Centercode vs. Listen Labs

A Listen Labs study is a one-time project that closes once it reaches the response limit you set. You can recruit from its network, bring your own people with a link, or use both in one study. Listen Labs says it returns answers quickly, with price tests and best-versus-worst trade-off questions, and that its Twins, digital models of real people it has interviewed, answer a quick question in seconds, though its Twins page leads to a demo booking. One of its articles says it does not replace simple structured surveys. Its articles also say larger companies start with a demo while smaller teams sign up themselves, and that recruiting spends credits. We found no pricing page.

Listen Labs’ Pulse repeats the same study in waves, meaning rounds at an interval you pick, and lets you add timely questions to any wave. Its release note says to switch Pulse on in the study builder, while its help center says to ask your account team. Its articles list diary studies, where people log what they do over a set period, among its formats, and its release notes say a study can require a signed agreement before it starts. We found no help center page on diary entries and no tool for dated phases, gates between events, or running a beta, and one of its articles says it covers the stages before a beta.

Listen Labs says Pulse is always-on research that runs your study in waves and tracks themes in what people say from wave to wave, and it has to be switched on first. Its articles say teams use its bring-your-own-people option for customer advisory programs, and its help center says sending email invitations from inside Listen Labs is off until you ask support. We found no space in Listen Labs where a standing group of your people can join, post, or talk with each other between waves.

Listen Labs’ articles say a template library covers concept testing, brand perception, usability, and more, and one says you reach the library by booking a demo. Its help center does not mention that library. It describes starting a study by describing your goals so the AI drafts the guide, uploading a guide, or duplicating a study. We found no ready-made templates for betas, advisory boards, or other kinds of program.

Listen Labs says its AI-moderated interviews can replace or add to the small internal group in user acceptance testing, and its articles say teams bring beta users into its studies. We found no ready-made program a customer can run for AI red teaming, ambassadors or advocates, bug bounties or operational acceptance testing anywhere on its site, and no help center page or plan details for acceptance testing.

Listen Labs says you can share a study link with your own customer list, community, or employees, or upload a contact file and send invitations from Listen Labs or from your own email domain, or paste the link into your own email tool, and you can mix them with its network in one study. Its built-in email campaigns are off until you ask support, and sending from your own domain needs setup with its customer success manager and your IT team. Its user guide says people you bring use fewer credits than people from its network. We found no referral tool or sign-up page for your own community; the sign-up page we found invites people to hear about more studies and does not set up a community for you.

Listen Labs says it finds participants who match your audience from a global network, with a consumer pool and a separate professional pool. Its pages describe that network in different ways: its docs say it draws on outside panel companies, other articles say it routes across partner panels alongside its own database, and several articles call it a proprietary network or database. Listen Labs says professional and very niche audiences cost more per completed interview, and for very specialized audiences its docs and user guide point you to its recruitment team to confirm what is possible and what it costs. Its articles say larger companies buy through a demo while smaller ones can sign up themselves.

Listen Labs’ screener questions, the opening questions that decide who gets in, mark each answer as qualifying or disqualifying, and quotas set a minimum, maximum, or exact share for a group, though its user guide limits how many questions a study can set quotas on unless you ask support. Its docs list targeting by age, gender, income, work status, location, and job details, say people answer general questions when they join its panel, and say behavior is asked about in screeners inside your study, while its role, industry, and article pages say participants can be matched on behaviors. We found no profile you build or keep up to date for your own people. The closest are details you add to each person’s study link, which can drive quotas and question logic, and a file of your own customer details that can be matched to responses afterward.

Listen Labs lets you bring your own list into any study by link or uploaded contacts, but we found no place inside it to keep people as a community across projects. Its Saved Audience feature saves a screener’s questions for reuse, not the people, uploaded contacts go into that study’s email campaign, and its Research Library, where past studies are kept and searched, keeps findings across studies rather than a list of people.

Listen Labs says studies fill quickly instead of taking weeks, and its user guide says first responses usually arrive quickly, with general audiences finishing sooner than targeted ones and very niche ones taking longest. Its docs say a study pauses on its own when too few people qualify, and for very niche or hard-to-fill audiences its guide sends you to its recruitment team to confirm what is possible and what it costs.

Listen Labs says you can recruit by country, region, or city and set quotas for each market, and its docs say your questions and people’s answers are translated automatically in both directions across many languages, with spoken read-aloud in fewer. Its pages give different counts for the countries, languages, and people its network covers, so we leave the figures out.

Listen Labs says participants share their screen, with camera or voice, while an AI interviewer asks about what they do, and its docs describe the recording as synchronized with their spoken narration. Its docs say each response gets a transcript and AI summaries, that highlight reels can be cut from the clips, and that showing screen content in reels is switched on by its support team on request. Its pages disagree on phones: a product update post limits phone recording to select customers on an iOS app, while its docs say it works on iOS and Android.

Listen Labs says its AI moderator holds the interviews, and we found no way to book, host, or observe a live session that you moderate yourself. Listen Labs says its video imports let you upload recordings of interviews held elsewhere and get a transcript, themes, and clips beside your other research, and its AI moderator page suggests running an AI study after a few moderated sessions.

Listen Labs says its AI moderator holds interviews by text, voice, or video, listens to what each person says, and decides whether to follow up, with the depth set question by question. Its docs describe full transcripts, a short summary of the main point for each question, and a report organized around your study goals, and its notice to participants calls them AI-powered research interviews. Its feature pages have a Try for Free button that opens account creation, we found no public plans, and its help center says each completed response uses credits, with its user guide adding that people it recruits cost more credits than people you bring.

Its docs describe MaxDiff, where people pick the best and worst option from small sets, with a head-to-head view and a view of the mix of options that reaches the most people, and Listen Labs says it also offers a separate price test that walks each buyer up or down a list of prices to chart demand and revenue. We found no feature page or help page for a study that puts features and prices in one choice. Its articles disagree: some list conjoint, which weighs features and prices together, among the methods it supports, while another says teams whose whole program is self-programmed conjoint should keep a separate tool beside it. A line in its developer reference says conjoint questions are not returned, but we found no page that shows how to set one up.

Listen Labs says its usability tests have people share their screen while they complete tasks, with an AI interviewer that asks follow-ups, and an interview can ask participants to upload a file such as a screenshot. Some of its articles suggest placing an always-on interview link inside your product. That is feedback inside a session you design, and we found no report form, idea box, or feedback widget for your own site or app.

Listen Labs says it handles incentive payments for the studies it recruits, and one of its articles says those incentives are built into the per-participant credit cost. For people you bring yourself, its docs say your team funds an incentive balance and presses Pay Incentive for one person or many, each person gets an email and picks a gift card, a prepaid card, or a charity donation, and it will not pay someone who did not finish the whole study. Its docs open by saying rewards go out automatically, while their steps have you press the button.

Listen Labs says its Email Campaign sends study invitations from the platform to a contact list you upload, from its own address or, after a setup step with its customer success team, yours, and it is off until its support team turns it on. Its docs say a gift card reaches each person by email when you press Pay Incentive. We found no reminders, text or in-app messages, message schedules, tracking of who opened a message, or rules that nudge and thank people on their own. Some of its articles suggest sharing your study link through your own email or app, which is you sending it.

Listen Labs says its research comes from real, human interviews held by an AI moderator, and that every claim in its results traces back to a real interview. Its Twins page recommends a full study for high-stakes decisions and says real people are one click away in the same platform. Its help center says you can recruit from its own network, bring your own people, or mix both in one study.

Listen Labs says its Twins are AI models of real people that answer questions about concepts, names, pricing, and campaigns in seconds, for directional questions too small or too urgent for a full study. Its articles set Twins apart from synthetic personas. We found no generic AI personas or AI experts, and no page saying Twins complete surveys or tasks alongside real participants, in its product pages, help center, release notes, case studies, articles, or blog. The Twins page offers only a demo request.

Listen Labs says each Twin is a one-to-one AI model of a real person, built from an in-depth interview with that person and enriched with the customer’s own data, and that every answer comes with its reasoning, the interview quote behind it, and a confidence score. It says Twins are built for one customer’s use and never shared with anyone else. Its pages say you can click an answer to trace it to the real person behind it, and one article calls the source a named individual’s interview. We found no page that spells out what you see about that person, such as a name, and no help center page about Twins. The Twins page offers only a demo request.

Listen Labs’ docs say you can import data you collected elsewhere to analyze in its tools: spreadsheets of answers, transcripts as text or Word files, and recorded interviews, though only admins and researchers can start an import. Its release notes say each video becomes a response with a transcript, themes, and clips. We found no automatic feed from other systems: its public developer interface (API) creates studies and reads results, its reference lists no way to load feedback, and its partner guides cover passing participant IDs and sending people from the partner tool into an interview and back. We found no scan of public pages or forums for customer feedback, and one of its articles calls it a primary research platform rather than a social listening tool. Its page for Research Agent, the assistant that analyzes results and builds decks, lists web search for outside context, while its docs say that agent does not use web data.

Listen Labs’ docs say interviews get automatic transcripts, each response gets a one-line summary with bullet points, each question gets a key finding with themes, and a written report with an executive summary is created as responses are analyzed. The question-by-question analysis starts when an editor asks for it and can need a refresh as responses arrive. Listen Labs says every theme links back to the interview. We found no plan named for any of this.

Listen Labs’ docs describe highlight reels you ask the chat to make or build from quotes, then trim, reorder, and share as a link that anyone can open without an account, or download as video. Reels can also combine clips from several studies through its Research Library. Full transcripts export as files, and a public study link can show viewers the responses as well as the analysis. Screen-share footage in reels is turned on by request to its support team, and its docs say some workspaces can ask support for a version with anonymized faces and voices, and voice-only studies show captions over a waveform.

Listen Labs’ docs say its AI groups the answers to each question into themes, shows how many responses mention each, and opens the quotes behind it, and Listen Labs says you can edit the coding when you disagree. That grouping sits inside one question of one study. We found no step that merges matching feedback from different studies or sources into one item, though its Research Library chat can be asked about recurring pain points across studies.

Listen Labs’ docs say each theme shows how many responses mention it, which covers how many people raised something. Listen Labs says its journey maps help teams prioritize the fixes with the biggest effect on satisfaction and retention. We found no score that weighs how serious a problem is against how many people raised it, and no list of insights ranked by impact.

Listen Labs says its Pulse tracker repeats the same study in waves, turns open answers into themes you can trend beside ratings such as Net Promoter Score (a customer loyalty rating), and shows emerging themes before the headline numbers move. Its articles add that Pulse forecasts how brand-health measures will move. We found no single engagement or success grade for a whole program and no predicted star rating. Pulse has to be switched on first.

Listen Labs’ docs describe a Responses tab it calls a real-time dashboard for incoming interviews, charts that switch between counts and percentages and can be split by group and downloaded, a chat where you ask questions or request charts in plain English, and a link that lets anyone view the analysis. Its articles also mention a Pulse dashboard that shows themes beside ratings. We found no boards you build yourself and no scheduled email summaries: its account settings offer email notices for study events, and its docs say the analysis can go out of date until you click Update now.

Listen Labs’ enterprise-focused articles say it connects with Jira and Slack, that study notifications and findings can be routed into Slack channels, and that its outputs can show up in Microsoft Teams, with connector setups reviewed during enterprise onboarding. Its docs mention Slack only in the list of what admins manage and as a place to paste a reel link, and we found no help center setup guide for any of these tools. What its docs do describe is a connector that lets AI assistants such as Claude and ChatGPT search studies and read transcripts and analysis, a public developer interface (API) that reads results out, exports to Google Sheets, and decks that open in Google Slides. Its Pulse page and articles also say Pulse connects to the survey tools you already run, and Pulse has to be switched on first.

Listen Labs says you describe a goal and its AI drafts the study’s goals, screener, and interview guide, its AI moderator holds the interviews, and Research Agent turns the results into decks, memos, and highlight reels. Its home page calls this a single place to run research from start to finish, and it says ChatGPT, Claude, and other AI tools you connect can build and launch a study from a goal. We found no single named agent that carries a project from setup through recruiting, interviews, and reporting. Its help center names Research Agent for analysis and deliverables and mentions a presentation agent that builds decks in your template. Studies spend credits, and we found no plan named for these features.

Listen Labs has controls in several places rather than one setting for an agent. You choose how many follow-up questions its AI moderator may ask on each question, and Listen Labs says the moderator works only inside the objectives and topic limits you approve and that its study builder fills in gaps when you want it to. Admins write standing guidelines its AI follows when it drafts studies, reports, and decks, its Study Advisor shows suggestions beside each question for you to apply, and its help center has a per-study switch for emotion analysis. We found no setting that turns an agent’s capabilities off, suggest-only, or fully on, one at a time.

Listen Labs’ help center says its platform writes a report on its own as a study’s analyzed responses reach set counts and builds a deck on its own when a study closes, and account settings can email you when first responses arrive, a study finishes, or a report or deck is ready. These follow the study rather than a daily or weekly schedule, and we found no briefing from an agent of its own. Its connector post lists a customer example where an outside AI tool turns the month’s top insights into a newsletter for the team. One of its articles says study notifications and findings summaries can be routed into Slack, and a checklist in another says to schedule Research Agent to build deliverables when fieldwork ends, though we found no matching setting in its help center.

Its help center says Collaborators can build and edit studies but cannot launch them, while its user guide calls the role read-only, and Listen Labs says Admins can review a Collaborator’s work before it goes out. A connected AI tool acts as you, so its help center says it can launch only where your role allows. Listen Labs says its research skill for Claude, a set of instructions you give Claude, has you review what participants will see and approve the launch and its cost before anything goes live. Its help center says you send rewards to people you bring yourself by clicking Pay Incentive. We found no queue where an AI agent’s messages or rewards wait for an owner.

Listen Labs lets admins write standing guidelines that its AI follows when it drafts studies and when it generates reports and decks, and its help center calls Research Library the collective memory of your research program, where answers cite the studies behind them. Listen Labs also says you can edit the AI’s coding of an interview when you disagree. All of these are things you write, ask, or change, and we found no page saying the notes you leave on its work, or corrections you make to its analysis, become lasting memory that shapes what it proposes next. Listen Labs says it never trains its AI models on customer data.

Listen Labs says ChatGPT, Claude, and other AI tools that support MCP, a common way to plug AI tools into other software, can connect to your account, search across studies, read transcripts and analysis, and create, edit, and launch studies as you, within your permissions. Its connector post says existing customers reach out to their account team for access, while its help center says to add the connector yourself, and we found no plan named for it. We found no page on logging connector calls (one article says audit logging covers all platform activity) or a workspace switch to turn it off. Its help center says removing the connector from your AI tool does not revoke it and to contact support for that, while its page on agent credentials says to revoke it in account settings.

We found no way to ship units to participants or track them in Listen Labs’ help docs, product pages, or release notes. The closest wording is in its articles and one customer story, and none of it says who sends the product. One article sets out a home-use testing method with a step to ship product to category purchasers and says Listen Labs delivers its whole method as one system. Another lists participants recording the unboxing of a delivered product with a mobile app. The wear-testing customer story has people wear the product on camera in a recorded interview.

Listen Labs’ help docs let you limit a study to iOS devices, Android devices, or desktop, and its API guide describes the same setting as restricting which devices can take a study. Screening questions can qualify people on what they say they own. We found no device list and no targeting by model or software version, and no plan named for the setting.

Listen Labs says in articles on its site that it supports diary-style and ethnographic formats for in-home usage tests, where participants use a product in their own setting and report back to an AI interviewer over a set period. We found no diary feature in its help docs or product pages, and no help doc or product page on getting products to participants. Its articles disagree: others say AI moderation does not suit studies where a participant handles a physical product, and that such studies belong with a human moderator.

Listen Labs says in a product post that you can require participants to sign a contract, usually an NDA, before a study starts. You upload the contract in the study builder, and anyone who declines is screened out. Participants recruited through Listen Labs can sign without using their name, and each signed, time-stamped copy shows in the Responses tab with the respondent ID. We found no help article on it, nothing on keeping versions of the agreement, and no plan named for it.

Listen Labs says its Pulse tracker repeats the same study, audience, and screening wave after wave and charts themes from open answers next to the numbers a team already reports, and its docs say Research Library can compare sentiment across studies over time. For use, the nearest thing is what a participant does on a shared screen during an interview, which Listen Labs says its Visual Insights reads for desktop studies, plus logs of what people did that you attach in a spreadsheet file. We found no built-in view of how use changes over weeks and no plan named for these. Pulse has to be switched on first.

We found no way in Listen Labs’ help docs, product pages, or release notes to ship units to participants, on the market or not. Listen Labs says studies often show work that is not public yet, such as concepts, ads, and designs, and it offers a watermark on study images and videos, plus a signed NDA before a study starts. The work is shown on screen as images, video, or prototypes.

Listen Labs says an in-house research team is there to opt into for hands-on support, with terms set in each customer’s contract, and that its insights team helps hands-on with complex projects, in one article from study design through the final recommendation. It also says the choice between self-serve and managed is made study by study, and that larger companies start through a sales demo, which is also the way in to that help. We found no customer story where its team ran a whole project from plan to report; one describes a healthcare research firm joining its work with clients as part of the research team, without saying who runs the project.

We found no page saying Listen Labs’ team ships products to participants, tracks them, or arranges returns. Listen Labs says it supports in-home product tests as diary-style studies, where participants use the product in their own setting and report back in video interviews, and that page does not say who supplies the product. A customer story about wear testing has customers try a product on camera and does not say who sends it, and one of its guides lists shipping a product as a step without saying who does it.

Listen Labs’ published terms say the customer keeps its own data and the outputs the service generates, and that Listen Labs will not use the customer’s data to train its AI. Its documentation lets you export transcripts, recordings, answers, the study guide, and reports. The same terms keep its templates and methods with Listen Labs, limit the participant data it supplies to the customer’s internal research, and give access to the platform for the length of the order. We found nothing about handing a project from its team to the customer’s team.

Listen Labs’ sign-in page has a company single sign-on option, which lets people log in with their company account, and it opens a screen titled Enterprise SSO. Its help center also mentions people who sign in this way, and many of its articles say enterprise single sign-on is supported. We found no setup guide, no page naming the plan that includes it, and no page saying it supports SCIM, the standard that adds and removes users automatically.

Listen Labs’ help center lists the workspace roles Admin, Researcher, and Collaborator, plus a read-only Viewer role that the same page says the invite menu does not offer. Each study can also be shared as full access, collaborator, or viewer, and people outside the workspace can be given collaborator or viewer access. It keeps earlier versions of a study and of a report that you can restore, and several of its articles say it keeps audit logs or audit trails, but we found no help page describing a log of who did what, and no page naming the plan that includes any of this.

Listen Labs’ product updates page describes a setting called Redact PII (PII means personal details that identify someone), which you turn on for a study before it fields. It swaps names, contact details, addresses, and company names in interview responses collected after you switch it on for placeholders, though not what shows on a shared screen or in a video background. Its help center says some workspaces can ask support for highlight clips with anonymized faces and voices, and its reels page has a setting for which participant details appear in a reel, applied to every reel in the study. One of its articles says permissions by role can limit who sees personal details, but we found no help page or setting that lets some teammates see names and emails while others see them masked.

Listen Labs has a public, versioned API, which lets other software read from and write to it, with a published reference. It can create and launch studies, read responses and transcripts, and request deletions, though its product updates page says study creation covers only studies where you bring your own participants. Its help center documents hand-off links to a partner platform and two survey tools, plus a connector that lets AI assistants build and read studies. Its articles say it works with Jira, Slack, Figma, and Microsoft Teams. We found no documented webhooks (automatic messages to other software) apart from a sample automation recipe in one article. Its pages disagree on who can make API keys, and its guide for AI agents says access is granted per organization and can be requested.