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Product Analytics Services

Product Analytics Consulting That Tells You What Users Actually Do

Most teams don’t have a product analytics problem they have a trust problem. Events fire inconsistently, three dashboards report three different activation numbers, and nobody’s confident enough in the data to make a call without a gut check. Kaliper builds product analytics you can actually act on: clean tracking plans, properly implemented platforms, and funnels, retention curves, and experiments that hold up under scrutiny. We’ve done this for 100+ clients over 8+ years, on Mixpanel, Amplitude, PostHog, Heap, and Statsig.
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THE PROBLEM

Why Product Teams Stop Trusting Their Own Data

It usually starts small. A new feature ships, someone adds tracking for it without checking the existing taxonomy, and now “Signed Up” means three different things across web, iOS, and Android. Six months later, the product team is debating whether activation is up or down 8%, and the honest answer is nobody knows because nobody owns the definitions.

This is the normal state of product analytics at companies that bought a tool and skipped the implementation work. The platform isn’t the problem. Mixpanel, Amplitude, Heap, and Statsig are all capable of answering hard questions about user behavior. What’s missing is the unglamorous layer underneath: a tracking plan everyone follows, naming conventions that don’t drift, and someone responsible for catching breakage before it reaches a board deck.

That’s the layer we build.

SERVICES

What We Do

Tracking Plan & Event Taxonomy Design

We map every event, property, and user trait your product needs to track, and we name them in a way that survives new feature launches without retrofitting. This is the foundation every platform implementation below depends on skip it, and you're rebuilding in a year.

Platform Implementation
Hands-on setup of your chosen product analytics platform instrumentation, identity resolution across web and mobile, server-side and client-side event capture, and QA against the tracking plan before anything ships to production.
Funnel, Retention & Cohort Analysis
We build the funnels and retention views that actually answer your questions where users drop off, which cohorts stick, and which onboarding paths predict long-term retention versus which just look good in a demo.
A/B Testing & Experimentation
Experiment setup and analysis using your product analytics platform's native testing tools or a dedicated experimentation layer, with proper statistical rigor so you're not shipping changes based on noise.
Dashboards & Reporting
Reporting built for the people who'll actually use it product managers tracking feature adoption, growth teams watching activation, leadership reviewing retention trends not a generic dashboard nobody opens twice.
Ongoing Support
Most teams don't need a full-time analytics hire; they need someone to keep the tracking plan enforced, fix breakage fast, and extend instrumentation as the product evolves. We offer retainer support for exactly that.
PLATFORMS

Platform Implementation, Done Right

Mixpanel

Mixpanel rewards teams that get the event taxonomy right from day one its self-serve dashboards are only as good as the data feeding them. We handle Mixpanel implementation end-to-end: event tracking setup, identity merging, and the kind of clean instrumentation that makes self-serve actually self-serve for your team.

Amplitude

Amplitude’s strength is depth cohort analysis, predictive insights, and behavioral graphs that go further than most teams use. We set it up to match, including Amplitude-native experimentation for teams running structured A/B tests.

Heap

Heap’s auto-capture is genuinely useful, but auto-capture without governance turns into thousands of meaningless events. We implement Heap with the naming and tagging discipline that keeps auto-captured data usable, not just abundant.

PostHog: PostHog

PostHog’s appeal is having product analytics, session replay, feature flags, and experimentation in one open-source platform, with the option to self-host for teams that want full control over their data. We implement PostHog with the same tracking-plan discipline we bring to every platform: clean event taxonomy from day one, proper identity resolution across web and mobile, and instrumentation that takes advantage of session replay and feature flagging without turning into yet another tool nobody fully uses.

Statsig

For product teams building experimentation into their core workflow, Statsig combines analytics with feature flagging and experiment management in one place. We implement Statsig for teams that want analytics and experimentation running on the same data, not two disconnected tools. Not sure which platform fits your product? That’s usually the first conversation we have — the right call depends on your team’s technical resources, your testing velocity, and what you’re already running upstream.
DIFFERENTIATION

Why Companies Bring Us In

We're consultants, not a platform reseller.

We don’t make money from which tool you pick. Our recommendations are based on your product, your team’s technical depth, and what you’re trying to learn not a partnership incentive.

We fix what's already broken as often as we build new.

A large share of our product analytics work starts with an audit of an existing Mixpanel or Amplitude setup that’s drifted duplicate events, broken identity resolution, dashboards built on bad assumptions. We’ve seen the failure modes before.

8+ years, 100+ clients, across SaaS, mobile, and marketplace products.

We’ve implemented product analytics for companies at very different stages, from pre-PMF startups that need their first clean funnel to scaling products untangling years of inconsistent tracking.

You get documentation, not a black box.

Every engagement ends with a documented tracking plan and schema your team can maintain so you’re not dependent on us for every future event you want to add.
PROCESS

How We Work

1. Audit & Discovery

We review your current analytics setup (or your goals, if you’re starting fresh) and identify where the data is unreliable, missing, or untracked.

2. Tracking Plan Design

We define the events, properties, and naming conventions your product needs built to scale with new features, not break with them.

3. Implementation

We implement the tracking plan in your chosen platform, including identity resolution across devices and platforms, and server-side tracking where client-side isn’t reliable enough.

4. QA & Validation

Every event is tested against the tracking plan before launch. No assumptions, no “looks about right.”

5. Dashboards, Funnels & Training

We build the views your team will actually use, then train your team to read and extend them confidently.

6. Ongoing Support

Retainer support to keep the implementation clean as your product changes new features, new platforms, new questions.

Stop Guessing About What Your Users Do

If your product team is debating whose dashboard is right instead of debating what to build next, that’s a tracking problem, not a roadmap problem. Let’s fix the data first.
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FAQ

Frequently Asked Questions

What is product analytics consulting, and do we actually need it?
Product analytics consulting is help with the layer between buying a tool and getting reliable answers from it: defining what to track, instrumenting it correctly, and building the funnels, retention views, and experiments your team will actually use. You need it when you have a tool but don’t trust the numbers coming out of it, when you’re choosing a platform and don’t want to lock into the wrong one, or when nobody internally owns the tracking plan. If your team is confident in its data and has a dedicated analytics engineer keeping it clean, you may not need outside help. Most teams we talk to are not in that position.
Marketing analytics (GA4, ad platform pixels) measures how people find and arrive at your product. Product analytics (Mixpanel, Amplitude, Heap, Statsig) measures what they do once they’re inside it: which features they use, where they drop off, and what predicts retention. Most companies need both, connected to the same identity.
It depends on what your product is optimizing for, but most products need a clear North Star metric, an activation metric (the moment a new user gets real value), retention by cohort, a core engagement metric, and conversion through your key funnels. The mistake we see most often is tracking dozens of events but none of the ones that actually predict whether a user sticks. Part of the tracking plan work is deciding what’s worth measuring, not just measuring everything because the tool allows it.
It depends on your team’s technical resources and what you’re optimizing for. Mixpanel suits teams that want fast, self-serve answers. Amplitude suits teams that want depth and predictive analysis. Heap suits teams that want broad auto-captured coverage. PostHog suits teams that want product analytics and session replay in one open-source platform, with the option to self-host. Statsig suits teams that want analytics and experimentation in one workflow. We’ll walk through this with you based on your specific product and team.
A clean implementation for a single product with web and mobile typically takes 4 to 6 weeks: tracking plan design, instrumentation, QA, and dashboard build-out. Multi-product or multi-platform setups, or migrations from an existing messy implementation, usually run 8 to 12 weeks. We already have Mixpanel/Amplitude set up. Do you only work with new implementations? Most of our product analytics engagements start with an existing implementation that’s drifted over time. We audit what’s there, identify what’s broken or inconsistent, and rebuild the tracking plan and instrumentation without necessarily starting from zero.
Most product analytics platforms can export raw event data to a warehouse like Snowflake, BigQuery, or Redshift, either natively or through your CDP. We set this up so your product usage data sits alongside your other business data (Salesforce, billing, support tickets), which lets you answer questions a standalone product analytics tool can’t, like how feature usage relates to revenue or which behaviors predict churn for high-value accounts. Since data engineering is a core part of what we do, the warehouse side is something we handle directly rather than hand off.
Yes, this is one of the most common reasons teams come to us. Product analytics lets you identify the behaviors that separate users who stay from users who leave, spot the drop-off points where at-risk users disappear, and build cohorts you can target before they churn rather than after. The analytics itself doesn’t reduce churn; it tells you where to act. We build the retention and cohort views that make those patterns visible, and connect them to your messaging tools if you want to act on them automatically.
PII accidentally captured in event properties is one of the most common implementation problems we find in audits: email addresses in search events, names in form fields, user data in URLs that gets auto-captured. We design tracking plans with PII handling as a requirement from the start: what gets masked, what gets excluded, what requires consent gating, and how to structure events so compliance doesn’t require rebuilding instrumentation later. If your users include EU residents or you’re in a regulated industry, we factor that into the implementation design explicitly, not as an afterthought.
It depends on scope: platform, number of surfaces (web, iOS, Android), whether it’s a new build or fixing an existing implementation, and whether you want ongoing retainer support afterward. New implementations typically start in the range of a defined project fee; audits and fixes on existing setups are usually smaller in scope. We’ll give you a clear estimate after the discovery call, not a vague range that doesn’t help you plan.
Both options are available. Some clients need a one-time implementation with documentation handed off to their internal team. Others keep us on retainer to maintain the tracking plan, fix breakage, and extend instrumentation as the product evolves, which is usually more cost-effective than a full-time hire for this specific work.
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