Scenario
User, decision moment, available action and expected value.
Sales · analytics · Machine Learning
A model helps
choose the next action.
I design analytics scenarios for sales — demand forecasting, customer prioritisation, next-best-offer recommendations and event probability assessment — after validating data and business processes.
Business problem
First we define the decision the model must improve
Managers treat clients of different potential the same way.
Offers are prepared without considering history, context and constraints.
Forecasting is done manually and shows no uncertainty range.
CRM data exists, but it's incomplete or reflects the process with distortions.
You can't separate model quality from how well the recommendation is executed.
Project workflow
From hypothesis to a controlled pilot
Data
Contact history, sales, products, features and outcomes.
Baseline
A simple rule or current process the model is measured against.
Pilot
A limited segment, a recommendation log and a control group.
Decision
Quality, economics, adoption, risks and scaling conditions.
Possible scenarios
Only after validating data availability
Lead prioritisation
Probability of moving to the next stage and the order of manager actions.
Next best action
The right next contact or offer, given constraints.
Demand forecasting
Planning at product, segment or period level with an error range.
Churn risk
A signal to investigate causes and make a timely contact, not an automated verdict.
Pilot criteria
More than accuracy
Data qualitycompleteness, bias and target variable leakage
Model qualitythe metric aligns with the business cost of errors
Adoptionthe recommendation is available at the moment of decision
Incremental effectcompared to the current process or a control group
Risk controlexplainability, personal data and degradation monitoring
Result foundation: the pilot ties the model to data quality, the sales process and team actions — so you can measure real value before scaling.
First step