Method, apparatus, and computer program product for determining closing metrics

Patent metadata

Field	Value
Publication number	US-12026757-B2
Application number	US-202117375428-A
Country	US
Kind code	B2
Filing date	Jul 14, 2021
Priority date	Oct 4, 2012
Publication date	Jul 2, 2024
Grant date	Jul 2, 2024

Abstract

A method, apparatus and computer program product are provided for calculating closing metrics regarding a contract between a promotion service and provider. A promotional system may calculate a probability of closing, and an estimated time to close. The promotion service may offer a promotion to consumers for a discounted product or service, to be honored by the provider. A category, lead source, historical data, stage in sales, and/or size of the provider may be used in calculating a probability of close and/or time to close. An example method may comprise supplying a classifying model with a dataset, wherein the dataset comprises an identification of a provider and attributes corresponding to the provider and identifying a class of the provider in accordance with the plurality of corresponding attributes, wherein the identification is determined based on one or more patterns determinative of a return rate by the classifying model.

First claim

That which is claimed:

1. An apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the processor, cause the apparatus to at least:
automatically perform at least one of automated forward selection, automated backward elimination, or a combination thereof on a classifying model to select a set of determinative attributes from attributes comprising a provider, a geographic area in which the respective provider is located, a provider category, a lead source, and historical data relating to the one or more providers, wherein the determinative attributes are selected by at least the processor based on which attributes stabilize the classifying model;
train a set of classifying models to utilize the set of determinative attributes, wherein each classifying model indicates a probability of close with a particular provider within a respective time period;
supply the set of trained classifying models with a dataset, wherein the dataset comprises an indication of one or more providers in a given geographic area, a plurality of available attributes corresponding to the one or more providers, wherein the available attributes comprise at least one of a provider category, a lead source or historical data relating to the one or more providers; and
determine a probability of close with the particular provider for each of the respective time periods, wherein the probability of close indicates the probability that the particular provider will contract with a promotion service to offer a promotion within the respective time period, wherein the promotion is offered to a consumer for purchase from the promotion service in exchange for at least one of a discounted service or discounted product from the provider.

2. The apparatus of claim 1 , wherein the at least one memory and the computer program code are further configured to, with the processor, cause the apparatus to at least:
calculate an estimated time to close the contract between the particular provider and the promotion service.

3. The apparatus of claim 1 , wherein the at least one memory and the computer program code are further configured to, with the processor, cause the apparatus to at least:
determine a sales stage of an opportunity to contract offered by the promotion service to the provider, wherein the probability of close is based on at least the sales stage of the opportunity.

4. The apparatus of claim 1 , wherein the at least one memory and the computer program code are further configured to, with the processor, cause the apparatus to at least:
analyze a history of previous contracts between the promotion service and the particular provider, wherein the probability of close is based on at least the history analysis.

5. The apparatus of claim 1 , wherein the probability of close is calculated based on at least one of a size of the particular provider, merchant quality score of the particular provider, or a demand forecast.

6. The apparatus of claim 1 , wherein the at least one memory and the computer program code are further configured to, with the processor, cause the apparatus to at least:
rank a plurality of providers based on their respective probabilities to close.

7. The apparatus of claim 1 , wherein the providers are further ranked based on respective calculated return rates of promotions purchased by consumers.

8. A computer-implemented method comprising:
automatically performing at least one of automated forward selection, automated backward elimination, or a combination thereof on a classifying model to select a set of determinative attributes from attributes comprising a provider, a geographic area in which the respective provider is located, a provider category, a lead source, and historical data relating to the one or more providers, wherein the determinative attributes are selected by at least the computer-implemented method based on which attributes stabilize the classifying model;
training a set of classifying models to utilize the set of determinative attributes, wherein each classifying model indicates a probability of close with a particular provider within a respective time period;
supplying the set of trained classifying models with a dataset, wherein the dataset comprises an indication of one or more providers in a given geographic area, a plurality of available attributes corresponding to the one or more providers, wherein the available attributes comprise at least one of a provider category, a lead source or historical data relating to the one or more providers; and
determining a probability of close with the particular provider for each of the respective time periods, wherein the probability of close indicates the probability that the particular provider will contract with a promotion service to offer a promotion within the respective time period, wherein the promotion is offered to a consumer for purchase from the promotion service in exchange for at least one of a discounted service or discounted product from the provider.

9. The computer-implemented method of claim 8 , further comprising:
calculating an estimated time to close the contract between the particular provider and the promotion service.

10. The computer-implemented method of claim 8 , further comprising:
determining a sales stage of an opportunity to contract offered by the promotion service to the provider, wherein the probability of close is based on at least the sales stage of the opportunity.

11. The computer-implemented method of claim 8 , further comprising:
analyzing a history of previous contracts between the promotion service and the particular provider, wherein the probability of close is based on at least the history analysis.

12. The computer-implemented method of claim 8 , wherein the probability of close is calculated based on at least one of a size of the particular provider, merchant quality score of the particular provider, or a demand forecast.

13. The computer-implemented method of claim 8 , further comprising:
ranking a plurality of providers based on their respective probabilities to close.

14. The computer-implemented method of claim 13 , wherein the providers are further ranked based on respective calculated return rates of promotions purchased by consumers.

15. A computer program product comprising at least one non-transitory computer-readable storage medium having computer-executable program code instructions stored therein, the computer-executable program code instructions comprising program code instructions to:
automatically perform at least one of automated forward selection, automated backward elimination, or a combination thereof on a classifying model to select a set of determinative attributes from attributes comprising a provider, a geographic area in which the respective provider is located, a provider category, a lead source, and historical data relating to the one or more providers, wherein the determinative attributes are selected by at least the program code instructions based on which attributes stabilize the classifying model;
train a set of classifying models to utilize the set of determinative attributes, wherein each classifying model indicates a probability of close with a particular provider within a respective time period;
supply the set of trained classifying models with a dataset, wherein the dataset comprises an indication of one or more providers in a given geographic area, a plurality of available attributes corresponding to the one or more providers, wherein the available attributes comprise at

Assignees

• Groupon Inc

Inventors

• Mullins Brian
• Deland Matt
• Ferdowsi Zahra
• Lang Stephen
• Stokvis John
• Finn Nolan
• Shariff Shafiq

Classifications

• G06Q10/06311

  Scheduling, planning or task assignment for a person or group · CPC title

• G06Q30/0201

  Market modelling; Market analysis; Collecting market data · CPC title

• G06N20/10

  using kernel methods, e.g. support vector machines [SVM] · CPC title

• G06Q10/06315

  Needs-based resource requirements planning or analysis · CPC title

• G06Q40/00

  Finance; Insurance; Tax strategies; Processing of corporate or income taxes · CPC title