Abstract: A method and apparatus for predicting customer churn from an organization. The method and apparatus determine an interaction churn score based on analyzing an interaction between the customer and the organization and related data. Optionally, the method and apparatus determine also a transactional churn score based on additional data related to the customer or the organization, and a combined churn score based on the interaction churn score and the transactional churn score. The interaction churn score is determined by assessing a categorization score for the interaction in association with one or more categories, and combining the categorization scores. Of further use is a retention offer mechanism for making a retention offer to the customer.

Abstract: A method for classifying a pair of audio signals into an agent audio signal and a customer audio signal. One embodiment relates to unsupervised training, in which the training corpus comprises a multiplicity of audio signal pairs, wherein each pair comprises an agent signal and a customer signal, and wherein it is unknown for each signal if it is by the agent or by the customer. Training is based on the agent signals being more similar to one another than the customer signals. An agent cluster and a customer cluster are determined. The input signals are associated with the agent or the customer according to the higher score combination of the input signals and the clusters. Another embodiment relates to supervised training, wherein an agent model is generated, and the input signal that yields higher score against the model is the agent signal, while the other is the customer signal.

Abstract: A computerized method of predicting customer churn from an organization, including: receiving at a computer server a recorded customer interaction with an agent of the organization; analyzing the received customer interaction to extract basic features that provide an indication regarding the churn probability of the customer; extracting the entity information of the customer from the recorded interaction; retrieving from a database accessible by the server previous interactions for the same entity and extracting advanced features that provide an indication regarding the churn probability of the customer by comparing multiple interactions of the same entity; predicting a churn probability for the received interaction by applying a statistical customer churn model to the extracted basic features and extracted advanced features; and wherein the interaction and the previous interactions are recordable from more than one type of communication channel by which the customer can communicate with the organization.

Abstract: A method for classifying a pair of audio signals into an agent audio signal and a customer audio signal. One embodiment relates to unsupervised training, in which the training corpus comprises a multiplicity of audio signal pairs, wherein each pair comprises an agent signal and a customer signal, and wherein it is unknown for each signal if it is by the agent or by the customer. Training is based on the agent signals being more similar to one another than the customer signals. An agent cluster and a customer cluster are determined. The input signals are associated with the agent or the customer according to the higher score combination of the input signals and the clusters. Another embodiment relates to supervised training, wherein an agent model is generated, and the input signal that yields higher score against the model is the agent signal, while the other is the customer signal.

Abstract: A method and apparatus for predicting customer churn from an organization. The method and apparatus determine an interaction churn score based on analyzing an interaction between the customer and the organization and related data. Optionally, the method and apparatus determine also a transactional churn score based on additional data related to the customer or the organization, and a combined churn score based on the interaction churn score and the transactional churn score. The interaction churn score is determined by assessing a categorization score for the interaction in association with one or more categories, and combining the categorization scores. Of further use is a retention offer mechanism for making a retention offer to the customer.