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Mobiloitte offers a privacy-first mobile app analytics platform with real-time tracking, LTV prediction, churn scoring, and activation pipelines to boost growth, product, and data decisions.
Event taxonomy & governance for iOS, Android, React Native & Flutter with clear data rules, ownership & unified metrics tracking.
Low-latency pipelines with Kafka, Flink & Spark plus dbt & Delta/Iceberg transform events into instant dashboards, alerts & AI triggers.
With consent, Mobiloitte stitches user signals (deterministic and probabilistic) into trusted profiles that CRM, CDP, and lifecycle tools can use.
Teams see drop-offs and loops across onboarding, paywalls, subscriptions, and retention so they can fix the biggest blockers first.
ML models spot high-value & at-risk users, guiding actions measured by uplift & payback—ideal for predictive churn in subscription apps.
Stat-sound tests (sequential, CUPED, bandits) with guardrails help teams learn fast without p-hacking.
Willingness-to-pay models, segments, and usage clusters keep the roadmap and pricing tied to real behaviour.
Reverse ETL and feature stores push segments and scores to messaging, ads, and in-app channels in near real time.
Support for SKAdNetwork attribution for iOS apps, incrementality tests, and marketing mix modelling for mobile apps gives a clearer view of spend and impact.
Freshness, completeness, schema drift, anomalies, lineage, and SLA alerts mean leaders never decide on broken data.
Cheaper storage, compression, partitions, z-ordering, vectorised queries, caching, and FinOps dashboards keep speed high and costs low.
Consent flows, access control, masking, retention, and purpose limits make regulators, boards, and CISOs comfortable.
Analytics fails when it is split, slow, or built for slides instead of decisions. Mobiloitte builds on the warehouse/lakehouse to cut lock-in, enforce simple shared meanings, and give every team the same trusted truth. Pipelines, models, and dashboards are versioned and reproducible. Predictive models are monitored so they do not drift, slow down, cost too much, or become unfair.
Well coded Testable dbt models, CI/CD, automated data checks, and model registries.
Responsive Embedded pods work with PMs, marketers, and data teams for quick wins.
Fast growing Built to scale from 100K to billions of events/day without re-platforming.
Multipurpose One governed stack for insights, ML, testing, and personalisation.
Before Any Event is Added, Mobiloitte Sends a Simple, Strict Analytics Contract. KPIs, Event Names, Metric Meanings, Roles, Retention Rules, and Privacy are Aligned So Engineers Can Build, Analysts Can Trust, and Leaders Can Measure Success
KPI tree, metric dictionary, semantic layer
Event taxonomy, tracking plan, data contracts
Identity strategy (deterministic + probabilistic) and consent framework
Data architecture (streaming, lakehouse/warehouse, reverse ETL)
Cost model and SLAs for performance, freshness, availability
Mobiloitte Sets Up Streaming and Batch Ingestion, dbt Transforms, BI Dashboards, LTV/Churn/Propensity Models, and a Safe Experimentation Setup. Everything Is Reproducible and Monitored With CI/CD
Airflow/Prefect orchestration with Kafka/Flink/Spark pipelines
dbt transforms, semantic layer, and clear lineage
BI (Looker, Power BI, Mode, Superset) with ready templates
ML models for LTV, churn, ARPU, propensity, and uplift
Experimentation stack: guardrails, Bayesian/bandits, sequential tests
Mobiloitte Keeps Analytics Fast, Clear, Lawful, and Profitable. Models Are Retrained, Data Is Checked, Drift Is Fixed, and Costs Are Tuned. Growth Playbooks Help Improve CAC, Retention, and Pricing Over Time
Model registry, feature store, auto-retraining, and evaluations
Data observability: freshness, anomalies, drift, lineage, SLAs
FinOps: budgets, alerts, infra optimization
Playbooks, training, governance and compliance reviews
24×7 support and roadmap guidance
Mobiloitte aligns KPIs, journeys, taxonomy, governance, and compliance. The team estimates ROI, effort, and data readiness to pick the first wins.
Ingestion, modeling, BI, predictive models, and experiments go live with CI/CD. Both test and real traffic are used to check accuracy, speed, and business value.
Pipelines, models, tests, and costs are watched and improved. Product and growth teams partner with Mobiloitte to raise retention, lower CAC, lift ROAS, and shorten payback.
Firebase • Segment • Snowplow • RudderStack • AppsFlyer • Branch • Kafka • Flink • Spark • dbt • Airflow/Prefect • Delta/Iceberg/Hudi • Snowflake/BigQuery/Redshift • Superset • Looker • Power BI • MLflow • W&B • Feast
Mobiloitte builds with privacy by design. That means consent flows, least-privilege access, masking of sensitive fields, clear retention rules, DPIAs where needed, and audit logs. The stack supports SOC2, GDPR, HIPAA, and safe data sharing across teams.
They start from business KPIs and key user journeys, not tools. Every event has a clear purpose, owner, and required properties, plus versioning and end-of-life rules. Data contracts and automated checks keep the plan stable as the app grows.
Yes. With consent, they combine deterministic IDs (like login) and probabilistic methods to link sessions safely. Access is role-based, usage is logged, and retention is limited, so profiles stay useful and compliant.
They watch quality at ingestion, transforms, and activation. Nulls, uniqueness, schema drift, anomalies, and lineage are checked with alerts and playbooks. Teams receive regular quality reports so issues are fixed fast.
They pair models with action plans who should get an offer, when, and how to measure impact. Methods include survival models and ML (GBMs, DL) with a feature store for consistency. Outputs are monitored and recalibrated so predictions stay useful.
They use sequential tests with alpha spending, CUPED/variance reduction, and pre-registered hypotheses. Guardrail metrics protect the business while learning. A central stats engine and rules stop “test-until-you-win” behaviour.
Yes. They provide server-side tracking, incrementality testing, and SKAdNetwork attribution for iOS apps, plus MMM to fill signal gaps. Sensitive PII is minimised and controlled to keep programmes lawful.
They ship a simple semantic layer, curated dashboards, and short “how to read” guides. Workshops help PMs, marketers, finance, and leaders use data on their own without breaking rules. The result is fewer ad hoc requests and faster decisions.
Yes. Reverse ETL and feature stores send trusted segments and scores to CRM, CDP, ads, and in-app channels. Everything respects roles, consent flags, and retention rules.
Not always. A warehouse-first setup lets teams keep or swap tools without losing history. If current tools work, Mobiloitte integrates and governs them instead of replacing them.
They use efficient formats, partitions, compaction, vectorised engines, and caching. FinOps dashboards with budgets and alerts stop surprises. Query, pipeline, and model costs are reviewed often and tuned as volume grows.
They offer SLAs, playbooks, runbooks, training, and shared roadmap planning. Escalation paths are clear, and regular reviews keep the stack healthy, compliant, and current. Help is available 24×7.
A governed MVP with reliable dashboards often ships in 4–6 weeks. Predictive models, experiments, and activation loops usually land in 8–12 weeks or more. Each phase is tied to clear KPIs like retention lift or lower CAC.
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Stop guessing. With Mobiloitte, mobile analytics become a growth engine that connects clean data, simple tools, and clear goals so every change can be tested, measured, and improved.