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Onboarding intake template

User onboarding survey — personalize activation and segment new signups

A post-signup survey that captures the 4-7 fields product teams actually use — role, team size, primary jobs-to-be-done, prior tools, source of discovery — to personalize the first-session experience and feed CRM and product-analytics segmentation without blocking activation.

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User onboarding survey — personalize activation and segment new signups

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Who this template is for

An onboarding survey is the product team's most underused intake form. Signup forms get all the design attention — every team obsesses over the conversion math of the email-and-password page — but the survey that runs right after signup, the one that decides which template surfaces, which onboarding flow branches, which tooltips appear, and which sales-team alert fires for an enterprise-ICP signal, often gets bolted on as an afterthought with whatever questions someone proposed in Slack three sprints ago. The product teams that get this right (Notion's "what kind of work do you do," Linear's role question, Figma's role and team size, Cursor's developer-stack question, Vercel's framework question) treat the onboarding survey as the activation engine — the questions and the routing logic are A/B tested with the same rigor as the signup form, and the data feeds product personalization, CRM segmentation, and product-analytics cohort definitions simultaneously. This template gives you that structure. Four to seven questions that map to role / team size / jobs-to-be-done / prior tools / source, conditional logic so users only see the questions relevant to their primary use case, routing to your stack (Mixpanel / PostHog / Amplitude for product analytics, HubSpot / Salesforce / RD Station for CRM, Segment for both), and the option to surface in-app conditionally during first session rather than block activation. Use it for B2B SaaS post-signup, for consumer-app first-launch personalization, for developer-tool stack matching, and for any product where the answer to "what do you actually want to do here" should change what the user sees next.

From signup completion to personalized first session in under 90 seconds

A user completes signup (email/password, OAuth, or magic link) and lands on the onboarding survey. The survey is 4-7 questions in a single page, with conditional logic so a user who selects "engineering" as their role sees follow-up questions about tech stack while a user who selects "marketing" sees follow-up questions about campaign types. Completion takes 60-90 seconds. On submission, the responses fire to three destinations simultaneously: product analytics (Mixpanel, PostHog, Amplitude, Segment as the routing layer) for cohort segmentation that informs activation and retention dashboards; CRM (HubSpot, Salesforce, Pipedrive, RD Station for Brazilian programs) for sales-side segmentation and enterprise-ICP-alert workflows when the role + company size match; and in-app personalization (LaunchDarkly, Statsig, PostHog feature flags, or your own user-property storage) to control which template surfaces, which onboarding flow branches, which tooltips appear in the first session. The user's first session reflects their answers immediately — a marketing manager at a 50-person company sees the team-collaboration template gallery; an engineer at a 5-person startup sees the API-integration onboarding; a solo creator sees the simple template flow. The downstream personalization compounds over weeks — the welcome email sequence is segmented by role, the in-app upgrade prompts reference the team-size-appropriate plan, and the sales-team outreach (when triggered) opens with context that the user filled out three days earlier.

What's included

Each field below is here because experienced product and growth teams have learned it predicts activation and retention. Customize the field set per your product's specific personalization model — a B2B project-management tool needs role + team size; a consumer fitness app needs goal + skill level + frequency; a developer tool needs stack + experience level — but resist the urge to add more than 7 questions because completion rate drops sharply past that threshold.

Built for the product categories where onboarding personalization moves activation rates

  • B2B SaaS (the dominant use case)

    Notion, Linear, Figma, Vercel, Stripe, Airtable, Zapier — every major B2B SaaS product has an onboarding survey, because the first-session experience for a marketing manager is genuinely different from the first-session experience for an engineer, and routing them to different templates / tutorials / use-case-specific demos lifts activation rates measurably (typically 10-30% lift on activation when the survey is well-implemented). The standard fields: role (Engineer / Designer / Product / Marketing / Sales / Operations / Founder / Other), team size (Just me / 2-10 / 11-50 / 51-200 / 201-1000 / 1000+), primary use case (the one or two use cases your product supports best, with text-open for outliers), prior tool (which competitor or non-competitor they're switching from — critical competitive intelligence), and source (how they heard about you — LinkedIn / Twitter / Google search / referral / podcast / specific named source). For Brazilian B2B SaaS (RD Station, Pipefy, Conta Azul, Bling, ContaWise, Omie, Movidesk, Octadesk, Resultados Digitais ecosystem), the same pattern applies with locale-specific role and team-size options.

  • Consumer apps with personalization (fitness, finance, learning)

    The category where the onboarding survey directly determines the first-session value. A fitness app's first session for a beginner doing bodyweight workouts at home should look entirely different from the first session for an advanced lifter at a commercial gym, and the only way to know which experience to show is to ask. Standard fields: primary goal (lose weight / build muscle / improve endurance / general fitness — vs. learn a skill / save money / build a habit, depending on category), current level (beginner / intermediate / advanced), available equipment or context (home / gym / outdoor; budget / advanced; smartphone-only / laptop), frequency they intend to engage (daily / 3-4x weekly / weekly / occasionally). For US consumer apps, Strava, Peloton, Noom, MyFitnessPal, Headspace, Calm all use variants. For language learning, Duolingo's onboarding survey is famously well-tested. For Brazilian consumer fintech (Nubank, Inter, C6 Bank, PicPay, Mercado Pago), the onboarding survey captures financial goals and current accounts to personalize the in-app feature surfacing.

  • Marketplaces (matching buyers and sellers)

    Two-sided marketplaces need two onboarding surveys — one for buyers, one for sellers — because the activation goals differ entirely. For sellers, the survey captures the category they're listing, the volume they expect, the existing channels they sell on, the prior platform they're moving from (if any), and the pricing strategy. For buyers, the survey captures the category they're shopping for, the budget tier, the urgency, and the discovery preference. Airbnb runs different onboarding for hosts vs. guests; Etsy for sellers vs. buyers; Mercado Livre for sellers vs. buyers; Vinted, Wallapop in Spain; Enjoei, OLX, Magazine Luiza Marketplace in Brazil. The B2B marketplace category (Faire for retail wholesale, Bolt for fintech, Vendr for procurement) similarly runs separate buyer and seller onboarding surveys.

  • Developer tools (technical role + stack + use case)

    Different vocabulary than B2B SaaS. Developer-tool onboarding surveys capture role (backend / frontend / full-stack / devops / ML / data / security), primary language and framework (TypeScript / Python / Go / Rust / Java; React / Next.js / Vue / Svelte), the cloud and infrastructure context (AWS / GCP / Azure / Vercel / Fly.io / Railway / self-hosted), and the use case (CI/CD / monitoring / database / authentication / API client / IDE feature). For modern AI-developer tools (Cursor, Windsurf, Continue, Cline, Aider, Anthropic Claude Code), the survey also captures comfort-with-AI / preferred-model / company-size-for-pro-tier-eligibility. Vercel, Stripe, Twilio, Datadog, PostHog, Linear all run variants tuned to their specific developer audience.

  • Vertical SaaS (industry-specific products)

    Onboarding for vertical SaaS captures the sub-industry vertical, the specific workflow the customer wants to automate or improve, and the integration context for the dominant tools in that vertical. A vertical SaaS for restaurants captures cuisine type / seating capacity / current POS (Toast, Square, Lightspeed, Clover, Stone POS for Brazil) / integration intent; a vertical SaaS for clinics captures specialty / patient volume / current EHR (Epic, Cerner, athena, eClinicalWorks, iClinic and Doctoralia for Brazilian clinics) / compliance scope (HIPAA, LGPD, sector-specific); a vertical SaaS for construction captures trade / company size / current project-management tool (Procore, PlanGrid, Buildertrend, ConstructConnect). The vertical context lets the onboarding survey ask much more precise questions than a horizontal product, and the data feeds vertical-specific product-team prioritization.

  • AI products (use case + comfort + integration intent)

    The category where onboarding surveys have grown most since 2022. AI product onboarding captures the user's primary use case (writing / coding / research / customer support / image generation / video generation / agent / data analysis), their comfort with AI prompting (novice / intermediate / advanced), their integration intent (API for development / web app for use / browser extension / mobile app), and their existing AI stack (ChatGPT / Claude / Gemini / open-source local / company-specific deployment). For enterprise AI products, the survey also captures company size + sector + compliance scope (because AI compliance — HIPAA, GDPR, LGPD, the EU AI Act tier classification — varies significantly by sector). For Brazilian AI products (the wave of Brazilian AI startups since 2023 — Liminal, ZenoCloud and the Brazilian wrappers of foundation models), the survey captures Portuguese-language-specific use case and the comfort with code-switching between Portuguese and English in AI workflows.

Tune the survey to your product's personalization model

Start with the 4-7 question set. The minimum useful onboarding survey is 4 questions (role / team size / use case / source); the maximum that maintains acceptable completion rates is 7. Past 7, completion drops sharply and the responses you get are biased toward users with unusually high motivation to complete. Each question should map to a downstream personalization or routing decision — don't ask a question whose answer your product doesn't use. Use conditional logic so users only see the questions relevant to their primary use case (an engineer sees stack questions, a marketer sees campaign-type questions). Decide whether the survey is required or optional. Required surveys gate app access until completed — they yield higher data quality but lower activation rates (a small percentage of users abandon at the survey). Optional surveys appear conditionally during first session and let users skip — they yield lower completion rates (40-70% typical) but no activation loss. Most teams use required for B2B SaaS where the activation tradeoff is acceptable for the personalization gain; optional for consumer apps where activation matters more than data completeness. Route the responses to three destinations simultaneously: product analytics (Mixpanel, PostHog, Amplitude — for cohort segmentation), CRM (HubSpot, Salesforce, Pipedrive, RD Station — for sales-side workflow when enterprise-ICP signals match), and in-app personalization (LaunchDarkly, Statsig, PostHog feature flags — for the actual first-session experience differentiation). Translate the survey into the languages of your audience — Spanish and Portuguese versions for products with growing Latin American user bases lift completion rates 20-40% in those segments.

User onboarding survey FAQ

4-7 questions for most products. Below 4, you're not collecting enough signal to differentiate personalization meaningfully. Above 7, completion rates drop sharply (often by 50%+ at 10 questions vs. 6) and the responses you do get are biased toward users with unusually high motivation. The right cuts are usually conditional logic: instead of asking everyone 12 questions, ask everyone 5 core questions and use conditional follow-ups to gather depth only where the user's answers indicate it matters. For B2B SaaS, 5-6 questions is typical (role / team size / primary use case / prior tool / source / optional company name). For consumer apps, 3-5 questions is typical (goal / level / frequency / optional preference). For developer tools and AI products, conditional question paths can support up to 7-8 questions because the audience tolerates more depth in exchange for highly personalized first-session experiences.
Required for B2B SaaS where the personalization payoff justifies the activation tradeoff; optional for consumer apps where activation matters more than data completeness. The math: required surveys block app access until completed, which means a small percentage (typically 5-15%) of new signups abandon at the survey, but the completion rate for users who continue is 95%+ and the data quality is high. Optional surveys appear conditionally in the first session (often as a modal or in-app banner with a skip option), which means no activation loss but completion rates of 40-70% and selection bias toward more engaged users. Hybrid pattern: require 2-3 critical questions (role, team size) to gate app access for B2B segmentation, and surface the remaining 3-5 questions as optional follow-ups during first session. Most teams settle on required for B2B SaaS with the survey kept short (4-5 questions); optional for consumer products with the survey kept very short (3-4 questions).
Depends on whether you've decided the survey is required. If required: yes, the survey blocks the first navigation to the app dashboard until the user completes it. This is the standard pattern for Notion, Linear, Figma, Vercel, Stripe, and most B2B SaaS. If optional: the app loads to the dashboard normally, and the survey appears as a modal on first navigation or as an in-app banner that the user can dismiss. The decision should follow the required-vs-optional choice rather than be a separate decision. A pattern to avoid: requiring the survey but allowing skip via a small "skip for now" link — this combines the activation hit of required (users frustrated by the gate) with the data sparsity of optional (most users skip). Either commit to the gate or commit to optional; the mushy middle yields the worst of both.
All three, simultaneously, via webhook fan-out. Product analytics (Mixpanel, PostHog, Amplitude, Segment as the routing layer) receives user-property events that tag the user's cohort — this drives activation and retention dashboards, A/B-test segmentation, and product-team prioritization. CRM (HubSpot, Salesforce, Pipedrive, RD Station for Brazilian B2B programs) receives the contact-segmentation data — this drives email-marketing sequence segmentation, sales-team alerts when enterprise-ICP signals match (role = decision-maker title + team size = 200+ + industry = target vertical), and the lifecycle stage transitions. In-app personalization (LaunchDarkly, Statsig, PostHog feature flags, or your own user-property storage) receives the actionable subset — this drives template-gallery filtering, onboarding-flow branching, tooltip visibility, and the entire first-session experience. The webhook fan-out is the right architecture because the three systems have different data models, retention policies, and access controls; sending the same survey response to all three lets each system store and use what it needs.
Three layers worth testing. Question wording (the same field can be phrased multiple ways — "What's your role?" vs. "What do you do?" vs. "How would you describe your work?" — and the response distribution shifts meaningfully with phrasing). Answer options (the choices you offer determine the buckets, and adding or removing options changes the distribution and the activation outcomes that follow). Required-vs-optional gating (the most impactful test, because it directly affects activation rates). For statistical significance, you typically need 1,000+ signups per variant per week for question-wording tests, 500+ for answer-option tests, and 200+ per variant for the activation-rate impact of required-vs-optional. Most teams use Statsig, PostHog, Optimizely, LaunchDarkly Experimentation, or VWO for the testing infrastructure. The metric to optimize is downstream — activation rate at day-3 or day-7, not survey completion rate. A survey with 60% completion that leads to 40% activation beats a survey with 90% completion that leads to 30% activation.

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