Embodiments of the present invention relate to efficiently computing variable predictiveness such that an indication of variable predictiveness can be provided in real time. In this regard, aspects of the present invention enable a user (e.g., digital marketer) to input a query and, in response, receive an indication of variable predictiveness. To efficiently compute variable predictiveness in response to a submitted user query, mutual information is computed offline and, thereafter, used to generate, in real time, conditional mutual information of variables for a specified date range. The concept of conditional mutual information can be utilized to represent variable predictiveness or otherwise indication variable predictiveness, such as to identify a set of variables that accurately predict a metric. Using such an approach effectively reduces a number of data access attempts and calculations performed in real time thereby reducing utilization of a processor(s).
Please help me write a proper abstract based on the patent claims. CLAIM: 1. One or more computer storage media storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform operations to facilitate computation of variable predictiveness, the operations comprising: receiving a predictiveness query that requests an indication of variable predictiveness relative to a metric, the predictiveness query including a date range for which the indication of variable predictiveness is desired; in response to the predictiveness query, utilizing at least one predetermined mutual information for a variable corresponding with time range to compute a conditional mutual information for the variable; and utilizing the conditional mutual information associated with the variable to provide an indication of variable predictiveness of the variable relative to the metric in relation to the date range. 2. The one or more computer storage media of claim 1, wherein the conditional mutual information for the variable represents the mutual information conditioned on distribution of visit patterns for one or more days within the date range. 3. The one or more computer storage media of claim 1, wherein the predictiveness query specifies the metric for which an indication of variable predictiveness is desired and the date range. 4. The one or more computer storage media of claim 1, wherein the at least one predetermined mutual information for a variable is computed in an offline process prior to reception of the predictiveness query. 5. The one or more computer storage media of claim 1, wherein the at least one predetermined mutual information for the variable comprises a plurality of predetermined mutual information values for the variable, wherein each of the plurality of predetermined mutual information values corresponds with a unique day occurring within the date range. 6. The one or more computer storage media of claim 1, wherein the indication of variable predictiveness of the variable relative to the metric comprises conditional mutual information. 7. The one or more computer storage media of claim 1, wherein the indication of variable predictiveness of the variable relative to the metric comprises designating the variable as an accurate predictor of the metric. 8. The one or more computer storage media of claim 1, wherein the conditional mutual information for the variable is computed by: for each day in the date range, multiplying a predetermined mutual information corresponding with that day by a probability associated with that day to obtain a product value in association with the day, wherein the probability comprises an occurrence count on that day divided by the total occurrence count for the date range, and summing the product values in association with each day within the date range to obtain the conditional mutual information for the variable. 9. A computerized method for facilitating computations of variable predictiveness indicating extents to which variables can predict metrics, the computerized method comprising: computing, via a first computing process, a mutual information value for a variable in relation to a metric for at least each day within a date range; receiving, via a second computing process, a query requesting an indication of variable predictiveness in association with the date range; in response to the query, using, via a third computing process, the mutual information values computed in association with the variable for each day within the date range to compute a conditional mutual information in association with the variable for the date range; and based on the conditional mutual information, providing, via a fourth computing process, an indication of variable predictiveness to indicate an extent to which the variable predicts the metric, wherein the first, second, third, and fourth computing processes are performed by one or more computing devices. 10. The method of claim 9 further comprising storing the mutual information values computed in association with the variable for each day within the date range, wherein the stored mutual information values are used to compute the conditional mutual information. 11. The method of claim 10, wherein the mutual information values are stored in association with an indication of the variable and a corresponding date. 12. The method of claim 9, wherein the indication of variable predictiveness to indicate the extent to which the variable predicts the metric comprises the computed conditional mutual information. 13. The method of claim 9, wherein the indication of variable predictiveness to indicate the extent to which the variable predicts the metric comprises an indication that the variable accurately predicts the metric. 14. The method of claim 9, wherein the conditional mutual information for the variable represents the mutual information values for each day conditioned on distribution of visit patterns for the day as compared to visit patterns for the date range. 15. A computerized system comprising: one or more processors; and one or more computer storage media storing computer-useable instructions that, when used by the one or more processors, cause the one or more processors to: for each time unit within a time range, perform an offline calculation of a mutual information value for a variable with respect to a metric; store the mutual information values for each time unit with a number of user visits to a website associated with the corresponding time unit; upon receiving a request for an indication of variable predictiveness in relation to the time range, reference and use the mutual information values for each time unit within the time range and corresponding number of user visits to the website to calculate, in real time, a conditional mutual information for the variable with respect to the metric; and use the conditional mutual information for the variable to provide an indication of variable predictiveness. 16. The system of claim 15 further comprising a data store to store the mutual information values for each time unit with the number of user visits to the website associated with the corresponding time unit. 17. The system of claim 15, wherein the request for the indication of variable predictiveness in relation to the time range is initiated by a user via a user device. 18. The system of claim 15, wherein the indication of variable predictiveness is provided to the user that initiated the request for the indication of variable predictiveness. 19. The system of claim 15 further comprising automatically using the indication of variable predictiveness to customize the website. 20. The system of claim 15, wherein the conditional mutual information for the variable is compared to at least one other conditional mutual information for at least one other variable to select the variable as accurately predicting the metric.