# Understand your entire user base

> Manifolds identifies the people and companies behind your product signups. Join that data with PostHog, Amplitude, or your warehouse to analyze product behavior by role, company, industry, and more.

Product teams use Manifolds to understand who is actually using their product.

Join Manifolds data with PostHog, Amplitude, or your warehouse and you can analyze product behavior by role, company, industry, technicality, or whatever else matters to your business.

Which features do engineers use that PMs don't? What are your non-technical users doing? How do founders at small companies behave differently from PMs at larger ones? Which kinds of users convert?

Manifolds gives you the data to answer those questions across your entire user base.

*Figure.* An example breakdown of one user base, cycling through technicality, role, industry, company size, and technicality by seniority. The figures are illustrative.

## There are only so many users you can talk to

User interviews are one of the best ways to understand how people use your product. They're also necessarily a small sample.

That gets especially tricky with PLG. Once you have tens or hundreds of thousands of users, there can be entire groups of people using your product that your product team barely knows exist.

We've seen this repeatedly with developer tools. A company builds for developers, then discovers a surprisingly large group of non-technical users in the product.

*Figure: USER BASE · ILLUSTRATIVE.* An illustrative user base of 100,000 users, broken down as 58 percent developers, 16 percent product, 9 percent founders, 7 percent sales and revops, and 10 percent other.

## See what different kinds of users actually do

Once Manifolds data is in PostHog, Amplitude, or your warehouse, you can combine information about who someone is with everything you already know about what they do.

What are non-technical users doing in the product?

Which features do engineers use that PMs don't?

Do founders at small companies activate differently from PMs at larger companies?

Are enterprise users finding their way to parts of the product you didn't expect?

*Figure: PERSON + PRODUCT BEHAVIOR.* Manifolds identifies the user as a PM. PostHog records that they used feature X, invited 6 teammates, and converted to paid. Together they show that PMs are using feature X and converting.

## Understand how different segments convert

A signup funnel can tell you that 8% of users converted.

The more interesting questions usually start one level deeper.

How do founders at small companies convert compared with PMs at medium-sized companies? Do technical users activate faster? Are users at larger companies more likely to invite teammates?

With Manifolds, you can break down conversion by those groups and see the differences.

*Figure: SEGMENT CONVERSION · ILLUSTRATIVE.* Illustrative conversion by segment. Small-company founders activate at 62 percent and convert at 14 percent. Mid-market PMs activate at 48 percent and convert at 21 percent. Enterprise engineers activate at 71 percent and convert at 18 percent.

*Illustrative signup, activation and paid conversion rates for three user segments*

|  | SIGNUP | ACTIVATE | PAID |
| --- | --- | --- | --- |
| small-company founders | 100% | 62% | 14% |
| mid-market PMs | 100% | 48% | 21% |
| enterprise engineers | 100% | 71% | 18% |

ILLUSTRATIVE FIGURES

## Find the users you didn't know you had

Consider a developer tools company with 50,000 monthly signups.

The product is built primarily for developers, but a meaningful chunk of the user base turns out to be PMs, founders, salespeople, RevOps teams, or other non-technical users.

Once you can identify those groups, you can see what they're actually doing in the product.

Sometimes a group that looks like an edge case turns out to be thousands of users.

*Figure: NON-TECHNICAL USERS.* One user base splitting into technical users and non-technical users, where the non-technical group contains product, founders, sales and revops, all of which lead into feature usage.

## Segment users around your actual product

Role, seniority, company size, and industry cover a lot of useful product analysis, but sometimes you need something much more specific.

Is this person technical? Does their company sell software to developers? Is the company building an AI product? What technologies does it use?

Manifolds can research custom attributes about your users and their companies, then send those fields into your product analytics alongside everything else.

*Figure: RESEARCH FIELDS.* A signup passes through Manifolds, which researches company attributes including vertical tags, capability tags, YC backing and funding, and those fields are sent on to PostHog or your warehouse.

## See how your user base changes

Your user base isn't static.

You might start getting more PMs. Enterprise users might become a larger share of signups. A new industry might suddenly start adopting the product.

You can see those shifts in PostHog, Amplitude, or the reports you're already running in your warehouse.

*Figure: USER MIX OVER TIME.* Across four quarters the mix shifts. Q1 and Q2 are developers, founders and PMs. By Q3 and Q4 founders have dropped out of the top three, and PMs and enterprise users are both growing.

## Put the data where your product team already works

A lot of teams send Manifolds data into PostHog or Amplitude and attach it to their existing users.

Others put it in their warehouse and join it against product events there.

That gives you product events and Manifolds data on the same users:

*Figure: DATA JOIN.* Your own product data (signup, activation, feature usage, retention and conversion) joined with Manifolds data (role, seniority, technicality, company, company size, industry and custom research) to produce your product analysis.

From there, your product team can build reports and ask questions about the user base that weren't possible before.

## How it works

1. ### A user signs up

   Send their email address to Manifolds.

   REQUEST

   ```
   POST https://api.manifolds.run/v0/enrichments
   Authorization: Bearer mf_live_...
   Content-Type: application/json

   {
     "email": "p.singh@hoolimail.com"
   }
   ```

2. ### Manifolds identifies and researches them

   Manifolds identifies the person and company and runs any additional research you've configured.

   RESULT · enrichment\_card.created

   ```
   {
     "event": "enrichment_card.created",
     "version": 1,
     "enrichment_id": "enr_33333333-3333-3333-3333-333333333333",
     "occurred_at": "2026-04-13T16:00:00.000Z",
     "data": {
       "person": {
         "email": "p.singh@hoolimail.com",
         "fullName": "Priyanka Singh",
         "jobTitle": "Software engineer",
         "roleSeniority": "ic",
         "emailAffiliation": "personal"
       },
       "company": {
         "name": "Pied Piper",
         "website": "https://piedpiper.com",
         "sizeBucket": "11-50",
         "sizeMin": 11,
         "sizeMax": 50,
         "isYc": false,
         "verticalTags": ["software_it"],
         "capabilityTags": ["custom_software_services"]
       }
     }
   }
   ```

3. ### Add the data to your analytics

   Push the result into PostHog, Amplitude, your warehouse, or another system where you keep product data.

4. ### Compare product behavior across different users

   Now you can break down activation, feature usage, retention, conversion, and other product behavior by the people and companies behind those events.

## Why Manifolds

**Personal emails matter**

PLG products get a lot of personal-email signups. Among our customers, they account for more than 70% of signups.

Identifying those users gives you a much more complete dataset to analyze.

**Match rate matters**

Missing half your users can give you a pretty distorted picture of your user base.

Manifolds combines multiple data sources and does its own research to identify more of the signup stream.

**Research what matters to your product**

Technicality might matter enormously to one company and not at all to another.

Manifolds can research the attributes that matter to your product, so those attributes can become part of your analysis.

**Built for large PLG user bases**

Manifolds was built to research large streams of product signups continuously.

As new users arrive, their data can flow into the same product analytics systems your team already uses.

## Know who's using your product

Your product analytics already contain a huge amount of information about what your users do.

Add Manifolds and you can understand who those users are, too.

[Get started](https://manifolds.run/signup)

Canonical page: https://manifolds.run/use-cases/product