Beneath AnalyticsAI Consulting & Development
Data Science
Data science answers the questions your dashboards cannot: who will churn, what demand will be, which segments matter. Beneath Analytics builds churn models, forecasts, and segmentation on your own data, integrates them into the tools your team already uses, and validates them with properly designed experiments.
Technology we use
- Analytics & measurement
- Mixpanel
- BI & reporting
- Looker Studio
- Data & warehousing
- BigQuery
What this includes.
We apply data science where it creates measurable business value: churn prediction, demand forecasting, customer segmentation, and experimentation frameworks. GDPR-native delivery, EU data residency as standard.
Churn prediction models
Behavioural models that identify at-risk customers 30 to 60 days before they cancel. Integrated with your CRM or CS tooling so action can be taken.
Customer segmentation
Data-driven customer segments based on behaviour, value, and intent: not just demographics. Actionable for marketing, product, and retention teams.
Demand and revenue forecasting
Statistical forecasting models built on your historical patterns. Useful for inventory planning, headcount decisions, and investor reporting.
A/B test design and analysis
Statistically valid experiment design, running, and interpretation. Stop making decisions on underpowered tests with inconclusive results.
What changes when it lands.
4.2% to 2.8% in four months
Monthly churn
45 days earlier warning
Churn visibility
About €280K in year one
ARR retained
Common questions.
Usually twelve to twenty-four months of history with the outcomes you care about recorded. If the data is missing or inconsistent, we start with a pipeline and measurement project rather than promising a model that cannot be trained.
We validate against holdout data, ship the model behind the workflow it supports, and track live performance against the same metric used during development, so degradation is visible rather than assumed away.
Wherever the decision happens: your CRM, customer success dashboard, Slack alerts, or a Looker Studio report. A model nobody can act on has no value, so integration comes first.
Yes. We define hypotheses, calculate the sample size needed for a conclusive result, run the test, and interpret it: which usually ends underpowered experiments that were never going to answer the question.
Related solutions.
- Tracking & Analytics
Analytics & Business Intelligence
Analytics and business intelligence means Beneath Analytics turns your tracked data into decisions: attribution modelling, predictive models, and dashboards built around the questions your team actually asks and adopted by the people who use them.
View solution - AI Consulting & Development
Applied AI
Applied AI puts machine learning and language models into the workflows where they create measurable value: recommendations, classification, document processing, and anomaly detection. Every engagement starts with a business problem, is trained on your data, and ships with monitoring so you know when model performance starts to drift.
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