# US 2019/0318378 A1 · Adaptive lead generation for marketing

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- Number: US 2019/0318378 A1
- Document ID: US20190318378A1
- Status: Published application
- Kind: A1
- Application number: 16/453,471
- Filed: 2019-06-26
- Published: 2019-10-17
- Earliest claimed: 2016-05-13
- Assignees: Zeta Global Corp.
- Applicants: Zeta Global Corp.
- Inventors: Pavan Korada; Sunpreet Singh Khanuja; Weiwei Zhang; Bharat Goyal
- Web page: https://patentgrep.com/patent/US20190318378A1

## CPC

- G06Q 30/0243: {Comparative campaigns}
- G06F 16/24578: {using ranking}
- G06F 16/9535: Search customisation based on user profiles and personalisation
- G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30)
- G06Q 30/016: After-sales
- G06Q 30/0251: Targeted advertisements
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

Various examples are directed to systems and methods for adaptively generating leads. A marketing system may determine that a first lead score for a first lead is greater than a first lead score threshold and determine that a second lead score for a second lead is less than the first lead score threshold. The marketing system may generate a set of filtered leads including the first lead information from the first lead. The marketing system may determine a scrub rate that describes a portion of first execution cycle data having lead scores greater than the first lead score threshold and determine that the scrub rate is greater than an analysis window scrub rate by more than a scrub rate threshold. The marketing system may select a second lead score threshold that is lower than the first lead score threshold.

## Claims

### Claim 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 execution cycle data comprising a first lead and a second lead, wherein the first lead comprises first lead information describing a first potential customer and the second lead comprises second lead information describing a second potential customer;
determine a first lead score for the first lead based at least in part on a first lead scoring model;
determine that the first lead score for the first lead is greater than a first lead score threshold;
determine that a second lead score for the second lead is less than the first lead score threshold;
generate a set of filtered leads including the first lead information from the first lead;
determine that a quality of the first execution cycle data is different than a quality of 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 execution cycle data;
receive second execution cycle data comprising a third lead comprising third lead information describing a third potential customer;
determine a third lead score for the third lead based at least in part on the second lead scoring model;
determine that the third lead score for the third lead is greater than the first lead score threshold; and
generate a second set of filtered leads including the third lead information from the third lead.

### Claim 2 (depends on 1)

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

### Claim 3 (depends on 1)

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

### Claim 4 (depends on 1)

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.

### Claim 5 (depends on 1)

The system of claim 1, wherein determining that the quality of the first execution cycle 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 execution cycle data having lead scores greater than the first lead score threshold;
determine that the scrub rate is greater than an analysis window scrub rate by more than a scrub rate threshold;

### Claim 6 (depends on 5)

The system of claim 5, 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.

### Claim 7 (depends on 5)

The system of claim 5, further programmed to:
determine a mean scrub rate of a plurality of historical scrub rates to generate the analysis window scrub rate; and
determine that the scrub rate threshold is more than two standard deviations higher than the mean scrub rate.

### Claim 8 (depends on 1)

The system of claim 1, wherein determining that the quality of the first execution cycle 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.

### Claim 9 (depends on 1)

The system of claim 1, where the system is further programmed to:
before generating the second set of filtered leads, send a message to an administrative user; and
determine that more than a threshold time has passed since the sending of the message.

### Claim 10 (depends on 1)

The system of claim 1, wherein the first execution cycle data consists of leads received during an observation period shorter than the analysis window, and wherein the system is further programmed to receive lead score data describing lead scores for at least one lead received during the observation period and at least one lead received during the analysis window and not during the observation period.

### Claim 11 (depends on 1)

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.

### Claim 12 (depends on 1)

The system of claim 1, wherein the first execution cycle data and the second execution cycle data consist of leads with a first common value for a first lead category, and wherein the first execution cycle data and the second execution cycle data consist of leads with a second common value for a second common lead value.

### Claim 13

A method for adaptively generating leads, comprising:
receiving first execution cycle data comprising a first lead and a second lead, wherein the first lead comprises first lead information describing a first potential customer and the second lead comprises second lead information describing a second potential customer;
determining a first lead score for the first lead based at least in part on a first lead scoring m;
determining that the first lead score for the first lead is greater than a first lead score threshold;
determining that a second lead score for the second lead is less than the first lead score threshold;
generating a set of filtered leads including the first lead information from the first lead;
determining that a quality of the first execution cycle data is different than a quality of 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 execution cycle data;
receiving second execution cycle data comprising a third lead comprising third lead information describing a third potential customer;
determining a third lead score for the third lead based at least in part on the second lead scoring model;
determining that the third lead score for the third lead is greater than the first lead score threshold; and
generating a second set of filtered leads including the third lead information from the third lead.

### Claim 14 (depends on 13)

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

### Claim 15 (depends on 13)

The method of claim 13, wherein the determining the second lead scoring model comprises modifying a scoring parameter of the first lead score model.

### Claim 16 (depends on 13)

The method of claim 13, further comprising, after determining the second lead scoring model, determining that application of the second lead scoring model is permitted by a limitation rule.

### Claim 17 (depends on 13)

The method of claim 13, wherein determining that the quality of the first execution cycle 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 execution cycle data having lead scores greater than the first lead score threshold;
determine that the scrub rate is greater than an analysis window scrub rate by more than a scrub rate threshold;

### Claim 18 (depends on 17)

The method of claim 17, 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 least in part on the average scrub rate.

### Claim 19 (depends on 17)

The method of claim 17, further comprising:
determining a mean scrub rate of a plurality of historical scrub rates to generate the analysis window scrub rate; and
determining that the scrub rate threshold is more than two standard deviations higher than the mean scrub rate.

### Claim 20

A non-transitory machine-readable medium comprising instructions which, when read by a machine, cause the machine to perform operations comprising:
receiving first execution cycle data comprising a first lead and a second lead, wherein the first lead comprises first lead information describing a first potential customer and the second lead comprises second lead information describing a second potential customer;
determining a first lead score for the first lead based at least in part on a first lead scoring m;
determining that the first lead score for the first lead is greater than a first lead score threshold;
determining that a second lead score for the second lead is less than the first lead score threshold;
generating a set of filtered leads including the first lead information from the first lead;
determining that a quality of the first execution cycle data is different than a quality of 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 execution cycle data;
receiving second execution cycle data comprising a third lead comprising third lead information describing a third potential customer;
determining a third lead score for the third lead based at least in part on the second lead scoring model;
determining that the third lead score for the third lead is greater than the first lead score threshold; and
generating a second set of filtered leads including the third lead information from the third lead.