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Despite the name, the Analytics section doesn't encompass all analytics capabilities in GitLab. Whether categories like Value Stream Analytics, or capabilities like Issue, Release, or CI/CD analytics those are not within the scope of the Analytics section.
The Analytics Section closes the DevOps loop. We provide app owners and developers the tools to learn about and engage with users so that they can move beyond improving their efficiency to accelerating their effectiveness. Our vision is to continue to extend DevOps across its most painful gap - measuring user value.
The Analytics Section is comprised of a single Stage, the Analyze stage. This stage has two groups:
The Product Analytics group focuses on allowing users to analyze product data to uncover insights about how their apps are being used. This is done through preconfigured dashboard and reports as well as enabling users to build their own reports.
The Product Intelligence group focuses on empowering both internal and external users to send usage data to GitLab by providing SDKs and a stable reporting platform. Internally, this means tools like Service Ping and Snowplow as well as providing support to the groups that use them. External users will use the SDK that this group publishes to instrument their own apps.
We know that many groups and teams within GitLab as well as users rely on data to understand how they are using GitLab. There are multiple approaches to produce and consume data currently, which introduces confusion and friction. Our long-term vision is to unify these so that there is a Single Source of Truth for both how to contribute data as well as how to consume it. There are additional details on this vision at our cross-stage directional vision page.
The primary personas we address, in priority order, are:
We may 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.
We have the right to win in this new Section because:
We will pursue this opportunity with the following principles:
Our 1 year plan is to:
Our Performance Indicators are TBD.
We are conducting research on critical jobs to be done for the Analytics section.
The Analytics section will further extend GitLab's lead in being the One DevOps platform by consolidating yet another set of existing tools required to deliver software value to users.
All current DevOps platforms define their water's edge at Monitoring - ensuring a deployed idea is available and performant for users. The process of visualizing and learning from usage, collecting and organizing feedback, engaging and enabling users - those are all left to specialist vendors positioning their products at Marketing, Growth and Product teams.
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.
Due to the heavy emphasis on SaaS and the high data volumes - most pricing in this market is consumption-based.
One critical trend in this market is a clear move to first-party data (partially a result of ITP) as the use of third-party SaaS services to store user data increasingly causes data privacy compliance and brand concerns.
From conversations analysts we expect the market definitions to become crisper and to see a new segmentation that includes developer-focused user engagement products called Product Analytics.
The traditional markets for this stage are fragmented across IT Operations, Marketing Automation, and Customer Data Platform markets. The market most closely aligned to our intent is Customer Data Platforms - a market that IDC states was $1.3B in 2020 and expected to grow to $3B by 2025 (18% CAGR).