Which of the following sequence is correct?

Question

BlackTom AI · Accepted Answer

Question deconstructs the logical order for a data-driven demand optimization workflow. 
Option 1: 'Perform price optimization -> use machine learning techniques to predict demand -> collect data' suggests acting on price without first gathering data or predicting demand. This is backwards: without data collection, the model has nothing to learn, and price optimization would be baseless. 
Option 2: 'Collect data -> perform price optimization -> use machine learning techniques to predict demand' places optimization before modeling. Price optimization relies on a predictive model to forecast demand or price elasticity; skipping the modeling step would undermine the basis for optimization and could render the optimization unreliable. 
Option 3: 'Use machine learning techniques to predict demand -> perform price optimization -> collect data' starts with prediction but after predicting demand, it attempts optimization before any new data collection. If the model is already trained, you still typically collect fresh data to update the model and validate predictions; this sequence ignores the iterative data-gathering loop that sustains accuracy over time. 
Option 4: 'Collect data -> use machine learning techniques to predict demand -> perform price optimization' establishes a logical chain: first gather data, then build/predict using ML, then act on those predictions with price optimization. This aligns with standard data science workflows, where data collection feeds models, which in turn inform optimization decisions. 
In summary, the forward flow from data collection to modeling to optimization ensures each step has the necessary input from the previous step, supporting reliable, data-driven pricing decisions.

Which of the following sequence is correct?

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