Abstract: Apparatuses, methods, and systems for detecting changes in customer behavior are disclosed. One method includes detecting customer action data, receiving, by a marketing platform server, the customer action data over a period of time, determining, customer parameters including a mean, and a standard deviation of the customer action data, generating a normalization value when the standard deviation is detected to be less than a deviation threshold, calculating, by the marketing platform server, a value of deviation from expectation based at least on the mean, the normalization value, and a noise factor, calculating a current cumulative sum value of the customer action data based on a prior cumulative sum value and the value of the deviation from expectation, comparing the current cumulative sum value with a threshold, and generating an electronic communication for the merchant server when the current cumulative sum value satisfies a compared condition with the preselected threshold.

Abstract: Apparatuses, methods, and systems for detecting changes in customer behavior are disclosed. One method includes detecting customer action data, receiving, by a marketing platform server, the customer action data over a period of time, determining, customer parameters including a mean, and a standard deviation of the customer action data, generating a normalization value when the standard deviation is detected to be less than a deviation threshold, calculating, by the marketing platform server, a value of deviation from expectation based at least on the mean, the normalization value, and a noise factor, calculating a current cumulative sum value of the customer action data based on a prior cumulative sum value and the value of the deviation from expectation, comparing the current cumulative sum value with a threshold, and generating an electronic communication for the merchant server when the current cumulative sum value satisfies a compared condition with the preselected threshold.

Abstract: Apparatuses, methods, and systems for detecting changes in periodic sub-user behavior data for determining a condition of a network are disclosed. One method includes sensing the user data of sub-users, calculating a plurality of thresholds based on a size of the user data, calculating a rolling mean for each sub-period of a larger period, generating difference values based on comparing current sub-period values to corresponding sub-period rolling mean, accumulating a cumulative sum based the difference values, detecting an anomaly based on comparing the cumulative sum with the plurality of thresholds, detecting a condition of a state of communication between the user server and the platform server when the comparing of the cumulative sum with the plurality of threshold exceeds a condition detection threshold, and feeding the condition of the state of communication back to the user server when the condition is detected.

Abstract: Apparatuses, methods, and systems for detecting changes in customer behavior are disclosed. One method includes detecting customer action data, receiving, by a marketing platform server, the customer action data over a period of time, determining, customer parameters including a mean, and a standard deviation of the customer action data, generating a normalization value when the standard deviation is detected to be less than a deviation threshold, calculating, by the marketing platform server, a value of deviation from expectation based at least on the mean, the normalization value, and a noise factor, calculating a current cumulative sum value of the customer action data based on a prior cumulative sum value and the value of the deviation from expectation, comparing the current cumulative sum value with a threshold, and generating an electronic communication for the merchant server when the current cumulative sum value satisfies a compared condition with the preselected threshold.

Abstract: Apparatuses, methods, and systems for detecting changes in customer behavior are disclosed. One method includes detecting customer action data, receiving the customer action data over a period of time, determining customer parameters including a mean, and a standard deviation of the customer action data, determining a modified noise factor based on the value of the customer action data, to make the change detection system more sensitive to certain values, calculating a value of deviation from expectation based at least on the mean, the standard deviation, and the modified noise factor, calculating a current cumulative sum value of the customer action data based on a prior cumulative sum value and the value of the deviation from expectation, comparing the current cumulative sum value with a preselected threshold, and generating an informative electronic communication when the current cumulative sum value satisfies a compared condition with the preselected threshold.