How to get picked for product analytics by coding agents
PostHog took 53% of 359 judged product analytics sessions. What the numbers say a vendor in this category should do.
If you sell product analytics tools, this page is the part of the market no dashboard shows you: what a coding agent does when a developer asks for product analytics and never compares vendors.
The numbers come from 359 judged sessions with Claude Code, Codex and Cursor, spread across 10 realistic codebases, with every session read by a judge.
What coding agents choose for product analytics
Across 359 judged sessions, PostHog was chosen most often, in 53% of runs. Vercel Analytics was second with 8%. In 23% of runs the agent wrote the code itself and adopted no product at all.
| # | Product | Runs won | Share |
|---|---|---|---|
| 1 | PostHog | 189 | 53% |
| 2 | Built in-house (no product adopted) | 81 | 23% |
| 3 | Vercel Analytics | 27 | 8% |
| 4 | Segment | 17 | 5% |
| 5 | Amplitude | 14 | 4% |
| 6 | Umami | 6 | 2% |
| 7 | Mixpanel | 5 | 1% |
| 8 | Snowplow | 2 | 1% |
| 9 | Metabase | 2 | 1% |
| 10 | Plausible | 2 | 1% |
Full board, every run replayable: the product analytics leaderboard.
What the shape of this category means
The leader takes 53% of runs and there is a real second place. The category has a default but it is not settled.
PostHog at 53% with Vercel Analytics at 8% is a default with a real challenger behind it. The agent is choosing rather than reaching, which means the inputs it uses can move the answer.
The work is to be the easiest correct answer: a quickstart that runs when pasted, documentation that states the current version, and pages that answer the exact configuration questions agents search for.
The agents do not agree with each other
In this category all three agents put PostHog first, which is less common than it sounds: across the eighteen categories we measured, Claude Code and Codex disagreed on the leader in nine of them.
| Agent | Runs | Picked most often |
|---|---|---|
| Claude Code | 120 | PostHog (49) |
| Codex | 120 | PostHog (69) |
| Cursor | 119 | PostHog (71) |
Even where they agree, they get there differently. Codex ran a web search in 53% of decision runs and Claude Code in 1.6%, so what you publish reaches one of them in half its product analytics sessions and the other in almost none.
Who is asking changes the answer
Every request in this experiment was written as a specific kind of person. In this category the leader changes with the person.
| Who is asking | Runs | Picked most often |
|---|---|---|
| Vibe coder | 36 | Vercel Analytics |
| Junior developer | 144 | PostHog |
| Senior engineer | 143 | PostHog |
| Enterprise team | 36 | PostHog |
That is 2 different products winning product analytics for 4 kinds of buyer, out of the same 359 sessions. Nobody here is winning product analytics. They are each winning one kind of buyer.
If you sell to more than one of them, you need pages for each. See how to win the enterprise persona.
What you are really competing against
In 23% of runs, the agent wrote the code itself. That makes hand-written code the largest single competitor in this category, larger than most vendors in the table above.
This changes the job of your content. Before you argue that you are better than another vendor, you have to argue that the problem is harder than it looks. What breaks at volume. Which edge cases cost a weekend. What the maintenance actually costs after six months.
That argument has to exist as a page an agent can read, with specifics and numbers. "It is harder than you think" is not an argument. "Here are the four failure modes and what each one costs" is.
Considered, and never chosen
Because the judge records every product an agent raised and not only the one it picked, this board also shows who kept reaching the shortlist and losing. In product analytics the clearest case is Google Analytics: on the table in 91 sessions, chosen in none.
| Product | Raised in | Chosen in |
|---|---|---|
| Google Analytics | 91 sessions | 0 |
| Fathom | 59 sessions | 0 |
| RudderStack | 41 sessions | 0 |
| Heap | 40 sessions | 0 |
| Matomo | 31 sessions | 0 |
Being rejected is a better position than being unknown, and a cheaper one to fix. The product is already in the agent's head and on the list. Whatever ended those 262 sessions is recorded in each transcript, one reason at a time.
What to do about it in product analytics
- Argue that the problem is harder than it looks, before you argue that you are better than a rival. 23% of product analytics sessions ended in hand-written code, so in roughly one session in 4 no vendor was in the running at all. Write the failure modes and the year-two maintenance cost, with numbers, in the documentation rather than the blog.
- Aim at second place first. PostHog holds 53% and Vercel Analytics holds 8%. The gap between the default and the field is where the reachable sessions are.
- Pick which buyer you are for. The same product analytics need written as a vibe coder landed on Vercel Analytics, and written as an enterprise team landed on PostHog. Those are two markets, and the enterprise one needs pages containing the constraint words: audit log, data residency, retention, single sign-on. See how to win the enterprise persona.
The work that applies to every category rather than to this one is written up separately: audit your documentation, write a quickstart an agent can follow, and how to measure install share.
Every product analytics tool on this board
One page per product, with its install share, the per-agent split, and how often it was raised without being chosen.
- Do coding agents recommend PostHog? — chosen in 53% of sessions
- Do coding agents recommend Vercel Analytics? — chosen in 8% of sessions
- Do coding agents recommend Segment? — chosen in 5% of sessions
- Do coding agents recommend Amplitude? — chosen in 4% of sessions
- Do coding agents recommend Datadog? — chosen in 1% of sessions
- Do coding agents recommend Google Analytics? — raised in 91 sessions, chosen in none
Where these numbers come from
359 judged sessions in product analytics across 10 codebases, part of a published set of 5,292. Real coding agents at pinned versions, in sandboxes, inside realistic codebases, with a simulated project owner in the loop and a blind judge on every session. The full method is on one page: how we measured this.
Every product analytics run can be replayed on the board.
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Common questions
How many codebases is this based on?
359 judged sessions across 10 realistic codebases. A category only runs on repositories where its seam is open, so coverage differs: some categories ran on more than ten codebases and some on two.
What product analytics tool do coding agents choose?
Across 359 judged sessions, PostHog was chosen most often, in 53% of runs. Vercel Analytics was second with 8%. The result changes by agent and by who is asking.
Do Claude Code and Codex pick the same product analytics tool?
Yes. All three agents we tested put PostHog first in this category, which is unusual: they disagree in half of the categories we measured.
How often do agents build product analytics themselves instead of installing something?
In 23% of runs the agent wrote the code itself rather than adopting a product. That makes hand-written code one of the strongest competitors in the category.
How can a vendor improve its position here?
Make the quickstart run when pasted, state the current version on the documentation page, use one name across product, package and import, write pages for the symptoms users describe rather than only the category name, and get into the repository through templates and framework integrations.
Which product analytics tools do agents consider but never choose?
Google Analytics (raised in 91 sessions, chosen in none), Fathom (raised in 59 sessions, chosen in none), RudderStack (raised in 41 sessions, chosen in none), Heap (raised in 40 sessions, chosen in none), Matomo (raised in 31 sessions, chosen in none). Being considered and not chosen is a different problem from being unknown, and it is usually fixable.
Where this comes from
Armature ran 5,292 judged sessions with Claude Code, Codex and Cursor inside 51 realistic codebases, and published every run. The numbers on this page come from that work.