Methodology and system for adjusting default parameters in dynamic lightweight personalized analytics

A method for dynamically adjusting default parameters in dynamic lightweight personalized analytics (DLPA), the method comprising the steps of:

• receiving one or more customer parameters and remittance trends, wherein the one or more customer parameters comprise social media data, support data and communications data;
• accessing, via a customer account database, one or more key performance indicators (KPIs);
• accessing one or more DLPA metrics;
• wherein the DLPA metrics are impacted by one or more responses to a customer remittance suggestion;
• responsive to the KPI and DLPA metrics, adjusting a time window that represents a time period during which DLPA engine executes prior to updating new parameters;
• the step of adjusting further comprising:
• determining whether a change has occurred during a most recent time window (TW) of a DLPA computation to at least one KPI wherein the time window represents a given time frame for calculating a set of parameters;
• responsive to determining a change has occurred, determining whether an incremental change type is random;
• responsive to determining the incremental change type is random, incrementing the most recent time window (TW) by a random time and otherwise, incrementing the most recent time window (TW) by a linear time to generate an adjusted time window;
• automatically selecting a next change type wherein the next change type is selected from one of:
• random and linear;
• dynamically processing a plurality of transaction data, for a duration of the adjusted time window, based on the one or more adjusted at least one array to achieve memory conservation and optimal computation and to further generate a set of remittance suggestions and financial suggestions;
• and
• communicating, via a communication network, the set of remittance suggestions and financial suggestions.

A computer-readable medium comprising instructions which, when executed by a computer, cause the computer to carry out steps of:

• receiving one or more customer parameters and remittance trends, wherein the one or more customer parameters comprise social media data, support data and communications data;
• accessing, via a customer account database, one or more key performance indicators (KPIs);
• accessing one or more DLPA metrics;
• wherein the DLPA metrics are impacted by one or more responses to a customer remittance suggestion;
• responsive to the KPI and DLPA metrics, adjusting a time window that represents a time period during which DLPA engine executes prior to updating new parameters;
• the step of adjusting further comprising:
• determining whether a change has occurred during a most recent time window (TW) of a DLPA computation to at least one KPI wherein the time window represents a given time frame for calculating a set of parameters;
• responsive to determining a change has occurred, determining whether an incremental change type is random;
• responsive to determining the incremental change type is random, incrementing the most recent time window (TW) by a random time and otherwise, incrementing the most recent time window (TW) by a linear time to generate an adjusted time window;
• automatically selecting a next change type wherein the next change type is selected from one of:
• random and linear;
• dynamically processing a plurality of transaction data, for a duration of the adjusted time window, based on the one or more adjusted at least one array to achieve memory conservation and optimal computation and to further generate a set of remittance suggestions and financial suggestions;
• and
• communicating, via a communication network, the set of remittance suggestions and financial suggestions.