1. A method comprising: implementing a goal model for a first goal from a plurality of goals using machine learning, the goal model being at least one of a neural network or an expert system; factorizing raw data to a set of data factors for the first goal, the raw data including query data from a user search query and at least one of relevance of an item title, temporal data, transaction data, impressions of item listings, item demand, or item supply; assigning a plurality of impact scores to each of the set of data factors, a first impact score of the plurality of impact scores corresponding to the first goal and a second impact score of the plurality of impact scores corresponding to a second goal in the plurality of goals, the plurality of impact scores respectively measuring the degree to which changes in a data factor influence a corresponding model's output, the first impact score corresponding to revenue generation and the second impact score corresponding to cross product marketing; ranking the set of data factors based on the first impact score; selecting a plurality of data factors from the set of data factors based on the ranking, the plurality of data factors being a proper subset of the set of data factors; modifying the values of the plurality of data factors by respective weights prior to being inputted into the goal model; inputting, responsive to the search query, the plurality of data factors into the goal model to: select search results from a database using the goal model; and create a model output, the model output including a ranking for each result in the search results; and presenting, to a user, an ordered list of search results based on the ranking for each result in the search results from the model output.