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TCB Infotech | Expert Odoo & ERPNext Implementation Partner

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Predictive Analytics

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.

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Payment Risk Score
Illustrative
72
Risk of late payment on this invoice
Out of 100, higher means higher risk
Payment history, last 6 invoicesStrong driver
Order value vs typical orderModerate driver
Days since last contactMinor driver
Figures are illustrative
1
Decision, scored well
Your
History, not a generic benchmark
6-9 wk
To a first working model
Explained
Drivers behind every score
Why It Matters

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.

Built for One Decision

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.
A predictive score being reviewed for a business decision
Drivers, Not Just a Number

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.
A manager reviewing the drivers behind a predictive score
The Score

What a working score looks like

A number for the decision at hand, and the drivers that explain where it came from.

Machine Failure Risk
Illustrative
58
Risk of unplanned downtime, next 30 days
Out of 100, higher means higher risk
Vibration readings, last 2 weeksStrong driver
Hours run since last serviceModerate driver
Ambient temperature varianceMinor driver
Figures are illustrative
How It Works

From past outcomes to a score you can use tomorrow

1
Define the decision
We agree exactly what the model should score and how the score will be used.
2
Gather known outcomes
We pull the past cases where the actual outcome is already known from your systems.
3
Build and test
We build the model and test its accuracy against outcomes it has not seen.
4
Add the explanation layer
We surface the drivers behind each score, in language the decision-maker actually uses.
5
Deploy and monitor
The model runs live, and its accuracy is tracked against real outcomes going forward.
What It Scores

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 Changes

What a working predictive score changes

Earlier
Sight of a risk before it lands
Focused
Attention on the highest-risk cases
Explained
Reasoning behind every score
Tested
Accuracy against real outcomes
Consistent
Criteria applied to every case
Human
Judgement still makes the call
Why Us

Why teams choose TCB Infotech for predictive analytics

1
We start with your real outcomes
We build ERP systems, so we know how to pull clean, known-outcome data out of them.
2
Narrow scope, tested properly
One decision, built and tested against outcomes you already know, not a broad promise.
3
Drivers, not black boxes
Every score is explained in language the person using it actually understands.
4
Honest about accuracy
We report what the model can and cannot do, and track its real-world accuracy afterward.
5
Model-agnostic build
We choose the approach that fits your data and hosting rules, with no lock-in to one vendor.
6
Proven delivery
50+ projects across industries, with zero failed projects since 2021.
FAQ

Predictive analytics questions

What is predictive analytics?
It is a model built from your own historical data that scores the likelihood of a specific outcome, such as a customer defaulting, a machine failing or a deal closing. The score is meant to support a decision a person already makes, not replace their judgement.
How is this different from the forecasting service?
Forecasting projects a number forward over time, such as next quarter's revenue. Predictive analytics scores an individual case, such as this specific customer's risk of late payment, so it is built around a decision rather than a trend line.
What decisions can a predictive model support?
Any decision where you have enough past examples with known outcomes, such as which customers are likely to pay late, which leads are likely to close, or which machines are likely to need maintenance soon.
Can we see why the model gave a particular score?
Yes. Every score is shown with the main factors that drove it, in plain language, so the person using the score can understand and challenge it rather than accept a number on faith.
How much data do we need?
It depends on the decision, but a useful starting model usually needs a few hundred past cases with a known outcome. We assess your available data honestly before committing to a build.
How long does a first model take to build?
A first model for one well-defined decision usually takes six to nine weeks, including testing against outcomes you already know before it is trusted on new cases.
In Short

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.