01
The Same KPI Has Multiple Totals
MMP, ad platform, store, product analytics, subscription, and backend systems count users, dates, attribution, and revenue differently.
Mobile Measurement, Attribution & MMP
Know Which Growth Signals You Can Trust
Grovix audits, implements, and migrates the measurement layer connecting acquisition, product behavior, privacy frameworks, subscriptions, and verified revenue, so growth decisions are based on defined, tested data rather than dashboard consensus.
For growth, product, data, and engineering teams scaling paid acquisition, repairing attribution, migrating an MMP, or aligning campaign performance with downstream value.
Illustrative measurement audit view
MMP ↔ BackendRevenue reconciled
The Decision Risk
When the Numbers Disagree, Growth Slows Down
Measurement becomes a business constraint when systems use different definitions, events lack ownership, privacy paths are blended, or teams cannot explain which number should drive the next decision.
02
Events Exist Without Ownership
Names, triggers, properties, identifiers, and revenue rules drift across platforms until teams cannot trust or safely optimize to them.
03
Privacy Data Is Read as Real-Time Truth
ATT, SKAN, consent states, aggregation, thresholds, and postback delays are mixed into one report without their measurement limits.
The Engagement
Choose the Measurement Engagement You Actually Need
Start with a forensic audit, a net-new implementation plan, or a migration path. Each route keeps definitions, dependencies, ownership, and acceptance evidence explicit.
Engagement format
Independent measurement audit, implementation specification, MMP migration, or journey validation and sign-off support.
Timing
The sequence is confirmed after the priority journeys, platforms, access, and release constraints are mapped. Audit, implementation, and migration remain explicit phases.
What you receive
- Measurement risk map and source-of-truth matrix
- Implementation-ready event, identity, and data-flow specification
- Journey QA evidence, acceptance criteria, and remediation sequence
What we need from you
- SDK, platform, link, event, and export configuration access
- Representative acquisition-to-revenue user journeys
- Growth, product, data, and engineering decision owners
Our Method
One Measurement System, Clear Source Ownership
The engagement starts with the decisions your teams need to make and ends with tested data contracts, owners, and acceptance rules.
Define the Decisions
We identify the acquisition, activation, retention, monetization, and lifecycle decisions the measurement system must support before changing events or dashboards.
Assign Source Ownership
Attribution, product behavior, store conversion, media delivery, spend, and verified revenue remain distinct, with one accountable source for each definition.
Validate the Real Journey
We test click, store, first open, attribution, deep link, in-app event, revenue, consent, export, and reporting behavior, not only whether an SDK initializes.
Engagement Scope
Audit, Implementation & Migration Scope
We scope the work around the channels, privacy paths, platforms, user journeys, and reporting risks that materially affect growth decisions.
MMP Audit, Setup & Migration
Provider selection, SDK and S2S architecture, partner data sharing, attribution windows, fraud controls, migration mapping, QA plans, and cutover risk management.
Event & Identity Taxonomy
Governed events, properties, triggers, user and device identifiers, revenue definitions, owners, environments, deduplication, and versioning.
ATT & Privacy Measurement
Consent-state implementation review, prompt timing tradeoffs, privacy documentation inputs, event sharing, and consent-aware reporting boundaries.
SKAN / AdAttributionKit Readiness
Privacy-safe iOS measurement design, conversion-value or event mapping, partner setup, reporting-delay expectations, and campaign-volume constraints.
Deep Links & Web-to-App
Universal or app links, deferred routing, campaign parameters, fallback behavior, owned-channel links, and device-level QA across the real journey.
Raw Data & Reconciliation
Real-time callbacks, scheduled storage exports, warehouse-ready schemas, freshness checks, spend and revenue joins, discrepancy rules, and reporting ownership.
Engagement Outputs
What Your Team Leaves With
The outcome is not another architecture diagram. It is an implementation-ready measurement plan with evidence, ownership, and explicit sign-off criteria.
Measurement Risk Map
An evidence-backed inventory of SDKs, events, identities, attribution settings, privacy paths, partner mappings, links, exports, dashboards, and decision-critical discrepancies.
Implementation-Ready Specification
An actionable event taxonomy, data-flow map, source-of-truth matrix, QA cases, suggested ownership, dependencies, and rollout sequence.
Journey Validation & Sign-Off
Representative user journeys tested across systems by definition, timezone, window, cohort, event source, consent state, and data freshness, with acceptance evidence.
Audit
A senior-led step with a clear owner and decision rule.
Prioritize
A senior-led step with a clear owner and decision rule.
Implement
A senior-led step with a clear owner and decision rule.
Learn
A senior-led step with a clear owner and decision rule.
Engagement Fit
Built for Teams Making High-Stakes Growth Decisions
The engagement works across new implementations, migrations, and forensic audits where attribution or downstream-value uncertainty is blocking confident optimization.
Good fit · 01
Marketing and product teams report different acquisition, activation, or revenue numbers.
Good fit · 02
You are implementing or migrating Adjust, AppsFlyer, Branch, or another MMP.
Good fit · 03
iOS privacy changes have made campaign quality, attribution, or SKAN reporting hard to interpret.
Good fit · 04
Deep links, partner events, subscriptions, raw exports, or warehouse joins fail in parts of the real user journey.
Measurement
Success Means Fewer Measurement Unknowns
The first outcome is knowing which data is complete, timely, correctly defined, and fit for each decision before campaign or product teams optimize against it.
Data Integrity
Event completeness, schema validity, duplicate rate, identity coverage, revenue reconciliation, environment leakage, and SDK or S2S consistency.
Attribution Coverage
Paid and owned source coverage, partner mappings, window logic, organic classification, reattribution, consent state, and privacy-framework reporting.
Operational Reliability
Data freshness, export success, dashboard latency, broken-link rate, QA pass rate, documented ownership, and time required to resolve discrepancies.
Attribution is a rules-based allocation of credit, not proof of causality. We keep platform delivery, MMP attribution, product behavior, store conversion, and verified backend transactions distinct, then reconcile them for the decision at hand.
Frequently Asked Questions
Which MMP should we choose?
The right choice depends on channel mix, scale, privacy requirements, raw-data needs, deep-link workflows, fraud controls, integrations, internal expertise, and total operating cost. We document requirements first, then compare providers against the same decision criteria.
Can all dashboards become one single source of truth?
Not literally. Different systems own different facts: an MMP owns attribution logic, product analytics owns behavior, stores own store-side conversion, ad platforms own delivery, and backend or BI should own verified transactions. We create one governed decision layer with explicit definitions and reconciliation rules.
Are ATT and SKAN the same measurement framework?
No. ATT governs access to device-level tracking signals such as IDFA when consent and policy allow. SKAN provides delayed, aggregated Apple attribution and cannot be joined back to ATT users. We design and report the two paths separately.
Can you migrate our MMP without losing data?
We minimize loss and reporting discontinuity with a documented event crosswalk, parallel validation where practical, partner and link migration, version-aware rollout, and cutover acceptance criteria. Historical provider data and live user adoption still create constraints, so we do not promise literal zero loss.
Do you implement dashboards too?
Yes, when the underlying definitions and data are trustworthy. We first establish taxonomy, ownership, validation, and freshness; then build reporting around the decisions each team needs to make.
Is this the same as a full MarTech audit?
No. This engagement concentrates on mobile measurement, attribution, privacy reporting, links, events, and reconciliation. If the decision also involves CRM, lifecycle, CDP, experimentation, subscription tooling, vendor overlap, or stack cost, start with the Mobile MarTech Audit.
Stop Debating Dashboards. Start Trusting Decisions.
Start with a senior-led audit of your measurement architecture, attribution rules, event taxonomy, privacy paths, and highest-risk user journeys.
Request a Mobile Measurement Audit