Predictive Analytics
Most predictive analytics pitches promise to forecast the whole business.
Why It Matters
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.
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.
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.
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.
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.
Scoring the likelihood a customer pays late, to guide credit terms and collection priority.
Scoring which machines are likely to need maintenance soon, from sensor and usage data.
Scoring which leads or open deals are likely to close, to guide where sales spends time.
Scoring which customers or employees are showing signs of leaving, early enough to act.
Scoring which items are likely to run short against demand before the next reorder.
Scoring which batches or orders carry a higher risk of a quality issue, based on past patterns.
We agree exactly what the model should score and how the score will be used.
We pull the past cases where the actual outcome is already known from your systems.
We build the model and test its accuracy against outcomes it has not seen.
We surface the drivers behind each score, in language the decision-maker actually uses.
The model runs live, and its accuracy is tracked against real outcomes going forward.
Sight of a risk before it lands
Attention on the highest-risk cases
Reasoning behind every score
Accuracy against real outcomes
Criteria applied to every case
Judgement still makes the call
We build ERP systems, so we know how to pull clean, known-outcome data out of them.
One decision, built and tested against outcomes you already know, not a broad promise.
Every score is explained in language the person using it actually understands.
We report what the model can and cannot do, and track its real-world accuracy afterward.
We choose the approach that fits your data and hosting rules, with no lock-in to one vendor.
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.
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.
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.
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.
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.
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.
Related
The other services in this section, and related pages from TCB Infotech.
No commitment. A practical look at what your data could support.
