GLOSSARY
Machine learning
Machine learning uses examples to learn patterns for predictions or decisions. Its usefulness depends on which situations the data represents and what counts as a successful outcome. A model can reproduce established sales habits alongside useful relationships.
Turning customer records into priorities
A sales team might use previous opportunities to rank incoming enquiries. If the records mainly cover large accounts receiving intensive support, their conversion rate may partly reflect that attention. The resulting score then shapes the next allocation of resources: highly rated contacts receive more support and generate further positive examples.
The business team therefore needs a say in the target outcome, data selection and exceptions. A transparent rule may initially suit a new market segment. Comparing the model with that baseline helps establish whether its additional operating effort is justified.
Evidence that supports a decision
Evaluate later cases that were not used for development, with results separated by relevant customer groups. Count missed opportunities, unnecessary work and realised contribution margin alongside accurate predictions. Observe the relationship between scores and outcomes without attributing every improvement to the model. Changes in the market or offering can require fresh validation.