Data Analytics
From diagnosing what already happened to forecasting what comes next. Analysis that answers why and anticipates portfolio, demand and risk.
Talk to a specialistA dashboard shows what happened. Analytics answers why and what comes next.
A good dashboard answers the first question: margin fell. It rarely answers the second, which is the one that matters: why did it fall, in which portfolio segment, and will it continue next month?
That is where analytics comes in. Not as a prettier dashboard, but as a different kind of work: crossing variables nobody has crossed, isolating what actually explains the result and, when there is enough history, projecting what is likely to happen. The practical difference is moving from reacting to the close to acting before it.
Two layers of analysis
The first almost always has to come before the second: forecasting without understanding produces a number nobody trusts.
- Diagnostic. Why the indicator moved. Segmentation, basket analysis, ABC curve, cross-channel and customer cohort analysis. It is the layer that turns "it fell 8%" into "it fell 8% because a specific portfolio segment changed behaviour".
- Predictive. What is likely to happen. Machine Learning models applied to demand forecasting, propensity to buy, credit risk and churn. Always with the margin of error stated a forecast without a confidence interval is a guess dressed as science.
Where the return usually comes fastest
These are the areas where analytics usually pays for itself first in the projects we run.
- Customer portfolio. Who is actually profitable after cost to serve, freight and discount. It is common to find that part of the portfolio loses money on high revenue.
- Demand forecasting. To size stock and production without choosing between stockouts and idle capital.
- Risk and delinquency. Prioritising collection by what has a real chance of recovery, instead of treating everyone the same.
- Churn. Spotting the customer who is leaving while there is still time to talk to them.
What you are left with
- The documented model. Variables used, how it was trained and when it needs retraining.
- The written analysis. The finding in plain language, with a recommended action not just the chart.
- Monitoring in production. The forecast compared with what actually happened, so you can see whether the model still gets it right.
- Team training. To read the result with the right scepticism and know when the model does not apply.
How we run it
We start with the business question, not the algorithm and the initial assessment exists to check whether there is data of sufficient quality and history to answer it. Often the answer is that there is not yet, and saying so in week one is more honest than delivering a good-looking model trained on bad data.
How we measure results
The criterion is always the decision the analysis changed, not accuracy in isolation. In our projects the most frequent indicators are margin gained by portfolio segment, lower operational loss and forecast accuracy compared with the method used before.
We now have a far more robust, fast and visual view of each management area’s results.
Sales director national fuel distributor
About this service
Do we need a lot of historical data?
For the diagnostic layer, no: it works with what the operation already records. For the predictive layer, yes a model needs history and variation to learn from. The initial assessment measures exactly that and says whether it is feasible, before you invest.
Is this the same as artificial intelligence?
Machine Learning is a kind of AI, so partly yes. But we avoid the label because it promises too much: what we deliver are statistical models trained on your data, with a known and explainable error not a system that decides on its own.
What if the model gets it wrong?
It will get things wrong, and the right question is by how much. Every model we deliver comes with the margin of error measured and compared with your current method. If it is not better than what you already do, that shows up in testing and we do not put it into production.
Do we need dashboards first?
It helps a lot, but it is not required. What is required is reliable data and that is why analytics usually comes after governance or alongside it. Analysing dirty data produces a wrong conclusion with the appearance of rigour.
Who runs the model afterwards?
Your team, with the documentation and training that are part of the delivery. Models need retraining from time to time; we leave written guidance on when and how. If you prefer, we maintain it under an outsourcing contract.
What your data already knows?
One conversation is usually enough to identify which question already has an answer in what you record.
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