Data & artificial intelligence / Predictive analytics
Anticipate what
is going to happen.
Models that learn from your history to predict demand, customer churn, fraud or breakdowns before they happen. So you can decide in advance instead of reacting when it is already too late.
What it is
From “what happened”
to “what is likely to happen.”
Descriptive analytics tells you what already happened: reports, dashboards, last month’s sales. Predictive analytics goes one step further and uses that same historical data to estimate what is likely to happen next: how much you will sell next week, which customers are about to leave, or which transaction looks fraudulent.
The key difference is the verb. You move from “what happened” to “what is likely to happen” and, with it, from reacting to anticipating. But it pays to be honest: a predictive modeldoes not foresee the future, it computes probabilities from patterns that recur. Its value is not being right every time, but being right often enough and early enough to change a decision. It is part of our data and artificial intelligence practiceand it almost always relies on machine learning to build the models.
What you can predict
From demand
to breakdown.
Demand and sales (forecast)
How much you will sell by product, store or week. It helps you plan purchasing, production and staffing without running short or piling up idle stock.
Customer churn
Which customers have a high probability of leaving in the coming weeks, so you can act before you lose them rather than after.
Fraud and anomalies
Operations or transactions that fall outside the normal pattern and deserve a review, flagged in near real time.
Predictive maintenance
Which machine or asset is most likely to fail, so you can intervene before the breakdown instead of after the stoppage.
Risk and scoring
The probability of default, claim or non-compliance, so you can approve, prioritise or set conditions with sound judgement.
Stock and out-of-stocks
Which items will run out and when, so you can restock in time and avoid both lost sales and excess inventory.
What it needs to work
A model learns
from your past.
A prediction does not come out of nowhere. For a model to work it needs a minimum foundation; without it, any result is smoke:
- Enough historical data: the model learns from what already happened
- A clear, measurable goal: what exactly you want to predict
- Relevant variables: the factors that truly drive that outcome
- Data quality: no gaps or serious errors that pollute the learning
- Examples of the event to predict: enough cases of churn, breakdowns or fraud
- A decision the prediction can change: if nothing changes, the model is redundant
Cases by sector
The same idea, applied
to each business.
| Sector | What is predicted | What it is for |
|---|---|---|
| Retail and e-commerce | Demand by product and out-of-stocks | Restock in time, plan campaigns and reduce tied-up inventory. |
| Banking and insurance | Default risk and transaction fraud | Approve with judgement and stop suspicious operations before they complete. |
| Industry and manufacturing | Machine breakdowns and quality deviations | Maintenance before failure and fewer unplanned stoppages. |
| SaaS and subscription | Customer churn | Retain at-risk customers before they cancel. |
| Energy and utilities | Grid consumption and demand | Size production and anticipate demand peaks. |
In one line
Predicting isn't guessing: it's deciding in advance.
Benefits and outcomes
What you can
expect.
Anticipate, don’t react
You see the churn, the stockout or the breakdown coming weeks in advance. That margin is what turns a problem into a decision.
Lower costs and losses
Less idle stock, less completed fraud, fewer machine stoppages, fewer lost customers. The saving is concrete and measurable.
Prioritise resources
Instead of treating every case the same, you focus effort where the probability — and the impact — is highest.
Decide with probabilities
You swap “I have a feeling” for “there is an 80% probability.” The decision is still yours, but better informed.
Continuous improvement
The model learns from every new data point. The more you use it and correct it, the better it predicts.
Scale without effort
Once in production, the model scores thousands of cases instantly, with no added manual work.
How we build it
Reliable models,
no smoke.
A model that is right in the lab and wrong in production is worthless. That is why we build with statistical honesty: a modest model you can trust beats a spectacular one on paper that deceives you.
We always validate on data the model has not seen (train/test), because a model only proves its worth on new cases, not by repeating the ones it already knows. We choose withhonest metrics and compare them against a baseline: if a model does not clearly beat a simple rule or current intuition, we say so.
We watch for overfitting (memorising the past instead of learning the pattern) anddrift (the world changing and the model falling behind); when it appears, we monitor and retrain. And we prioritise explainability: a model whose decision can be justified builds trust; a black box does not.
We don’t sell AI for the sake of it. If your data cannot support a reliable model, we tell you before building anything.
CPPA methodology
From the question
to the model in production.
Frame the problem
We start with the decision, not the algorithm. What do you want to predict, how far in advance, and what will you do with the prediction? If no decision changes, there is no project.
Prepare the data
We gather the history, clean it and build the variables that genuinely explain the outcome. Much of the model’s success is decided here, far more than in the choice of algorithm.
Train and validate
We train several models, compare them against a baseline and test them on data they have not seen. We choose on honest metrics, not on the flashiest number.
Production and monitoring
We embed the model into your operations, measure its real accuracy and watch for drift. When the world changes, we retrain. A model is a living system, not a deliverable you sign off and forget.
Examples
How it looks
in practice.
Demand forecast
- Sales history by product and store
- Variables: seasonality, price, promotions, holidays
- Model training and validation
- Weekly prediction by item
- Adjustment of purchasing and production
- Accuracy measurement and retraining
Churn prediction
- Usage, contract and support data
- Calculation of risk signals
- Churn probability model
- Prioritised list of at-risk customers
- Action by the retention team
- Measurement of the effect on real churn
Fraud detection
- Near real-time transaction stream
- Anomaly model per operation
- Instant risk score
- Blocking or review of suspicious cases
- Traceable log for audit
- Retraining with confirmed cases
Predictive maintenance
- Sensor data and past breakdowns
- Wear and usage variables
- Failure probability model
- Alert before the breakdown arrives
- Planned maintenance order
- Adjustment with every real intervention
Risks and mitigation
What can go wrong
(and how we avoid it).
A poorly framed predictive model does harm: it gives false confidence. These are the real risks and how we control them:
Overfitting
The model memorises the past instead of learning the pattern, and fails on new data. We avoid it by always validating against data it has not seen.
Data drift
The world changes and the model becomes outdated. We monitor it in production and retrain it when its accuracy starts to slip.
Biased data
If the history carries a bias, the model perpetuates it. We audit the data and the results to detect and correct it in time.
False certainties
A probability is not a guarantee. We always communicate the margin of error and avoid presenting a prediction as if it were a fact.
Predicting without acting
A prediction that changes no decision is an expensive luxury. We start with cases where the action is clear and the impact is measurable.
A black box with no explanation
A model no one understands builds no trust. We prioritise explainability so every decision made with it can be justified.
Frequently asked questions
What accuracy can a predictive model reach?
How much data do I need?
Is it a crystal ball?
How is it different from machine learning?
How long does it take to be ready?
How does it integrate with what I already have?
Related services
Keep exploring.
Machine learning
The models that make prediction possible. If you want to understand the technique underneath, start here.
View →
Business intelligence and dashboards
Before predicting, it helps to understand the present well. Dashboards turn your data into day-to-day decisions.
View →
Data engineering
Without clean, well-organised data there is no reliable prediction. Data engineering builds that foundation.
View →What would you like to be able to anticipate?
Request a proposal →Want to see how we think through a real business from its data? Read ourCPPA X-RAY on 100 Montaditos.