Sprig

Overview

Sprig is an in-product survey and user research platform that helps product teams capture qualitative feedback during real product use, analyze open-text responses with AI-powered theme clustering, and watch session replays showing user behavior alongside survey responses. The no-code platform launches studies in minutes without engineering resources, achieving 30%+ response rates vs 2-5% for email surveys by capturing feedback in-context.

Used by product teams at Coinbase, Loom, and Figma, Sprig serves SMB SaaS companies and tech startups with 1,000-25,000 monthly active users (MAU) needing continuous user feedback to prioritize product roadmap, optimize onboarding flows, and understand feature adoption without manual research analysis or dedicated UX researchers.

At a glance:

• Category: User Research & Product Feedback
• Best for: Product teams 3-10 people at SaaS companies with 1K-25K active users
• Pricing: FREE-$175/mo (FREE: 1 survey/month + 5K MTUs, Starter: $175/mo + 25K MTUs)
• Free plan: 1 in-product survey or replay per month, up to 5K monthly tracked users
• Setup: 1-2 hours to install SDK and launch first in-product survey

Ideal For

• Product teams at PLG SaaS companies: Product managers and UX researchers at 3-10 person product teams with 1K-25K active users needing continuous feedback on feature requests, pain points, and usability - Sprig's 30%+ response rates and AI analysis deliver insights without dedicated research resources.
• Teams optimizing onboarding and activation: Growth-focused product teams using in-app surveys to understand where users get stuck during onboarding, why they don't activate, or what causes drop-off - session replays show behavior alongside feedback for root cause analysis.
• Lean startups validating product-market fit: Early-stage SaaS companies (100-5,000 users) testing product hypotheses with rapid user feedback loops - Sprig's no-code surveys launch studies in minutes vs weeks of user interviews, FREE plan validates use case before scaling investment.

Not Ideal For

• Large-scale consumer apps 100K+ MAU: Starter plan caps at 25K monthly tracked users (MTUs), Enterprise plan required for high-volume products - better alternatives exist for consumer scale (Hotjar $32-80/mo unlimited sessions, Pendo volume pricing).
• Teams needing quantitative product analytics: Sprig focuses on qualitative feedback (surveys, replays) vs quantitative metrics (funnels, cohorts, retention) - use Mixpanel ($0-899/mo) or Amplitude (FREE-$995/mo) for behavioral analytics, combine with Sprig for qualitative insights.
• Enterprise requiring sophisticated segmentation: Sprig's targeting is basic (user attributes, page URL, actions taken) vs advanced behavioral segmentation in Pendo (cohorts, usage frequency, feature adoption patterns) - enterprise teams need deeper targeting.

The Bottom Line

For SMB product teams (3-10 people) at SaaS companies with 1K-25K active users, Sprig delivers best-in-class in-product surveys and AI-powered feedback analysis at FREE-$175/mo. The 30%+ response rates (vs 2-5% email surveys) and automated theme clustering save 10-20 hours monthly of manual analysis. FREE plan (1 survey/month, 5K MTUs) validates use case, Starter plan ($175/mo, 25K MTUs) sweet spot for SMB teams. Pair with Mixpanel/Amplitude for quantitative analytics to get complete product insights.

Sprig helps product teams capture continuous user feedback through in-product surveys triggered during real product use, analyze open-text responses with AI-powered theme clustering, and watch session replays showing user behavior alongside feedback responses. The platform surfaces actionable insights (feature requests, pain points, usability issues) without manual analysis or dedicated research resources, enabling data-driven product decisions at 10X faster speed than traditional user research.

Core Capabilities

1. In-Product Surveys (30%+ Response Rates): Launch surveys inside website or mobile app triggered by user actions (completed onboarding, used feature 5 times, visited pricing page) - captures feedback in-context achieving 30%+ response rates vs 2-5% for email surveys sent days later.
2. Sprig AI Theme Clustering: Automatically analyzes open-text survey responses clustering similar feedback into clear themes and patterns - reduces manual analysis time 80% by surfacing top pain points, feature requests, and usability issues without reading hundreds of individual responses.
3. Session Replays + Feedback: Watch video recordings of user sessions showing mouse movements, clicks, and scrolling alongside survey responses - understand not just what users say but what they do, identifying behavior patterns causing frustration or confusion.
4. No-Code Survey Builder: Create and launch in-product surveys in minutes using visual builder with question templates, logic branching, and customizable targeting - no engineering required, product managers deploy studies without waiting for dev resources.
5. 20+ Integrations: Connects with Segment, Mixpanel, Amplitude, Jira, Slack, Intercom to trigger surveys based on behavioral data, sync feedback to product analytics, create tickets from feature requests, and notify teams of urgent feedback.

How It Works

1. Install Sprig SDK on website (JavaScript) or mobile app (iOS/Android native SDKs) - 30-60 minute setup, no ongoing engineering support required
2. Create in-product survey using no-code builder with question templates (NPS, feature request, usability testing, exit intent) and custom branding matching product UI
3. Configure targeting to show survey to specific users based on actions (completed onboarding), attributes (paid vs free), or page views (visited pricing 3 times)
4. Survey launches automatically when users meet targeting criteria, capturing responses in real-time with optional session replay recording user behavior before/during survey interaction
5. Sprig AI analyzes responses clustering open-text feedback into themes (e.g., "Feature Request: Export to CSV - 47 responses", "Pain Point: Slow Loading - 32 responses") with sentiment scoring
6. Review insights dashboard showing top themes, response rates, session replays of key moments - export data to Jira (create tickets from feature requests), Slack (notify team of negative feedback), or product analytics tools

Our Take: Sprig is excellent for qualitative user research at SMB product teams with 1K-25K active users. The 30%+ response rates are game-changing vs 2-5% email surveys - capturing feedback in-context during product use drives 10X+ higher engagement. Sprig AI theme clustering saves 10-20 hours monthly of manual analysis for lean product teams without dedicated researchers. Downsides: 25K MTU cap on Starter ($175/mo) creates scaling ceiling, and quantitative analytics are limited (use Mixpanel/Amplitude alongside for complete picture). Best value at Starter plan for SMB SaaS with 5K-25K MAU needing continuous feedback loops to inform roadmap.

1. In-Product Surveys (30%+ Response Rates)

Launch surveys inside product triggered by user actions (completed feature X, visited page Y, clicked button Z) capturing feedback in-context during real use. No-code builder creates studies in minutes with question templates (NPS, multiple choice, open-text, Likert scale) and custom branding - achieves 30%+ response rates vs 2-5% for email surveys sent days later when context is lost.

Why it matters: Email surveys suffer from low response rates (2-5%) and recall bias (users don't remember experiences from days ago) - in-product surveys capture feedback immediately while experience is fresh, increasing response rates 10-15X and improving response quality with contextual memories vs generic "how was your experience?" questions.

2. Sprig AI Automated Theme Clustering

AI analyzes open-text survey responses automatically clustering similar feedback into themes and patterns with sentiment scoring. Reduces manual analysis time 80% by surfacing top pain points (e.g., "Slow Loading - 47 mentions, 85% negative sentiment") and feature requests (e.g., "CSV Export - 32 requests, 92% positive importance") without reading hundreds of individual responses.

Why it matters: Manual analysis of 100-500 open-text survey responses takes 5-15 hours monthly for product teams - Sprig AI does it in seconds, freeing product managers to act on insights vs spending time reading and categorizing responses. Critical for lean teams without dedicated UX researchers who need insights without analysis overhead.

3. Session Replays Alongside Survey Responses

Watch video recordings of user sessions showing mouse movements, clicks, scrolling, and rage clicks (rapid clicking indicating frustration) alongside survey responses. Understand not just what users say but what they do - identify behavior patterns causing confusion, frustration, or drop-off that users might not articulate in survey responses.

Why it matters: Users often can't articulate why they're frustrated ("it just feels slow") - session replays show exactly where they struggled (e.g., clicked button 5 times because loading state unclear, abandoned after error message with no explanation). Combining qualitative feedback (surveys) with behavioral data (replays) provides complete picture for debugging usability issues.

4. No-Code Survey Builder & Targeting

Create and launch in-product surveys in minutes using visual builder with question templates, logic branching (show question B only if user answered A with X), and customizable targeting (show survey when user completes onboarding, or visits pricing page 3 times, or uses feature X for first time). No engineering required - product managers deploy studies without dev resources.

Why it matters: Traditional user research requires weeks of coordination (researcher writes study, engineer implements survey, QA testing, deploy) - Sprig's no-code builder reduces time-to-insights from weeks to hours. Product teams iterate rapidly on hypotheses testing 5-10 studies monthly vs 1-2 quarterly research projects with engineering dependencies.

5. 20+ Integrations (Segment, Mixpanel, Amplitude, Jira, Slack)

Connects with product analytics platforms (Segment, Mixpanel, Amplitude) to trigger surveys based on behavioral data (user completed event X, is in cohort Y) and export survey responses for correlation with quantitative metrics. Creates Jira tickets automatically from feature requests, sends Slack notifications for negative feedback, syncs user attributes from Intercom for personalized targeting.

Why it matters: Isolated survey tool generates feedback in vacuum without behavioral context - Sprig's integrations combine qualitative feedback (what users say) with quantitative data (what users do) for complete product insights. Example: Correlate NPS scores with usage frequency in Mixpanel to identify "power users hate feature X despite high usage" vs "casual users love it" insights informing roadmap prioritization.

1. Optimize Onboarding Funnel with In-App Surveys

Scenario: SaaS company with 5,000 trial users and 20% activation rate (users completing onboarding and experiencing "aha moment") wants to understand why 80% drop off before activation to improve product-led growth.

Workflow:

1. Product team creates in-product survey triggered when users abandon onboarding (exit page, close tab, idle 5 minutes without completing step)
2. Survey asks 2 questions: "What stopped you from completing setup?" (open-text) and "How could we make this easier?" (open-text)
3. Sprig captures 250 responses over 2 weeks (30%+ response rate from 800 abandoners)
4. Sprig AI clusters responses into themes: "Unclear instructions - 87 responses", "Too many steps - 64 responses", "Missing feature I need - 43 responses", "Technical errors - 31 responses"
5. Session replays show users struggling with specific onboarding steps (e.g., 40% drop off at integrations screen when encountering OAuth errors)
6. Product team prioritizes fixes: simplify instructions (+15% completion), reduce steps from 7 to 4 (+12%), fix OAuth errors (+8%), defer "missing features" to sales-led conversations
7. Activation rate increases from 20% to 35% (55% improvement) over 6 weeks based on survey insights and replay analysis

Outcome: Identified root causes of 80% onboarding drop-off through in-app surveys capturing feedback from abandoners in real-time. Sprig AI surfaced top 4 themes from 250 responses in seconds vs 10+ hours manual analysis. Activation improvement 20% → 35% (55% increase) drove 75% more activated trial users for sales team to convert.

2. Prioritize Product Roadmap with Feature Request Analysis

Scenario: Product team at 10,000 user B2B SaaS receives 500+ feature requests monthly across support tickets, sales calls, and email - needs systematic way to quantify demand, cluster similar requests, and prioritize roadmap based on user voice vs founder intuition.

Workflow:

1. Product team launches continuous "Feature Request Survey" triggered when users complete core workflows (sent email campaign, analyzed report, exported data)
2. Survey asks "What feature would make [product] more valuable for you?" (open-text) and "How important is this to you?" (1-5 scale)
3. Sprig captures 200 feature requests monthly (30% response rate from 650 users completing workflows)
4. Sprig AI clusters requests into themes with importance scores: "CSV Export - 64 requests, 4.7/5 importance", "Advanced Filtering - 52 requests, 4.3/5", "Mobile App - 47 requests, 3.8/5", "Multi-User Collaboration - 41 requests, 4.5/5"
5. Product team cross-references feature themes with usage data in Mixpanel - "CSV Export" requested by 80% power users (high value segment), "Mobile App" requested by 90% free tier (low value)
6. Roadmap prioritization: Build CSV Export (high demand + high value users), Multi-User Collaboration (moderate demand + high importance), defer Mobile App (low value segment)
7. After CSV Export launch, 35% of requesters upgrade to paid plan within 30 days (feature-driven conversion)

Outcome: Systematized feature request collection through in-app surveys vs scattered feedback across channels. Sprig AI clustered 200 monthly requests into 15-20 themes with quantified demand and importance scores, eliminating founder intuition bias. CSV Export launch drove 35% conversion rate among requesters proving value of user-driven roadmap prioritization.

Monthly Tracked Users (MTUs): Unique users who load your product in a given month (regardless of survey participation)

Surveys vs Replays: Each plan includes combined limit for in-product surveys OR session replays (1 survey + 1 replay = 2 total on Starter)

What We Recommend

For small SaaS (1K-5K users): Free plan ($0, 1 survey/month, 5K MTUs) validates use case with limited but sufficient capacity for monthly NPS surveys or quarterly feature feedback studies - upgrade to Starter when you need continuous feedback (2+ surveys monthly).

For SMB SaaS (5K-25K users): Starter plan ($175/mo, 2 surveys/month, 25K MTUs) delivers best value for product teams needing continuous feedback loops - run NPS survey + feature request survey simultaneously, or alternate onboarding survey with retention survey monthly.

For larger products (25K+ users): Enterprise plan (custom pricing) required when you exceed 25K MTUs - contact sales for volume discounts and advanced features (API access, SSO, dedicated success manager).

ROI Calculation: If in-app surveys increase activation 10-20% (typical based on case studies), and you have 1,000 trial users monthly, that's 100-200 more activated users. At $50 LTV per activated user, that's $5K-10K monthly value vs $175/mo cost = 28-57X ROI. Pays for itself if you capture just 4 incremental activations monthly.

1. Sign up for FREE plan at sprig.com - 1 survey or replay per month, up to 5K MTUs validates use case before committing to paid plan

2. Install Sprig SDK on website (JavaScript snippet) or mobile app (iOS/Android native SDKs) - 30-60 minute setup, no ongoing engineering support required

3. Create first in-product survey using no-code builder - start with NPS survey or feature request template, customize questions and branding

4. Configure targeting to show survey to specific users (completed onboarding, visited pricing 3 times, used feature X) based on actions or attributes

5. Launch survey and monitor responses in real-time dashboard - Sprig AI automatically clusters open-text responses into themes as responses come in

6. Review insights showing top themes, session replays of key moments, and response rates - export data to Jira (create tickets from feature requests) or Slack (notify team of feedback)

Pro tip: Start with NPS survey triggered after users complete core workflow (sent first campaign, created first report) to measure satisfaction among activated users vs all users (includes never-activated). Use Sprig AI to cluster open-text "why did you give this score?" responses into themes showing top drivers of promoters vs detractors. Create Jira tickets from top detractor themes for prioritized fixes, track NPS improvement month-over-month as you address feedback.

• Sprig Website
• In-Product Surveys Overview
• Start Free Plan
• G2 Reviews
• TrustRadius Reviews
• Tekpon Reviews

• Mixpanel: Quantitative product analytics (funnels, cohorts, retention) pairs with Sprig's qualitative surveys - use Mixpanel to identify behavior patterns, Sprig to understand why users behave that way
• Segment: Customer data platform feeding behavioral events to Sprig for survey targeting - trigger surveys based on user actions (completed onboarding) and export survey responses to data warehouse
• Jira: Project management receiving feature requests and bug reports from Sprig surveys automatically - converts user feedback into actionable tickets with context and priority scoring