From descriptive and diagnostic analytics to predictive modelling, prescriptive AI, customer analytics, product analytics, and marketing attribution — Cinovic delivers end-to-end analytics solutions that tell you not just what happened, but why, what will happen next, and what to do about it.
We partner with CEOs, CMOs, product leaders, and data teams who are done making decisions based on gut feel and spreadsheets — and are ready to build analytics capabilities that drive measurable revenue, retention, and operational efficiency.
We don't deliver dashboards and disappear — we embed analytics into your decision-making process, define the metrics that matter, build the models that predict what's next, and train your team to act on data every single day.
From descriptive reporting and diagnostic root-cause analysis to predictive modelling, prescriptive AI, customer analytics, product analytics, and marketing attribution — we build analytics capabilities that compound in value as your business grows.
Build reliable reporting foundations — KPI dashboards, automated report generation, cohort analysis, funnel analysis, root-cause investigation frameworks, and data quality audits that give your team a single, trusted view of business performance before moving to prediction.
Build and deploy production-grade predictive models — customer churn prediction, demand forecasting, lead scoring, revenue forecasting, risk scoring, propensity modelling, and time-series forecasting — using Python, scikit-learn, XGBoost, LightGBM, and PyTorch, with MLflow model versioning and monitoring.
Unlock deep customer intelligence — customer lifetime value (CLV) modelling, RFM segmentation, behavioural cohort analysis, churn prediction and early warning systems, next-best-action recommendations, and customer health scoring — to acquire better customers, retain them longer, and grow their value over time.
Build product analytics frameworks — funnel analysis, retention curve analysis, feature adoption tracking, user journey mapping, activation and engagement scoring, A/B test analysis, and product-led growth (PLG) metrics — using Mixpanel, Amplitude, PostHog, or custom SQL-based analytics on your data warehouse.
Demystify your marketing performance — multi-touch attribution modelling (first-touch, last-touch, linear, data-driven), marketing mix modelling (MMM) for channel contribution analysis, campaign ROI tracking, paid media analytics, SEO performance analytics, and customer acquisition cost (CAC) by channel and cohort.
Go beyond prediction to recommendation — prescriptive analytics, optimisation models, scenario simulation, decision trees, Monte Carlo analysis, and AI-powered decision support systems that tell your teams not just what will happen, but the optimal action to take in response.
Build reliable reporting foundations — KPI dashboards, automated report generation, cohort analysis, funnel analysis, root-cause investigation frameworks, and data quality audits that give your team a single, trusted view of business performance before moving to prediction.
Build and deploy production-grade predictive models — customer churn prediction, demand forecasting, lead scoring, revenue forecasting, risk scoring, propensity modelling, and time-series forecasting — using Python, scikit-learn, XGBoost, LightGBM, and PyTorch, with MLflow model versioning and monitoring.
Unlock deep customer intelligence — customer lifetime value (CLV) modelling, RFM segmentation, behavioural cohort analysis, churn prediction and early warning systems, next-best-action recommendations, and customer health scoring — to acquire better customers, retain them longer, and grow their value over time.
Build product analytics frameworks — funnel analysis, retention curve analysis, feature adoption tracking, user journey mapping, activation and engagement scoring, A/B test analysis, and product-led growth (PLG) metrics — using Mixpanel, Amplitude, PostHog, or custom SQL-based analytics on your data warehouse.
Demystify your marketing performance — multi-touch attribution modelling (first-touch, last-touch, linear, data-driven), marketing mix modelling (MMM) for channel contribution analysis, campaign ROI tracking, paid media analytics, SEO performance analytics, and customer acquisition cost (CAC) by channel and cohort.
Go beyond prediction to recommendation — prescriptive analytics, optimisation models, scenario simulation, decision trees, Monte Carlo analysis, and AI-powered decision support systems that tell your teams not just what will happen, but the optimal action to take in response.
We combine statistical rigor, machine learning engineering, domain expertise, and data storytelling to deliver analytics capabilities that are not just technically sound but commercially relevant and decision-ready.
Design and build end-to-end experimentation platforms — feature flagging integration (LaunchDarkly, Statsig, GrowthBook), experiment assignment and tracking pipelines, Bayesian and frequentist statistical frameworks, sequential testing, CUPED variance reduction, and experiment result dashboards — so every product and marketing change is tested before it's scaled.
Transform complex analytics outputs into clear, compelling narratives — designing executive briefings, data-driven board presentations, insight reports, and analytics summaries that communicate findings in plain business language, with actionable recommendations, confidence intervals explained simply, and clear 'so what' conclusions.
Build real-time analytics capabilities — streaming event processing with Kafka and Flink, live KPI monitoring, real-time fraud and anomaly detection, operational alerting systems, and sub-minute dashboard refresh for eCommerce, logistics, and financial services teams who need to act on data as it happens — not hours later.
Apply advanced statistical and spatial analytics — geospatial analysis with PostGIS and Kepler.gl, survival analysis for customer and product lifetime modelling, causal inference and uplift modelling, network graph analytics, Bayesian inference, and time-series decomposition — for organisations where standard analytics isn't sufficient.
Stay ahead with practical guides, model deep-dives, and case studies on predictive analytics, customer churn modelling, marketing attribution, A/B testing, product analytics, and building data-driven organisations.
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A 2026 enterprise guide to building high-performance Power BI data models, from star schema design to DAX time intelligence, predictive analytics, and row-level security.
Hyvä migration delivers measurable ROI: Magento stores see LCP drop from 4.8s to 1.2s and 50%+ achieve green Core Web Vitals. See how Cinovic turns performance into profit.
Tell us about the decisions you struggle to make with confidence, the metrics you wish you understood better, and the analytics capabilities you want to build — and we'll map out a practical roadmap to get you there.
Data analytics services cover the full spectrum of turning raw data into actionable business intelligence — from descriptive analytics (understanding what happened), diagnostic analytics (understanding why), predictive analytics (forecasting what will happen), and prescriptive analytics (recommending what to do). Services include data modelling, statistical analysis, machine learning model development, customer and product analytics, marketing attribution, and decision support systems.
Business intelligence (BI) focuses on reporting and visualising historical data — answering 'what happened' through dashboards, reports, and KPI tracking. Data analytics is broader — it includes BI but extends into predictive modelling, statistical analysis, experimentation, machine learning, and prescriptive decision support. BI tells you your sales dropped last quarter. Analytics tells you why, predicts whether it will continue, and recommends what to do about it.
Predictive analytics uses historical data, statistical algorithms, and machine learning models to forecast future outcomes — such as which customers are likely to churn, which leads are most likely to convert, how much demand a product will see next quarter, or which transactions are likely fraudulent. The business value is in acting early: retaining customers before they leave, focusing sales on the highest-probability leads, and planning inventory before stockouts happen.
focused predictive model — such as a customer churn predictor or lead scoring model — typically takes 4–8 weeks from data discovery to production deployment. More complex models with large feature engineering requirements, multiple data sources, or real-time scoring needs take 8–16 weeks. Full analytics platforms with multiple models, monitoring, and retraining pipelines take 3–6 months. We scope everything during your free analytics strategy session.
Marketing mix modelling (MMM) uses statistical regression to measure the contribution of each marketing channel to overall business outcomes — using aggregate data without requiring user-level tracking, making it privacy-safe and effective in a cookieless environment. Multi-touch attribution (MTA) tracks individual user journeys across touchpoints to assign credit. MMM is better for strategic budget planning across channels. MTA is better for tactical in-campaign optimisation. Most mature marketing analytics programmes use both together.