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E-commerce · Product analytics · React Native

An app that understands why customers buy - and why they leave.

For e-commerce brands, retailers, and D2C stores with serious mobile traffic, the app is not just a sales channel - it drives decisions on conversion, campaigns, and assortment. Whether you are building from scratch, launching mobile for the first time, or modernizing an existing app, shape it around data from day one.

Below: what to measure in a mobile store app, which tools to use, and how it translates into brand revenue.

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Purchase path as a data sourceeach step = a measurable signal
100%
Entry
source, campaign, device
-38%
62%
Browse
categories, search, scroll
-55%
28%
Product page
time, variants, image zoom
-61%
11%
Cart
value, abandonment, delivery
-64%
4%
Purchase
payment, margin, LTV

100%

Entry

source, campaign, device

62%

Browse

categories, search, scroll

28%

Product page

time, variants, image zoom

11%

Cart

value, abandonment, delivery

4%

Purchase

payment, margin, LTV

Without analytics you only see the last column. With analytics you see every point where you lose customers - and can win them back.

Sample funnel for illustration - not a promise of results for your store.

Why nowPhilosophyFoundationWhat we measureToolsData to revenueReact NativeRolloutRoles

00 / Why now

Project kick-off is the cheapest moment to get analytics right.

In larger stores and retail brands, app data often sits beside web analytics, CRM, ads, and ERP - without a shared event language. The mobile app is sometimes built "fast": a few events added late, unnamed screens, marketing measured separately from sales. On a greenfield build, first mobile channel, or modernization you define every screen and customer action anyway - so the tracking plan can be designed properly from the start instead of patched in after launch.

For e-commerce and marketing teams where every conversion point matters, the difference is simple: instead of an app you only know "works", you get a product that suggests what to improve to sell more - and lets you validate in days instead of guessing for a quarter.

Our approach

We do not bolt analytics on at the end. We treat it like payments or login - part of the app planned from the start.

01 / Philosophy

From “what happened” to “why” to “what next”.

Data can be read on three levels. Most e-commerce organizations stop at the first. The insights that move margin and LTV appear two levels up.

Level 1, description

What is happening?

How many users, what revenue, which screens. A starting point, not the goal.

Level 2, diagnosis

Why is it so?

Where and who drops off, which product variants block purchase, what separates buyers from the rest.

Level 3, action

What do we do next?

Tests, personalization, and timely reminders. What moves conversion and basket value.

02 / Foundation

Data layer: one consistent event language.

Before we measure anything, we design a tracking plan - an agreed dictionary of events and attributes (e.g. product_viewed, add_to_cart, checkout_started). App, web, and marketing data then speak the same language.

  • Collection layer (CDP). We emit events once and route them to analytics, marketing, and the warehouse. Changing tools does not require rewriting the app.
  • Server-side measurement. We confirm key events (purchase, payment) on the server - ad-blocker resistant and more accurate.
  • Consent and privacy (GDPR, iOS ATT). Compliance from day one, built on first-party data.
  • Data quality. Event validation so reports are trustworthy. Decisions on clean numbers, not noise.

Why this matters at e-commerce scale

With a large catalog, seasonality, and many campaigns, consistency matters: the same metrics in the app, web store, and ad reports. You add new tools quickly without digging into code each time.

03 / What we can measure

Full customer behavior, not just app revenue.

  • Conversion funnels - step by step from entry to payment, with precise drop-off points.
  • Cohorts and retention - whether customers return after the first order and which groups are most loyal.
  • LTV and segmentation (RFM) - who buys often and big, and who is starting to churn.
  • Paths and navigation - how people actually move through the app.
  • Search and catalog - what they search for and fail to find.
  • Qualitative signals - session replay and heatmaps where frustration does not show in numbers alone.

04 / Tools

We pick the stack for your goals, scale, and budget.

For brands with high traffic and many channels there is no single “best” stack. Below are categories we work in, with example tools - from MVP to stacks handling hundreds of thousands of sessions per month.

AProduct analytics
GA4 + Firebase

Market standard, strong Google Ads integration. A sensible starting point at no extra tool cost.

AmplitudeMixpanel

Advanced funnels, cohorts, and paths without developers for every question.

PostHog

Analytics, session replay, A/B tests, and feature flags in one, with self-hosted option and full data control.

BigQueryMetabase

Your own data warehouse for deep custom analysis and tailored reports.

BCollection and data pipeline (CDP)
SegmentRudderStack

One event source, routed to all tools. Less code, easier vendor swaps.

Server-side tagging

Accurate, ad-blocker-resistant measurement of key conversions on the server.

CAttribution and marketing
AppsFlyerAdjust

See which campaigns bring buyers, not just installs. Less wasted ad spend.

Deep links

Ads land on the right product in the app, not the home screen.

DExperiments and feature flags
PostHogStatsig

Live A/B tests and gradual rollouts. Decisions from results, not opinions.

Firebase Remote Config

Change copy, promo prices, or layout without an app store release.

ESession replay and heatmaps
UXCamSmartlook

Watch real sessions and see exactly where customers get stuck and leave.

Rage taps

Automatic detection of repeated taps - a signal of bugs or unclear UX.

FNotifications and CRM
BrazeCleverTap

Automated, personalized push and messages based on real behavior, e.g. abandoned cart.

OneSignal

Lightweight, cost-effective start with push notifications and audience segments.

GStability and performance
SentryCrashlytics

Catch errors and crashes before customers report them. Every checkout crash is lost revenue.

Performance monitoring

We track screen load times. A slow app simply converts less.

Listed tools are examples. The final stack is chosen for your business goals, traffic scale, and budget.

05 / From data to revenue

Concrete mechanisms that drive revenue.

Analytics alone does not sell. What you do with it does. A few moves that usually pay back fastest:

01

funnel optimization

We fix the step where you lose the most

We find the step with the biggest drop-off (often delivery or checkout registration), simplify it, and measure impact. Analytics shows which change to ship first - and with React Native we usually deploy in days and see results immediately.

02

cart recovery

We recover abandoned purchases automatically

Customers who added items and left get a timely reminder, sometimes with an incentive. One of the fastest-payback automations in e-commerce.

03

personalization

We show the right product to the right person

Recommendations and home feed tailored to history and behavior raise basket value and shorten the path to purchase. More data means better suggestions.

04

retention and LTV

We bring customers back for a second and third order

Acquiring new customers is expensive. Data-driven segments let you reach at-risk users before they churn and reward your most valuable ones.

05

experiment culture

We replace guessing with A/B tests

Every major change (product page layout, copy, promotion) is tested on part of traffic and shipped only when it improves results. Less risk, faster growth.

Honestly

We do not promise magic growth percentages - every brand, catalog, and season is different. We promise data-driven decisions, fast hypothesis tests, and steady improvement of revenue metrics.

06 / React Native advantage

Why React Native fits analytics at e-commerce scale.

  • One tracking plan for iOS and Android - define events once, they work on both platforms.
  • OTA updates - UX fixes and A/B variants without waiting for store review.
  • Modern RN and Expo architecture - smoother UI; a faster app converts better.
  • Rich SDK ecosystem - most tools have ready React Native libraries.
  • Faster iteration - one team, both platforms, more data-driven cycles for the same budget.

More on the stack: React Native

07 / How we roll it out

Four phases. Value from phase one.

  1. Phase

    0

    Data foundation (Discovery, DDT)

    Right after kick-off we define the tracking plan and collection layer - in parallel with a greenfield build, a first mobile channel, or modernizing an existing app in React Native. We treat analytics like payments: part of the product from the first release.

  2. Phase

    1

    Measurement and visibility

    We launch funnels, dashboards, and session replay. For the first time you see the full picture: where and why you lose customers.

  3. Phase

    2

    Optimization

    A/B tests, cart recovery, push automations. We systematically lift conversion and basket value.

  4. Phase

    3

    Prediction and personalization

    Recommendations, predictive segments, churn prediction. The app starts acting proactively.

08 / Roles

Foundation on our side, decisions on yours.

We build the foundation: tools, integrations, clean data, ready dashboards. We own that entirely. But wiring alone does not sell - your team does, because they know customers, seasonality, promotions, and brand policy.

In larger e-commerce organizations that is usually performance marketing, CRM, or the mobile product team - the people who already own campaigns, push, and offer tests. We install the cockpit; they fly, because they know customers, assortment, and the retail calendar.

On our side

  • Tracking plan and event wiring in the app
  • Integrations, tool wiring, and server-side measurement
  • Clean, consistent data and ready dashboards
  • Team onboarding and technical support

On your side

  • Reading data and forming hypotheses
  • Campaign and automation setup in marketing tools
  • Decisions on what to test and change
  • Regular action based on insights

We do not leave you alone

At launch we hand over dashboards, an event dictionary, and short team onboarding so the barrier to use is as low as possible.

On this page

  • Why now
  • Philosophy
  • Foundation
  • What we measure
  • Tools
  • Data to revenue
  • React Native
  • Rollout
  • Roles

Frequently asked questions

Can analytics be added to an existing app later?
Yes, but cost goes up: you revisit screen by screen, align event names, and backfill gaps in history. On a greenfield build, first mobile channel, or major modernization it is cheaper and faster to define a tracking plan from day one, in parallel with the rest of the product.
Who configures marketing campaigns and push automations?
GMI builds the foundation: tracking plan, integrations, server-side measurement, clean data, and dashboards. Campaigns, promotions, and abandoned-cart reminders are configured by your marketing team - you know customers, seasonality, and offers better than anyone outside. We hand over onboarding and an event dictionary at launch.
Is GA4 enough, or do we need Amplitude or Mixpanel?
GA4 plus Firebase is a sensible start, especially with Google Ads. When product and marketing teams want to explore funnels, cohorts, and paths without developers for every question, consider Amplitude, Mixpanel, or PostHog. We pick the final stack for your goals, traffic scale, and budget - with CDP layer to avoid lock-in.
How does analytics fit into the DDT process?
Right after kick-off, in Discovery workshops, we define the most important events, attributes, and measurement goals. That is the first step of a greenfield build, mobile channel launch, or modernization - not a separate IT project. The product collects data from day one of production traffic.
Will data collection comply with GDPR and iOS (ATT)?
Yes - consent, first-party data, and field minimization are planned from the start. We also confirm key conversions server-side, which improves accuracy under cookie and ad-ID restrictions.

What happens after you sign

We start with the foundation that pays back fastest.

Right after signing we run a Discovery workshop (DDT) to agree key events and pick an analytics stack - whether we are building from scratch, launching mobile, or modernizing an existing product. It is part of the app project, not a separate IT workstream.

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about the project.

Have an app idea or need technological support? Write to us — we'll prepare a preliminary analysis and estimate within 48h. Projects that go through our DDT process (Discovery, Design & Technology) come with a price guarantee and a fixed-price agreement — a key differentiator for us.

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