Trustworthy Product Metrics
A governed event tracking plan, funnels, cohorts, and feature adoption metrics give product teams consistent numbers in tools such as Amplitude, Mixpanel, or your own warehouse.
Industries
Technology, Media & Telecom
Turn Every Event and Subscriber Signal Into Action
Software companies, digital publishers, streaming services, and telecom operators partner with us on the data platforms behind their products. Our engineers build event pipelines, subscriber models, and real-time analytics that product, marketing, and network teams act on.
What We Deliver
Where We Make the Difference
Digital businesses capture huge volumes of clickstream, usage, and network data, but tracking plans drift and metrics disagree between teams. Product decisions slow down while analysts reconcile numbers. We fix the data foundation first, then build the models and streaming analytics that product and revenue teams need.
Churn and Lifetime Value
Models trained on usage, billing, and support history estimate churn risk and customer lifetime value, so retention and pricing teams can target the right accounts.
Streaming Data Pipelines
Apache Kafka, Amazon Kinesis, or Azure Event Hubs pipelines process clickstream and telemetry as it arrives, feeding personalization, fraud checks, and operational alerts.
Network Performance Analytics
Telecom operators combine network telemetry, capacity data, and trouble tickets to spot congestion, anticipate outages, and prioritize where infrastructure spending will matter most.
Our Approach
How We Deliver Technology, Media & Telecom
Step 1: Agree on the Metrics That Matter
Product, marketing, and finance leaders align on core metric definitions and rank analytics use cases by revenue impact and data readiness.
Step 2: Fix the Event Foundation
We audit tracking, clean up event schemas, and design batch and streaming pipelines sized to your actual data volumes and latency needs.
Step 3: Ship Models Into the Product
Churn, recommendation, and scoring models are built in short sprints and exposed through APIs so product and marketing systems can use them directly.
Step 4: Measure and Iterate
Experiment frameworks and model monitoring show whether each change moved the metric, so your own team keeps learning and improving after launch.
Should we use a product analytics tool or build in our warehouse?
It depends on who asks the questions and how much data you have. Many teams use a tool like Amplitude for product managers and a warehouse for deeper analysis, sharing one event schema between them.
Do we really need real-time data?
Only for use cases where minutes matter, such as fraud checks, personalization, or network alerts. We recommend streaming where it pays off and simpler batch pipelines everywhere else.
Can you work with our existing engineering team?
Yes. We usually embed with your data and product engineers, follow your code review and deployment practices, and leave behind pipelines your team owns.
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