A Score for the One Decision You Make Every Week
Most predictive analytics pitches promise to forecast the whole business. We build something narrower and more useful: a model scoring one specific decision you already make regularly, such as which customer is likely to pay late or which machine is likely to fail next, built from your own history, with the drivers behind each score shown plainly.
50+ ERP and software projects. Zero failed projects since 2021.
Out of 100, higher means higher risk
A narrow model beats a broad promise
A model built for one decision, on your own outcomes, holds up. A model built to predict everything usually predicts nothing well.
A model that knows exactly what it is scoring
Instead of a general-purpose analytics platform, the model is built around one decision you already make, such as approving a credit limit or scheduling maintenance. That focus is what makes it testable against real past outcomes and trustworthy enough to actually use.
- One clearly defined decision, not a broad promise.
- Tested against outcomes you already know.
- Accuracy measured honestly, not oversold.
You can see why the score is what it is
A score with no explanation is hard to trust and impossible to challenge. Every score comes with the main factors that pushed it up or down, in plain language, so the person making the decision can weigh the score against what they know that the model does not.
- Main drivers shown behind every score.
- Explained in plain business language.
- The person's own knowledge stays part of the decision.
What a working score looks like
A number for the decision at hand, and the drivers that explain where it came from.
Out of 100, higher means higher risk
From past outcomes to a score you can use tomorrow
The decisions most worth a predictive model
Decisions with enough past examples and a known outcome are the best fit.
Payment and credit risk
Scoring the likelihood a customer pays late, to guide credit terms and collection priority.
Equipment failure risk
Scoring which machines are likely to need maintenance soon, from sensor and usage data.
Deal and lead scoring
Scoring which leads or open deals are likely to close, to guide where sales spends time.
Churn and attrition risk
Scoring which customers or employees are showing signs of leaving, early enough to act.
Stock-out risk
Scoring which items are likely to run short against demand before the next reorder.
Quality risk
Scoring which batches or orders carry a higher risk of a quality issue, based on past patterns.
What a working predictive score changes
Why teams choose TCB Infotech for predictive analytics
Predictive analytics questions
What is predictive analytics?
How is this different from the forecasting service?
What decisions can a predictive model support?
Can we see why the model gave a particular score?
How much data do we need?
How long does a first model take to build?
Score the decision you actually make, not the whole business
Predictive analytics earns its keep when it is scoped to one real decision, tested against outcomes you already know, and honest about how confident it actually is. A model that claims to predict everything is usually a model nobody can verify.
Built narrow, explained clearly and checked against real results, a predictive score becomes one more piece of evidence the person making the decision can weigh, alongside everything else they already know.
Find the Decision Worth Scoring
Book a free consultation. Tell us about a decision you make regularly with known past outcomes, and we will assess whether a model would help.
Book a Free Consultation →No commitment. A practical look at what your data could support.
What Is Inside a Predictive Model
A score is only useful if it is trustworthy and explainable. Here is how the model learns from your own outcomes, how it explains itself, and how its accuracy is kept honest over time.
From known outcomes to a score you can rely on
Learned from what actually happened, not assumptions
The model is trained on past cases where the real outcome is already known, such as which invoices were actually paid late or which machines actually failed. This grounds the model in your own reality rather than a general industry assumption that may not match how your business runs.
- Trained on your own historical, known-outcome cases.
- Reflects your business, not an industry average.
- Retrained as new outcomes become available over time.
Every score comes with its own reasoning
Rather than a single opaque number, each score is broken down into the factors that contributed to it, ranked by how much they mattered. This lets the person using the score sanity-check it against context the model does not have.
- Contributing factors ranked and shown per score.
- Presented in plain business language, not statistics.
- Built to be checked, not simply accepted.
Measured against reality, not just at launch
Every prediction is eventually compared against what actually happened, and that comparison is tracked openly over time. If accuracy drifts as conditions change, this shows up in the tracking rather than being discovered months later in a bad decision.
- Predictions compared against real outcomes over time.
- Accuracy visible, not hidden behind a one-time claim.
- Drift detected early, prompting a retraining cycle.
Everything the predictive model build includes
Scored case list
Every open case ranked by score, so attention goes to the highest-priority ones first.
Threshold alerts
Notification when a case crosses a risk level your team sets as worth acting on.
Scheduled retraining
The model retrained periodically as new outcomes accumulate, keeping it current.
ERP-connected scoring
Scores posted directly against the relevant record in your ERP or CRM.
Accuracy reporting
A regular report comparing past predictions against actual outcomes.
Model-agnostic build
Built so the underlying modelling approach can be upgraded without a rebuild.