SYSTEM AND METHODS FOR EFFICIENT AND SUCCESSFUL OUTBOUND CAMPAIGNS IN CONTACT CENTER

1 . A dynamic call queue system comprising:

a processor and a non-transitory computer readable medium operably coupled thereto, the non-transitory computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform operations which comprise:

training a generative artificial intelligence (AI) model on past customer data, past customer activity, and past customer interaction transcripts, to output a product recommendation for each of a plurality of customers;

querying the generative AI model for the product recommendation for each of the plurality of customers;

extracting keywords from the product recommendation for each of the plurality of customers;

applying a term frequency-inverse document frequency (TF-IDF) text vectorizer to the keywords;

generating a first vector of the keywords from the application of the TF-IDF text vectorizer;

receiving a description of a new product;

applying the TF-IDF text vectorizer to the description of the new product;

generating a second vector of the description of the new product from the application of the TF-IDF text vectorizer;

calculating a cosine similarity score (CSS) between the first vector and the second vector for each of the plurality of customers;

generating a customer likelihood score (CLS) for each of the plurality of customers from each CSS and the description of the new product;

calculating a sentiment score for each of the plurality of customers based on the past customer interaction transcripts;

retrieving a customer category score (CCS) for each of the plurality of customers;

calculating a customer propensity score (CPS) for each of the plurality of customers based on the CSS, the CLS, the sentiment score, and the CCS for each of the plurality of customers;

sorting the plurality of customers based on the CPS;

generating a dynamic list of customers in real-time, wherein a customer having a higher CPS is higher on the dynamic list than a customer having a lower CPS; and

scheduling outbound interactions based on the dynamic list of customers.

2 . The dynamic call queue system of claim 1 , wherein the past customer data and past customer activity comprise behavioral data, demographic data, psychographic data, and geographic data.

3 . The dynamic call queue system of claim 1 , wherein the operations further comprise training a random forest algorithm to output the CLS.

4 . The dynamic call queue system of claim 1 , wherein calculating the CPS comprises determining a weighted average of a sum of the CSS, the CLS, the sentiment score, and the CCS.

5 . The dynamic call queue system of claim 1 , wherein the operations further comprise:

determining an outcome of at least a portion of the scheduled outbound interactions; and

determining a value for a plurality of performance indicators of one or more agents handling the scheduled outbound interactions.

6 . The dynamic call queue system of claim 5 , wherein the operations further comprise rewarding an agent with a successful outcome.

7 . The dynamic call queue system of claim 5 , wherein the operations further comprise:

generating a report including the dynamic list of customers, the outcome of at least a portion of the scheduled outbound interactions, and the value of the plurality of performance indicators of the one or more agents handling the scheduled outbound interactions; and

automatically emailing the generated report to a supervisor of the one or more agents.

8 . The dynamic call queue system of claim 7 , wherein the operations further comprise:

identifying the scheduled outbound interactions with a CPS higher than a predetermined threshold and a value of the plurality of performance indicators lower than a predetermined threshold; and

automatically assigning training to one or more agents who handled the identified scheduled outbound interactions.

9 . A method for generating and scheduling a dynamic queue of customers, which comprises:

training a generative artificial intelligence (AI) model on past customer data, past customer activity, and past customer interaction transcripts, to output a product recommendation for each of a plurality of customers;

querying the generative AI model for the product recommendation for each of the plurality of customers;

extracting keywords from the product recommendation for each of the plurality of customers;

applying a term frequency-inverse document frequency (TF-IDF) text vectorizer to the keywords;

generating a first vector of the keywords from the application of the TF-IDF text vectorizer;

receiving a description of a new product;

applying the TF-IDF text vectorizer to the description of the new product;

generating a second vector of the description of the new product from the application of the TF-IDF text vectorizer;

calculating a cosine similarity score (CSS) between the first vector and the second vector for each of the plurality of customers;

generating a customer likelihood score (CLS) for each of the plurality of customers from each CSS and the description of the new product;

calculating a sentiment score for each of the plurality of customers based on the past customer interaction transcripts;

retrieving a customer category score (CCS) for each of the plurality of customers;

calculating a customer propensity score (CPS) for each of the plurality of customers based on the CSS, the CLS, the sentiment score, and the CCS for each of the plurality of customers;

sorting the plurality of customers based on the CPS;

generating a dynamic list of customers in real-time, wherein a customer having a higher CPS is higher on the dynamic list than a customer having a lower CPS; and

scheduling outbound interactions based on the dynamic list of customers.

10 . The method of claim 9 , which further comprises training a random forest algorithm to output the CLS.

11 . The method of claim 9 , wherein calculating the CPS comprises determining a weighted average of a sum of the CSS, the CLS, the sentiment score, and the CCS.

12 . The method of claim 9 , which further comprises:

determining an outcome of at least a portion of the scheduled outbound interactions; and

determining a value for a plurality of performance indicators of one or more agents handling the scheduled outbound interactions.

13 . The method of claim 12 , which further comprises rewarding an agent with a successful outcome.

14 . The method of claim 12 , which further comprises:

generating a report including the dynamic list of customers, the outcome of at least a portion of the scheduled outbound interactions, and the value of the plurality of performance indicators of one or more agents handling the scheduled outbound interactions; and

automatically emailing the generated report to a supervisor of the one or more agents.

15 . The method of claim 14 , which further comprises:

identifying the scheduled outbound interactions with a CPS higher than a predetermined threshold and a value of the plurality of performance indicators lower than a predetermined threshold; and

automatically assigning training to one or more agents who handled the identified scheduled outbound interactions.

16 . A non-transitory computer-readable medium having stored thereon computer-readable instructions executable by a processor to perform operations which comprise:

training a generative artificial intelligence (AI) model on past customer data, past customer activity, and past customer interaction transcripts, to output a product recommendation for each of a plurality of customers;

querying the generative AI model for the product recommendation for each of the plurality of customers;

extracting keywords from the product recommendation for each of the plurality of customers;

applying a term frequency-inverse document frequency (TF-IDF) text vectorizer to the keywords;

generating a first vector of the keywords from the application of the TF-IDF text vectorizer;

receiving a description of a new product;

applying the TF-IDF text vectorizer to the description of the new product;

generating a second vector of the description of the new product from the application of the TF-IDF text vectorizer;

calculating a cosine similarity score (CSS) between the first vector and the second vector for each of the plurality of customers;

generating a customer likelihood score (CLS) for each of the plurality of customers from each CSS and the description of the new product;

calculating a sentiment score for each of the plurality of customers based on the past customer interaction transcripts;

retrieving a customer category score (CCS) for each of the plurality of customers;

calculating a customer propensity score (CPS) for each of the plurality of customers based on the CSS, the CLS, the sentiment score, and the CCS for each of the plurality of customers;

sorting the plurality of customers based on the CPS;

generating a dynamic list of customers in real-time, wherein a customer having a higher CPS is higher on the dynamic list than a customer having a lower CPS; and

scheduling outbound interactions based on the dynamic list of customers.

17 . The non-transitory computer-readable medium of claim 16 , wherein the operations further comprise:

determining an outcome of at least a portion of the scheduled outbound interactions; and

determining a value for a plurality of performance indicators of one or more agents handling the scheduled outbound interactions.

18 . The non-transitory computer-readable medium of claim 17 , wherein the operations further comprise rewarding an agent with a successful outcome.

19 . The non-transitory computer-readable medium of claim 17 , wherein the operations further comprise:

generating a report including the dynamic list of customers, the outcome of at least a portion of the scheduled outbound interactions, and the value of the plurality of performance indicators of one or more agents handling the scheduled outbound interactions; and

automatically emailing the generated report to a supervisor of the one or more agents.

20 . The non-transitory computer-readable medium of claim 19 , wherein the operations further comprise:

identifying the scheduled outbound interactions with a CPS higher than a predetermined threshold and a value of the plurality of performance indicators lower than a predetermined threshold; and

automatically assigning training to one or more agents who handled the identified scheduled outbound interactions.