# US 2019/0171957 A1 · System and method for user-level lifetime value prediction

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2019/0171957 A1
- Document ID: US20190171957A1
- Status: Published application
- Kind: A1
- Application number: 16/208,773
- Filed: 2018-12-04
- Published: 2019-06-06
- Earliest claimed: 2017-12-06
- Assignees: Cognant LLC
- Applicants: Cognant LLC
- Inventors: Wei Yang; Yifan Zhao; Doug Loyer; Arun Kejariwal
- Web page: https://patentgrep.com/patent/US20190171957A1

## CPC

- G06Q 30/0202: Market predictions or forecasting for commercial activities
- A63F 13/792: for payment purposes, e.g. monthly subscriptions
- A63F 13/795: for finding other players; for building a team; for providing a buddy list
- G06F 18/24323: {Tree-organised classifiers}
- G06N 7/00: Computing arrangements based on specific mathematical models
- A63F 2300/5513: involving billing
- A63F 2300/5546: using player registration data, e.g. identification, account, preferences, game history
- A63F 2300/57: details of game services offered to the player
- 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
- A63F: CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A method, a system, and an article are provided for determining a lifetime value of a user of a client application. An example method includes: obtaining data including a history of interactions between a plurality of users and a client application on a plurality of respective client devices; developing, using the data, a first model to predict a likelihood that a new user of the client application will be a payer; developing, using the data, a second model to predict an amount of revenue generated by the new user of the client application; providing the client application to a plurality of new users; using the first model and the second model to predict the likelihood and the revenue for each new user in the plurality of new users; and adjusting, based on the predicted likelihood and the predicted revenue, a method of acquiring additional users of the client application.

## Claims

### Claim 1

A method, comprising:
obtaining data comprising a history of interactions between a plurality of users and a client application on a plurality of respective client devices;
developing, using the data, a first predictive model to predict a likelihood that a new user of the client application will be a payer;
developing, using the data, a second predictive model to predict an amount of revenue generated by the new user of the client application;
providing the client application to a plurality of new users;
using the first predictive model and the second predictive model to predict the likelihood and the revenue for each new user in the plurality of new users; and
adjusting, based on the predicted likelihood and the predicted revenue, a method of acquiring additional users of the client application.

### Claim 2 (depends on 1)

The method of claim 1, wherein the history of interactions comprises a record of user activity in the client application.

### Claim 3 (depends on 1)

The method of claim 1, wherein the data further comprises a record of user activity prior to installation of the client application.

### Claim 4 (depends on 1)

The method of claim 1, wherein the data further comprises at least one of a user characteristic and a client device characteristic.

### Claim 5 (depends on 1)

The method of claim 1, wherein the first predictive model and the second predictive model each comprise a chain of predictive models, wherein each model in the chain is configured to make a prediction using data for a distinct user age.

### Claim 6 (depends on 1)

The method of claim 1, wherein the predicted likelihood and the predicted revenue comprise predictions for an initial time after the client application was first provided to the new user.

### Claim 7 (depends on 6)

The method of claim 6, wherein using the first predictive model and the second predictive model comprises:
extrapolating the predictions for the initial time to a later time using one or more multipliers.

### Claim 8 (depends on 1)

The method of claim 1, wherein using the first predictive model and the second predictive model comprises:
providing the first predictive model and the second predictive model with input data comprising a history of interactions between the plurality of new users and the client application.

### Claim 9 (depends on 1)

The method of claim 1, wherein the method of acquiring additional users comprises presenting content related to the client application to a set of prospective additional users.

### Claim 10 (depends on 1)

The method of claim 1, wherein the client application comprises a multiplayer online game.

### Claim 11

A system, comprising:
one or more computer processors programmed to perform operations comprising:
obtaining data comprising a history of interactions between a plurality of users and a client application on a plurality of respective client devices;
developing, using the data, a first predictive model to predict a likelihood that a new user of the client application will be a payer;
developing, using the data, a second predictive model to predict an amount of revenue generated by the new user of the client application;
providing the client application to a plurality of new users;
using the first predictive model and the second predictive model to predict the likelihood and the revenue for each new user in the plurality of new users; and
adjusting, based on the predicted likelihood and the predicted revenue, a method of acquiring additional users of the client application.

### Claim 12 (depends on 11)

The system of claim 11, wherein the history of interactions comprises a record of user activity in the client application.

### Claim 13 (depends on 11)

The system of claim 11, wherein the data further comprises a record of user activity prior to installation of the client application.

### Claim 14 (depends on 11)

The system of claim 11, wherein the first predictive model and the second predictive model each comprise a chain of predictive models, wherein each model in the chain is configured to make a prediction using data for a distinct user age.

### Claim 15 (depends on 11)

The system of claim 11, wherein the predicted likelihood and the predicted revenue comprise predictions for an initial time after the client application was first provided to the new user.

### Claim 16 (depends on 15)

The system of claim 15, wherein using the first predictive model and the second predictive model comprises:
extrapolating the predictions for the initial time to a later time using one or more multipliers.

### Claim 17 (depends on 11)

The system of claim 11, wherein using the first predictive model and the second predictive model comprises:
providing the first predictive model and the second predictive model with input data comprising a history of interactions between the plurality of new users and the client application.

### Claim 18 (depends on 11)

The system of claim 11, wherein the method of acquiring additional users comprises presenting content related to the client application to a set of prospective additional users.

### Claim 19 (depends on 11)

The system of claim 11, wherein the client application comprises a multiplayer online game.

### Claim 20

An article, comprising:
a non-transitory computer-readable medium having instructions stored thereon that, when executed by one or more computer processors, cause the one or more computer processors to perform operations comprising:
obtaining data comprising a history of interactions between a plurality of users and a client application on a plurality of respective client devices;
developing, using the data, a first predictive model to predict a likelihood that a new user of the client application will be a payer;
developing, using the data, a second predictive model to predict an amount of revenue generated by the new user of the client application;
providing the client application to a plurality of new users;
using the first predictive model and the second predictive model to predict the likelihood and the revenue for each new user in the plurality of new users; and
adjusting, based on the predicted likelihood and the predicted revenue, a method of acquiring additional users of the client application.