Which Metrics Matter for a New App?
Contents
A new app does not need a dashboard full of numbers. It needs a small set of metrics that answer the decisions in front of you.
Before launch, you may care about whether people complete the first workflow. After launch, you may need to understand why they return, pay, stop using the app, or ask for support.
The right metrics change as the product changes.
- Track the first meaningful action before worrying about large traffic numbers. - Define activation in terms of user value, not account creation. - Use cohorts to understand retention instead of relying on total active users. - Connect revenue metrics to the behavior that creates revenue. - Add crash, error, and support signals because a growth chart can hide a broken product.
Start with the decision, not the dashboard
Ask what you need to decide in the next few weeks.
Examples:
- Are new users reaching the main result?
- Do users return when the problem happens again?
- Which acquisition source brings the right users?
- Are people starting a trial but not paying?
- Is a technical failure blocking a valuable workflow?
Each question needs different data. If you track everything before choosing a question, you will collect events without knowing what action they should change.
[INTERNAL-LINK: how to add analytics and crash reporting to a mobile app → event planning, funnels, crash reporting, and privacy]
Define the first meaningful action
The first meaningful action is the point where a user experiences the product's main value.
It may be:
- creating a first project
- completing a booking
- uploading a document and receiving a result
- sending a request that another person can act on
- completing a lesson or workout
- saving a plan they intend to follow
Account creation is usually a setup step. It becomes activation only if creating the account itself is the product's main value.
Write the activation event as a sentence:
"A new user is activated when they complete [specific action] within [relevant period]."
Keep the rule simple enough that another person on the team can apply it consistently.
Track activation rate
A basic activation rate is:
Activated users / new users x 100
For example, if 40 new users sign up and 18 complete the core action, the activation rate is 45 percent.
The number is useful only when the event is meaningful. If you define activation as opening the app twice, you may get a nice percentage without knowing whether users solved their problem.
Look at the steps between signup and activation:
| Step | Question |
|---|---|
| Account created | Did registration work? |
| Setup completed | Did users understand what to enter? |
| First action started | Did they find the main path? |
| Core action completed | Did the product deliver value? |
| Result saved or shared | Did the outcome matter enough to keep? |
[INTERNAL-LINK: how to design an app onboarding flow that converts → improve the steps between account creation and first value]
Measure retention with cohorts
Retention asks whether users return after their first useful experience.
A cohort groups users by a shared start date or event. For example, users who first completed the core workflow during the same week can be measured together.
Useful questions include:
- How many return the next day?
- How many return the following week?
- Do users return because the problem recurs?
- Does retention differ by acquisition source?
- Do users who complete onboarding retain better?
Do not compare a new cohort with an old cohort without checking product changes, audience differences, and seasonality.
A task app may need frequent use. A tax document app may be valuable even if users return only during a specific period. Retention must fit the problem.
Track conversion when money is involved
For a paid app, measure the steps that lead to revenue:
- pricing page viewed
- trial started
- checkout started
- payment completed
- subscription renewed
- cancellation started
- refund requested
A simple conversion rate is:
Paying users / eligible users x 100
Define "eligible users" clearly. Paying users divided by all website visitors answers a different question from paying users divided by trial users.
[INTERNAL-LINK: how to price a subscription app → pricing decisions, trial structure, and recurring revenue considerations]
Connect metrics to the product model
Different products need different primary signals.
| Product type | Useful primary signal |
|---|---|
| Booking app | Completed bookings and repeat bookings |
| AI app | Completed useful outputs and repeat usage |
| Team app | Active workspaces and completed shared actions |
| Marketplace | Successful matches or transactions |
| Subscription app | Retained paying accounts |
| Service portal | Requests completed and response time |
Do not copy a popular startup's metric because it sounds impressive. A number matters when it represents the value your product promises.
Measure acquisition quality
Acquisition metrics tell you where users came from. They do not tell you whether the users were a good fit.
Track:
- source or campaign
- landing page conversion
- signup completion
- activation rate by source
- paid conversion by source
- support issues by source
- retention by source
A channel that sends many visitors but few activated users may be less useful than a smaller channel that brings people with a real need.
[INTERNAL-LINK: how to get your first 100 mobile app users without paid ads → practical early acquisition channels and manual outreach]
Track product quality as a first-class metric
A product can show growth while users struggle with crashes, slow screens, failed uploads, or confusing permissions.
Track:
- crash-free sessions
- failed API requests
- upload failures
- payment failures
- notification delivery problems
- support requests by workflow
- time to resolve a reported issue
The exact technical measurements depend on your stack. The principle is simple: measure the places where the product fails to deliver its promise.
Name events around actions
Good event names describe a product action.
Examples:
- account_created
- onboarding_completed
- project_created
- core_result_generated
- subscription_started
- payment_failed
- referral_qualified
Add properties that help you compare behavior, such as plan, platform, acquisition source, or workflow type. Do not include sensitive content just because the analytics tool allows it.
Create an event dictionary before the dashboard becomes difficult to maintain. For each event, record the name, trigger, properties, owner, and decision it supports.
Build one useful dashboard
A first dashboard can contain:
- New users
- Activation rate
- Core workflow completion
- Return usage by cohort
- Paid conversion when relevant
- Crashes, failures, or support volume
Add a note beside every chart: "If this changes, what will we do?"
If the answer is nothing, the chart may not deserve space on the first dashboard.
Avoid vanity metrics
Vanity metrics are not always useless. They become a problem when they create confidence without guiding action.
Examples include:
- total downloads without activation
- total registered accounts without recent activity
- total page views without qualified signups
- social followers without product usage
- messages sent without successful outcomes
Use these numbers for context if needed. Do not use them as proof that the product works.
Diagnose patterns instead of chasing one number
A few common patterns:
| Pattern | Possible meaning |
|---|---|
| High signup, low activation | Onboarding or value explanation problem |
| High activation, low return usage | The problem may not recur or the result is weak |
| High trial starts, low payment | Pricing, trust, or product value issue |
| Good retention, low acquisition | Distribution problem |
| Good acquisition, high support volume | Audience mismatch or usability problem |
| Usage drops after an update | Regression or changed workflow |
These are starting points, not automatic diagnoses. Talk to users and inspect the workflow before changing the product.
Handle privacy carefully
Analytics can contain personal information if you send messages, filenames, locations, or uploaded content as event properties.
Before tracking an event, ask:
- Do we need this property?
- Can we use an identifier instead of the content?
- Does the user understand this collection?
- Is the data protected and retained appropriately?
- Does the privacy policy describe it?
Analytics should help you improve the product without turning every user action into a record you do not need.
[INTERNAL-LINK: how to add a privacy policy and terms to a mobile app → data inventory, permissions, analytics, providers, and deletion]
Review metrics on a regular rhythm
A useful review can be short:
- What changed?
- Which user behavior explains the change?
- What evidence supports that explanation?
- What action will we take?
- When will we check again?
Do not redesign the dashboard every week. Keep definitions stable long enough to compare cohorts, then change them when the product or business model changes.
Build a focused analytics system with Huxly
Huxly helps you build apps with analytics events, authentication, databases, payments, AI features, testing, and preparation for TestFlight or Google Play. You can define the events that support product decisions while building the workflow, rather than adding random tracking after launch.
FAQ
What is the most important metric for a new app?
The most important metric is usually the completion of the first meaningful workflow. The exact event depends on what the app promises to do.
How many metrics should a new app track?
Start with a small dashboard that covers acquisition, activation, retention, revenue when relevant, and product quality. Add metrics when a decision requires them.
What is the difference between activation and retention?
Activation measures whether a new user reaches initial value. Retention measures whether users return or continue using the product later.
Should I track every button tap?
No. Track actions connected to a product question. Excessive events make analysis harder and can create unnecessary privacy work.
How often should I review app metrics?
Review them on a regular schedule that fits the product. A new app may need frequent checks during a beta, while a stable product can use a weekly or monthly review.
What if the numbers conflict?
Look for segment differences, tracking errors, and changes in the product. A high signup rate with low activation means something different from high activation with low retention.
