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Welcome to the GitLab Product Analytics category direction.
The Product Analytics group vision is in alignment and contributes to the Monitor Stage vision and simply stated is ". . to continue to extend DevOps across its most painful gap - measuring user value." Giving teams the tools they need to stay user-focused can have positive impact on their performance, job satisfaction and productivity as noted in the Accelerate State of DevOps 2023.
GitLab's Product Analytics feature empowers businesses building applications to make data-driven decisions, optimize user experiences, drive growth, and stay competitive by focusing on customer value. Product Analytics provides key information about who your users are, how they behave, their common adoption patterns and workflows, and any friction points in key business critical funnels through your application. By keeping this information close to your development teams, they can quickly see how their code translates to user outcomes.
Combining Product Analytics with the comprehensive suite of tools offered by GitLab’s One Platform yields a multitude of benefits for teams and organizations. By leveraging Product Analytics alongside GitLab's robust version control, issue tracking, CI/CD pipelines, and collaboration features, teams gain unprecedented visibility into the entire secure software development lifecycle. They can seamlessly track user behavior, gather valuable adoption insights, and make data-driven decisions right from within their development environment. This integration empowers teams to iterate rapidly, prioritize features effectively, and optimize user experiences based on real-time feedback. Additionally, by centralizing data within GitLab, teams can streamline communication, foster collaboration, and ensure alignment across departments, ultimately driving greater efficiency and innovation throughout the development process.
Product managers can track an issue all the way from identification to release, and now go a step further to validate that the bug resolution or new feature has the expected customer impact, all without leaving the GitLab platform. The same goes for the development team, who now no longer need access to a separate tool to ensure that the code they are writing has the desired effect on the user outcomes.
With our GA release of Product Analytics, we also aim to simplify the instrumentation process that is a common pain point for customers. We are collaborating with the Analytics Instrumentation Group who are providing interfaces to developers to enable the gathering of insights. And, by storing dashboard configuration-as-code (CaC), we can follow developer best practices for iterative development and version control of their custom visualizations. In addition to the benefits mentioned previously, our ability to store the user's application code presents a significant longer-term opportunity. By leveraging this contextual information, we can offer more comprehensive analytics solutions than any other product. For instance, we could explore the possibility of suggesting or automatically adding analytics instrumentation code to different parts of the app. While this requires extensive research, one potential outcome could be direct suggestions within merge requests (MRs) along with code snippets to automatically instrument newly added sections of code.
Finally, GitLab's unique capabilities with respect to AI provide opportunities that will be difficult for other companies to replicate. GitLab Duo and other AI features will give us the ability to help address pain points we've identified as well as helping improve user workflows. Some ideas around this relate to offering automatic code for instrumentation, automated suggestions for reporting and charting of collected data, and providing a question-and-answer interface for engaging with the data. Since AI is so new, we are actively exploring this area and tracking it in this epic.
There are many use cases for Product Analytics. One way you can think about these is to segment them by the type of digital product analyzes and the subsequent questions those who create it would seek to be answer.
Our initial use case is focused on our ability to dogfood GitLab’s own applications. Our initial starting criteria for a customer profile is that they:
The ideal customers for Product Analytics are customers already invested in the GitLab Ultimate DevSecOps platform. Customers may be exploring GitLab's Product Analytics offering for various reasons. They may be on the journey of tool consolidation and see the vision of how the GitLab One Platform can streamline their workflows. Other customers may already have invested in a solution like MixPanel or Amplitude, but feel like it is a siloed tool (meaning that only a PM or product leadership reviews the data) and are looking to reevaluate the way they integrate this data into their day to day analysis. Finally, they may have no product analytics tracking today, leaving them with a blindspot around customer behavior and adoption trends that are critical to building a data-driven roadmap for their applications. Any of these use cases make them an ideal candidate for evaluating Product Analytics.
Understanding the relationship between how your system operates and how your customers perceive the value of that system is critical to maintaining an optimized environment. Because GitLab is uniquely positioned to collect, store, and visualize data from multiple sources, we can provide an end-to-end solution for monitoring that complete story.
Note that while the above use cases and personas were not the initial focus of Product Analytics, we see the unique opportunity GitLab platform has to provide a unified visualization experience that combines system health and customer value.
Our Strategy to achieve this vision is to start by helping users store, query and visualize quantitative data to measure user value. We will collaborate with the Analytics Instrumentation team to give users the tools they need to instrument and collect data from deployed applications.
Given our focus on developers, the software delivery value stream, and DevOps - we will compose our new DevOps stage, Analyze, based on the set of categories we commonly see in User Engagement competitors.
Currently the Product Analytics group includes two categories.
In the future we envision more categories as we broaden our scope to cover additional personas and user cases. Those new categories include:
There are a number of existing (or considered) product categories in GitLab that could be considered part of the outer loop that the Analytics section will partner closely with to ensure we provide a cohesive experience. Those include:
We plan to collaborate and build with these categories where possible, rather than re-inventing new solutions for these related use cases.
The Product Analytics team is now focused on expanding the data visualization capabilities to handle more complex, cross-stage use cases that include Observability and Value Stream data sources. We are working to add table stakes dashboard features that empower our customers to query data from multiple sources in a unified manner, thus unlocking the ability to tell an end-to-end, system health-to-customer value story in one space.
Additionally, the team is evaluating our deployment options, architecture, and infrastructure options to ensure that customers have a seamless experience when onboarding both Observability and Product Analytics.
Some of these features to improve the overall experience are:
We will respond to feedback to ensure we maintain a focus on building a "Lovable" product.
With release 16.11, Product Analytics capabilities became generally available for all users to take advantage of, just like any other capability within GitLab Ultimate. Our minimal feature set for GA includes:
Our first iterations were focused on an internal preview of Product Analytics that we can dogfood, and we will continue to pursue this model throughout the company. This let us work through the technical questions of how to best develop Product Analytics, how to host and maintain relevant infrastructure, as well as how to use it like an end-user would. This culminated with us adding Product Analytics to the internal handbook, Pajamas, Metrics Dictionary, docs, and VersionApp for dogfooding purposes. You can also see our end-to-end walkthrough video or check out the 16.11 LinkedIn Live event for a demo!
Due to the heavy emphasis on SaaS and the high data volumes - most pricing in this market is consumption-based.
Our Performance Indicator is Weekly Active Users defined as users viewing a dashboard and is being tracked in Sisense (internal link).
We are conducting research on critical jobs to be done for the Analytics section.
The existing personas we serve are below, in priority order. We will likely need additional personas in the future.
Some nuance can be added to our personas and how we approach them. Nearly all analytics questions, workflows, funnels, or any metrics gathering will require technical work to add instrumentation, test, and deploy it. This is the reason we are focusing on Sasha as our primary persona before Parker. We are addressing Sasha in the context that they are supporting Analytics efforts for their team. This means they are interested in how to do tasks related to adding instrumentation code, deploying it, and debugging it in support of analytics-related questions and projects. This is a more focused version of the Sasha overall persona.
As part of considering these personas, consider what personas we are not including in this initial list. Specifically, we are not targeting executive personas or Directors with the initial offering. Sasha and Parker are individual contributors and have unique needs different than Directors or executives. They are focused mainly on specific applications and the analytics related to them, whereas executives and Directors will be concerned about multiple, or a "fleet", of applications. We intend to go after these personas eventually and will not intentionally create capabilities that exclude them, but they are not our primary focus at this point.
The market is divided between big tech entrants building on top of complete Marketing Automation platforms marketed towards enterprise marketing orgs and stand-alone tools user engagement tools that are marketed towards Product (and occasionally Development) teams.