Predictive Analytics
Forecasts that change decisions, not slide decks.
Demand forecasting, churn prediction, and propensity models built into the workflows where decisions actually happen — with honest accuracy reporting and monitoring for the day the model starts to drift.
A Prediction Nobody Acts On Is Just Trivia
The hard part of predictive analytics is rarely the model. Gradient boosting on clean tabular data is close to a solved problem. The hard part is getting a prediction in front of the right person, at the moment they decide, in a form they trust enough to act on.
So we start at the decision, not the algorithm. Who acts on this, how often, and what would they do differently if the number were in front of them? That determines the latency, the interface, and the accuracy genuinely required — which is often lower than teams assume.
We report accuracy honestly, including where a model performs poorly. A churn model that is 85% accurate overall but unreliable on your highest-value accounts is worse than useless if nobody flags that limitation, because it will be trusted exactly where it should not be.
Service Inclusions
Demand Forecasting
Time-series models with seasonality, promotions, and external factors — with prediction intervals, not just point estimates.
Churn & Retention
Propensity models that identify at-risk accounts early enough for intervention to still be possible.
Explainability Built In
SHAP-based attribution on every prediction, so the people acting on it can see which factors drove it.
Drift Detection
Monitoring on feature distributions and prediction quality, alerting when reality diverges from training data.
Rigorous Backtesting
Walk-forward validation on held-out history, benchmarked against your current approach — including doing nothing.
Delivered Into Workflow
Predictions surfaced in the CRM, dashboard, or system where decisions happen — not in a notebook nobody opens.
A Process Built for Clarity
No black boxes. No surprise invoices. Every project at Mornis Global follows a disciplined four-phase process designed to reduce risk and maximise value at every stage.
Decision Framing
Identify who acts on the prediction, how often, and what accuracy would actually change behaviour.
Data & Feasibility
Assess whether history, volume, and label quality can support the target. Sometimes the answer is no.
Baseline First
Establish a simple benchmark — often a heuristic. Anything more complex must beat it to justify its cost.
Model Development
Feature engineering and model selection with walk-forward validation against the baseline.
Integration
Serving via API or scheduled scoring, surfaced in the tools your team already uses.
Monitoring & Retraining
Drift detection, performance dashboards, and an agreed retraining cadence.
The Tech Stack
We select technologies based on performance, scalability, and long-term maintainability, not trends.
Python
Specialized implementation of Python in the Language space.
scikit-learn
Specialized implementation of scikit-learn in the Modelling space.
XGBoost / LightGBM
Specialized implementation of XGBoost / LightGBM in the Gradient Boosting space.
Prophet
Specialized implementation of Prophet in the Time Series space.
MLflow
Specialized implementation of MLflow in the Experiment Tracking space.
SHAP
Specialized implementation of SHAP in the Explainability space.
Real-World Impact
EatLocal
The Challenge
“Inventory decisions were made on last week's averages, producing consistent overstock on slow days and stockouts during local events.”
The Solution
We built a demand forecasting model incorporating seasonality, weather, and local event data, delivered as daily per-location predictions with intervals directly in the existing ops dashboard.
Key Performance Indicators
Common Inquiries
Everything you need to know about our specialized services.
What Decision Would a Good Forecast Change?
Tell us the decision you make repeatedly with incomplete information. We will assess whether your history can support predicting it.