer intelligence tools usually cover more of the loop. They generate new customer input as well as analyze it, and they push what they find into other teams' workflows. If your team already has plenty of data and needs help reading it, customer insights software may be enough. If your team is short on fresh, honest customer input in the first place, you need something that collects it, too.
For a deeper look at evaluation criteria, our guide on how to choose a customer intelligence platform walks through the platform types and the questions to ask every vendor.
The best methods for collecting customer intelligence combine direct conversations with passive signal capture. Direct methods tell you why. Passive methods tell you what, and how often. Strong B2B programs use at least one of each.
Mining calls, chats, and tickets for customer signal is standard practice now. The harder problem is that customers are volunteering less. Qualtrics XM Institute found that only 32% of consumers share direct feedback with a company after a very poor experience, down 7.7 points since 2021, based on a study of more than 23,000 consumers. That's consumer data, not B2B, but the lesson carries over. Analyzing the conversations you already have tells you about the customers who happen to talk to you. Continuous, structured outreach tells you about the ones who don't.
Existing customers carry most of the revenue in B2B. Forrester reports that current customers account for 61% of B2B revenue through renewal and expansion (Forrester). If most of your revenue comes from people who already bought, not understanding them gets expensive, and you usually find out late, in a renewal forecast.
Leaders know it. In a Gartner survey of 283 customer service and support leaders, 84% said customer data and analytics were very or extremely important to hitting their goals (Gartner). The gap comes from lack of communication between teams. Feedback sits with CS, call recordings sit with sales, research sits with product, and customer stories sit with marketing. Each team collects plenty, and very little of it reaches whoever is making the next decision.
Each tool below is strong at a different collection method. Very few B2B companies need all eight. Most need two or three that work together.
Deeto is an AI-native voice of the customer platform built for the step most stacks skip, getting customer intelligence to the people who act on it. It runs on five connected modules: Listen, Learn, Activate, Analyze, and Orchestrate.
That's the activation layer described earlier. A theme report only pays off when it reaches the product marketer writing the launch brief or the account manager walking into a renewal call. Deeto relaunched its platform in January 2026 around this idea, treating customer voice as a system of record that teams can use in real time (Business Wire).
Analysis-only tools read input someone else gathered. Deeto generates it. The same customer conversation can produce a win-loss insight, a sentiment signal, a roadmap request, and an approved quote for a sales deck. That matters most for customer marketing teams, who run customer advocacy programs and are often closest to customers while seeing the least of the intelligence those relationships produce.
Best for B2B teams that want one connected source of customer voice across product marketing, customer marketing, sales, and CS. In published Deeto customer stories, Clarivate reported a 75% productivity gain with a two to four week time to value, and Wrike grew its reference pool by 50 to 75%.
Gong is a revenue AI platform built on conversation intelligence. It records and transcribes sales calls, meetings, and emails, then flags deal risk, competitor mentions, and recurring themes across conversations. Its newer AI agents handle tasks like deal reviews and CRM updates (ZoomInfo review of Gong features).
Gong is excellent at mining the conversations your reps already have. Its limit for customer intelligence is scope. It hears customers mostly during the sales cycle and on calls a rep set up, so it misses post-sale sentiment from customers who never get on a call.
Best for sales leaders and revenue operations teams that want deal visibility and coaching from recorded calls.
Dovetail started as a research repository and now positions itself as an AI-native customer intelligence platform. It pulls in interviews, calls, surveys, support tickets, and NPS data, uses AI to find themes and track sentiment, and answers questions directly in Slack or Teams.
Dovetail fits well when you have a lot of qualitative data and a research team that needs to analyze and share it. In the terms above, it's mostly customer insights software. You still need a way to generate fresh customer input.
Best for UX research, product, and insights teams managing large volumes of interviews and qualitative feedback.
Enterpret is a customer feedback analytics platform that unifies feedback from more than 50 channels, including support tools, sales calls, reviews, and communities. Its October 2025 launch added a customer knowledge graph that connects feedback to accounts and opportunities, plus agents that flag emerging issues automatically (Business Wire).
Enterpret's adaptive taxonomy removes most manual tagging, which saves real time on high-volume feedback. The customers it names publicly, including Canva, Notion, Strava, Monday.com, and Perplexity, are mostly product-led companies with large user bases, so B2B companies with lower feedback volume may get less out of it.
Best for product and CX teams at product-led companies drowning in support and in-app feedback.
Clozd is a dedicated win-loss analysis platform. It runs live and AI-assisted buyer interviews after deals close, handles outreach and scheduling, and summarizes themes across interviews into Slack, Teams, and CRM (Clozd platform overview).
Clozd goes deep on one method, and that's the point. If win-loss insights are your top priority, it's a specialist worth a look. If you need intelligence from the rest of the customer lifecycle, you'll need another tool alongside it.
Best for product marketing and competitive intelligence teams running formal win-loss programs.
Gainsight is a customer success platform built around account health. It combines health scores, a Customer 360 view, renewal forecasting, and playbooks. Its Staircase AI product analyzes emails, meetings, tickets, and Slack to detect sentiment and churn risk (ZoomInfo review of Gainsight).
Gainsight is strongest at operationalizing what CS already knows, and it's a bigger project than most tools on this list. Gainsight says a core customer success framework can be up in about eight weeks, with verified outcomes arriving in roughly four months on average (Gainsight). Plan for an admin to own it after launch.
Best for mid-market and enterprise CS organizations managing renewals and expansion at the account level.
Productboard is a product management platform with a strong customer feedback layer. It centralizes feedback from connected sources, uses AI to surface trending topics and summarize pain points, and links each insight to the feature ideas it supports, with importance scores by customer segment.
Productboard is the right home when the product team is the main consumer of customer intelligence. It's less useful for marketing or sales teams that need customer proof or messaging input. If roadmap decisions are your use case, our guide to using customer feedback to build a product roadmap pairs well with it.
Best for product managers who need to tie feature requests to real customer demand.
6sense is a B2B account intelligence and ABM platform. It collects buying signals, including third-party intent data and anonymous website visits, to show which accounts are researching your category and when (TrustRadius).
6sense answers a different question than the rest of this list. It tells you who is likely to buy, not what your customers think. That makes it a useful complement for demand gen and sales, but it won't replace direct customer voice.
Best for demand generation and sales teams prioritizing accounts based on in-market signals.
The chart compares each tool by primary collection method, best fit team, standout capability, and main limitation. Vendor capabilities change often, so confirm current features with each vendor before you buy.
Start with the decision you're trying to improve, not the feature list. A launch messaging problem, a churn problem, and a roadmap problem need different inputs.
Then figure out where your data gap actually is. If you have plenty of calls and tickets but no structured customer conversations, prioritize a tool that generates input. If it's the other way around, prioritize analysis.
Think about who will read the output. Tools built for researchers rarely get opened by sales reps, so look for delivery into Salesforce, HubSpot, Slack, or your enablement platform. Deeto's CRM and workflow integrations push customer voice into those systems directly.
Check how much of the lifecycle a tool can see. Many see customers at only one stage, and pre-sale, onboarding, renewal, and advocacy each surface different signals. Positive and negative voice belong together, too. The customer who flags a gap this quarter may be your best reference next quarter, and splitting those conversations into separate systems loses the context.
Last, be honest about time to value. A multi-month implementation should be considered a cost when the board is asking about retention this quarter.
For product marketing teams, the bar is simple. Can you walk into a launch with current, verified customer language and know it reflects the market, not one loud account?
What are customer intelligence tools?
Customer intelligence tools are software platforms that collect customer data from sources like interviews, sales calls, surveys, support tickets, and product usage, then analyze it for themes, sentiment, and trends. The best tools also deliver findings into the systems sales, marketing, product, and customer success teams already use, so the intelligence shapes decisions instead of sitting in a report.
What are the best methods for collecting customer intelligence?
The most effective methods are customer interviews, win-loss interviews, sales call analysis, surveys and in-product microfeedback, support ticket and review analysis, product usage data, buyer intent data, and advocacy conversations. Strong B2B programs combine at least one direct method that explains why customers act with one passive method that shows patterns at volume.
What is the difference between customer intelligence tools and customer insights software?
Customer insights software usually analyzes feedback a company already has, such as survey responses, transcripts, or tickets, to find themes. Customer intelligence tools typically cover more of the loop, generating new customer input and pushing findings to other teams as well as analyzing them. Many vendors use both terms, so check whether a tool collects data or only reads it.
Which customer intelligence tool is best for B2B product marketing teams?
Product marketing teams need current customer language, competitive context, and proof they can use in messaging. A voice of the customer platform like Deeto covers interviews, sentiment, and customer evidence in one place. Teams focused heavily on competitive deals often add a win-loss specialist like Clozd or conversation intelligence like Gong for call-level detail.
Do I need more than one customer intelligence tool?
Most B2B companies use two or three tools that cover different collection methods. A common pairing is a platform that generates and connects customer voice across teams, plus a specialist such as conversation intelligence for sales calls or intent data for demand gen. Aim for the smallest stack that covers the methods you need, with one place where the results come together.
Every B2B company has customer intelligence. Most of it is stuck in the tool where it was collected, and the best customer intelligence tools are the ones that get it out.
If your biggest gap is analysis, a specialist will help. If your biggest gap is fresh, honest customer input that reaches marketing, sales, product, and CS at the same time, you need a connected system, which is the problem Deeto is built to solve. See how Deeto works and hear what your customers are ready to tell you.
What are customer intelligence tools? Compare 8 B2B options, collection methods, and how to pick the right fit.
Six in ten B2B buyers would rather not talk to your sales team at all. That puts more weight on every email, in-app message, and text you send, and on what those messages actually say.
Omnichannel customer engagement tools are platforms that unify customer data and coordinate personalized messages across email, SMS, push, in-app, web, and messaging apps from one place. They decide who gets what message, on which channel, and when. The best ones now use AI agents to build and optimize campaigns with less manual work.
This guide compares eight leading platforms on channels, AI, data, and pricing models. It also covers the gap none of them fill, which is authentic customer proof that makes each message credible.
Omnichannel customer engagement tools are software platforms that connect every channel a customer uses into one coordinated program. Instead of running email in one tool and SMS in another, a team builds a single customer profile and triggers messages from it wherever the customer is most likely to respond.
Omnichannel engagement software typically has four layers:
The difference between multichannel and omnichannel is coordination. Multichannel means you're present on many channels. Omnichannel means those channels share context, so a customer who just replied on WhatsApp doesn't get a generic email ten minutes later.
Buyers are doing more of the work alone. A Gartner survey of 632 B2B buyers found that 61% prefer a rep-free buying experience, and 73% actively avoid suppliers who send irrelevant outreach.
That second number is the one customer marketing leaders should sit with. Irrelevant outreach costs you the buyer. A message has to be timely and it has to be credible, and engagement platforms only handle the first part.
Buyers are also checking your claims somewhere else first. In G2's 2025 Buyer Behavior Report, enterprise buyers named review sites (56%) and AI search (55%) as their top two research sources. Both run largely on what other people say about you, not what you say about yourself.
Engagement automation decides when a buyer hears from you, while customer voice decides whether they believe you.
We used each vendor's own product and pricing pages as the primary source and looked at six things: the company type and use case each platform is built around, the owned and paid channels it supports natively, its AI and automation features (agents, decisioning, content generation), how it unifies customer data, its pricing model, and its placement in the 2026 Gartner Magic Quadrant for Multichannel Marketing Hubs.
Gartner published that report on September 22, 2026, and evaluated 12 vendors. According to Braze's summary of the results, Braze, Salesforce, and Adobe were named Leaders. Klaviyo and Insider One were Challengers, Bloomreach was a Visionary, and Iterable was a Niche Player. HubSpot wasn't among the vendors evaluated.
A Gartner Magic Quadrant scores vendors on two measures, ability to execute today and completeness of vision for where the market is going. Leaders score high on both. Challengers execute well with a narrower vision, Visionaries have strong strategy but less proven delivery, and Niche Players often focus on a specific segment. A Niche Player can still be the best fit for your team.
The first three platforms fit B2B customer marketing programs, where you're engaging accounts, buying committees, and existing customers over long cycles. The last five are built mainly for high-volume consumer messaging.
HubSpot Marketing Hub is the most accessible option here for B2B teams. It's CRM-native, so marketing, sales, and service data live in one place without a separate integration project.
It covers email, SMS, WhatsApp, social publishing and inbox, ads, personalized web content, and live chat. The Breeze Assistant and a set of AI agents (Content, Nurture, Campaign, Customer, and Prospecting) handle content creation and lead nurturing.
For customer marketing, that shared record is the point. Onboarding sequences, renewal nurtures, and expansion campaigns can all key off lifecycle stage and deal data that sales and CS already keep up to date, so you're not working from a stale export.
HubSpot has a free plan with automated email, forms, and live chat, plus paid tiers. Teams with complex, high-volume consumer messaging usually outgrow it, but for mid-market B2B it covers most engagement automation needs.
Salesforce Marketing Cloud is the obvious choice for companies already running sales and service on Salesforce. Its newer product, Marketing Cloud Next, builds Agentforce AI agents into email, SMS, and web, and adds paid advertising activation and cross-channel analytics.
The Campaign Agent generates channel-ready content and optimizes sends from your goals and guardrails. Salesforce also pitches agentic demand generation, with agents that prospect target accounts, qualify inbound demand, and nurture buyers. That makes it one of the few tools on this list with an explicit B2B motion.
For enterprise customer marketing teams, the draw is shared account context. A renewal or advocacy campaign can draw on the same account, opportunity, and service history that sales and support see. Pricing isn't published on the product page.
Adobe Journey Optimizer is a real-time orchestration engine built on Adobe Experience Platform. It covers email, push, in-app, SMS and MMS, and web personalization, with AI decisioning for next best actions and a Journey Agent that helps build and analyze programs.
Its strength is depth. Real-time profiles, streaming data, and governance controls come from Experience Platform, which suits large enterprises with strict data requirements. That depth is also the trade-off. Journey Optimizer pays off most for teams already invested in the Adobe stack. Pricing isn't published.
Braze is built for consumer brands in retail, travel, media, gaming, and on-demand services, and was named a Gartner Leader for the fourth year in a row. Its pricing page lists email, push, SMS, RCS, WhatsApp, in-app, and more in every edition, with quote-based pricing tied to monthly active users. There's no free plan.
Klaviyo calls itself an autonomous B2C CRM and says it powers more than 205,000 brands across 100 countries. Its Composer agent builds campaigns from a prompt, and you can start on Klaviyo for free.
Insider One, which rebranded from Insider in December 2025, covers web, email, SMS and RCS, mobile app, WhatsApp, and web push. Its Agent One feature plans and optimizes campaigns, and the industries it lists lean toward retail, beauty, automotive, travel, financial services, and telecom.
Bloomreach Engagement pairs real-time customer and product data with Loomi AI decisioning across 13+ channels. It cites more than 1,400 brands and enterprises as customers, and predictions for purchase, churn, and returns make it especially useful for commerce teams.
Iterable is a cross-channel platform for consumer brands in fintech, marketplaces, retail, media, and streaming. Its support documentation lists nine channels, and its Nova layer adds AI agents and next-best-action decisioning. Pricing runs through sales.
Every platform above is good at timing and targeting. None of them can make the message itself credible. They'll personalize a subject line in milliseconds, but they can't produce a customer quote or a peer reference your buyer will trust.
That's where most programs break down. The proof usually lives somewhere else: testimonials, case studies, and reference customers sit in slide decks, Slack threads, and someone's inbox. The same few happy customers get every reference request because nobody can see who else is willing, and they burn out. And what customers say in interviews and surveys rarely reaches the person building the campaign in a form they can use.
Sending faster won't fix a message nobody believes.
Engagement tools decide when to reach someone. Deeto decides what's worth saying when you do.
Deeto is a voice of the customer platform. It turns authentic customer voice into intelligence and routes it into the tools your team already uses, including Salesforce, HubSpot, and Slack. It works alongside your engagement platform rather than replacing it.
Picture a nurture sequence in HubSpot. A director of operations at a mid-sized fintech company downloads your compliance guide and enrolls in a five-email track. Email three is where proof usually goes, and it's usually a logo bar or a stat lifted from your homepage.
With Deeto in the mix, that slot carries a short customer quote from an operations leader at another fintech customer, captured in an AI interview a few weeks after their rollout. Deeto's AI Match Agent picks the customer whose situation looks most like the prospect's. When the deal reaches sales, that same customer is ready as a reference, and nobody has to dig through old decks to find them.
The email is just as timely as before. Now it's believable too.
For customer marketing teams, that's the shift. Your engagement platform keeps handling timing and delivery. Deeto keeps it stocked with fresh, relevant customer voice, and spreads asks across a wider customer advocacy bench so the same few customers don't burn out.
Back to the Gartner number from earlier. When 73% of buyers avoid suppliers who send irrelevant outreach, a message carrying a real peer's words has a better shot than one carrying yours. If you're comparing proof tools too, our breakdown of customer testimonial software platforms covers that side of the stack.
Start with your business model, then work down to features. A few questions narrow the field fast:
What are omnichannel customer engagement tools?
Omnichannel customer engagement tools are platforms that unify customer data and coordinate personalized messages across channels like email, SMS, push, in-app, web, and WhatsApp. They use one customer profile to decide who gets which message, on which channel, and when. Examples include Braze, Salesforce Marketing Cloud, Adobe Journey Optimizer, HubSpot, and Klaviyo.
What is the difference between omnichannel and multichannel engagement?
Multichannel engagement means a brand is present on several channels, often managed in separate tools. Omnichannel engagement connects those channels so they share data and context. A customer's action on one channel changes what they see on the next, which avoids repeated or conflicting messages.
Which omnichannel engagement software is best for B2B companies?
For B2B companies, HubSpot Marketing Hub and Salesforce Marketing Cloud Next are the strongest fits because they're built around CRM data, accounts, and pipeline. HubSpot suits mid-market teams that want fast setup and a free starting plan. Salesforce suits larger companies already running sales on Salesforce.
What is engagement automation?
Engagement automation is the use of software to trigger and sequence customer messages based on behavior, timing, and profile data instead of manual sends. It includes onboarding flows, renewal reminders, re-engagement campaigns, and advocacy requests. Modern platforms add AI agents that build and optimize these programs within guardrails a team sets.
How much do omnichannel customer engagement platforms cost?
Pricing varies widely. HubSpot and Klaviyo offer free starting plans with paid tiers. Braze, Salesforce, Adobe, Insider One, Bloomreach, and Iterable use quote-based pricing tied to factors like active users, contacts, message volume, and channels. Ask vendors for a multi-year total cost that includes implementation.
Do omnichannel engagement tools replace customer advocacy platforms?
No. Engagement tools manage when and where messages reach customers. Customer advocacy and voice of the customer platforms like Deeto capture the testimonials, references, and feedback that make those messages credible. Connecting the two lets customer proof flow into every channel you run.
Engagement platforms have gotten very good at timing. Braze, Salesforce, Adobe, HubSpot, and the rest can now plan and run campaigns with AI agents doing much of the work.
The harder problem is trust. Buyers research on their own and lean on peers, and most will walk away from a vendor that sends them noise. A perfectly timed message with nothing credible inside it still gets ignored.
Pick the engagement platform that fits your business model and stack. Then make sure what flows through it carries your customers' voices, not just your own. See how Deeto works to put authentic customer proof into every channel you run.
Compare 8 omnichannel engagement platforms on channels, AI, and pricing, plus the gap automation can't fill.
Your customers decide to renew, expand, or churn based on things they told someone on your team months ago. Chances are, nobody did anything with that information.
Voice of the Customer for Software as a Service companies, shortened as VoC for SaaS, is the practice of continuously capturing what customers say across the subscription lifecycle, from first demo to renewal, and turning it into decisions about product, messaging, sales, and retention. A strong voice of the customer strategy connects each piece of feedback to the team that can act on it while the account is still active.
This guide covers what a SaaS VoC strategy includes, where it usually breaks, how the main VoC methods compare, and a five-step plan to build one.
Voice of the customer (VoC) is a structured way to collect, organize, and act on what customers need and expect. In SaaS, it carries extra weight. Revenue arrives in installments, so every customer decides again at every renewal whether the product is still worth paying for.
A VoC strategy for SaaS includes four parts:
Done well, a VoC program tells you why you lost the deal, which features your best accounts can't live without, and the words buyers actually use for the problem you solve.
A one-time purchase gives you one shot at the sale. A subscription gives the customer a vote every billing cycle.
Forrester's B2B research found that customer-obsessed companies grow revenue 28% faster, with 33% higher profitability growth and 43% better customer retention than companies without that focus. Customer obsession sounds like a culture slogan, but in practice it comes down to one main question, does customer feedback reach the people making decisions, fast enough to change them?
Retention is where SaaS economics live. Fred Reichheld's research at Bain found that in financial services, a 5% increase in customer retention produces more than a 25% increase in profit, because returning customers buy more over time and cost less to serve. Subscription software runs on the same math where every renewal you protect compounds.
For customer marketing and advocacy teams, VoC is the best source of proof you'll ever have. Every interview, QBR note, and renewal conversation tells you who's getting value, what result they got, and how they describe it.
That's your reference pool, your next case study, and your shortlist of advocates, already sitting in what customers have said. Most customer marketers still find them the hard way, by asking CSMs for names and chasing busy champions for a quote. A connected VoC program flips that. The customer who just told a CSM they cut onboarding time in half shows up as a story candidate the same week, with their own words attached. That's how customer advocacy scales without burning out the same five customers.
The same evidence protects the rest of the business. Pendo analyzed usage across 615 software products and found that 80% of features in the average product are rarely or never used. Someone built each of those features believing customers wanted it. A working VoC program would have tested that belief before engineering spent a quarter on it.
The feedback already exists in most SaaS companies but it lands in the wrong place at the wrong time.
Here is where it usually breaks down:
Each of these is a routing problem more than a collection problem. Adding another survey tool won't fix it. What fixes it is one system that turns customer voice into intelligence and sends it to the right team. That's the gap Deeto was built to close, and it shapes how you should build the program.
Most SaaS companies already run three or four of the methods below, but they don't talk to each other, and none of them were built to carry the customer's own words from first demo to renewal.
Gong records the calls your reps and CSMs were already having. Pendo collects usage data, in-app polls, and NPS from the people using your product, mostly to inform the roadmap. Both do those jobs well. Neither is built to go interview the economic buyer about why they almost churned, or to turn a champion's story into a reference a rep can use in a late-stage deal.
Deeto doesn't replace either of them. It pulls their signals together with direct customer conversations, then ties everything to the account and routes it to the team that needs it.
Start with the decisions you need to make and work backward. Most teams do it the other way around: buy a survey tool, collect a pile of responses, then try to work out what they're for.
Pick three to five decisions coming up in the next two quarters. Examples: positioning for the Q1 launch, renewal risk across your top 50 accounts, the next two roadmap bets. Every listening activity should map to at least one of them. If it doesn't, cut it.
SaaS customers say different things at different stages. List the moments that matter for your business: the closed-lost deal, onboarding week three, the first QBR, 90 days before renewal, the expansion conversation. A customer journey map makes the gaps obvious. Pre-sale signals belong here too. A structured win/loss analysis is often the fastest way to learn what buyers compare you against.
Annual surveys produce a snapshot. Continuous capture produces a trend line. Short, conversational check-ins tied to lifecycle moments get higher response rates and richer answers than a 30-question survey. Deeto runs AI-led customer interviews at each of those moments, so the feedback arrives with context and in the customer's own language.
SaaS customer insights are only useful if people can find them. Store every interview, quote, score, and story against the account and contact it came from. Connect it to the tools your teams already work in through CRM and Slack integrations. A rep should be able to see what a customer said six months ago without filing a request.
This is where most programs stall. Decide in advance who gets what:
A VoC program earns its budget when it changes outcomes. Track a small set of metrics tied to the decisions you named in step one:
Deeto customers see sales cycles 20 to 30% shorter when reps have customer proof inside the deal, not buried in a quarterly deck.
What is VoC for SaaS?
VoC for SaaS is a structured approach to capturing what customers say across the subscription lifecycle, from evaluation through onboarding, adoption, and renewal. It turns that feedback into decisions about product priorities, positioning, sales proof, and retention. The goal is to get customer evidence to the team that can act on it while the account is still active, rather than in a quarterly report.
How is a voice of the customer strategy different from collecting customer feedback?
Collecting feedback is one activity. A voice of the customer strategy is the system around it. It defines which decisions feedback should inform, where to listen in the customer lifecycle, how to store and analyze what you hear, and who is responsible for acting on it. Many SaaS companies collect plenty of feedback but have no strategy, so it sits unused in survey tools and call recordings.
What are the best sources of SaaS customer insights?
The strongest SaaS customer insights come from combining sources. Customer interviews explain the reasons behind behavior. Product analytics show what users actually do. Conversation intelligence captures what comes up in sales and CS calls. Surveys like NPS and CSAT track sentiment over time. Review sites show how customers describe you in public. No single source gives the full picture on its own.
Who should own a VoC program in a SaaS company?
Ownership varies, but the program works best with one accountable owner and clear consumers. Product marketing, customer marketing, or customer experience often own it day to day. Product, sales, and customer success are the main consumers. What matters most is that someone is responsible for routing findings to those teams and tracking whether they acted on them.
How often should SaaS companies collect voice of the customer data?
Continuously, tied to lifecycle moments rather than a calendar. Trigger short check-ins after onboarding, after key feature launches, before renewals, and after closed-won or closed-lost deals. An annual survey can still serve as a benchmark, but it's too infrequent to catch churn risk or shifts in buyer priorities in a market that changes every quarter.
How does AI change VoC for SaaS?
AI makes continuous VoC practical at scale. It can run conversational interviews, transcribe and tag responses, detect sentiment shifts, and group feedback into themes across thousands of comments. The bigger change is speed. AI can surface a pattern the week it appears and push it to the right person, instead of waiting for an analyst to compile a quarterly readout.
Most SaaS companies have plenty of customer feedback. What they're missing is a reliable path from what customers say to what teams decide. A good VoC strategy for SaaS starts with the decisions that matter most this year, listens at the lifecycle moments that shape them, keeps everything in one place, and routes findings to the people who can act.
Get that right and you'll see it in renewals and win rates first. The roadmap takes longer to change, but it's how you stop building the features nobody uses.
Deeto is a voice of the customer platform built for this, used by SaaS and technology companies to capture customer voice continuously and put it to work across product, marketing, sales, and CS. See how Deeto works.
What is VoC for SaaS? Learn how to capture customer voice and turn it into decisions that drive renewals.
Less than a third of consumers give feedback directly to a company, according to Qualtrics' 2025 Consumer Trends report. Everyone else leaves clues in support tickets, call recordings, reviews, and renewal conversations.
So how can sentiment analysis be used to improve customer experience? Sentiment analysis uses AI to read that language and classify the emotion behind it, down to the specific topic. For CX teams, that means finding out why a score dropped and which accounts are drifting, usually well before either shows up at renewal. Customer marketing teams get the other half of the picture: the happy customers who are ready to become references and advocates.
This guide covers what sentiment analysis measures, how it compares to survey scores, and six ways to put it to work.
Sentiment analysis in customer experience is the process of using natural language processing (NLP) to measure how customers feel about a company, product, or interaction based on what they say and write. It turns unstructured feedback (open-text survey answers, support chats, interview transcripts) into structured data a team can track over time.
Early tools sorted comments into three buckets: positive, negative, or neutral. Unfortunately, these buckets don’t tell you much. Take a comment like "onboarding took forever, but your support team saved us." A basic model averages it into something close to neutral, and you lose both signals.
Modern customer sentiment analysis works in layers:
In PwC's 2025 Customer Experience Survey, about nine in ten executives said customer loyalty had grown in recent years. Only four in ten consumers agreed.
Part of that gap comes from using scores rather than a true sentiment analysis. An NPS of 42 could either mean that customers are mildly annoyed about pricing or furious about a broken integration. The average hides the reason, and the reason is what you'd actually fix.
It's an expensive thing to miss. Qualtrics XM Institute found that 34% of consumers reduce spending after a negative experience and 13% stop spending entirely. In B2B, the pullback rarely shows up as a complaint. You see it in a smaller seat count, a champion who stops joining calls, or a renewal that slips a quarter without much explanation.
Customers rarely say they're leaving, but they'll hint at it in tickets and call notes long before the renewal date. Sentiment analysis is how you catch those hints while there's still time to respond.
Catching them is only half the job, though. In most companies, customer voice is scattered. Support owns the tickets, CS owns the call notes, product owns the survey verbatims, and customer marketing rarely sees any of it. Sentiment improves the experience only when what it finds reaches the person who can act on it.
Survey scores and sentiment analysis answer different questions. Scores like NPS, CSAT, and CES measure how customers rate an experience. Sentiment analysis explains why they rated it that way, using language customers already produce without being asked.
The bigger practical difference is that sentiment analysis doesn't need anyone to fill out a survey. As response rates fall, any score that depends on a response gets noisier. Sentiment analysis runs on the tickets, calls, and conversations that happen anyway.
In practice, you want both. Survey scores give you the trend, and sentiment analysis on the verbatims and support conversations tells you what moved it.
Sentiment analysis improves customer experience when it ties a feeling to a cause and to someone responsible for fixing it. Here are six ways B2B teams use it.
When NPS drops, leadership wants to know why. Aspect-based sentiment can answer that in hours rather than weeks of research. Tag every detractor comment by topic, and you might find that most mention implementation delays while pricing complaints haven't moved.
Then map those topics against your customer journey map to see which stage is causing the friction. A dip in the first 90 days needs a different fix than a dip at renewal.
Most teams use sentiment analysis defensively, to find problems. The positive side deserves the same attention, especially for customer marketing. Every comment that praises a specific outcome is a potential reference, case study, or review, and most of them sit unnoticed in support threads and call notes.
For customer marketing and customer advocacy leaders, positive sentiment tagged by topic becomes a shortlist of advocates. A customer who wrote "we cut onboarding time in half" is a ready candidate for your reference program, and their own words are the first draft of customer stories and social proof. The same tags show which outcomes customers mention most, so campaigns are built on what customers value rather than what the team assumes.
Timing changes the ask, too. Inviting a customer to speak right after they praised your team lands very differently than a cold reference request at quarter end.
Tone changes before behavior does. If a customer wrote "love the new dashboard" in March and "still waiting on a fix" in June, that's a signal, and it arrives well before usage drops.
Sentiment trends across tickets, call notes, and QBR transcripts add context to churn prediction models that usage data can't provide on its own. For customer success teams, the useful output is simple: a list of accounts to call this week, ranked by risk, with the reason attached.
Ticket volume is a poor proxy for impact. Two hundred tickets about a confusing button might matter less than twenty angry billing complaints from your largest accounts.
When you weight topics by how often they come up and how strongly customers feel about them, product teams get a more honest ranking. They also get the customer's own words to back it up, which tends to shorten roadmap debates. Our guide on how to use customer feedback to build a product roadmap walks through that process step by step.
A frustrated comment doesn't help anyone if it stays in the support tool. Send sentiment alerts to where account owners already work, whether that's the CRM or Slack.
Then set a rule people can follow. For example, any strongly negative comment from an account in its renewal window goes to the CSM and the account executive within 24 hours, with the original quote attached. Customers notice when someone gets back to them the next day.
Conversation-level sentiment shows you where calls go wrong. If sentiment keeps dropping at the moment agents explain a policy or transfer a case, you have a script or process problem, and those can be fixed.
Managers can also pull calls that started badly and ended well, then use them for training. Real conversations with your own customers teach more than a generic soft-skills course.
"Great, another outage" reads as positive to a basic model. B2B language adds more traps, since words like "aggressive" or "critical" can be neutral or even positive in a technical conversation. Use models tuned on business conversations, and have someone spot-check a sample of scored comments each month.
Sentiment analysis can only read what customers actually say. If most of them stay quiet, the model works from a thin, skewed sample, usually your angriest and happiest customers. The fix is to give the middle more chances to speak up, through short in-product questions and AI-led customer interviews that pick up detail surveys miss.
A sentiment dashboard with no owner turns into wallpaper. Every tracked topic needs a team responsible for it and a threshold that triggers action. If nobody would change a decision based on a metric, stop tracking it.
This is the scattered-voice gap from earlier, and it's the most common reason sentiment programs stall. Each team sees its own slice of the customer, and nobody sees the whole account.
Closing it takes one system that turns customer voice into intelligence and routes it to the teams that can act. Deeto, a voice of the customer platform, is built that way. Listen captures customer voice from interviews, surveys, and in-product questions. Learn organizes it in one system of record tied to each account. Activate delivers the right signal to CS, product, or customer marketing in the tools they already use. Analyze applies AI sentiment analysis to every response and tracks patterns over time. Orchestrate runs the workflows that connect it all, so a frustrated comment becomes a CSM alert and a glowing one becomes an advocacy invitation.
For customer experience and success teams using Deeto this way, results include 10 to 15% higher renewal rates and 25% faster visibility into risk signals.
How can sentiment analysis be used to improve customer experience?
Sentiment analysis improves customer experience by showing how customers feel about specific topics, such as onboarding, pricing, or support, across every channel they use. Teams use it to find the cause behind falling scores, flag at-risk accounts before renewal, prioritize product fixes by emotional impact, and route negative feedback to the right owner quickly.
Does sentiment analysis replace NPS and CSAT?
No. Sentiment analysis works best alongside survey scores. Surveys give you a consistent benchmark to track over time, but they depend on customers responding and rarely explain why a score changed. Sentiment analysis reads tickets, calls, interviews, and open-text answers to show what's behind a score moving up or down.
What data sources work best for customer sentiment analysis?
The best sources are the ones where customers explain themselves in their own words: support tickets, call and meeting transcripts, open-text survey answers, customer interviews, CSM notes, and product reviews. Combining several sources per account gives a more accurate picture than any single channel, since each one captures a different moment in the relationship.
Can sentiment analysis predict customer churn?
Sentiment analysis can surface early warning signs of churn. A steady decline in tone, rising frustration about one topic, or phrases like "evaluating alternatives" often appear before usage drops. Combined with product usage and account data, sentiment trends give churn models context that usage alone misses, and they give customer success teams a specific reason to reach out.
What is aspect-based sentiment analysis?
Aspect-based sentiment analysis ties each emotion in a piece of feedback to a specific topic. Instead of scoring a whole comment as neutral, it separates "setup was painful" from "the support team was excellent" and records both. CX and product teams can then see which parts of the experience drive satisfaction and which cause frustration.
How accurate is AI sentiment analysis?
It depends on the model and the data it was trained on. Modern models handle straightforward feedback well but can struggle with sarcasm, mixed opinions, and industry jargon. Teams get better results by using models tuned to business conversations, scoring sentiment by topic rather than by whole comment, and having a person review a sample of results each month.
So how can sentiment analysis be used to improve customer experience? Mostly by telling teams what to fix and who owns it, based on the language customers already produce.
You don't need to start big. Pick one outcome, connect the feedback you already collect, and give every topic an owner. Teams that get value from sentiment use it to decide who to call this week. Teams that don't end up with a monthly report nobody opens.
Deeto brings customer voice from interviews, surveys, and conversations into one place, then turns sentiment into alerts and next steps for CS, product, and marketing. To see how that works for your team, book a demo.
What is sentiment analysis? Learn how it compares to NPS, where it catches churn, and how to put it to work.
Buyers check your claims before they trust them. Social proof in marketing is evidence that other people have bought your product and gotten value from it. It includes reviews, customer stories, ratings, video testimonials, and endorsements. It works because buyers use the choices of people like them as a shortcut when a decision feels risky.
Here are four ways to put social proof to work, along with the data behind each one and a simple way to measure whether it is paying off.
Social proof is the tendency to treat other people's choices as evidence of what is safe, correct, or worth buying. In marketing, social proof is any signal that real customers chose you and were glad they did. It includes reviews, testimonials, case studies, customer logos, ratings, awards, user-generated content, and word of mouth.
Psychologist Robert Cialdini lists social proof as one of six principles of persuasion, and his research adds a detail marketers should use. According to W. P. Carey School of Business at Arizona State University, the effect grows when the proof comes from multiple people and from people similar to the buyer. That is why a quote from someone in your prospect's exact role and industry often carries more weight than a bunch of anonymous five-star ratings.
The table below compares the six most common types of social proof and where each one fits best.
Teams often collect several of these types and store them in different places, which makes the right proof hard to find when a buyer needs it. Deeto gets the right customer story or piece of social proof to the right buyer at the right moment, instead of leaving it to sit in a folder until someone remembers to look.
Buyers now verify before they commit, and they verify with other customers. G2's 2026 Buyer Behavior Report found that review sites (38%) and AI chatbots (37%) are the two biggest sources shaping software shortlists. Buyers let AI narrow the field, then lean on peer proof to decide which vendors earn their trust. Your customers' words now influence the shortlist twice, once directly and once through the AI tools that summarize them.
TrustRadius tells a similar story. Its 2026 B2B Buying Disconnect Report found that 63% of technology buyers used AI during their purchase process and that 94% of the AI users fact-check its answers at least some of the time. AI helps buyers find options, and other customers help them trust one.
Social proof helps marketing teams in four concrete ways.
Four approaches cover most of what works, and they build on each other. Start with the one your buyers meet first.
Reviews are a well-researched form of social proof. Northwestern University's Spiegel Research Center found that a product with five reviews had a purchase likelihood 270% greater than a product with none. The lift was bigger for pricier items, with conversion rising 380% for higher-priced products and 190% for lower-priced ones. The study looked at retail purchases, so read the exact percentages as directional for B2B. The direction matches what software buyers say about review sites.
There are two main details in the data that stand out. The benefit of extra reviews shrinks quickly after the first five, and purchase likelihood peaks when average ratings sit between 4.0 and 4.7, because a perfect 5.0 looks too good to be true.
Reviews also feed the AI answers buyers read first. When G2 asked what would raise buyers' confidence in a chatbot's answer, the top response was a citation from a review site. Reviews now convince the person reading them and shape the answer that person sees before they ever visit your site.
A generic testimonial can let potential clients know that your product works, but a matched story says it worked for someone like the person reading it. Picture a VP of Finance at a 500-person fintech reading a success story from a 20-person retail startup. The result might be impressive, but it doesn't answer her real question, which is whether this will work at a company like hers.
The Cialdini research above explains why the mismatch hurts. Build each story around the details buyers use to judge similarity, which are industry, company size, the role of the person quoted, and the problem being solved. Then include the parts that make a story evidence instead of a brochure:
Pairing each story with a measured result, the kind captured in ROI and impact studies, turns an anecdote into something a CFO will accept. Video testimonials add another layer, because buyers can see and hear a real person. Keep them short and tied to a single outcome.
Product marketing teams usually own the story library as part of their product marketing strategy. The library only pays off if a rep or campaign manager can find the right story in seconds, filtered by industry, company size, and use case.
Social proof tends to live on a website, while a lot of the deciding happens in calls, emails, and shared docs where the website is not in the room. A champion inside the buying group needs something to forward to a skeptical CFO or security lead, and it has to arrive at the right moment.
Deeto helps sales teams put matched customer evidence in front of a champion at the right stage, and customers see 20% to 30% faster sales cycles from that kind of contextual customer intelligence.
The simple fact is that customer proof goes stale. A story from three years ago about a product that has since changed loses credibility with every release. The fix is a steady flow of new customer voices instead of one yearly push for testimonials.
Advocates also create proof you cannot write yourself, including unprompted posts, referrals, and conference talks. Deeto helps teams capture customer feedback continuously and route the best voices to the teams that need them.
Social proof fails in predictable ways.
Measure social proof by tying each type to a behavior it should change. Track how much proof you have, whether buyers see it, and whether deals move when they do.
What is social proof in marketing?
Social proof in marketing is evidence that other people have bought your product and gotten value from it, shown through reviews, ratings, testimonials, case studies, logos, and endorsements. It works because buyers treat the choices of similar people as a shortcut when a decision feels risky, which lowers perceived risk and builds trust before a sales conversation.
What are the main types of social proof?
The main types are reviews and ratings, customer stories and case studies, video testimonials, customer logos, peer references, and social mentions or user-generated content. Reviews and logos help early shortlisting, stories and video help mid-funnel evaluation, and live references help late-stage risk reduction. Match each type to the moment a buyer needs it.
Why does social proof work?
Social proof works because people look to others when a choice feels uncertain. Psychologist Robert Cialdini lists it as one of six principles of persuasion, and his research shows the effect grows when the proof comes from multiple people who resemble the buyer. In B2B, that means peers in the same role or industry.
How many reviews do you need for social proof to work?
Fewer than most teams expect. Northwestern's Spiegel Research Center found that purchase likelihood rose sharply once a product had five reviews, then gained less from each additional review. More reviews still help credibility, but the first five do the most work. Average ratings between 4.0 and 4.7 performed best.
How do you use social proof in B2B marketing?
Publish reviews on the sites buyers use, match customer stories to each prospect's industry and role, and put proof inside the sales process through tagged libraries, live references, and CRM delivery. Then keep it fresh with ongoing advocacy and feedback. B2B buyers verify claims with peers, so proof from similar customers carries the most weight.
How do you measure social proof?
Track review volume and ratings, conversion on pages with and without proof, how often reps use proof in active deals, win rate when proof was shared, and mentions of your brand in AI answers. Pair win-rate data with win/loss interviews, because deals where reps share proof may already be stronger deals.
Buyers still want to hear from someone like them, and now they check what AI tells them against what customers actually say. Social proof in marketing works when it reaches the right buyer, in the right format, at the right moment. That means reviews where buyers look, stories matched to the reader, proof inside the deal, and a steady supply of new customer voices.
Start with one question: what are your customers already saying that your buyers never hear?
Deeto is a voice of the customer platform that helps teams capture, organize, and deliver authentic customer proof across marketing, sales, and success. To see how it works with your own customer evidence, book a demo.
What is social proof in marketing? Learn 4 ways to use reviews, stories, and advocacy to build buyer trust.
A customer marketing team needs three new case studies for a launch. They pull a list of thirty accounts, send the ask, and wait. Two weeks later they have three replies. Meanwhile, on calls that already happened last month, at least four customers said something that would have made a great quote. Nobody heard it, because nobody was listening for it.
Call intelligence software analyzes sales and customer calls automatically to pull out sentiment, intent, and specific moments worth acting on, without tagging or re-listening to a recording after the fact. It runs on every call a company has, not just the ones someone remembered to flag, and it treats a good moment with the same attention as a bad one.
That's what Deeto Signals does. It's the part of Deeto that turns what customers actually say on a call into intelligence that reaches marketing, customer success, product, and sales, instead of staying locked in a recording only one person ever heard.
Here's what that changes.
Most teams already record their calls. Almost none of them get read back.
A transcript search only finds the words you already thought to search for. If a customer says "this made my week" instead of "I'm happy with the product," a keyword search misses it entirely. Sentiment doesn't live in the words on the page. It lives in tone, emphasis, the moment right before someone says something honest, and none of that survives a transcript dump into a shared drive.
So the calls pile up. Reps move to the next deal. CS moves to the next renewal. The thing a customer said that would have made a perfect testimonial sits in a recording nobody opens again.
Deeto Signals runs automatically on every sales and customer call, the moment a call source is connected. There's no tagging, no manual review, and no workflow to remember. Connect Zoom, Google Meet, or Gong, and it starts working on the next call.
For every person on every call, Signals:
That last part matters more than it sounds. A flagged moment with no path back to the actual audio is a claim someone has to take on faith. A flagged moment with a timestamp is something a customer marketing manager can verify in ten seconds before they act on it.
Signals runs automatically on every sales and customer call as soon as a call source is connected. No tagging, no manual review, nothing to remember. Connect Zoom, Google Meet, or Gong, and it starts working on the next call.
For every person on every call, it pulls out what was actually said (not what got typed into notes afterward), scores sentiment and relevance person by person so a five-person call doesn't get flattened into one generic summary, and links every signal back to the exact moment in the recording. A flagged moment with no path back to the actual audio is a claim someone has to take on faith. One with a timestamp is something a customer marketing manager can check in ten seconds before acting on it.
Call intelligence tools are built to catch churn risk, but that only covers half the job. The good moments are just as detectable, and they're what an advocacy program runs on. When a customer says a feature saved their team hours, or that switching to the product was the easiest vendor decision they've made all year, that's not just nice to hear. It's a signal.
Signals surfaces who's advocacy-ready based on what a customer actually said, not an NPS score or a CSAT survey someone has to remember to send. It suggests candidates before anyone goes looking, the same way a well-run customer reference program depends on knowing who to ask before the ask feels cold. Your next case study subject identifies themselves. You just finally hear it.
Most customer sentiment analysis tools weren't built for that. They're tuned to catch what's going wrong. Few catch what's going right, at the level of one specific person on one specific call, in a form a customer marketing team can actually use that same day.
None of this comes at the expense of risk detection. Signals runs the same pass for renewal risk and product friction as it does for advocacy-ready moments. There is no separate setup, no second tool to connect, and no tradeoff between watching for the good and watching for the bad.
A frustrated comment about a broken integration gets flagged the same way a glowing comment about time saved does. Both go to the person who needs to see them, linked to the moment they happened.
Every signal Signals surfaces is available over MCP, so the assistant a team already uses, such as Claude or ChatGPT, can answer questions from what customers actually said instead of filling gaps with a plausible-sounding guess.
Ask what customers are saying about onboarding this quarter, and the answer comes from real call moments instead of a summary someone wrote from memory three weeks ago.
Signals doesn't need a new process. It needs a call source. Connect Zoom, Google Meet, or Gong, and every call from that point forward gets the same pass: sentiment scored per person, advocacy-ready moments flagged, renewal risk caught, all of it linked back to the exact moment it happened.
The recording isn't the deliverable. What someone said on it is.
What is call intelligence software?
Call intelligence software analyzes sales and customer calls automatically to extract sentiment, intent, and specific moments worth acting on. It works without manual tagging or re-listening, scores each person on a call individually, and links flagged moments back to the exact point in the recording where they happened.
How is this different from call recording or transcription?
Recording and transcription capture what was said. They don't tell anyone which parts matter. Call intelligence software adds automatic sentiment scoring, per-person relevance, and moment-level linking on top of the raw transcript, so the useful parts surface without anyone reading the whole thing.
Does call intelligence software only flag negative signal, like churn risk?
Most tools in this category are built primarily for risk detection. Signals scores positive and negative signal in the same automatic pass, so advocacy-ready moments get surfaced with the same rigor as renewal risk instead of being an afterthought.
Can call intelligence data feed an AI assistant?
Yes. Connected over MCP, the customer intelligence a tool like Signals extracts can be queried directly by an LLM such as Claude or ChatGPT, so answers about customer sentiment or product feedback come from real call moments instead of a summary or a guess.
What is call intelligence software? Learn how it scores every call automatically and surfaces advocacy-ready customers.
See how Deeto helps you turn customer voice into a GTM advantage.
