Forecast sales and demand
We use your sales history and the patterns behind it, such as school terms, paydays or the rains, to estimate how much you are likely to sell, so you can plan stock, cash and staff ahead of time.
Data Science
Plan for what is coming next, using the records you already have.
Reports tell you what has already happened. Data Science looks ahead. Using your past sales, customer and payment records, we build models that forecast demand, group customers who behave alike, and flag the customers or loans most likely to need attention, so you can stock, staff and lend with more confidence. We explain every model in plain language, including how sure it is, so it supports your decisions instead of replacing them.
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For example
Picture a chain of agrovet shops in the Rift Valley that runs out of maize seed and fertiliser at planting time, then sits on unsold stock for months afterwards. Using a few years of sales by shop and season, we build a forecast of how much of each product every shop is likely to sell in the coming weeks, so orders go out earlier and stock is moved to the shops that will need it.
What we do
How Data Science works for you.
Group your customers
We sort customers into groups that buy in similar ways, such as frequent small buyers or occasional large orders, so offers and follow-ups reach the right people.
Score churn and risk
We flag customers who look likely to stop buying, and score credit or default risk, whether for customers who buy on account or for a lender or SACCO reviewing loan applications.
Test what works
Before you roll out a price change, promotion or new message to everyone, we help you try it on a small group first and measure fairly whether it made a difference.
What you get
What we hand over.
Every project is scoped around your business, but this is what you can expect.
- A working model, such as a forecast or a risk score, built on your own records
- A plain-language explanation of how it works, how accurate it is and where it can go wrong
- Results delivered where your team will use them, such as a spreadsheet, a dashboard or your Business System
- A simple routine for refreshing the model as new data comes in
- A handover for your team, and support while you start using it
Questions
About Data Science.
What is the difference between Data Science and Data Analysis?
Data Analysis explains what has already happened, through reports and dashboards. Data Science uses that history to predict what is likely to happen next, such as next month's demand or which customers may stop buying. Many clients start with Data Analysis and add Data Science once their numbers are in good shape.
How much data do we need?
It needs some history. For a sales forecast, a year or more of sales records is a good start. We look at what you have first and tell you honestly whether a model is worth building yet.
Can we trust the predictions?
A prediction is a well-informed estimate, not a guarantee. We test every model on past records it has not seen, tell you how often it gets things right, and keep the final decision with your team.
Let's talk
Wondering if Data Science fits your business?
Tell us how you work today. We'll talk it through and tell you honestly what would help.