Sprig

Today, we're proud to launch Sprig AI Recommendations, the final step in our Sprig 2.0 vision. With Sprig AI as your product partner, you can now generate specific, actionable recommendations to achieve your goals. Sprig AI Recommendations empowers you to: ???Get instant, data-backed solutions to optimize your product. ???Make informed roadmap decisions with confidence. ???Boost key metrics with actionable, real-time insights. Whether you're trying to improve onboarding, increase conversion, or enhance usability, AI Recommendations provides a powerful way to know exactly what to do next in your mission to build products your users love. Start generating tailored solutions at scale—create a free Sprig account today! ?? Try it out here: https://lnkd.in/gpRixWcT

Here’s an actual use case on AI saving a product team hours and hours each week (without losing output quality): Roger Liu, former Director of Product at Novo, uses Sprig AI to automatically extract insights from survey data, a process that took countless hours before Sprig. Without Sprig AI, Roger would have to run a survey, download a CSV of all the responses, and run his own script in hopes of finding some themes and takeaways. With Sprig AI, Roger just has to log into his account and Sprig AI has already prepared a holistic, real-time view of survey responses, key themes, and product recommendations based on them. There is no manual work required. The time it takes him to enter his email and password into Sprig is the time it takes him to get actionable insights. It’s 2025. If you’re not leveraging AI for high-scale data analysis, you’re making your job unnecessarily complicated. Thank you to Roger Liu and Cecilia Liu for participating in this webinar!

Here’s one of our favorite real-life user adoption case studies (resulting in a +194% boost in feature adoption): Chipper Cash, a fintech platform, struggled to gain adoption for its cryptocurrency feature. They needed to figure out why this was the case. Here’s what they did: 1. Break down key questions The team segmented the challenge into smaller, actionable questions, such as: - Are users familiar with cryptocurrency? - Do users know Chipper offers a cryptocurrency feature? - What prevents users from trying it? 2. Deploy in-product surveys Chipper launched targeted, in-product surveys to collect real-time insights directly from active users with Sprig. These surveys achieved high response rates, offering immediate feedback on feature awareness and sentiment. 3. Analyze results at scale with AI Sprig AI’s analysis of the survey results revealed that 58% of Ugandan respondents were unaware of the cryptocurrency feature. Another subset of customers expressed hesitation due to limited knowledge about cryptocurrency. 4. Act on insights Now that they knew what the issue was, the Chipper Cash team had to act on it. They invested in targeted awareness campaigns via social media and in-app messaging. Additionally, they launched a cryptocurrency education initiative, offering resources to help users understand Bitcoin, Ethereum, and basic investing concepts. 5. Iterate based on metrics The team monitored feature adoption rates and iterated based on performance data. As a result of these data-driven product decisions, adoption of their cryptocurrency feature increased by 194%.

Invoice2go increased onboarding completion by 25% with Sprig. Here’s how: Invoice2go is an all-in-one app for small businesses to get paid fast by customers and manage cash flow. They prioritized user research early on—the company had several UXRs on staff before they reached 100 team members! While expanding from an invoicing platform to a full-service solution, the company launched Invoice2go Money—a banking solution enabled by an integration with Plaid. Early on, though, the team saw a problem: Customers were dropping off after signing up for Invoice2go Money. Here’s how they solved their user drop-off issue: 1. Determined there was an issue in the first place Customers had to go through a new set of flows to use the payment and banking capabilities. However, the team quickly noticed a stark drop-off between users applying for the card payment offering, getting approved, and actually using it. They needed to find the obstacle. 2. Launched an in?product Sprig survey to understand where the obstacle was The team had a hunch: Using Plaid to link a bank account presented an issue to users, causing the drop-off. To collect data on this, they launched an in-product survey with Sprig, targeting users who applied for the new card payment system, were approved, but didn’t actually start using the app. This was the best plan of action to isolate the customers they wanted to reach. 3. Used the survey results to validate their hypothesis The survey included an open-text question to get feedback on areas for improvement and friction. As expected, 30% of users cited Plaid and uncertainty with the platform as reasons they didn’t provide bank details. Additional issues with usability and awareness also presented themselves here. The Results: Feature update decreased drop?off and increased completion by 25%. With answers from the in-product Sprig surveys, Invoice2Go quickly introduced a new flow to mitigate these issues by increasing awareness of Plaid and improving usability. If you want to replicate their success: 1. Target the right users with in-product surveys triggered at critical points in their journey. Invoice2go targeted users who had begun but not completed the process of implementing card payments. By asking only those users, they could understand the exact concerns standing in the way. 2. Use a quantitative approach to qualitative research. Invoice2go surfaced qualitative insights at a quantitative scale by asking open-ended questions to validate hypotheses. Sprig AI can analyze these responses to extract the top themes. 3. Use research to prioritize the roadmap and make iterative improvements to existing products. Hunches—even strong ones—aren’t enough to invest time, energy, and resources into a project. Validating that hypothesis with data lets you prioritize the roadmap with conviction. We hope you found this helpful! Let us know if you have any questions.

You can’t improve your product without understanding users’ wants and needs. Here’s the easiest way to understand them: The key for UXRs and product teams is to seamlessly gather this valuable feedback without disrupting the product experience or burdening their team with excessive effort. The best way to do this is to launch a Sprig Feedback study. This will allow you to capture continuous feedback directly within your product or website, keeping you constantly informed of user needs. The template shown below is free and can be easily installed wherever you want on your site. For the questions themselves, we encourage you to keep them at a higher level because you’re addressing a larger audience. Some example questions: - How satisfied are you with our products/services? - On a scale of 1 to 10, how would you rate your overall experience with our brand? - On a scale of 1 to 10, how would you rate the product experience? Collection is really only the first step, though. Analysis is the second. Here are some tips for analyzing product feedback results that will yield the most actionable insights: 1. Uncover trends and patterns: Examine your data to identify patterns and trends that reflect areas of opportunity across your product (Sprig AI helps with this). 2. Put results in context: Think about how things have changed since the last time you researched similar questions. Also, consider whether your findings are related to market changes or other research within your company. 3. Visualize results: Use dashboards to help your team understand complex information and effectively communicate results and next steps. These insights can help you make informed product decisions, improve the user experience, and optimize key metrics like conversion and adoptions. If you have any questions, feel free to leave a comment below!

We spent years developing our AI Analysis for Surveys to empower research, design, and product teams. The result? The easiest way to analyze survey answers and generate relevant insights. Below, you’ll see a demo of Sprig AI Analysis with a sample survey. Key highlights: (1) Open-text analysis Sprig AI groups open-text answers into the top themes, making it easy for your team to understand the survey takeaways without manually reading through endless responses. Plus, you can click on each theme to view the specific responses that contribute to it. But Sprig AI’s power doesn’t end there. (2) Sprig AI Insights Sprig AI generates custom summaries of your survey results tailored to your specific study goal. Based on that survey data, it also extracts actionable opportunities, trends, correlations, and strengths for your product. In short, it’s easier and faster than ever for UXRs and product teams to get relevant, timely survey insights. If you want to see more on this, book a free demo from our bio.

?? Companies like Ramp use Sprig AI to analyze product feedback data and provide insights to product teams based off them. If you’re looking to make informed and impactful product decisions, improve your customer journey, and optimize key product metrics, you should use it too. Solving product challenges is easy with Sprig AI Insights. The process couldn’t be more simple: 1. Instantly launch a Sprig study Sprig AI will generate targeted Replay, Heatmap, Survey, and Feedback studies across your product to capture user behavior and feedback data. From there… 2. Sprig AI will surface product challenges. You can automatically see your study's most valuable product takeaways, including product opportunities, correlations, trends, and strengths. 3. You can take action with Sprig’s guidance. Review Sprig AI’s custom insights for each opportunity to see exactly what product changes to make to improve usability, conversion, and more. Importantly, the insights automatically update as new feedback comes in—ensuring the insights?you see are always up to date. After that, act on it as you see fit! We hope you find this helpful! Head to our site to read more about Sprig AI.

?? Introducing Always-On Replays! We’re excited to announce Always-On Replays, bringing continuous, AI-powered session insights to your product experience! With Always-On Replays, you can: ?? Automatically recreate user sessions across your entire product ?? Leverage AI-powered insights to surface friction points instantly ?? Organize replays effortlessly by product area, user segment, or interaction type No matter which part of your product you're optimizing—onboarding, checkout, or beyond—Always-On Replays gives you the full picture so you can make product decisions with confidence. ?? Now in beta! Be among the first to try it—link in the comments below!

We’ve rounded up the modern UXR tool stack for 2025! See if your favorites made the list: Research Repositories → Dovetail, Notion ?? Dovetail makes a team’s research insights easily accessible in a searchable hub. Plus, their active Slack community is a great resource for research-related and Dovetail-specific questions. ?? Notion feels like a bullet journal reimagined as a powerful, wildly successful tech product. Researchers can leverage its intuitive, easy-to-organize workspace for data management, including research reports and insights. It’s also a great option for user research libraries that need a structured hierarchy of folders and documents. Participant Management → User Interviews ?? User Interviews enables researchers and UX designers to seamlessly recruit quality participants for studies, with advanced targeting options based on job titles, skills, and consumer behavior. (Check out our integration!) Unmoderated Research → Sprig ?? Sprig powers targeted in-product surveys, session replays, and heatmaps, helping research teams quickly capture in-context user sentiment and behavior directly within their product. With Sprig AI, teams can instantly analyze responses and session clips to uncover patterns and extract key insights—turning raw feedback into real-time, actionable intelligence. It’s the fastest way to get deep, in-the-moment user insights while engaging participants directly within the product experience. Moderated Research → Grain ?? Grain makes it easy for researchers to record, transcribe, and clip key moments from research interviews, sales calls, and customer meetings—transforming conversations into actionable insights. We hope this roundup is helpful! What’s in your UXR tool stack? Let us know in the comments! ??

?? Did you know you can instantly analyze user feedback and behavior across your entire product experience to get holistic insights that drive smarter, faster product decisions? …only if you use Sprig AI Explorer, to be fair. The process is simple. All you have to do is: 1. Launch Sprig in-product studies in just a few clicks 2. Generate your Sprig AI Explorer report 3. Watch Sprig AI reveal actionable insights across your product 4. Explore the experience data behind each insight From there, you can make product decisions based on what your users want—because they told you they did. Check out this brief demo of how it works below!