[{"data":1,"prerenderedAt":784},["ShallowReactive",2],{"/en-us/blog/three-faces-of-user-calls":3,"navigation-en-us":38,"banner-en-us":438,"footer-en-us":448,"blog-post-authors-en-us-Viktor Nagy":688,"blog-related-posts-en-us-three-faces-of-user-calls":702,"assessment-promotions-en-us":735,"next-steps-en-us":774},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":26,"isFeatured":12,"meta":27,"navigation":28,"path":29,"publishedDate":20,"seo":30,"stem":34,"tagSlugs":35,"__hash__":37},"blogPosts/en-us/blog/three-faces-of-user-calls.yml","Three Faces Of User Calls",[7],"viktor-nagy",null,"product",{"slug":11,"featured":12,"template":13},"three-faces-of-user-calls",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How product managers can get more out of user calls","There are 3 types of user calls. Here's how GitLab product managers approach them and how we leverage our transparency value to better understand our users.",[18],"Viktor Nagy","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749682372/Blog/Hero%20Images/michal-czyz-ALM7RNZuDH8-unsplash.jpg","2022-07-20","\n\nOne of the core jobs of product managers is to speak with users to better understand their needs, pain points and the context in which they operate and use our products. But not all user calls are the same. \n\nThere are 3 prominent types of user calls:\n\n- Discovery or problem validation calls\n- Roadmap discussions\n- Solution validation calls\n\nHere's an in-depth look at how we approach the three types of user calls at GitLab.\n\n## Discovery calls\n\nDiscovery or problem validation calls are product managers' most crucial conversations with users. Discovery calls are typically set up to learn about our users in a targeted way. These calls help build a better understanding of users' pain points. \n\nFor discovery, we need a recipe for repeatable, comparable user calls. For this reason, we should create an interview script and follow that script on all the user calls. This does not mean these calls are robotic and devoid of improvisation, not at all! The script should provide the backbone of the discussions. We can adjust it either during the call or in advance based on prior knowledge about the user. Good discovery calls typically take the form of a deep-dive conversation: we know the script by heart and can run back and forth around it, always asking the questions that fit the conversation. \n\nFinding the right users is one of the most challenging parts of discovery calls. Thankfully, with GitLab, this is relatively easy. We can always reach out to the most active users on issues and invite them to a call. Another technique I employ is to find users in the [Cloud Native Computing Foundation](https://www.cncf.io) and Kubernetes communities' Slack channels and articles on [Medium](https://medium.com). This way, I can also find non-GitLab users, a set of people likely more valuable to interview than existing users. Finally, we can recruit users with the support of the account managers. They are always helpful in connecting PMs with users. Asking the users about their needs shows them that we genuinely care about them.\n\nThere are at least two distinct discovery calls: PM-led or UX-led. UX research typically works on projects with a strict scope. For PM-driven calls, a great framework is [\"Continuous discovery\" calls by Teresa Torres](https://www.producttalk.org/continuous-discovery/). With continuous discovery, we build a deep understanding of our users and get well-understood opportunities. The technique allows us to get a broad view and to dive deep into specific aspects of our problem space when needed.\n\n## Roadmap discussions\n\nRoadmap discussion calls are typically initiated by sales or account management teams. Product managers are asked to join the prospect/customer call to strengthen our positions and show how much we care for the customer. \n\nTo prepare for roadmap discussions, PMs should have an effective way to present the roadmap. This typically happens in the form of slides. A diligent PM might even prepare something specifically for the client.\n\nDuring these calls, the user/customer/prospect will typically ask the questions, and the PMs respond. Our role in these calls is to represent the truth. We might be tempted to paint a rosier picture about the current or expected state of the product than is actually true, and we should avoid making time-bound promises.\n\nWhat are the expected outcomes of roadmap discussions? They can help strengthen our position with the user. Remember that these calls primarily cater to our customers/users and customer-facing teams. As such, they are unlikely to provide deep learning about our users. \n\nIf we approach these calls with the intention to prove that our roadmap is correct, we will likely fall victim to both response and confirmation biases. There are techniques to validate a roadmap, but they are more aligned with problem validation than roadmap discussion calls. For example, UX researchers should be able to help validate a roadmap as a UX research project.\n\n## Solution validation calls\n\nLast but not least, we have solution validation calls. These calls serve our learning but are way more focused than discovery calls. Solution validation calls require some form of a prototype for a specific problem we want to test and get feedback on from our users.\n\nAt GitLab, the prototypes are typically built by product design or engineering. The product manager might miss some of these calls in an empowered and autonomous team. But, as these calls are great learning experiences, we should aim to be there to support and learn if we can.\n\nA solution validation call might be started with a concise roadmap discussion. Unlike in sales calls, our aim is not to influence the user but to set the scene for solution validation. The central part of the call should be around the proposed solution. We should provide the least amount of guidance to our users since there are no humans available to direct our users when they are working with the actual product. If much guidance is required, that is a sign that we might want to rethink our UX approach.\n\nFinding suitable interview candidates for a solution validation call might be tricky. For GitLab, we often use the shortcut of inviting users based on their activity on relevant issues. Sometimes, when our issues provide enough context, we might get some solution validation asynchronously as users give their feedback directly in the issue.\n\n## How many calls?\n\nHow often does a good PM have all these calls? For discovery calls, I aim to have 3 calls per week. Above this, I don’t mind taking 1 sales call. While I prefer the product designer to run solution validation calls, I try to participate there too. Not every solution requires dedicated validation, so having a target number for solution validation calls is unrealistic. The better the discovery calls are, the fewer solution validation calls you might need. Still, even the best discovery cannot and should not answer all the questions of a solution validation. Often there are different (and totally valid) approaches to the same problem, and we need to pick the one that is the easiest for users to understand.\n\nI think we need to speak to our users every day. Working at GitLab, sometimes this might take the form of issue comments, but face-to-face calls are a must. In any case, during these discussions we should aim to learn from our users, not just answer their questions. A handy question in issues is to ask for more context from our users. The response might highlight unknown use cases or edge cases we missed previously.\n\n## Take the calls\n\nIt is helpful to remember all the user call types we practice as PMs. As mentioned, I think the most crucial user calls for PMs are the discovery calls. If we don’t make discovery calls, nobody will; also, PMs might not be needed in the other calls. That said, a product manager's job is to also help the business be viable. So we should be able to support sales and always have a deck ready for roadmap calls. 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GitLab metrics and registry features help reduce CI/CD bottlenecks","See how CI/CD Job Performance Metrics and Container Virtual Registry, currently in beta, help platform teams quickly spot slow jobs and simplify multi-registry container pulls.",[708],"Talia Armato-Helle","https://res.cloudinary.com/about-gitlab-com/image/upload/v1771438388/t6sts5qw4z8561gtlxiq.png","Platform and DevOps engineers spend too much time piecing together visibility across fragmented tools and managing infrastructure that should just work.\n\nTwo new GitLab features currently in beta tackle this from different angles but share the same goal: giving practitioners direct control over the CI/CD infrastructure they depend on, without adding another third-party tool. One surfaces job-level performance data right where you monitor pipelines. The other simplifies how you pull container images from multiple registries with built-in caching.\n\nBoth features are open for feedback now. Your input will help shape what ships next.\n\n## CI/CD Job Performance Metrics\n\n* **Available tiers:** GitLab Premium, GitLab Ultimate\n* **Status:** Limited-availability beta on GitLab.com; available on GitLab Self-Managed and GitLab Dedicated when ClickHouse is configured\n\nToday, there’s no simple way to see when a particular job’s duration starts increasing or which jobs are quietly dragging down your pipeline runtimes. Most teams either build custom dashboards or manually dig through logs to answer basic questions like:\n\n* Which jobs are slowest?  \n* Where are failure rates climbing?  \n* Which stage is the real bottleneck?\n\nCI/CD Job Performance Metrics changes that by adding a new job-focused panel to the CI/CD analytics page at the project level.\n\nFor each job in your pipelines, you can see:\n\n* Typical (P50, median) and worst‑case (P95) job duration, so you can quickly view normal versus slowest runs  \n* Failure rate, so you can spot fragile or flaky jobs  \n* Job name and stage, covering the last 30 days by default\n\nThe table is sortable, searchable by job name, and paginated, so platform teams get a single view to answer questions that previously required separate tools or custom reporting.\n\n**Try it now**\n\n* Navigate to your project and select **Analyze \\> CI/CD analytics**.  \n* Look for the CI/CD job performance metrics panel and sort by duration or failure rate to find your slowest or least reliable jobs.\n\n**Documentation**\n\n* [CI/CD analytics – CI/CD job performance metrics](https://docs.gitlab.com/user/analytics/ci_cd_analytics/#cicd-job-performance-metrics)\n\n**What’s coming next**\n\nWe’re working on stage-level grouping, so you can view aggregated metrics across your build, test, and deploy stages, and quickly understand where to focus optimization work.\n\n**Share your feedback:**\n\n* [CI/CD job performance metrics epic](https://gitlab.com/groups/gitlab-org/-/work_items/18548)\n\n## Container Virtual Registry\n\n**Tier:** GitLab Premium, GitLab Ultimate\n**Status:** Beta, API-ready in 18.9\n\nMost organizations pulling container images into CI/CD pipelines rely on multiple registries: Docker Hub, Harbor, Quay, and internal registries, to name a few. Managing authentication, availability, and caching across all of them is operational overhead that slows pipelines down and introduces fragility.\n\nThe Container Virtual Registry lets you create a single GitLab endpoint that pulls from multiple upstream container sources with built-in caching.\n\nInstead of configuring credentials and availability for each registry individually in your pipeline configuration, you can:\n\n* Point your pipelines at one GitLab virtual registry endpoint  \n* Configure multiple upstream registries (Docker Hub, Harbor, Quay, and others using long-lived token authentication)  \n* Let GitLab resolve image pulls automatically, with pull-through caching to reduce bandwidth costs and improve reliability\n\nFor teams evaluating GitLab as a container registry replacement, this closes a critical capability gap. For teams already managing multi-registry container workflows, it centralizes image management into GitLab and cuts down on repeated pulls.\n\n**What the beta supports today**\n\n* Upstream registries using long-lived token authentication: Docker Hub, Harbor, Quay, and other compatible registries  \n* Pull-through caching so commonly used images are served from GitLab after the first pull  \n* API-first configuration, with UI management in progress++\n\nCloud provider registries requiring IAM authentication (such as Amazon Elastic Container Registry, Google Artifact Registry, and Azure Container Registry) are being considered for future iterations.\n\n**Test it today**\n\n* The Container Virtual Registry is API-ready in 18.9.  \n* SaaS (GitLab.com): Request access through your CSM or by commenting on the feedback issue below to have the feature flag enabled for your group.  \n* Self-managed: Enable the feature flag and configure the virtual registry using the API.\n\n**Documentation**\n\n* [Container Virtual Registry API](https://docs.gitlab.com/api/container_virtual_registries/)  \n* [Pull container images from the virtual registry](https://docs.gitlab.com/user/packages/virtual_registry/container/#pull-container-images-from-the-virtual-registry)\n\n\n Watch this walkthrough of the Container Virtual Registry Beta:\n   \n\n  \u003Ciframe src=\"https://player.vimeo.com/video/1167512082?title=0&byline=0&portrait=0&badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"20260223_Container Virtual Registry Beta_V1\">\u003C/iframe>\u003C\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n  \u003Cbr>\u003C/br>\n\n\n\n**Share your feedback:**\n\n* [Container virtual registry feedback issue](https://gitlab.com/gitlab-org/gitlab/-/issues/589630)\n\n## Help us build what matters\n\nEveryone in the GitLab community is a contributor. We built these betas based on community requests.\n\n* **CI/CD Job Performance Metrics** came from teams who had no easy way to see when build times started trending in the wrong direction, or which jobs were hurting pipeline reliability.  \n* **Container Virtual Registry** came from enterprise customers managing multiple registries and looking to reduce tool sprawl and bandwidth costs while evaluating GitLab as a central registry.\n\nYour feedback shapes what we create next. Try one or both of these betas, and share your experience in the linked feedback issues.\n\nThis is the first in a series of Core DevOps betas we plan to highlight. 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