Machine Learning & Advanced Analytics
We build forecasts and models on the records you already hold, and we tell you when a simple rule or a clean report would answer the question better.
Questions we help answer
- Can we forecast demand by product, site or season well enough to plan stock and staff?
- Which customers are likely to buy again, and which are about to stop?
- Can we spot unusual refunds, discounts or stock movements before month end?
- Do we have enough data for a model, or would a better report answer the question?
- How do we keep a model accurate after it has been built?
What we do
Demand and revenue forecasting
Forecasts by product, location and period with stated ranges, back-tested against a simple baseline such as last year's pattern, and used only where they beat it.
Segmentation and propensity
Customer groups by purchase behaviour and value, and scores for likelihood to buy again or lapse, delivered as lists the CRM and ad platforms can use.
Exception detection
Rules and models that flag unusual refunds, discounts, price changes and stock or cash variances for review before the period closes.
Experiment design and readouts
Hold-out and A/B tests sized for your volume, with significance checked against a simulated null and corrected for the number of comparisons made.
Model operations
Retraining schedules, accuracy monitoring and documentation, so a model that drifts is noticed and corrected.
- How an engagement with THEFOCUS startsDiscuss this with us
How we approach it
- 01
Check the data first
Records are validated for completeness, ranges and duplicates before modelling, and a single corrupt source is traced to its origin.
- 02
Beat a simple baseline
Every model is compared with the rule or average the business uses today, and kept only if it wins on data it was not trained on.
- 03
Test out of sample
Accuracy is reported on held-out periods or customers, never on the data used to build the model.
- 04
Put the output where decisions happen
Forecasts, scores and flags land in the planning sheet, BI report, CRM or ad platform that the decision-maker already uses.
Client results
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How an engagement with THEFOCUS starts
A first conversation is about your situation. If there is a fit, we propose a scoped diagnostic with a fixed deliverable before any larger commitment.
Discuss this with us01
A working conversation
Forty-five minutes with the person who would lead the work. We ask about the decision, the systems behind it and the deadline.
02
A scoped diagnostic
Read-only access, a fixed timeframe and a written finding: what the records show, what it costs and what to change first.
03
A plan you can act on
Changes, owners, measures and exclusions in writing. The same team then builds and measures, or hands over cleanly.
- One accountable team
- The people who diagnose also build the systems and measure the result.
- Partners chosen per problem
- Independent specialists join for the parts that need them, under one contract.
- Records before dashboards
- Orders, ledgers and logs are reconciled before any recommendation depends on them.
Questions executives ask
How much data do we need for machine learning?
It depends on the question and how noisy the outcome is. We check volume and quality first and say plainly when the data cannot support a model, in which case a rule or report is the better tool.
Will we be able to run the models ourselves?
Yes. Models are delivered with code, documentation and a retraining runbook in your own environment, and we can train your analysts to operate them.
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