The developer marketing metrics you should track (funnel-wise)

I shared 3 developer marketing strategies for each stage of the funnel recently, and the follow up question was how to measure those marketing efforts. Developer marketing metrics are rarely discussed in depth. In most devtool teams, reporting ends up being page views, signups, or community activity screenshots. None of these show whether developers are actually moving closer to adoption or advocacy.

This isn't the '7 best developer marketing metrics' kind of blog, this piece focuses on the core key metrics you can measure across the developer funnel: when a developer notices a tool (TOFU), when they try it (MOFU), and when they bring it into a project (BOFU). The goal is to track those metrics with consistency, so we can see where momentum builds and where the developer experience can be made clearer.

3 Developer marketing metrics for each stage of the developer funnel

Once you understand how a developer naturally moves from discovering a tool to trusting it in real work, the next step is deciding what to measure at each point in that developer journey. The purpose of developer marketing metrics is to understand whether developers are progressing forward. If they are not, the work is to find where friction is happening and reduce it.

We will walk through the funnel stage by stage, and identify the right metrics that signal movement.

TOFU: Discovery metrics

At this stage, the focus is on the tool being present in the developer’s environment. The aim is for the tool to be seen, recognized, and stored in memory for later use. Developers are observing, scanning, and collecting information during this phase.

1. Impressions and reach

This reflects how often the tool appears in spaces where developers look for solutions, learn, and exchange knowledge.

What to track?

Signal	Description
GitHub repo views	Developers encounter the tool while reviewing or exploring code
Stack Overflow question views	Developers see the tool during troubleshooting
Community thread visibility	The tool appears in ongoing discussions

How to capture this metric?

Where it happens	What to capture	How to set it up	Signal Meaning
GitHub Insights → Traffic	New users and top referrers	Record weekly numbers in a simple sheet or dashboard	Shows the tool is being noticed during code exploration
Stack Overflow	View counts on questions mentioning the tool	Log monthly view counts for the same set of URLs	Shows the tool is seen during debugging and learning
Reddit / Slack channel / Discord / Forums	Mentions in technical conversations	Weekly manual keyword search or TrackReddit alerts	Shows the tool is being acknowledged in shared team knowledge

2. Engagement on technical content

This measures whether a developer is actively trying to understand the tool in a hands-on way through developer videos or through content, in other words this is also called as the 'developer engagement'

What to track?

Signal	Description
Documentation depth of navigation	Developer moves beyond the landing page
Code sample copy events	Developer copies example snippets
Starter repo clones	Developer downloads or forks an example project

How to capture this metric?

Where it happens	What to capture	How to set it up	Signal Meaning
Documentation site	Event: docs_page_path_depth and scroll depth	Track via google analytics, Plausible, or PostHog	Shows exploration into workflow understanding
Code blocks	Event: copy_code_sample	Add a click event to copy buttons	Shows intent to test code in local environment
GitHub Insights → Traffic	Clone counts and referrers	Review weekly in the Traffic tab	Shows preparation for hands-on evaluation

3. Traffic from problem-oriented searches

This metric reflects whether developers encounter the tool when searching for solutions to a specific technical problem. At this stage, the developer is seeking guidance or patterns they can apply immediately. Appearance in these searches places the tool in the set of possible options for future evaluation.

What to track?

Signal	Description	Signal Meaning
Searches tied to the problem your tool solves	Developer is researching a task area, not a tool name	The tool is discovered at the point of need
Search-driven clicks to docs or tutorials	Developer selects your solution path	The tool enters the mental shortlist of possible approaches
Stable ranking for problem queries	The tool maintains presence in recurring problem searches	The tool remains discoverable over time

How to capture this metric?

Where it happens	What to capture	How to set it up	Signal Meaning
Google Search Console	Impressions and clicks for problem keyword queries	Review monthly in Performance → Queries	Shows the tool appears during solution research
Ahrefs / Semrush	Ranking movement for key problem topics	Track 10–20 problem-focused search terms	Shows consistency of presence at discovery points
Documentation or site search logs	Frequently searched problem phrases	Review internal site search reports	Shows what problems developers associate with your tool

MOFU: activation metrics

One of the important developer marketing metrics, at this stage, the developer is trying to understand whether the tool fits their environment. They are testing it in a low-risk space. The movement here is defined by reaching a small working result. The key signal is whether the developer goes from reading to running something.

1. API key or credential creation

This metric reflects the moment the developer prepares to test the tool in their own environment.

What to track?

Signal	Description
API key created	The developer has taken the step to authenticate and run the tool

How to capture this metric?

Where it happens	What to capture	How to set it up	Signal meaning
Developer Dashboard or Console	Event: api_key_created	Log key creation event with timestamp	Developer is preparing to evaluate the tool in real context
CLI / SDK	Event: auth_attempt or auth_success	Emit telemetry only on successful authentication (opt-in recommended)	Developer is connecting the tool to local runtime
API Gateway or Server Logs	First successful request using key	Track first request timestamp and status code	Developer reached an authenticated working state

2. First working setup signal

This metric captures the moment when a developer has successfully installed or initialized the tool and reached a basic working state. It marks the transition from understanding into trial.

What to track?

Signal	Description
Completion of the Quickstart or Getting Started path	The developer reached the end of a guided setup flow
Package, SDK, or CLI installed and run locally	The tool is now present in the developer’s environment
Starter project or example application running successfully	The developer has confirmed that the tool works in practice

How to capture this metric?

Where it happens	What to capture	How to set it up	Signal meaning
Documentation site	Start of setup path	Track event: setup_started on Quickstart or Getting Started links	Developer begins evaluating the tool directly
Package manager / dependency install	SDK downloads or installing a package, module.	Use registry dashboards for npm, pip, Go, NuGet, Cargo, etc.	Developer is preparing to run the tool locally
CLI / Local initialization	Successful setup command or first run	Emit event: setup_success after successful initialization (opt-in recommended)	Developer has reached a working initial state
Starter repo or template	Clone or download of example project	GitHub Insights → Traffic → Clone count + referrers	Developer intends to integrate or explore the tool in their workspace

What counts as a completed first setup?

A valid first setup is when the developer:

• Installs the package or dependency
• Follows one direct doc path
• Runs a basic example without modification
• Sees a successful output (even if minimal)

This is the smallest possible meaningful success.

3. Time to first success

This metric reflects how long it takes a developer to reach a basic working outcome after they begin setup. It shows the pace of understanding and how quickly confidence forms.

What to track?

Measurement	Description
Average time from Quickstart start → first successful run	The duration between beginning setup and seeing a working result
Median time across all developers	A stable signal that reduces the effect of outlier cases

How to capture this metric?

Where it happens	What to capture	How to set it up	Signal meaning
Documentation site	Starting the setup path	Track event: setup_started on the Quickstart or Get Started link	Marks the moment the developer begins setup
CLI, SDK, or local workflow	Completion of the first working run	Emit event: setup_success only on a successful run	Indicates the developer reached a basic functional state
Example project or starter repo	Time between clone and first commit or run	Use GitHub clone timestamp + local event or commit time (if available)	Shows how quickly the example leads to working output

Industry benchmarks

Tool Type	Healthy Time to First Success (Approx.)
Auth / API client libraries	5–15 minutes
Framework or SDK integration	15–45 minutes
Infrastructure or deployment tools	45–90 minutes

BOFU: adoption metrics

At this stage, the developer is deciding whether the tool becomes part of actual work. This is where the tool moves from testing into daily or recurring use.

1. Project-level adoption

This metric shows whether the tool is present in an active project. It reflects ongoing use inside a real codebase or environment.

What to track?

Signal	Description
The tool is present in dependency files or manifests	It is included in the project setup
The tool runs in CI or deployment workflows	It is required during development or release
Multiple contributors interact with or commit around it	The tool is part of the team workflow

How to capture this metric?

Where it happens	What to capture	How to measure it	Signal meaning
Package registry usage	Sustained installs from the same environment or org	Registry analytics for npm, pip, Go, NuGet, Cargo	The tool persists beyond the initial test
Project codebase	Dependency entry in lockfile or build file	SBOM scan, dependency monitoring, or internal registry logs	The tool is now part of application code
CI / Build pipeline	Recurring execution in build or test steps	CI logs or usage pings	The tool is active during ongoing development

2. Depth of use

This metric reflects how fully the tool is being used inside the project. It shows whether the developer is relying on core features only, or incorporating advanced capabilities as the project grows.

What to track?

Signal	Description
Use of multiple features or modules	The tool is supporting more than one task or integration point
Expansion to additional services or components	The tool appears in more areas of the codebase
Increasing API call volume or api usage	The tool is involved in ongoing or repeated activity

How to capture this metric?

Where it happens	What to capture	How to measure it	Signal meaning
API or service logs	Requests grouped by feature or endpoint	Count calls per feature and track weekly trends	The developer is using more parts of the system
Codebase or repo structure	References to multiple modules or configuration paths	Search across directories or analyze import/require statements	The tool is part of several parts of the project
Build and deployment pipelines	Commands or steps involving the tool	Review CI/CD workflow files or step execution history	The tool plays a role during active development cycles

3. Advocacy metrics

This metric reflects the moment when a developer begins to reference, recommend, or share the tool with others in the developer channels. It shows that the tool has earned trust through real use and has become part of how the developer explains or solves problems.

What to track?

Signal	Description
Mentions in internal team discussions or channels	The tool is being recommended inside a working environment
References in documentation, READMEs, or boilerplate setups	The tool has become part of shared patterns
Mentions in developer communities	The developer is sharing experience in public spaces

How to capture this metric?

Where it happens	What to capture	How to measure it	Signal meaning
Internal communication platforms	Mentions of the tool in Slack, Teams, or Discord	Light manual weekly logging or search alerts	The tool is being shared across teammates
Team or company repos	References in internal READMEs, skeleton repos, or code templates	Search repos for imports, config blocks, or setup steps	The tool is now part of organizational development patterns
Developer Community spaces	Mentions in Stack Overflow, GitHub issues, Reddit, dev blogs, or meetup talks	Set periodic alerts or manual sweeps	The developer is sharing lived experience with others

Wrapping up

Hackmamba is a developer marketing agency and we are analytical about how developer adoption happens. We track each stage of the journey closely, because every signal tells us something about how developers are moving. Discovery metrics show where awareness begins. Activation metrics show where a developer reaches a first working result. metrics signals show where the tool becomes part of real work.

We use these developer marketing metrics to understand progress. When a step slows down, we look at the material, the flow, or the environment around that moment. We adjust documentation, examples, messaging, or onboarding paths based on what the data shows.

This creates a clear and steady journey that supports developers as they move forward. When the path is clear, adoption rate grows in a way that lasts.

FAQs:

1. How should I plan a marketing budget based on these metrics?

Plan budget around the stages where developers show movement. If the discovery metrics are low, invest in visibility in problem-solving spaces and technical content. If activation metrics are slowing, direct budget toward improving documentation, examples, and the first working setup experience. If adoption signals are steady but not growing, focus on community education, deeper tutorials, and shared workflows. The budget follows the developer journey. You put resources where progress needs support.

2. What are the key performance indicators for developer marketing?

Key performance indicators can be grouped by funnel stage. Discovery KPIs include impressions in problem-solving spaces and documentation engagement. Activation KPIs include API key creation, first working setup, and SDK interactions. Adoption KPIs include project-level usage and recurring usage inside CI or development cycles.

3. What conversion rates matter in developer marketing?

Useful conversion rates are the ones that reflect progress. For example: impressions → documentation visits, documentation visits → API signups, API signups → first successful call, or first successful call → daily active usage. These conversions show movement through the funnel.

4. How do we measure success in developer marketing?

Success is measured by how many developers reach stable usage. Look at daily active users, sustained SDK interactions, recurring API calls, and presence in real projects. These signals show that the tool has become part of real work.

5. Are signups extremely important in developer marketing?

Signups are useful to track, but they matter most when they lead to first use. A signup shows interest. The key is what happens after it. Watch how many signups move to a first working setup, then to returning usage. This shows whether developers find value and continue forward.