Predictive Analytics & Machine Learning
We use your own sales history to answer two questions: what will happen next month, and why did last month go the way it did.
Machine learning (ML) sounds like a laboratory, but here it means something concrete: a program that reads your sales from the past few years and finds the patterns that repeat — the weekday that sells most, the month that drops, the product that pulls another one along. From those patterns it projects what is coming. It does not guess: it calculates, and you can measure whether it got it right.
What's included?
- Sales and demand forecasting by product, location, week or month.
- Seasonality: when your peak season starts and how long it lasts, measured on your own previous years.
- Inventory restocking: how much to order and when, so you don't run out of stock in the good week or fill the warehouse with what doesn't move.
- Corrective analysis: where the money is going — shrinkage, discounts nobody authorized, returns, products that sell a lot and leave little.
- Root cause analysis when an indicator drops: separating how much was the season, how much a price change, and how much is something you need to fix.
- A dashboard with the forecast and your business indicators live, plus the report that arrives in your inbox on its own.
Ideal for
Businesses that buy inventory and get it wrong in both directions: they run out of product exactly when demand was there, or they over-buy and end up clearing it at a discount. Also for anyone who sees the margin drop and cannot tell which part of the business it went to. You don't need to be a large company or have an IT team: an owner of three coffee shops with sales in a spreadsheet has everything needed to start.
How we work
First we look at your history and tell you whether it is enough. If it is, we define a single concrete question —how much of this will I sell next month, or where is my margin going— and work on that one. We build the model with open, well-known tools (Python with pandas and scikit-learn, Prophet or statsmodels for time series) and test it against a period the model never saw: if it cannot beat the simple rule of "sell the same as last year", we tell you. A model that cannot beat that rule is not worth what it costs to maintain.
What cannot be promised
A forecast is delivered with its measured margin of error, not as a bare number. No history contains what has not happened yet: a competitor opening across the street, a supplier raising prices, a road closure. When something like that happens the model gets it wrong — and the right response is not to hide it, but to retrain it with the new data. That is why the forecast is reviewed periodically and compared against what actually happened.
Frequently asked questions
How much sales history do I need?
It depends on what you want to predict. For the model to understand your peak season it needs at least two complete cycles: two years of sales. With one year you can project the trend, but not seasonality — the model has never seen a previous December to compare against. With three months there is no model, there is a guess with a pretty chart, and we will tell you that before charging you for it.
Everything is in a spreadsheet, I have no system. Does that work?
It works. A file with date, product and quantity sold is already a valid history. You don't need an ERP or a database to start: we take care of cleaning the file, fixing the names written three different ways, and putting it in order. If it later makes sense to move that into a real database, that is a separate step decided afterwards, not a requirement to begin.
Do I need someone technical to use the result?
No. What we hand over is a screen that opens in a browser or on a phone, with the numbers you asked for and the date of the last update. The data refreshes on its own at the frequency we agree. If you also want a summary sent by email or WhatsApp every Monday, it gets configured once and forgotten.
Is this the same as a dashboard?
No, although they are delivered together. A dashboard shows what already happened: how much you sold yesterday. Predictive analysis estimates what will happen, and corrective analysis explains why what happened, happened. The dashboard is the window; the model is what sits behind it. If you only need the window, that is Databases & BI and it costs less.