Analyze your product analytics data

You ask Kite a question the way you'd ask a colleague, and Kite works out which report answers it. These all work:

• "How many people who signed up in September created a project in their first week?"
• "Which signup source brings users who are still active after 30 days?"
• "Are people using the new reporting feature we launched two weeks ago?"
• "What do our most active accounts do that everyone else doesn't?"

Kite reads your project before it writes a single query, because your team's definitions are a better starting point than a guess. In Mixpanel, it reads the business context your team saved, the short notes that explain what a term like "Activated" means at your company, along with your saved reports, metrics, and dashboards. In PostHog, it reads your saved insights, your dashboards, and the actions your team defined. In every tool, it reads the event names and the properties attached to them, and it checks which values a property actually takes, so a breakdown by plan uses "Pro" and "Team" rather than names that don't appear in your data.

When a question is ambiguous, Kite asks before it answers. "How many active users do we have?" depends on what counts as active, so Kite proposes a definition based on what your team already uses, such as anyone who created or edited a project in the past seven days, and lets you confirm it.

Each answer comes with a short explanation of how Kite got it: the events it used, the date range, and any filters, such as leaving out your own team's accounts. In the Kite web app, the answer can include an interactive chart or table. Kite also saves the report in your analytics tool, so anyone on your team can open it, check it, and reuse it.

Kite checks each event's tracking before it trusts a result, because a broken event looks exactly like a drop in user behavior. It pulls the daily count of each event over the period it's studying. If "Project Created" fell to almost nothing on the day of a release while signups held steady, the problem is tracking, not users, and Kite tells you that first.

It also looks for the most common ways tracking goes wrong:

• Renamed events. A release that changed "Signed Up" to "Account Created" splits one step into two names, and a report built on the old name undercounts everyone after the change.
• Visitors and accounts counted as two people. When your product doesn't link an anonymous visitor to the account they create, everyone seems to vanish at signup.
• Events counted twice. When two sources send the same pageview, the top of a funnel looks bigger and every later step looks weaker.
• Events that stopped arriving. When Segment sits between your product and your analytics tool, Kite reads how many events each Segment destination delivered and how many failed, so it can find the day an event stopped reaching your analytics tool.

Kite reports tracking problems as their own findings, with the date each one started and the fix your developers need to make. You get the rest of the analysis based on the data that's still reliable.

Related How-to pages

• How to find broken or leaky funnels with Segment, PostHog: Check Segment's delivery numbers and PostHog's events to separate steps that broke on a date from steps where people really leave.

A funnel shows how many people complete each step of a journey in order, and the step where the share falls hardest is where you lose them. Kite builds funnels from the events your product already sends. A trial journey might run from viewing the pricing page to starting signup, completing signup, creating a first project, and inviting a teammate.

Kite chooses the funnel's settings to fit the journey, because those settings change what the funnel says. It sets the time limit for completing the steps based on how long people usually take, since signing up takes minutes but inviting a teammate can take days. It decides whether the steps must happen in order and whether to count people or repeat actions.

Then Kite decides which drop is worth fixing first. The step with the lowest conversion rate isn't always the most costly. Kite weighs how many people reach each step and how close the step is to revenue. It also breaks the weakest step down by device, country, plan, and the traffic source and campaign of each user's first visit. A signup step that loses most of its mobile visitors from one paid campaign calls for a very different fix from a step that loses everyone equally. To see why people leave, Kite looks at what they did instead, using path reports and, when your tool records them, session replays from a sample of people who dropped out.

Related How-to pages

• How to find where users drop out of a Mixpanel funnel: Build the funnel in your Mixpanel project, check each step's tracking, and break the weakest step down by who it loses.
• How to find where users drop out of an Amplitude funnel: Pull funnel data from Amplitude and find the step and the user group that cost you the most people.
• How to find where users drop out of your funnel with PostHog: Build a PostHog funnel, read the paths and recordings of people who left, and find the reason behind the biggest drop.

Retention shows what share of the people who started in a given week or month are still using your product later, and it's the clearest sign of whether new users are finding value. Kite builds retention reports grouped by when people signed up, so you can see whether September's signups are sticking better than August's. It can measure retention on any action that matters to you, such as returning to edit a project rather than just logging in.

The most useful retention questions for marketing compare groups. Kite breaks retention down by the traffic source and campaign recorded at signup, the plan people chose, their company size or role when your product records it, and the first actions they took. That's how you learn that users from one partner channel are twice as likely to be active a month later, or that people who connect a data source on day one rarely leave.

Kite uses other report types when they answer the question better:

• Stickiness shows how many days a week people use a feature, which separates habits from one-time visits.
• Lifecycle reports in PostHog split your active users into new, returning, returning after a break, and gone quiet, so you can see whether growth comes from new signups or from people coming back.
• User groups, called cohorts in most analytics tools, let Kite save a group such as "signed up from the webinar and created a project" so your team can use it in any later report.

For more on the work of turning early usage into lasting customers, see Get new users to value faster.

The most valuable product question for marketing is which early actions lead people to pay. Answering it means putting two records side by side: what each person did in your product and whether they paid, which usually lives in Stripe.

Kite links each person in your analytics tool to their Stripe customer, using a shared ID when your app saves one and email when it doesn't. It counts only behavior that happened before each person's first real payment, so actions that come with a paid plan don't masquerade as causes of payment. For each candidate behavior, Kite reports how many signups did it, what share of them paid, and what share of everyone else paid. It also checks whether the pattern holds across signup months and channels.

The result is a short list of actions worth steering new users toward, along with a saved group of recent signups who haven't taken those actions yet. That group is where the next piece of work starts, such as onboarding emails or in-app copy that points people toward the action. For the full job, see Turn free users into paying customers.

Related How-to pages

• How to see which product behaviors lead to paid customers with PostHog, Stripe: Link PostHog people to Stripe customers and compare what payers and non-payers did in the same early window before anyone paid.

Some teams keep their product data in a data warehouse, a database that collects copies of data from many tools in one place, or read it straight from their product's own database. Kite can work there too. With Google BigQuery connected, Kite lists your datasets and tables, reads each table's columns, and writes and runs the SQL query that answers your question. With Supabase connected, it reads your tables and their columns and runs read-only queries, so it can count signups, projects, or orders without changing anything.

This is useful when the number you want only exists in the database. Your analytics tool might show signup events, while the database shows which accounts are still on a paid plan today. Kite reads the table structure before writing a query, so it uses your real column names. It also tells you which table and filters each number came from, so an engineer can check the query in a minute instead of writing it from scratch.

Warehouse data works well for marketing reports. Kite can join product usage with signup source to show which campaigns brought in customers who are still paying, then publish the result as a report or dashboard your team can open from a link.

An analysis is only worth running if it changes something, so Kite ends each one with a proposed next step. Depending on the finding, that might be:

• A rewritten pricing, landing, or signup page where people drop out.
• Onboarding emails or in-app copy that steer new users toward the action that predicts payment.
• An experiment in PostHog or Mixpanel that tests a change to onboarding.
• A note to your developers with the exact tracking fix and the date the problem started.

Kite builds the work and posts it in the conversation with the evidence behind it. It asks for your approval before anything reaches your users, such as a published page or a launched experiment. Reading and analyzing your data is routine, so Kite does that without checking in.

After the change ships, Kite reruns the same report with the same settings, so the before and after are directly comparable. You can also ask Kite to repeat any analysis on a schedule, such as a summary of your PostHog dashboard every Monday that leads with the changes worth your attention and the likely cause of each. For a number you only want to hear about when it moves, such as daily signups in PostHog, Kite can keep a standing watch and tell you when it crosses a threshold you choose. Kite can also react to events your product records in PostHog as they happen, such as a new signup, and tell your team right away when one matters.

Related How-to pages

• How to get a weekly summary of your PostHog dashboard in Slack: Get a short weekly summary that compares each chart with the week before, sets aside normal noise, and gives the cause and a next step for each real change.