Method for accounting for impact of uncertainty in customer surveys

A computing apparatus for evaluating and filtering uncertainty in survey data, the computing apparatus comprising:

• a processor;
• a memory;
• and
• a communication interface coupled to each of the processor and the memory,
• wherein the processor is configured to:
• receive, via the communication interface, a plurality of survey responses with respect to a customer survey;
• obtain a first set of numerical survey data based on the received plurality of survey responses;
• generate a second set of numerical survey data based on a random sampling of a predetermined number of synthetically generated survey responses;
• adjust the second set of numerical survey data based on a predetermined intrinsic variability factor;
• compute an estimated error value for the adjusted second set of numerical survey data;
• determine an uncertainty of the first set of numerical survey data based on the computed estimated error value;
• and
• adjust the first set of numerical survey data based on the determined uncertainty,
• wherein each of the first set of numerical survey data and the second set of numerical survey data includes, for each respective survey response from among the received plurality of survey responses, a corresponding numerical value that falls within a predetermined numerical range, and
• wherein the processor is further configured to:
• divide the predetermined numerical range into a plurality of bins, each respective bin having a corresponding numerical sub-range that does not overlap with a numerical sub-range that corresponds to any other bin from among the plurality of bins;
• assign each respective survey response from among the received plurality of survey responses into a corresponding bin from among the plurality of bins based on the corresponding numerical value of the respective survey response;
• and
• when each respective survey response has been assigned to a corresponding bin, use a result of the assigning to determine a Net Promoter Score (NPS) that relates to the customer survey,
• wherein the processor is further configured to perform the assignment of each respective survey response into the corresponding bin by applying a predetermined machine-learning classification algorithm to the received plurality of survey responses.

A non-transitory computer readable storage medium storing instructions for evaluating and filtering uncertainty in survey data, the storage medium comprising executable code which, when executed by a processor, causes the processor to:

• receive a plurality of survey responses with respect to a customer survey;
• obtain a first set of numerical survey data based on the received plurality of survey responses;
• generate a second set of numerical survey data based on a random sampling of a predetermined number of synthetically generated survey responses;
• adjust the second set of numerical survey data based on a predetermined intrinsic variability factor;
• compute an estimated error value for the adjusted second set of numerical survey data;
• determine an uncertainty of the first set of numerical survey data based on the computed estimated error value;
• and
• adjust the first set of numerical survey data based on the determined uncertainty,
• wherein each of the first set of numerical survey data and the second set of numerical survey data includes, for each respective survey response from among the received plurality of survey responses, a corresponding numerical value that falls within a predetermined numerical range, and
• wherein the executable code further causes the processor to:
• divide the predetermined numerical range into a plurality of bins, each respective bin having a corresponding numerical sub-range that does not overlap with a numerical sub-range that corresponds to any other bin from among the plurality of bins;
• assign each respective survey response from among the received plurality of survey responses into a corresponding bin from among the plurality of bins based on the corresponding numerical value of the respective survey response;
• and
• when each respective survey response has been assigned to a corresponding bin, use a result of the assigning to determine a Net Promoter Score (NPS) that relates to the customer survey,
• wherein the executable code further causes the processor to perform the assignment of each respective survey response into the corresponding bin by applying a predetermined machine-learning classification algorithm to the received plurality of survey responses.