PREDICTIVE MODELS THAT SHIP AND KEEP WORKING

Quick Answer: NUUN Digital builds predictive analytics that get deployed and stay accurate — churn, LTV, demand forecasting, propensity-to-convert, and price-elasticity models. Every model ships with monitoring, retraining cadence, and production-quality code. Data-science projects that stall at proof-of-concept fail the business; we refuse to build them.

WHAT WE DELIVER

Churn and retention models. Probability-to-churn scoring with intervention recommendation.
Customer lifetime value (LTV) models. Cohort and individual-level LTV predictions.
Demand forecasting. SKU-, region-, and channel-level demand predictions.
Propensity models. Propensity-to-buy, -upgrade, -refer, -engage.
Price-elasticity models. Willingness-to-pay modelling for pricing and promotion.
Anomaly detection. Fraud, outlier, and quality-signal detection.

HOW WE DO IT

Problem framing. What decision does this model support, at what SLA?
Feature engineering. Domain-knowledge-driven feature design, not just AutoML.
Model development. Gradient-boosted trees, Bayesian approaches, or deep learning as appropriate.
Validate on business metrics, not just model metrics. AUC doesn't run the business.
Ship to production. With monitoring, drift detection, and documented retraining cadence.

WHEN IT FITS

Large customer bases with reasonable behavioural data depth.
Operational teams capable of acting on predictive scores (not just receiving them).
Strategic bets where modelled predictions drive real investment.
Existing models performing poorly and needing rebuild.

SELECTED WORK

Anonymized — subscription business — Churn model → save-rate up [X]% via targeted intervention. Read case →
Confidential retailer — LTV model → paid-media reallocation → CAC down, margin up. Read case →

SOURCES & FURTHER READING

Data & Analytics practice
AI & Digital Transformation practice
Data Management practice
MLflow — https://mlflow.org/
Google Cloud AI — https://cloud.google.com/products/ai

Frequently asked.

Do you use off-the-shelf or custom models?

Depends. Common problems (churn, LTV) often best-served by proven approaches tuned to your data. Novel problems get custom modelling. We resist over-engineering.

What does model monitoring look like?

Performance monitoring (accuracy against actuals), data-drift monitoring (input distributions shifting), and business-outcome monitoring (model supporting actual decisions). All three matter.

How often should models retrain?

Depends on data-drift velocity. Stable categories: quarterly retrain. Fast-changing categories (fashion retail, certain financial products): monthly or continuous. Retraining cadence is a model-design decision.

Can you integrate models into our existing data stack?

Yes. Snowflake, Databricks, BigQuery, and cloud-native ML platforms are standard deployment targets. Model-serving via APIs, reverse ETL to activation tools, or direct database integration.

What about interpretability?

Mandatory for regulated industries (financial services, healthcare) and recommended elsewhere. SHAP, LIME, and model cards standard. We won't ship black-box models into decisions that require explanation.