Finding
Churn driver
your history · not a benchmarkMAPE 5.8%
Data science services
Find what actually moves the number.
Then, where it earns its keep, put a model in the decision. Our data science services clean the mess, test the question, and only then train. Data science and machine learning as one piece of work — on your own history, in your environment.
forecast ›
On your history
5.8%
MAPE. Scored against what actually happened, not a benchmark.
01Where it earns its keep
Data science services: the question first, the model only if it pays.
Data science finds the driver. Machine learning puts a forecast, score or flag into the decision. We will not add a model to a job a clearer report should do.
01
What is actually driving the number
Not a hunch. Which product, channel or cohort is moving cash, margin or churn — with the mess cleaned first.
02
An experiment worth running
A price, a message, a process — tested against your own history, so you spend on what works.
03
A forecast you can plan on
Demand, cash, churn — from your own history, in time to act. The machine learning slice of the same job.
04
A score on the work that repeats
Leads, tickets, accounts — ranked so attention goes where it counts, with a person still approving the calls that matter.
05
A flag when something looks off
The unusual transaction, the drifting customer, the figure that should never sneak up.
02How we work
Investigate. Then, only if it pays, train.
- Step 01
We pin the question worth answering
What is driving churn, what you will sell, which leads are real. A clear question beats a vague model.
- Step 02
We clean and explore the history you already keep
Spreadsheets, CRM, shop exports. Cleaning and agreeing definitions is most of the work — and often the part that pays first.
- Step 03
We test what actually moves the number
Experiments and statistical checks, not a dashboard of hunches. If the signal is not there, we will be straight with you.
- Step 04
We put the useful model into the decision
03FAQ
What is data science?
Turning the history you already keep into answers you can act on: what is driving the number, what is likely next, and which experiment is worth running — then wiring the useful models into the work.
It sits under data and analytics, next to business intelligence. When the model should act across your tools, that is agentic AI. New to the idea? Read the plain-English guide to data science.
How is data science different from machine learning?
Data science is the investigation: clean the mess, test what actually moves the number, then choose a model. Machine learning integration is putting that model into a decision your team already makes. We do both, as one piece of work when that is what the question needs.
Do we need a data science team?
No. We do the analysis and keep the models honest. Your team uses the result.
What if our data is messy?
It usually is. Cleaning and agreeing definitions is most of the work — and often the part that pays first. We will be straight if there is not enough history to learn from.
Can you do both the analysis and the models?
Yes. One partner from the question through the model that stays in production. If a simpler report answers it, we will not add a model for the sake of it.
Where does this run, and is the data safe?
Analysis and models run on infrastructure we manage, behind a login. For sensitive work an open model runs on our own infrastructure, so the history is never sent to a third-party model provider.
Start here
Tell us the number you want explained — or predicted.
Send a few details and we’ll set up a short call to scope it and give you a tailored quote. No obligation, no hard sell.