Prediction turns your data into foresight. We build demand and sales forecasting, churn, and lifetime value models that show you what’s coming, so you can act before your competitors.
Speak to an ExpertWe build predictive models that turn your historical data into a view of what’s coming: demand, sales, the customers likely to leave, and the customers worth the most. Then we help you act on it, adjusting spend and plans before the curve arrives, not after.
Prediction is for businesses planning against uncertainty: setting budgets, inventory, or targets without a reliable view of future demand, or investing in every customer equally because they can’t tell who will churn and who will compound. If your planning method is last year plus a percentage, a model built on your actual data will do better.
Modelling future customer demand from your historical data, seasonality, and trends, so you can plan inventory, capacity, and campaigns with confidence.
Projecting revenue by product, channel, or region, giving marketing and finance a shared, defensible number to plan against.
Identifying the customers most likely to leave before they do, so retention offers and lifecycle campaigns reach them while it still matters.
Predicting the future value of customers so acquisition and retention budgets concentrate on the customers worth the most.
Using forecasts to set and adjust marketing budgets, concentrating spend in the periods and products where demand will actually be.
Our models are built in Python: Prophet, a proven open-source framework, for time-series forecasting, and machine learning models for customer-level predictions like churn and lifetime value. We favour approaches that are transparent and maintainable: you can see why the model says what it says, and the model and code are yours to keep.
A reliable model needs history to learn from. As a rule of thumb, we look for at least 12 months of data so the model can see a full seasonal cycle. If your data isn’t ready, we’ll tell you, and an exploratory data analysis is usually the right first step.
It’s proactive marketing in its purest form. By applying statistical modelling to your own data, we help you turn reactive decisions into predictive strategy. Every insight helps you plan smarter, spend better, and grow faster.
Assessing whether your historical data has the depth and quality a reliable model needs.
Building the model in Python: Prophet for time-series forecasting, machine learning models for churn and lifetime value, tuned to your data and business events.
Back-testing the model against held-out history so you know how accurate it is before you rely on it.
Turning predictions into decisions: budgets, targets, retention campaigns, and plans, with the model refreshed as new data arrives.
At least 12 months, so the model can learn a full seasonal cycle. More history and more granularity generally produce better forecasts.
It depends on your data and how volatile your market is. We back-test every model against your own history and report its accuracy, so you know how much to trust it before it informs decisions. Be wary of anyone quoting accuracy before they’ve seen your data.
Python, with Prophet for time-series forecasting and machine learning models for customer-level predictions, integrated with BigQuery or your existing warehouse where useful. The model and code are built in your environment and belong to you.
Yes. Alongside demand and sales forecasting, we build churn and lifetime value models. Our LTV Analysis and Customer Segmentation solutions are productised entry points if you want to start with a defined scope and price.
Don’t wait to see what happens. Predict it.
Let's discuss how Predictive Modelling can drive measurable results for your business.