US 2019/0318378 A1Published application

Adaptive lead generation for marketing

AssigneeZeta Global Corp.

InventorsPavan Korada, Sunpreet Singh Khanuja, Weiwei Zhang, Bharat Goyal

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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.

Claims20

Dependent claims

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.

9 more elements

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.

9 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/453,471Filed 26 Jun 2019
Priority: 13 May 2016
Published: 17 Oct 2019
Term ends: Not estimatedThe source records don’t support an estimate.

Classification8 codes

G06Q 30/0243
Determining effectiveness of advertisements: comparative campaigns
G06F 16/24578
With adaptation to user needs: using ranking
G06F 16/9535
Querying, e.g. by the use of web search engines: search customisation based on user profiles and personalisation
G06F 30/20
Computer-aided design [CAD]: design optimisation, verification or simulation

Family

Application62/336,514Us provisional application · filed 13 May 2016
Application15/594,104Division · filed 12 May 2017
Application16/453,471Division
US 11,144,949 B2Related patent
US 11,354,700 B2Related patent
US 2017/0330220 A1Published application
US 2019/0318378 A1This patent

From the US filing history. Foreign family members aren’t listed.

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