Machine learning earns its place where a business makes the same numerical judgement repeatedly and has history to learn from. That describes a specific and useful set of problems rather than a general capability. This page organises those problems by the function that owns them, with the data each requires and the honest baseline each must beat. Classical machine learning on tabular data remains the workhorse for all of these, and it is considerably cheaper and more reliable than the generative approaches receiving more attention.
Finance and Planning
Finance owns the forecasting problems, and these are the applications with the clearest financial case because the cost of being wrong is already quantified. They are also where machine learning faces the strongest existing baseline, since statistical forecasting is mature and frequently sufficient.
Demand and Revenue Forecasting
Predicting volume by product and period from history plus external drivers. Established statistical methods are a strong baseline that must be beaten rather than ignored.
Cash Flow Prediction
Forecasting receipts from invoice and payment history, including which customers pay late. Directly useful for working capital decisions.
Credit and Payment Risk Scoring
Predicting default or late payment from transaction and account history. Requires enough historical defaults to learn from, which stable customer bases lack.
Cost and Margin Anomaly Detection
Flagging unexpected movement in costs or margins for investigation. Needs no labelled failures, which makes it available immediately.
Data Requirements
Several years of history at usable granularity, with external drivers available at prediction time rather than only retrospectively. Our predictive analytics work assesses this first.
Customer Retention and Growth
Churn is the most attempted commercial machine learning application and the one most often built without the second half. Predicting who will leave produces no value unless someone acts on the prediction with an intervention that works, and that intervention is a business design problem rather than a modelling one.
Churn Prediction
Identifying accounts likely to leave from usage, support, and billing patterns. Well-suited to machine learning given sufficient historical churn.
Why Prediction Alone Returns Nothing
A list of at-risk accounts with no intervention changes nothing. Decide what happens to a flagged account before building the model.
Lifetime Value Estimation
Predicting long-term account value to prioritise acquisition and retention spend. Useful for allocating effort where returns justify it.
Next Best Action and Cross-Sell
Recommending what to offer which customer based on similarity and history. Our CRM AI automation work delivers this into the sales workflow.
Measuring Against a Control Group
Compare intervened accounts against a held-back control. Without one you cannot distinguish the model working from accounts that would have stayed anyway.
Pricing and Revenue Management
Pricing models carry more risk than most machine learning applications because they act directly on revenue and are visible to customers. Constraint handling matters more than optimisation quality, and the reputational dimension makes this a business decision as much as a technical one.
Price Elasticity Estimation
Modelling how demand responds to price by segment and product. Requires price variation in your history, which consistent pricing does not provide.
Dynamic Pricing Within Constraints
Adjusting price against demand and inventory inside defined floors and ceilings. Constraints are the engineering work, optimisation is the easier part.
Discount and Promotion Effectiveness
Estimating which discounts drive incremental volume rather than subsidising purchases that would have happened regardless.
Inventory and Markdown Timing
Predicting when to reduce prices to clear stock while protecting margin. Directly measurable and well-bounded.
The Fairness Consideration
Customers notice differential pricing and react badly to perceived unfairness. Define what is acceptable before the model can find what is profitable.
Fraud, Risk and Operations
These are the applications where machine learning most clearly outperforms rules, because fraud patterns adapt and static rules do not. The trade-off between false positives and missed cases is a business decision with real costs on both sides, and it needs deciding explicitly rather than defaulting to whatever the model produces.
Transaction Fraud Detection
Scoring transactions for fraud risk from behavioural patterns. Rules catch known patterns, models catch variations, and both are typically used together.
Anomaly Detection in Operations
Flagging unusual patterns in processes, systems, or usage without needing labelled examples of what went wrong previously.
Setting the False Positive Threshold
Blocking legitimate transactions costs customers, and missing fraud costs money. This threshold is a commercial choice, not a technical default.
Document and Claim Triage
Prioritising cases by likely complexity or risk so effort goes where it matters. Our business process automation work routes on these scores.
Adaptation Over Time
Fraud patterns change deliberately in response to your detection. These models degrade faster than others and need a defined retraining cadence.
Marketing and Analytics
Marketing applications are attractive because data is plentiful and experiments are cheap to run. The main pitfall is confusing correlation with effect, since a model identifying who converts is not identifying who was persuaded. Distinguishing those requires experimental design rather than better modelling.
Response and Conversion Prediction
Predicting who is likely to respond to an approach, used to prioritise limited outreach capacity.
Segmentation From Behaviour
Grouping customers by actual behaviour rather than assumed demographics, which frequently reveals segments teams had not recognised.
Attribution Modelling
Estimating channel contribution to conversion. Genuinely difficult and frequently overstated in confidence by the tools reporting it.
Incrementality Rather Than Correlation
A model finding who converts is not finding who was persuaded. Holdout testing is what separates those two questions.
Reporting That Gets Used
Model output needs to reach the people acting on it. Our business intelligence work handles that delivery.
FAQs
What business problems suit machine learning?
Repeated numerical judgements with sufficient history: demand and revenue forecasting, churn prediction, lifetime value estimation, price elasticity, fraud scoring, anomaly detection, and response prediction. All involve a decision someone makes regularly with measurable outcomes.
Why do churn prediction projects often fail?
Because prediction alone changes nothing. A list of at-risk accounts produces no value without an intervention that works and someone to deliver it. Decide what happens to a flagged account before building the model, not afterwards.
Does machine learning beat statistical forecasting?
Sometimes. Established statistical methods are a strong baseline, particularly for stable products with regular demand. Machine learning helps where many interacting external variables and irregular patterns are involved. Measure against the baseline rather than against no forecast.
How do I know if a model is actually working?
Compare against a held-back control group. Without one you cannot distinguish the model producing value from outcomes that would have occurred anyway, which is the most common reason machine learning benefits are overstated internally.
What data do these use cases need?
Several years of history at usable granularity, consistent field definitions across the period, enough examples of the outcome you are predicting, and the input variables available at prediction time rather than only retrospectively in a warehouse.
Do I need generative AI for these applications?
No. Classical machine learning on tabular data handles forecasting, churn, pricing, and fraud better, cheaper, and more explainably than generative approaches. Generative models suit text and document work, which is a different set of problems.
