Service

Forecasting

Predictive Analytics.

Historical data describes what happened. Predictive models anticipate what is going to happen. The difference between the two positions isn't technological — it's the ability to act before the problem appears or the opportunity disappears.

What is predictive analytics

Predictive analytics is the use of statistical and machine learning models to generate forecasts about future behaviour from past and present data. Unlike descriptive analysis — which answers what happened — or diagnostic analysis — which answers why it happened — predictive analytics answers what is going to happen and with what probability.

Demand forecasting

Anticipating which products, in what volume and in which markets will see demand in the coming quarters.

Conversion propensity

Identifying which leads are most likely to become customers based on their prior behaviour.

Churn risk

Detecting customers with high cancellation probability before they cancel, enabling proactive intervention.

Predictive segmentation

Grouping customers not just by what they are, but by what they are likely to do next.

How we do it

01

Business objective definition

A predictive model without a clear business objective is a technical exercise. We start by understanding which specific decision the model must improve: pricing, segmentation, sales prioritisation, inventory management.

02

Data audit and preparation

We assess the quality, completeness and structure of available data. Most predictive projects fail at this stage — not because the data doesn't exist, but because it wasn't ready to be used.

03

Modelling and validation

We build the model appropriate to the problem: regression, classification, time series, clustering. We validate against historical data using business metrics, not just statistical ones.

04

Integration and operationalisation

A model that only works in an analysis notebook has no operational value. We integrate predictions into existing decision processes: CRM, dashboards, marketing automations.

What results to expect

  • —Reduction in demand forecasting error margin by 20-40% compared to historical average methods.
  • —Sales pipeline prioritisation with propensity models that increase conversion rates in worked accounts.
  • —Early churn risk detection 30-60 days in advance, enabling proactive retention.
  • —Customer segmentations with greater predictive power than demographic or historical value segmentations.

Results depend on the quality and volume of available data. In the initial diagnosis we assess viability and estimated improvement potential for your specific case.

Frequently asked questions

How much data do I need to implement predictive analytics?

It depends on the type of model. For churn or propensity models, you generally need at least 12-24 months of historical data with at least 1,000-5,000 events of the behaviour you want to predict. In the initial diagnosis we assess whether your data is sufficient and its quality.

Do I need an internal data science team to use the models?

No. The models we build integrate into the tools your team already uses: CRM, dashboards, marketing platforms. No one on your team needs to know how to programme or train models to benefit from the predictions.

What is the difference between predictive analytics and business intelligence?

BI (Business Intelligence) describes and analyses what has already happened: last month's sales, CAC evolution, customer distribution by segment. Predictive analytics anticipates what is going to happen: which customers will cancel, which products will see higher demand, which leads will convert.

How long does it take to get a predictive model operational?

A first validation model can be ready in 4-8 weeks, depending on data readiness and problem complexity. Production models, with integration into existing systems and real-environment validation, typically take 2-4 months.

Want to see how it would work in your case?

Tell us the business problem you want to solve with data. We assess viability and improvement potential with no commitment.

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