Granted Patent B2

US 12,333,565 · App. 17/727,662 · Granted Jun 17, 2025

Inventors: Pavan Korada (San Rafael, CA); Sunpreet Singh Khanuja (San Jose, CA); Weiwei Zhang (Palo Alto, CA); Bharat Goyal (San Jose, CA)

Assignee: Zeta Global Corp.

G06Q30/0243G06F16/24578G06F16/9535G06F30/20G06Q30/016G06Q30/0251

Abstract

Claims (56)

1. A system comprising at least one processor and a memory in communication with the at least one processor, wherein the system is programmed to:

receive first data comprising a first set of leads and a second set of leads, wherein the first set of leads comprises first lead information describing a plurality of first potential customers and the second set of leads comprises second lead information describing a plurality of second potential customers;

determine a first lead score for the plurality of first potential customers in the first set of leads based at least in part on a first lead scoring model;

determine that the first lead score for a first lead is greater than a first lead score threshold for the plurality of first potential customers in the first set of leads;

determine that a quality of the first data is different than a quality of an analysis window data by more than a quality threshold, the analysis window data comprising a plurality of leads received during an analysis window time period;

determine a second lead scoring model based at least in part on the first data;

wherein determining that the quality of the first data is different than a quality of analysis window data by more than a quality threshold comprises:

determining a scrub rate, wherein the scrub rate describes a portion of the first data having lead scores greater than the first lead score threshold;

determining that the scrub rate is greater than a scrub rate of an analysis window by more than a scrub rate threshold; and

receiving lead score data describing lead scores for at least one lead received during an observation period and at least one lead received during the analysis window and not during the observation period.

2. The system of claim 1 , wherein determining the second lead scoring model comprises re-training the first lead scoring model based at least in part on the first data.

3. The system of claim 1 , wherein determining the second lead scoring model comprises modifying a scoring parameter of the first lead scoring model.

4. The system of claim 1 , further programmed to, after determining the second lead scoring model, determine that application of the second lead scoring model is permitted by a limitation rule.

5. The system of claim 1 , further programmed to:

determine an average scrub rate for a second plurality of leads received during the analysis window; and

determine the scrub rate threshold based at least in part on the average scrub rate.

6. The system of claim 1 , further programmed to:

determine a mean scrub rate of a plurality of historical scrub rates to generate the scrub rate of the analysis window; and

determine that the scrub rate threshold is more than two standard deviations higher than the mean scrub rate.

7. The system of claim 1 , wherein determining that the quality of the first data is different than a quality of analysis window data by more than a quality threshold comprises determining that a first value of the first lead information indicates that the first lead is not likely to convert.

8. The system of claim 1 , where the system is further programmed to: before generating the second set of leads, send a message to an administrative user; and

determine that more than a threshold time has passed since sending of the message.

9. The system of claim 1 , wherein the first data consists of leads received during an observation period shorter than the analysis window.

10. The system of claim 1 , wherein the system is further programmed to determine a scrub rate for a plurality of lead scores for leads received during the analysis window.

11. The system of claim 1 , further programmed to determine that a second lead score for the plurality of second potential customers in the second set of leads is less than the first lead score threshold, wherein the first data and second data consist of leads with a first common value for a first lead category, and wherein the first data and the second data include leads with a second common value for a second common lead value.

12. A method for adaptively generating leads, comprising:

receiving first data comprising a first set of leads and a second set of leads, wherein the first set of leads comprises first lead information describing a plurality of first potential customers and the second set of leads comprises second lead information describing a plurality of second potential customers;

determining a first lead score for the plurality of first potential customers in the first set of leads based at least in part on a first lead scoring model;

determining that the first lead score for a first lead is greater than a first lead score threshold for the plurality of first potential customers in the first set of leads;

determining that a quality of the first data is different than a quality of an analysis window data by more than a quality threshold, the analysis window data comprising a plurality of leads received during an analysis window time period;

determining a second lead scoring model based at least in part on the first data;

wherein determining that the quality of the first data is different than a quality of analysis window data by more than a quality threshold comprises:

determining a scrub rate, wherein the scrub rate describes a portion of the first data having lead scores greater than the first lead score threshold;

determining that the scrub rate is greater than a scrub rate of an analysis window by more than a scrub rate threshold; and

receiving lead score data describing lead scores for at least one lead received during an observation period and at least one lead received during the analysis window and not during the observation period.

13. The method of claim 12 , wherein determining the second lead scoring model comprises re-training the first lead scoring model based at least in part on the first data.

14. The method of claim 12 , wherein determining the second lead scoring model comprises modifying a scoring parameter of the first lead scoring model.

15. The method of claim 12 , further comprising, after determining the second lead scoring model, determining that application of the second lead scoring model is permitted by a limitation rule.

16. The method of claim 12 , further comprising:

determining an average scrub rate for a second plurality of leads received during the analysis window; and

determining the scrub rate threshold based at east in part on the average scrub rate.

17. The method of claim 12 , further comprising:

determining a mean scrub rate of a plurality of historical scrub rates to generate the scrub rate of the analysis window; and

determining that the scrub rate threshold is more than two standard deviations higher than the mean scrub rate.

18. The method of claim 12 , wherein determining that the quality of the first data is different than a quality of analysis window data by more than a quality threshold comprises determining that a first value of the first lead information indicates that the first lead is not likely to convert.

19. A non-transitory machine-readable medium comprising instructions which, when read by a machine, cause the machine to perform operations comprising:

receiving first data comprising a first set of leads and a second set of leads, wherein the first set of leads comprises first lead information describing a plurality of first potential customers and the second set of leads comprises second lead information describing a plurality of second potential customers;

determining a first lead score for the plurality of first potential customers in the first set of leads based at least in part on a first lead scoring model;

determining that the first lead score for a first lead is greater than a first lead score threshold for the plurality of first potential customers in the first set of leads;

determining that a quality of the first data is different than a quality of an analysis window data by more than a quality threshold, the analysis window data comprising a plurality of leads received during an analysis window time period;

determining a second lead scoring model based at least in part on the first data;

wherein determining that the quality of the first data is different than a quality of analysis window data by more than a quality threshold comprises:

determining a scrub rate, wherein the scrub rate describes a portion of the first data having lead scores greater than the first lead score threshold;

determining that the scrub rate is greater than a scrub rate of an analysis window by more than a scrub rate threshold; and

receiving lead score data describing lead scores for at least one lead received during an observation period and at least one lead received during the analysis window and not during the observation period.

20. The non-transitory machine-readable medium of claim 19 , wherein determining the second lead scoring model comprises re-training the first lead scoring model based at least in part on the first data.

Assignments (3)

Continuity (4)

References Cited (114)