Abstract: Statistical machine learning, in which an input module receives user input that defines a hypothesis associated with a particular output. The hypothesis defines one or more starting criteria that are proposed as being correlated with the particular output, and a recommendation engine initially provides recommendations that include the particular output based on the one or more starting criteria defined by the hypothesis. An experience analytics system receives feedback data related to whether the recommendations provided based on the one or more starting criteria defined by the hypothesis were successful and modifies the hypothesis based on the feedback data. Subsequent to the experience analytics system modifying the hypothesis, the recommendation engine provides recommendations that include the particular output based on the modified hypothesis.

Abstract: Predictive modeling, in which a first simulation model that simulates a first policy for managing a set of data is determined based on a pattern representative of the set of data and first operating variables for the first policy. The first simulation model is optimized and first prediction data that estimates performance of the first policy is computed using the optimized first simulation model. A second simulation model that simulates a second policy for managing the set of data is determined based on the pattern and second operating variables for the second policy. The second simulation model is optimized and second prediction data that estimates performance of the second policy is computed using the optimized second simulation model. The first prediction data is compared with the second prediction data, and a determination of whether the first policy is better than the second policy is made.

Abstract: Predictive modeling, in which a first simulation model that simulates a first policy for managing a set of data is determined based on a pattern representative of the set of data and first operating variables for the first policy. The first simulation model is optimized and first prediction data that estimates performance of the first policy is computed using the optimized first simulation model. A second simulation model that simulates a second policy for managing the set of data is determined based on the pattern and second operating variables for the second policy. The second simulation model is optimized and second prediction data that estimates performance of the second policy is computed using the optimized second simulation model. The first prediction data is compared with the second prediction data, and a determination of whether the first policy is better than the second policy is made.

Abstract: Statistical machine learning, in which an input module receives user input that defines a hypothesis associated with a particular Output. The hypothesis defines one or more starting criteria that are proposed as being correlated with the particular output, and a recommendation engine initially provides recommendations that include the particular output based on the one or more starting criteria defined by the hypothesis. An experience analytics system receives feedback data related to whether the recommendations provided based on the one or more starting criteria defined by the hypothesis were successful and modifies the hypothesis based on the feedback data. Subsequent to the experience analytics system modifying the hypothesis, the recommendation engine provides recommendations that include the particular output based on the modified hypothesis.

Abstract: A method and apparatus for automatic quality evaluation of an activity related to an organization, such as an agent of an organization which interacts with a calling party, a product, a campaign or the like, based on any combination of one or more of the following: the interaction itself and particularly its vocal part; meta data related to the call, to the call parties or to the environment; information extracted from the call or general information. The method may be activated off-line or on-line, in which case all alert can be generated for one or more calls.

Abstract: A method and apparatus for automatic quality evaluation of an activity related to an organization, such as an agent of an organization which interacts with a calling party, a product, a campaign or the like, based on any combination of one or more of the following: the interaction itself and particularly its vocal part; meta data related to the call, to the call parties or to the environment; information extracted from the call or general information. The method may be activated off-line or on-line, in which case all alert can be generated for one or more calls.