A causality system for predicting the effects of customer experience on campaign results

Abstract

The present invention relates to a system (1) for enabling marketing teams to understand the reasons behind changes in subscriber"behavior better and to develop more effective campaign strategies by means of the technique of causality analysis.

Description

[0001] A CAUSALITY SYSTEM FOR PREDICTING THE EFFECTS OF CUSTOMER EXPERIENCE ON CAMPAIGN RESULTS Technical Field

[0002] The present invention relates to a system for enabling marketing teams to understand the reasons behind changes in subscriber” behavior better and to develop more effective campaign strategies by means of the technique of causality analysis.

[0003] Background of the Invention

[0004] Marketing teams want to make more effective and strategic decisions by accurately measuring the financial effects of their campaigns. Existing data indicate that the pre-and post-campaign revenues and usage habits of subscribers who receive the campaign vary as compared with the most similar control groups. These variations make it difficult to fully understand the effect of marketing strategies on subscribers.

[0005] Therefore, it is understood that there is a need for a system for enabling marketing teams to better understand the reasons behind changes in subscriber” behavior and to develop more effective campaign strategies by means of the technique of causality analysis.

[0006] The Chinese patent document no. CN117557299A, an application included in the state of the art, discloses a computer-aided marketing planning method and system. The invention relates to the technical field of data-driven marketing, in particular to a computer-aided marketing planning method and system. The method comprises thesteps of: carrying out the matching and analysis of a time sequence of consumer behaviors through employing a dynamic time bending algorithm based on collected consumer behavior data, carrying out the mode recognition, and carrying out the recognition of a model; and generating a behavior time sequence analysis result. According to the method, consumer behavior time sequences are matched and analyzed through a dynamic time bending algorithm and a state space analysis method. Behavior pattern recognition and dynamic model construction are achieved; key transformation moments and pattern categories of consumer behaviors are recognized through a transformation point analysis method and a clustering analysis method. Thereby, target positioning is provided for a marketing strategy, and the marketing strategy is optimized. Through combination of a complex event processing method and a graph theory analysis method, a user journey map and a customer contact network are optimized, the insight of customer behavior analysis is enhanced. Also, behavior mode prediction and marketing strategy optimization are realized on a CRM platform through a survival rate analysis method and a causal relationship analysis method.

[0007] Summary of the Invention

[0008] The object of the present invention is to realise a system which is developed with the aim of enabling marketing teams to better understand the reasons behind changes in subscriber” behavior and to develop more effective campaign strategies by means of the technique of causality analysis.

[0009] Detailed Description of the Invention

[0010] “A Causality System for Predicting the Effects of Customer Experience on Campaign Results” realized to fulfil the objective of the present invention is shown in the figure attached, in which:Figure 1 is a schematic view of the inventive system.

[0011] The components illustrated in the figure are individually numbered, where the numbers refer to the following:

[0012] 1. System

[0013] 2. Application

[0014] 3. Database

[0015] 4. Server

[0016] The inventive system ( 1 ) which is developed with the aim of enabling marketing teams to better understand the reasons behind changes in subscriber” behavior and to develop more effective campaign strategies by means of the technique of causality analysis; comprises

[0017] at least one application (2) which is configured to enable the user to enter parameters such as campaign name, analysis period, previous and subsequent periods, sample size for simulation, characteristics to be simulated, model to be used and threshold value for synthetic control group;

[0018] at least one database (3) which is configured to keep a record of data such as customer behavior, revenue data and usage habits;

[0019] at least one server (4) which is configured to analyze customer behavior before and after a campaign, to measure campaign effects by creating a synthetic control group among groups that exhibit similar behavior, to determine the reasons behind campaign effects and customer behavior, and to visualize and report results to authorized teams.The application (2) included in the inventive system (1) is configured to establish communication and exchange data with the server (4) by using any communication protocol. The application (2) is configured to ensure that analysis results are transmitted to authorized teams through an interface.

[0020] The database (3) included in the inventive system (1) is configured to establish communication and exchange data with the server (4) by using any communication protocol. The database (3) is configured to be managed by the server (4).

[0021] The server (4) included in the inventive system (1) is configured to establish communication and exchange data with the application (2) and the database (3) by using any communication protocol. The server (4) is configured to be managed by the database (3). The server (4) is configured to enable the collection of customer data such as customer behavior, revenue data and usage habits before and after the campaign. The server (4) is configured to enable the segmentation of the masses who exhibit similar behavior, by means of unsupervised techniques such as K-Means Clustering, Principal Component Analysis. The server (4) is configured to ensure that synthetic control groups of customers who did not participate in the campaign on the basis of each segment but who exhibit the most similar behavior with the experimental group are formed; demographic information, past behavior and other relevant factors are taken into account when forming synthetic control groups; and that the effects of the campaign are compared with the said groups on a segment basis. The server (4) is configured to ensure that the financial effects of a campaign -that is conducted by performing an impact analysis- are measured by comparing the data before and after the campaign and determining the effect of the campaign on customer behavior, and the differences between groups receiving and not receiving campaigns are analysed. The server (4) is configured to determine the causes of the observed changes by means of causality analysis. The server (4) is configured to examine the mediators throughwhich a campaign’s effect on customer behaviors is realized by means of techniques such as mediation model. The server (4) is configured to ensure that advanced statistical techniques such as Regresyon Discontinuity Design (RDD), Quantile Regression, Bayesian Belief Networks, Propensity Score Matching (PSM) are used at causality analysis and the cause-and-effect relationships if the observed effects of the said techniques are modelled accurately. The server (4) is configured to ensure that the results obtained are examined by means of extensive additional analysis and to check whether the reasons affecting the results are within a logical approach. The server (4) is configured to ensure that the analysis results are presented to authorized teams.

[0022] Industrial Application of the Invention

[0023] The inventive system (1) enables marketing teams to understand the reasons behind changes in subscriber” behavior better and to develop more effective campaign strategies by means of the technique of causality analysis.

[0024] Within these basic concepts; it is possible to develop various embodiments of the inventive “A Causality System (1) for Predicting the Effects of Customer Experience on Campaign Results”; the invention cannot be limited to examples disclosed herein and it is essentially according to claims.

Claims

CLAIMS1. A system (1) which is developed with the aim of enabling marketing teams to better understand the reasons behind changes in subscriber” behavior and to develop more effective campaign strategies by means of the technique of causality analysis; comprisingat least one application (2) which is configured to enable the user to enter parameters such as campaign name, analysis period, previous and subsequent periods, sample size for simulation, characteristics to be simulated, model to be used and threshold value for synthetic control group;and characterized byat least one database (3) which is configured to keep a record of data such as customer behavior, revenue data and usage habits; at least one server (4) which is configured to analyze customer behavior before and after a campaign, to measure campaign effects by creating a synthetic control group among groups that exhibit similar behavior, to determine the reasons behind campaign effects and customer behavior, and to visualize and report results to authorized teams.

2. A system ( 1 ) according to Claim 1 ; characterized by the application (2) which is configured to establish communication and exchange data with the server (4) by using any communication protocol.

3. A system (1) according to Claim 1 or 2; characterized by the application (2) which is configured to ensure that analysis results are transmitted to authorized teams through an interface.

4. A system (1) according to any one of the preceding claims; characterized by the database (3) which is configured to establish communication and exchange data with the server (4) by using any communication protocol.

5. A system (1) according to any one of the preceding claims; characterized by the database (3) which is configured to be managed by the server (4).

6. A system (1) according to any one of the preceding claims; characterized by the server (4) which is configured to establish communication and exchange data with the application (2) and the database (3) by using any communication protocol.

7. A system (1) according to any one of the preceding claims; characterized by the server (4) which is configured to be managed by the database (3).

8. A system (1) according to any one of the preceding claims; characterized by the server (4) which is configured to enable the collection of customer data such as customer behavior, revenue data and usage habits before and after the campaign.

9. A system (1) according to any one of the preceding claims; characterized by the server (4) which is configured to enable the segmentation of the masses who exhibit similar behavior, by means of unsupervised techniques such as K- Means Clustering, Principal Component Analysis.

10. A system (1) according to any one of the preceding claims; characterized by the server (4) which is configured to ensure that synthetic control groups of customers who did not participate in the campaign on the basis of each segmentbut who exhibit the most similar behavior with the experimental group are formed; demographic information, past behavior and other relevant factors are taken into account when forming synthetic control groups; and that the effects of the campaign are compared with the said groups on a segment basis.

11. A system (1) according to any one of the preceding claims; characterized by the server (4) which is configured to ensure that the financial effects of a campaign -that is conducted by performing an impact analysis- are measured by comparing the data before and after the campaign and determining the effect of the campaign on customer behavior, and the differences between groups receiving and not receiving campaigns are analysed.

12. A system (1) according to any one of the preceding claims; characterized by the server (4) which is configured to determine the causes of the observed changes by means of causality analysis.

13. A system (1) according to any one of the preceding claims; characterized by the server (4) which is configured to examine the mediators through which a campaign’s effect on customer behaviors is realized by means of techniques such as mediation model.

14. A system (1) according to any one of the preceding claims; characterized by the server (4) which is configured to ensure that advanced statistical techniques such as Regresyon Discontinuity Design (RDD), Quantile Regression, Bayesian Belief Networks, Propensity Score Matching (PSM) are used at causality analysis and the cause-and-effect relationships if the observed effects of the said techniques are modelled accurately.

15. A system (1) according to any one of the preceding claims; characterized by the server (4) which is configured to ensure that the results obtained are examined by means of extensive additional analysis and to check whether the reasons affecting the results are within a logical approach.

16. A system (1) according to any one of the preceding claims; characterized by the server (4) which is configured to ensure that the analysis results are presented to authorized teams.

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