Data driven systems and methods for optimization of a target business

A system for determining a benchmark competitor of a target business, the system comprising one or more processors configured to perform a method comprising:

• determining at least one attribute associated with the target business and a business industry;
• receiving performance data related to competitors of the target business, the competitors being in the business industry, wherein the performance data includes the at least one attribute and performance variables associated with the competitors;
• pre-processing the performance data, wherein the pre-processing the performance data comprising:
• providing the performance data to a Natural Language Processing (NLP) model;
• identifying, using the NLP model, outliers in the performance data;
• removing the outliers identified using the NLP model;
• identifying, using the NLP model, missing data in the performance data;
• linking the performance data to reduce effects of the missing data identified using the NLP model;
• identifying, using the NLP model, structural errors in the performance variables, the structural errors comprising one or more of skipped delimiter, duplicate delimiter, absence of a unique ID, typos, mislabeled data, same attributes with different name, and mixed data types;
• correcting the structural errors identified using the NLP model, comprising correcting the one or more of skipped delimiter, duplicate delimiter, absence of a unique ID, typos, mislabeled data, same attributes with different name, and mixed data types;
• standardizing, using the NLP model, the performance variables of the competitors by normalizing the performance variables;
• and
• receiving, from the NLP model, pre-processed performance data;
• determining factor weights, the factor weights corresponding to the performance variables of the pre-processed performance data;
• determining multiple-criteria decision-analysis and analytic hierarchy process (MCDA-AHP) weights, the MCDA-AHP weights corresponding to the performance variables of the pre-processed performance data;
• determining adaptive weights based on the factor weights and the MCDA-AHP weights;
• applying each of the adaptive weights to each of the performance variables of the pre-processed performance data related to each competitor to generate adaptively-weighted performance variables;
• determining a performance score for each of the competitors, the performance score for each of the competitors being based on the adaptively-weighted performance variables, wherein the performance score for each of the competitors is a quantization representative of a performance of a respective competitor;
• and
• determining, the benchmark competitor based on the performance scores of the competitors.

A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform a method comprising:

• determining at least one attribute associated with the target business and a business industry;
• receiving performance data related to competitors of the target business, the competitors being in the business industry, wherein the performance data includes the at least one attribute and performance variables associated with the competitors;
• pre-processing the performance data, wherein the pre-processing the performance data comprising:
• providing the performance data to a Natural Language Processing (NLP) model;
• identifying, using the NLP model, outliers in the performance data;
• removing the outliers identified using the NLP model;
• identifying, using the NLP model, missing data in the performance data;
• linking the performance data to reduce effects of the missing data identified using the NLP model;
• identifying, using the NLP model, structural errors in the performance variables, the structural errors comprising one or more of skipped delimiter, duplicate delimiter, absence of a unique ID, typos, mislabeled data, same attributes with different name, and mixed data types;
• correcting the structural errors identified using the NLP model, comprising correcting the one or more of skipped delimiter, duplicate delimiter, absence of a unique ID, typos, mislabeled data, same attributes with different name, and mixed data types;
• standardizing, using the NLP model, the performance variables of the competitors by normalizing the performance variables;
• and
• receiving, from the NLP model, pre-processed performance data;
• determining factor weights, the factor weights corresponding to the performance variables of the pre-processed performance data;
• determining multiple-criteria decision-analysis and analytic hierarchy process (MCDA-AHP) weights, the MCDA-AHP weights corresponding to the performance variables of the pre-processed performance data;
• determining adaptive weights based on the factor weights and the MCDA-AHP weights;
• applying each of the adaptive weights to each of the performance variables of the pre-processed performance data related to each competitor to generate adaptively-weighted performance variables;
• determining a performance score for each of the competitors, the performance score for each of the competitors being based on the adaptively-weighted performance variables, wherein the performance score for each of the competitors is a quantization representative of a performance of a respective competitor;
• and
• determining, the benchmark competitor based on the performance scores of the competitors.