Predict a customer's next-year value from the first day: zeros and heavy tails, BG/NBD against a zero-inflated lognormal network, decile calibration, and the feedback loop of value-based bidding: one-hour boards for three levels.
Machine-learning engineers designing data, training and model systems.
Your approach: Explain the data, learning or model lifecycle in the brief. Support relevant quality and operating targets with evidence.
Design Customer Lifetime Value Prediction. Predict a customer's next-year value from the first day: zeros and heavy tails, BG/NBD against a zero-inflated lognormal network, decile calibration, and the feedback loop of value-based bidding: one-hour boards for three levels. Work from the scoping questions below. State assumptions for any unspecified load, guarantee or target, then trace your design end to end. Explain one difficult case and a credible alternative; the worked example is a reference, not a required implementation.
Resolve the scoping questions for Customer Lifetime Value Prediction. Separate stated behavior from assumptions, and identify what is outside your design.
Declare relevant volume, latency, freshness, quality or cost targets with units. Show calculations or an evaluation plan that can test them; unspecified targets are your assumptions, not hidden pass criteria.
Explain how your guarantees hold in a difficult case relevant to this subject. Address: Which platforms? Seasons?
Identify users, required behavior and exclusions. Answer: Which decisions? When is the value needed?
Define the information owned by the system and the inputs, outputs and errors at its boundaries. Resolve: Value of what? Scale?
Estimate the dominant workload and resource demand with units and explicit assumptions. For a learned system, also state how quality is measured and what data is available.
Draw or describe the responsibilities needed for Customer Lifetime Value Prediction. Trace a representative request, event or job from its input to a visible result; identify durable state owners.
Walk through a difficult case step by step, including detection and recovery. Consider: Signals on day one? How wrong may it be? Which platforms? Seasons?
Compare a credible alternative using your chosen workload and guarantees. Explain a remaining risk, a signal to watch and when you would change the design.