Methods, systems, and apparatus for prioritizing communications are described. Metadata that characterizes an electronic communication is obtained and a machine learning algorithm is applied to the metadata to generate a scoring model. A score for the electronic communication is generated based on the scoring model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for prioritizing an electronic communication, the system comprising: one or more hardware processors; and memory to store instructions that, when executed by the one or more hardware processors perform operations comprising: obtaining metadata that characterizes the electronic communication; applying machine learning to the metadata to generate a scoring model; and generating a score for the electronic communication based on the scoring model. 2. The system of claim 1, wherein the metadata comprises a caller identifier and the operations further comprise mapping the caller identifier to a location. 3. The system of claim 1, wherein the score is based on a recipient identifier. 4. The system of claim 3, wherein the recipient identifier is a telephone number called, an email address, or an Internet Protocol address. 5. The system of claim 1, wherein the score has a score type and the score type is based on a type of the electronic communication, a machine learning model, or a set of statistical features. 6. The system of claim 1, wherein the electronic communication is a telephone call, electronic mail, or an electronic message. 7. The system of claim 1, the operations further comprising prioritizing the electronic communication based on the score. 8. The system of claim 1, wherein the performance metric is an estimated revenue, an estimated profit, an estimated profit margin, an estimated probability of completing a transaction, or a probability of selecting a specified product type. 9. The system of claim 1, the operations further comprising generating the score based on an estimated revenue multiplied by an estimated probability of completing a transaction. 10. The system of claim 2, wherein the location is identified by a county or a zip code area. 11. The system of claim 2, wherein the location is associated with a customer whose probability to complete a transaction is greater than a threshold value. 12. The system of claim 1, the operations further comprising selecting a subset of features for use in generating the score and training the scoring model utilizing the selected subset of features to predict at least a part of the score. 13. The system of claim 1, wherein the scoring model is a linear model, a logistic model, a random forest model, or a support vector machine model. 14. The system of claim 1, wherein a formula of the scoring model includes a term to rank a new product, a new service or a new advertising channel within a predefined range, the predefined range based on an aggregate mean of the scoring model. 15. The system of claim 1, the operations further comprising processing sparse training data associated with a product, a service, or an advertising channel with other sparse training data associated with another product, another service, or another advertising channel, the sparse training data characterized by an amount of training data being below a statistically significant amount, the processing being one or more of combining, extrapolating, and simulating. 16. The system of claim 1, the operations further comprising modifying the scoring model to incorporate an additional polynomial term corresponding to a new product, a new service, or a new advertising channel. 17. The system of claim 1, the operations further comprising dynamically selecting the scoring model at runtime. 18. The system of claim 17, the dynamic selection allowing for a plurality of scoring models to be used based on a communication type, a customer type, a selected score type, or a selected score outcome. 19. The system of claim 1, wherein the model is a logistics model used to predict a propensity of a customer to purchase a product. 20. The system of claim 19, wherein inputs for the prediction are a telephone number associated with a customer and a telephone number called by a customer. 21. The system of claim 1, wherein the score is resultant from an amalgamation of multiple models. 22. A computer-implemented method for prioritizing an electronic communication, the method comprising: obtaining metadata that characterizes the electronic communication; applying machine learning to the metadata to generate a scoring model; and generating a score for the electronic communication based on the scoring model. 23. The computer-implemented method of claim 22, wherein the metadata comprises a caller identifier and further comprising mapping the caller identifier to a location. 24. The computer-implemented method of claim 22, wherein the score is based on a recipient identifier. 25. The computer-implemented method of claim 24, wherein the recipient identifier is a telephone number called, an email address, or an Internet Protocol address. 26. The computer-implemented method of claim 22, wherein the score has a score type and the score type is based on a type of the electronic communication, a machine learning model, or a set of statistical features. 27. The computer-implemented method of claim 22, wherein the electronic communication is a telephone call, electronic mail, or an electronic message. 28. The computer-implemented method of claim 22, further comprising prioritizing the electronic communication based on the score. 29. The computer-implemented method of claim 22, wherein the performance metric is an estimated revenue, an estimated profit, an estimated profit margin, an estimated probability of completing a transaction, or a probability of selecting a specified product type. 30. The computer-implemented method of claim 22, further comprising generating the score based on an estimated revenue multiplied by an estimated probability of completing a transaction. 31. The computer-implemented method of claim 23, wherein the location is identified by a county or a zip code area. 32. The computer-implemented method of claim 23, wherein the location is associated with a customer whose probability to complete a transaction is greater than a threshold value. 33. The computer-implemented method of claim 22, further comprising selecting a subset of features for use in generating the score and training the scoring model utilizing the selected subset of features to predict at least a part of the score. 34. The computer-implemented method of claim 22, wherein the scoring model is a linear model, a logistic model, a random forest model, or a support vector machine model. 35. The computer-implemented method of claim 22, wherein a formula of the scoring model includes a term to rank a new product, a new service or a new advertising channel within a predefined range, the predefined range based on an aggregate mean of the scoring model. 36. The computer-implemented method of claim 22, further comprising processing sparse training data associated with a product, a service, or an advertising channel with other sparse training data associated with another product, another service, or another advertising channel, the sparse training data characterized by an amount of training data being below a statistically significant amount, the processing being one or more of combining, extrapolating, and simulating. 37. The computer-implemented method of claim 22, further comprising modifying the scoring model to incorporate an additional polynomial term corresponding to a new product, a new service, or a new advertising channel. 38. The computer-implemented method of claim 22, further comprising dynamically selecting the scoring model at runtime. 39. The computer-implemented method of claim 38, the dynamic selection allowing for a plurality of scoring models to be used based on a communication type, a customer type, a selected score type, or a selected score outcome. 40. The computer-implemented method of claim 22, wherein the model is a logistics model used to predict a propensity of a customer to purchase a product. 41. The computer-implemented method of claim 40, wherein inputs for the prediction are a telephone number associated with a customer and a telephone number called by a customer. 42. The computer-implemented method of claim 22, wherein the score is resultant from an amalgamation of multiple models. 43. A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising: obtaining metadata that characterizes the electronic communication; applying machine learning to the metadata to generate a scoring model; and generating a score for the electronic communication based on the scoring model. 44. The non-transitory machine-readable storage medium of claim 43, wherein the metadata comprises a caller identifier and the operations further comprise mapping the caller identifier to a location. 45. The non-transitory machine-readable storage medium of claim 43, wherein the score is based on a recipient identifier. 46. The non-transitory machine-readable storage medium of claim 45, wherein the recipient identifier is a telephone number called, an email address, or an Internet Protocol address. 47. The non-transitory machine-readable storage medium of claim 43, wherein the score has a score type and the score type is based on a type of the electronic communication, a machine learning model, or a set of statistical features. 48. The non-transitory machine-readable storage medium of claim 43, wherein the electronic communication is a telephone call, electronic mail, or an electronic message. 49. The non-transitory machine-readable storage medium of claim 43, the operations further comprising prioritizing the electronic communication based on the score. 50. The non-transitory machine-readable storage medium of claim 43, wherein the performance metric is an estimated revenue, an estimated profit, an estimated profit margin, an estimated probability of completing a transaction, or a probability of selecting a specified product type. 51. The non-transitory machine-readable storage medium of claim 43, the operations further comprising generating the score based on an estimated revenue multiplied by an estimated probability of completing a transaction. 52. The non-transitory machine-readable storage medium of claim 44, wherein the location is identified by a county or a zip code area. 53. The non-transitory machine-readable storage medium of claim 44, wherein the location is associated with a customer whose probability to complete a transaction is greater than a threshold value. 54. The non-transitory machine-readable storage medium of claim 43, the operations further comprising selecting a subset of features for use in generating the score and training the scoring model utilizing the selected subset of features to predict at least a part of the score. 55. The non-transitory machine-readable storage medium of claim 43, wherein the scoring model is a linear model, a logistic model, a random forest model, or a support vector machine model. 56. The non-transitory machine-readable storage medium of claim 43, wherein a formula of the scoring model includes a term to rank a new product, a new service or a new advertising channel within a predefined range, the predefined range based on an aggregate mean of the scoring model. 57. The non-transitory machine-readable storage medium of claim 43, the operations further comprising processing sparse training data associated with a product, a service, or an advertising channel with other sparse training data associated with another product, another service, or another advertising channel, the sparse training data characterized by an amount of training data being below a statistically significant amount, the processing being one or more of combining, extrapolating, and simulating. 58. The non-transitory machine-readable storage medium of claim 43, the operations further comprising modifying the scoring model to incorporate an additional polynomial term corresponding to a new product, a new service, or a new advertising channel. 59. The non-transitory machine-readable storage medium of claim 43, the operations further comprising dynamically selecting the scoring model at runtime. 60. The non-transitory machine-readable storage medium of claim 59, the dynamic selection allowing for a plurality of scoring models to be used based on a communication type, a customer type, a selected score type, or a selected score outcome. 61. The non-transitory machine-readable storage medium of claim 43, wherein the model is a logistics model used to predict a propensity of a customer to purchase a product. 62. The non-transitory machine-readable storage medium of claim 61, wherein inputs for the prediction are a telephone number associated with a customer and a telephone number called by a customer. 63. The non-transitory machine-readable storage medium of claim 43, wherein the score is resultant from an amalgamation of multiple models.