CA3150580A1 - Method and system for intelligent marketing - Google Patents

Abstract

The present invention makes public a method and a system for intelligent marketing, which method comprises: obtaining variables and drawing a variable trend curve, and obtaining variables with monotonicity features or U-shape features on the basis of the variable trend curve and a preset trend recognizing rule; employing the variables with monotonicity features or U-shape features to train an intelligent marketing model, and simultaneously employing a stepwise regression operation to screen and obtain one or more target variable(s) adapted to the intelligent marketing model; and employing the target variable(s) and the intelligent marketing model to obtain a target customer, and pushing a commodity to the target customer.

Description

METHOD AND SYSTEM FOR INTELLIGENT MARKETING
BACKGROUND OF THE INVENTION
Technical Field [0001] The present invention relates to the field of big data technology, and more particularly to a method and a system for intelligent marketing.
Description of Related Art

[0002] With the development of information technology and the incessant expansion of commodity marketing businesses, the traditional commodity marketing mode can no longer meet the requirements of informatization of the modern society, and the intelligent marketing mode is often used instead to push commodities to target customers, so as to enhance purchasing possibilities of users and hence enhance marketing efficiency.

[0003] However, in such machine learning models of the logistical regression type as the intelligent marketing model, continuous variables are usually required to possess better monotonicity or least assume positive U-shapes, inverse U-shapes, so that the model achieves better prediction effect or interpretability; there are many types of variables in actual application, including such thousands of variables as user basic information, browsing behaviors, and purchasing behaviors, etc., and it is usually impossible to require the trend chart of each type of variables to exhibit strict monotonicity or assume U-shape, at this time it is needed to screen out variables with better degree of monotonicity or U-shape and then to input the same in the intelligent marketing model. The currently available method relies on manual inspection to check the variable trend curve, and this calls for a great deal of workload with low efficiency under the circumstance in which there are massive variables.

Date Recue/Date Received 2022-03-01 SUMMARY OF THE INVENTION

[0004] An objective of the present invention is to provide a method and a system for intelligent marketing, totally in place of manual work, to automatically screen out variables with better degree of monotonicity or U-shape and then input the same to an intelligent marketing model to acquire target customers, push commodities to the target customers, and enhance marketing efficiency.

[0005] In order to achieve the above objective, the present invention provides the following technical solutions.

[0006] There is provided a method for intelligent marketing, which method comprises:

[0007] obtaining variables and drawing a variable trend curve, and obtaining variables with monotonicity features or U-shape features on the basis of the variable trend curve and a preset trend recognizing rule;

[0008] employing the variables with monotonicity features or U-shape features to train an intelligent marketing model, and simultaneously employing a stepwise regression operation to screen and obtain one or more target variable(s) adapted to the intelligent marketing model; and

[0009] employing the target variable(s) and the intelligent marketing model to obtain a target customer, and pushing a commodity to the target customer.

[0010] Preferably, the variables are binned to obtain a plurality of bins, and the variable trend curve is drawn on the basis of the bins.

[0011] Further, the step of obtaining variables with monotonicity features on the basis of the variable trend curve and a preset trend recognizing rule includes:

[0012] calculating a total variation TV of the variables on the basis of the variable trend curve, wherein the total variation TV of the variables is a sum total of amplitudes between every Date Recue/Date Received 2022-03-01 two adjacent bins of the variable trend curve;

[0013] calculating an absolute value of a difference between left and right two endpoints of the variable trend curve and marking the absolute value as a first difference value ADi, and obtaining a monotonicity index M index of the variables according to the total variation TV and the first difference value ADi, where M index = TV/ADi; and

[0014] screening out variables with monotonicity features on the basis of a preset monotonicity index threshold.

[0015] Preferably, the step of obtaining variables with U-shape features on the basis of the variable trend curve and a preset trend recognizing rule includes:

[0016] obtaining the maximum value and the minimum value of the variable trend curve except for left and right two endpoints;

[0017] calculating a sum of absolute values of difference values between the left and right two endpoints of the variable trend curve respectively with respect to the minimum value of the variable trend curve and marking the sum as a second difference value AD2, and obtaining a positive U-shape index U index 1 of the variables according to the total variation TV and the second difference value AD2, where U index 1 = TV/AD2;
and/or

[0018] calculating a sum of absolute values of difference values between the left and right two endpoints of the variable trend curve respectively with respect to the maximum value of the variable trend curve and marking the sum as a third difference value AD3, and obtaining an inverse U-shape index U index 2 of the variables according to the total variation TV and the third difference value AD3, where U index 2 = TV/AD3; and

[0019] screening out variables with U-shape features on the basis of preset positive U-shape index threshold and inverse U-shape index threshold.

[0020] Preferably, the monotonicity index threshold, the positive U-shape index threshold and the inverse U-shape index threshold are valuated in the range of [1, 1.51.

[0021] Further, when the maximum value of the variable trend curve except for the left and right Date Recue/Date Received 2022-03-01 two endpoints is smaller than values of the left and right two endpoints simultaneously, the inverse U-shape index U index 2 is not calculated; and

[0022] when the minimum value of the variable trend curve except for the left and right two endpoints is greater than values of the left and right two endpoints simultaneously, the positive U-shape index U index 1 is not calculated.

[0023] Preferably, the step of employing the variables with monotonicity features or U-shape features to train an intelligent marketing model, and simultaneously employing a stepwise regression operation to screen and obtain one or more target variable(s) adapted to the intelligent marketing model includes:

[0024] data-preprocessing the variables with monotonicity features or U-shape features;

[0025] screening important variables out of the preprocessed variables; and

[0026] inputting the important variables in the intelligent marketing model, and simultaneously employing a stepwise regression operation to screen and obtain one or more target variable(s) adapted to the intelligent marketing model.

[0027] Preferably, the data-preprocessing includes filling missing values, processing abnormal values, and one-hot coding with respect to categorical variables.

[0028] Preferably, the step of screening important variables out of the preprocessed variables includes:

[0029] calculating IV values and PSI values of the variables respectively; and

[0030] screening out important variables whose IV values are greater than an IV threshold and whose PSI values are smaller than a PSI threshold.

[0031] There is provided a system for intelligent marketing, which system comprises:

[0032] a first variable screening module, a second variable screening module, and a marketing pushing module, of which:

[0033] the first variable screening module is employed for obtaining variables and drawing a Date Recue/Date Received 2022-03-01 variable trend curve, and obtaining variables with monotonicity features or U-shape features on the basis of the variable trend curve and a preset trend recognizing rule;

[0034] the second variable screening module is employed for employing the variables with monotonicity features or U-shape features to train an intelligent marketing model, and simultaneously employing a stepwise regression operation to screen and obtain one or more target variable(s) adapted to the intelligent marketing model; and

[0035] the marketing pushing module is employed for employing the target variable(s) and the intelligent marketing model to obtain a target customer, and pushing a commodity to the target customer.

[0036] In comparison with prior-art technology, the method and system for intelligent marketing provided by the present invention achieve the following advantageous effects.

[0037] The method for intelligent marketing provided by the present invention utilizes machine learning to replace the traditional step of manually checking the trend chart, automatically screens out variables with better monotonicity or U-shape degree from great many variables, inputs the screened variables to an intelligent marketing model to acquire target customers, pushes commodities to the target customers, recognizes the type of the variable trend curve and the monotonicity and U-shape degrees consistently as the manual, visually direct judgment, can replace manual work, and effectively enhances working efficiency on the basis of guaranteeing recognition quality.

[0038] The system for intelligent marketing provided by the present invention employs the aforementioned method for intelligent marketing, can automatically screen out variables, acquire target customers according to the screened variables, push commodities to the target customers, and effectively enhance working efficiency and customers' purchasing success rate after marketing.
Date Recue/Date Received 2022-03-01 BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings described here are meant to provide further comprehension to the present invention, constitute a portion of the present invention, and exemplary embodiments of the present invention and the descriptions thereof are meant to explain the present invention, rather than to restrict the present invention. In the drawings:

[0040] Fig. 1 is a flowchart schematically illustrating the method for intelligent marketing in the embodiments of the present invention;

[0041] Fig. 2 is a flowchart schematically illustrating the essential screening method for intelligent marketing in the embodiments of the present invention;

[0042] Figs. 3(a) ¨ 3(b) are views respectively illustrating examples of Condition 1 in which the variable trend curve is recognized as a positive U-shape and Condition 2 in which the variable trend curve is recognized as an inverse U-shape in the embodiments of the present invention;

[0043] Figs. 4(a)¨ 4(0 are views respectively illustrating circumstances possibly occurring when the type of the variable trend curve is judged; and

[0044] Figs. 5(a) ¨ 5(1) are views respectively illustrating examples of judging the type of the variable trend curve.
DETAILED DESCRIPTION OF THE INVENTION

[0045] In order to make apparent and comprehensible the aforementioned objectives, features and advantages of the present invention, the technical solutions in the embodiments of the present invention will be more clearly and comprehensively described below with Date Recue/Date Received 2022-03-01 reference to the accompanying drawings in the embodiments of the present invention.
Apparently, the embodiments as described are merely partial, rather than the entire, embodiments of the present invention. All other embodiments obtainable by persons ordinarily skilled in the art on the basis of the embodiments in the present invention without spending creative effort in the process shall all be covered by the protection scope of the present invention.

[0046] Embodiment 1

[0047] Please refer to Fig. 1, a method for intelligent marketing, comprising:

[0048] obtaining variables and drawing a variable trend curve, and obtaining variables with monotonicity features or U-shape features on the basis of the variable trend curve and a preset trend recognizing rule;

[0049] employing the variables with monotonicity features or U-shape features to train an intelligent marketing model, and simultaneously employing a stepwise regression operation to screen and obtain one or more target variable(s) adapted to the intelligent marketing model; and

[0050] employing the target variable(s) and the intelligent marketing model to obtain a target customer, and pushing a commodity to the target customer.

[0051] The method for intelligent marketing provided by the present invention utilizes machine learning to replace the traditional step of manually checking the trend chart, automatically screens out variables with better monotonicity or U-shape degree from great many variables, inputs the screened variables to an intelligent marketing model to acquire target customers, pushes commodities to the target customers, enhances commodity marketing success rate, achieves automatic model training, and effectively enhances working efficiency.

[0052] Please refer to Fig. 2, the step of obtaining variables with monotonicity features or U-Date Recue/Date Received 2022-03-01 shape features on the basis of the variable trend curve and a preset trend recognizing rule in the embodiments of the present invention includes:

[0053] binning the variables to obtain a plurality of bins, and drawing a variable trend curve of positive sample rate (Target Rate) relevant variables on the basis of the bins. The variables are continuous variables, while discrete variables are not taken into consideration in the present invention.

[0054] Suppose there are altogether N bins on the x axis in a variable trend curve of binned continuous variables, the N bins are sequentially Pi. P2, ..., PN, positive sample rates at the N bins on the variable trend curve are respectively Target Ratei, Target Rate2, Target RateN, and the minimum positive sample rate (Target Rate.) and the maximum positive sample rate (Target _Rate) except for the positive sample rates (Target Ratei and Target RateN) at the left and right two endpoints are obtained.

[0055] The total variation (TV) of the variable trend curve is calculated, the total variation (TV) reflects the degree of fluctuation of the variable trend curve, and can be obtained by calculating a sum total of amplitudes between every two adjacent bins of the variable trend curve, namely TV = EiN_i I Target _ Rate i i ¨ Target_ Rate i I .

[0056] A first difference value (ADO of the variable trend curve is calculated, the first difference value means an absolute value of a difference between the positive sample rates at the left and right two endpoints on the variable trend curve, namely ADi = I Target_ RateN ¨
Target _Rated.

[0057] A second difference value (AD2) of the variable trend curve is calculated, the second difference value means a sum of absolute values of differences between positive sample rates at the left and right two endpoints on the variable trend curve respectively with respect to the minimum positive sample rate except for the positive sample rates at the left and right two endpoints, namely AD2 = I Target_ Ratemin ¨ Target_ Rate' I
+

Date Recue/Date Received 2022-03-01 I Target_ RateN ¨ Target_ Ratemin I.

[0058] A third difference value (AD3) of the variable trend curve is calculated, the third difference value means a sum of absolute values of differences between positive sample rates at the left and right two endpoints on the variable trend curve respectively with respect to the maximum positive sample rate except for the positive sample rates at the left and right two endpoints, namely AD3 = 'Target_ Ratemax ¨ Target_ Rate' I
+
I Target_ RateN ¨ Target_ Ratemax I .

[0059] A monotonicity degree index (M index) of the variable trend curve is calculated, the monotonicity degree index can be calculated by calculating a ratio of the total variation (TV) of the variable trend curve to the first difference value (ADO, namely M
index =
TV/ADi. If the variable trend curve is completely monotonous, then the ratio TV/ADi =
1; if it is not monotonous, then the ratio TV/ADi > 1; the more the ratio TV/ADi approaches 1, the higher is the monotonicity degree of the variable trend curve, conversely, the lower is the monotonicity degree of the variable trend curve.
Accordingly, the monotonicity degree index of the variable trend curve is valuated in the range of M_index E [1, +00), when M index = 1, this indicates that the variable trend curve is completely monotonous, and the higher the value of M index is, the lower is the monotonicity degree of the variable trend curve.

[0060] A U-shape degree index of the variable trend curve is calculated, the U-shape degree index can include a positive U-shape degree index (U index 1) and an inverse U-shape degree index (U index 2). The U-shape degree is essentially the same as the V-shape degree, each being an analysis model index that judges and predicts the current status and development trend of a certain event with a special development process.

[0061] When and only when Condition 1 (Target Rate.. < Target Ratei, and Target Rate.. <
Target RateN) is established, the variable trend curve is possibly recognized as a positive Date Recue/Date Received 2022-03-01 U-shape, and hence its positive U-shape degree index (U index 1) can be calculated by calculating the ratio of the total variation (TV) of the variable trend curve to the second difference value (AD2), namely U index 1 = TV/AD2. If the variable trend curve assumes a strict positive U-shape, then the ratio TV/AD2 = 1; if it is not a strict positive U-shape, then the ratio TV/AD2 > 1; the more the ratio TV/AD2 approaches 1, the higher is the positive U-shape degree of the variable trend curve, conversely, the lower is the positive U-shape degree of the variable trend curve. Accordingly, the positive U-shape degree index of the variable trend curve is valuated in the range of U_index_l E [1, +00), when U index 1 = 1, this indicates that the variable trend curve assumes a strict positive U-shape, and the higher the value of U index 1 is, the lower is the positive U-shape degree of the variable trend curve.

[0062] When and only when Condition 2 (Target Rate.. > Target Ratei, and Target Rate.. >
Target RateN) is established, the variable trend curve is possibly recognized as an inverse U-shape, and hence its inverse U-shape degree index (U index 2) can be calculated by calculating the ratio of the total variation (TV) of the variable trend curve to the third difference value (AD3), namely U index 2 = TV/AD3. If the variable trend curve assumes a strict inverse U-shape, then the ratio TV/AD3 = 1; if it is not a strict inverse U-shape, then the ratio TV/AD3 > 1; the more the ratio TV/AD3 approaches 1, the higher is the inverse U-shape degree of the variable trend curve, conversely, the lower is the inverse U-shape degree of the variable trend curve. Accordingly, the inverse U-shape degree index of the variable trend curve is valuated in the range of U_index_2 E [1, +00), when U index 2 = 1, this indicates that the variable trend curve assumes a strict inverse U-shape, and the higher the value of U index 2 is, the lower is the inverse U-shape degree of the variable trend curve.

[0063] When the U-shape degree index of the variable trend curve is calculated, since the variable trend curve is possibly recognized as both the positive U-shape and the inverse U-shape at the same time, the positive U-shape degree index and the inverse U-shape Date Recue/Date Received 2022-03-01 degree index of the variable trend curve must be both calculated in this case, the lesser one of the two indexes is selected to judge whether the variable trend curve pertains to a positive U-shape or an inverse U-shape, and the lesser one of the two indexes is taken to serve as the U-shape degree index U index of the variable trend curve.

[0064] Variables with monotonicity features and U-shape features are screened out on the basis of preset monotonicity index threshold, positive U-shape index threshold and inverse U-shape index threshold. In this embodiment, the monotonicity index threshold, the positive U-shape index threshold and the inverse U-shape index threshold are all valuated in the range of [1, 1.51. However, in the case the variable trend curve is excellent in monotonicity degree and has many variables, this threshold range can be lessened, conversely, the threshold range is enlarged.

[0065] Moreover, the monotonicity degree index M index and the U-shape degree index U index are utilized to judge the type of the variable trend curve, and such judging process involves positive sample rates (Target Ratei, Target_RateN) at the left and right endpoints and the extreme values (Target Rate.õ Target _Rate) except for the positive sample rates at the left and right endpoints.

[0066] When the positive sample rate at the left endpoint is greater than the positive sample rate at the right endpoint ( Target_Ratei Target_RateN), there are the following six circumstances concerning size relationships between the positive sample rates at the left and right endpoints and the extreme values except for the positive sample rates at the left and right endpoints:

[0067] Circumstance Al: Target_Ratemax E (-00, Target_RateN);

[0068] Circumstance A2: Target_Ratemax E [Target_RateN, Target_Ratei);

[0069] Circumstance A3: Target_Ratemax E [Target_Ratei, +00);

[0070] Circumstance B 1: Target_Ratemin E (-00, Target_RateN);

[0071] Circumstance B2: Target_Ratemin E [Target_RateN, Target_Ratei);

Date Recue/Date Received 2022-03-01

[0072] Circumstance B3: Target_Ratemin E [Target_Ratei, +00).

[0073] There are nine combinational circumstances for the above six circumstances, namely:

[0074] Circumstance Al-Bl: Target_Ratemax E (-00, Target_RateN) and Target_Rate mmE (-00, Target_RateN);

[0075] Circumstance A1-B2: Target_Ratemax E (-00, Target_RateN) and Target_Ratemin E [Target_RateN, Target_Ratei);

[0076] Circumstance A1-B3: Target_Ratemax E (-00, Target_RateN) and Target_Ratemin E [Target_Ratei, +00);

[0077] Circumstance A2-B1: Target_Ratemax E [Target_RateN, Target_Ratei) and Target_Ratemin E (-00, Target_RateN);

[0078] Circumstance A2-B2: Target_Ratemax E [Target_RateN, Target_Ratei) and Target_Ratemin E [Target_RateN, Target_Ratei);

[0079] Circumstance A2-B3: Target_Ratemax E [Target_RateN, Target_Ratei) and Target_Ratemin E [Target_Ratei, +00);

[0080] Circumstance A3-B1: Target_Ratemax E [Target_Ratei, +00) and Target_Ratemin E (-00, Target_RateN);

[0081] Circumstance A3-B2: Target_Ratemax E [Target_Ratei, +00) and Target_Ratemin E [Target_RateN, Target_Ratei);

[0082] Circumstance A3-B3: Target_Ratemax E [Target_Ratei, +00) and Target_Ratemin E [Target_Ratei, +00).

[0083] In Circumstance Al-B1, since Target_Ratemax E (-00, Target_RateN), Condition 2 of being recognized as an inverse U-shape is not satisfied, it is impossible for the variable trend curve to be an inverse U-shape, so it is not required to calculate the inverse U-shape index; the curve may be judged as a positive U-shape, so the positive U-shape index should be calculated; the variable trend curve may also be judged as monotony decrease, so the monotonicity index should also be calculated. After the positive U-shape index U index 1 and the monotonicity index M index have been calculated, the magnitudes of Date Recue/Date Received 2022-03-01 the two are compared, if U index 1 < M index, the variable trend curve is judged as a positive U-shape, otherwise it is judged as monotony decrease.

[0084] In Circumstance Al-B2, since Target_Ratemin > Target_Ratemax is impossible to occur, such circumstance is nonexistent.

[0085] In Circumstance Al-B3, since Target_Ratemin > Target_Ratemax is impossible to occur, such circumstance is nonexistent.

[0086] In Circumstance A2-B1, since Target_Ratemax E [Target_RateN, Target_Ratei) , Condition 2 of being recognized as an inverse U-shape is not satisfied, it is impossible for the variable trend curve to be an inverse U-shape, so it is not required to calculate the inverse U-shape index; the variable trend curve may be judged as a positive U-shape, so the positive U-shape index should be calculated; the variable trend curve may also be judged as monotony decrease, so the monotonicity index should also be calculated. After the positive U-shape index U index 1 and the monotonicity index M index have been calculated, the magnitudes of the two are compared, if U index 1 <M index, the variable trend curve is judged as a positive U-shape, otherwise it is judged as monotony decrease.

[0087] In Circumstance A2-B2, since Target_Ratemax E [Target_RateN, Target_Ratei) , Condition 2 of being recognized as an inverse U-shape is not satisfied, it is impossible for the variable trend curve to be an inverse U-shape; since Target_Ratemin E
[Target_RateN, Target_Ratei, Condition 1 of being recognized as a positive U-shape is not satisfied, it is also impossible for the variable trend curve to be a positive U-shape.
Therefore, this variable trend curve can only be judged as monotony decrease, and it is merely required to calculate the monotonicity index M index.

[0088] In Circumstance A2-B3, since Target_Ratemin > Target_Ratemax is impossible to occur, such circumstance is nonexistent.

Date Recue/Date Received 2022-03-01

[0089] In circumstance A3-B1, since the variable trend curve not only satisfies Condition 1 of being recognized as a positive U-shape but also satisfies Condition 2 of being recognized as an inverse U-shape, it may be recognized as a positive U-shape and may also be recognized as an inverse U-shape. Therefore, it is required not only to calculate the positive U-shape index U index 1 but also to calculate the inverse U-shape index U index 2. The variable trend curve may also be recognized as monotony decrease, so the monotonicity index M index should also be calculated. After the three indexes have been calculated, the magnitudes of the three are compared, the least one is selected therefrom, and the variable trend curve is judged to be of the corresponding type.

[0090] In circumstance A3-B2, since the variable trend curve does not satisfy Condition 1 of being recognized as a positive U-shape but satisfies Condition 2 of being recognized as an inverse U-shape, so after the inverse U-shape index U index 2 has been calculated, the monotonicity index M index is further calculated, the magnitudes of the two are compared, the lesser one is selected therefrom, and the variable trend curve is judged to be of the corresponding type.

[0091] In circumstance A3-B3, since the variable trend curve does not satisfy Condition 1 of being recognized as a positive U-shape but satisfies Condition 2 of being recognized as an inverse U-shape, so after the inverse U-shape index U index 2 has been calculated, the monotonicity index M index is further calculated, the magnitudes of the two are compared, the lesser one is selected therefrom, and the variable trend curve is judged to be of the corresponding type.

[0092] When the positive sample rate at the left endpoint is smaller than the positive sample rate at the right endpoint (Target_Ratei < Target_RateN), the circumstance is similar to the aforementioned circumstance in which the positive sample rate at the left endpoint is greater than the positive sample rate at the right endpoint, and it suffices to change Date Recue/Date Received 2022-03-01 monotony decrease to monotony increase.

[0093] Moreover, the variable monotonicity degrees and the U-shape degrees are sorted by means of the monotonicity degree index M index and the U-shape degree index U
index.
Specifically, the monotonous variables are classified as one type (including monotony increase and monotony decrease), the U-shaped variables are classified as one type (including positive U-shape and inverse U-shape), these are sorted according to an ascending order of the corresponding indexes, so that the variables are sorted in a decreasing order of monotonicity degrees and in a decreasing order of U-shape degrees.

[0094] The step of employing the variables with monotonicity features or U-shape features to train an intelligent marketing model, and simultaneously employing a stepwise regression operation to screen and obtain one or more target variable(s) adapted to the intelligent marketing model includes:

[0095] data-preprocessing the variables with monotonicity features or U-shape features, wherein the data-preprocessing includes filling missing values, processing abnormal values, and one-hot coding with respect to categorical variables;

[0096] the step of screening important variables out of the preprocessed variables includes:
calculating IV values and PSI values of the variables respectively, and hence screening out important variables whose IV values are greater than an IV threshold and whose PSI
values are smaller than a PSI threshold;

[0097] inputting the important variables in the intelligent marketing model, and simultaneously employing a stepwise regression operation to screen and obtain one or more target variable(s) adapted to the intelligent marketing model.

[0098] The method provided by the present invention is applicable to general intelligent marketing response degree models, a marketing model for attracting new customers with respect to cash loan products is taken for example, the model includes variables of such dimensions as user basic information, browsing behaviors and purchasing behaviors, etc.
Date Recue/Date Received 2022-03-01 Since the ultimate objective of attracting new customers with respect to cash loan products is to grant credit, whether the credit is granted is taken here as the judging criterion of y label positive and negative samples. Empirically, once over 90%
of the users apply for the line of credit and pass risk control card A within seven days after marketing, it can then be judged whether the marketing for attracting new customers is succeeded, so the performance period is selected as seven days here. If credit is granted to a user within seven days after marketing, the user is marked as a positive sample, otherwise the user is marked as a negative sample. The observation point of the training set is a certain marketing day, and the observation point of the testing set is a certain marketing day after the observation point of the training set.

[0099] The technical personnel conducted an AB test, i.e., compared the result of the screening method in the present invention with the result of the manually screening method. In the traditional, manually screening process, a total of 498 variables was removed from 1061 variables screened out of the previous step, 563 variables were retained, it took 55 minutes to complete the process, in which 14 variables were put in the model after the screened variables had been subjected to another round of screening through stepwise regression, the AUC (area under curve) on the training set was 0.91, and the AUC on the testing set was 0.9. In the process of automatic screening of monotonicity degrees according to the present invention, a total of 516 variables was removed from variables screened out of the previous step, 545 variables were retained, it took 3 minutes to complete the process, in which 15 variables were put in the model after the screened variables had been subjected to another round of screening through stepwise regression, the AUC on the training set was 0.92, and the AUC on the testing set was 0.91.
The present invention enhances working efficiency, and the model effect is slightly enhanced as compared with that of the manual screening; moreover, manual screening relies on the judgment of human beings, so the recognition may not be precise, whereas the current automatic recognition algorithm makes use of monotonicity indexes to judge monotonicity degrees and U-shape degrees, so the recognition result is made more precise.

Date Recue/Date Received 2022-03-01

[0100] Embodiment 2

[0101] There is provided a system for intelligent marketing, which system comprises: a first variable screening module, a second variable screening module, and a marketing pushing module, of which the first variable screening module is employed for obtaining variables and drawing a variable trend curve, and obtaining variables with monotonicity features or U-shape features on the basis of the variable trend curve and a preset trend recognizing rule; the second variable screening module is employed for employing the variables with monotonicity features or U-shape features to train an intelligent marketing model, and simultaneously employing a stepwise regression operation to screen and obtain one or more target variable(s) adapted to the intelligent marketing model; and the marketing pushing module is employed for employing the target variable(s) and the intelligent marketing model to obtain a target customer, and pushing a commodity to the target customer.

[0102] The system for intelligent marketing provided by the present invention employs the method for intelligent marketing in the aforementioned Embodiment 1 to utilize machine learning to replace the traditional step of manually checking the trend chart, automatically screen out variables with better monotonicity or U-shape degree from great many variables, input the screened variables to an intelligent marketing model to acquire target customers, push commodities to the target customers, and effectively enhance working efficiency and customer purchasing success rate after marketing. As compared with prior-art technology, the system for intelligent marketing provided by this embodiment of the present invention achieves the same advantageous effects as achieved by the method for intelligent marketing provided by the aforementioned Embodiment 1, and the other technical features in the system for intelligent marketing are identical with the features disclosed by the method of the aforementioned Embodiment 1, so no repetition is redundantly made in this context.

Date Recue/Date Received 2022-03-01

[0103] The specific features, structures, materials or features described in the above embodiments are combinational in any suitable form with any one or more embodiment(s) or example(s).

[0104] What is described above is merely directed to specific embodiments of the present invention, but the protection scope of the present invention is not restricted thereby. Any variation or replacement easily conceivable to persons skilled in the art within the technical range disclosed by the present invention shall be covered within the protection scope of the present invention. Accordingly, the protection scope of the present invention shall be based on the Claims.

Date Recue/Date Received 2022-03-01

Claims (10)

What is claimed is:

1. A method for intelligent marketing, characterized in comprising:
obtaining variables and drawing a variable trend curve, and obtaining variables with monotonicity features or U-shape features on the basis of the variable trend curve and a preset trend recognizing rule;
employing the variables with monotonicity features or U-shape features to train an intelligent marketing model, and simultaneously employing a stepwise regression operation to screen and obtain one or more target variable(s) adapted to the intelligent marketing model; and employing the target variable(s) and the intelligent marketing model to obtain a target customer, and pushing a commodity to the target customer.

2. The method for intelligent marketing according to Claim 1, characterized in that the variables are binned to obtain a plurality of bins, and that the variable trend curve is drawn on the basis of the bins.

3. The method for intelligent marketing according to Claim 2, characterized in that the step of obtaining variables with monotonicity features on the basis of the variable trend curve and a preset trend recognizing rule includes:
calculating a total variation TV of the variables on the basis of the variable trend curve, wherein the total variation TV of the variables is a sum total of amplitudes between every two adjacent bins of the variable trend curve;
calculating an absolute value of a difference between left and right two endpoints of the variable trend curve and marking the absolute value as a first difference value AD1, and obtaining a monotonicity index M index of the variables according to the total variation TV and the first difference value AD1, where M index = TV/ADi; and screening out variables with monotonicity features on the basis of a preset monotonicity index Date Recue/Date Received 2022-03-01 threshold.

4. The method for intelligent marketing according to Claim 3, characterized in that the step of obtaining variables with U-shape features on the basis of the variable trend curve and a preset trend recognizing rule includes:
obtaining the maximum value and the minimum value of the variable trend curve except for left and right two endpoints;
calculating a sum of absolute values of difference values between the left and right two endpoints of the variable trend curve respectively with respect to the minimum value of the variable trend curve and marking the sum as a second difference value AD2, and obtaining a positive U-shape index U index 1 of the variables according to the total variation TV and the second difference value AD2, where U index 1 = TV/AD2; and/or calculating a sum of absolute values of difference values between the left and right two endpoints of the variable trend curve respectively with respect to the maximum value of the variable trend curve and marking the sum as a third difference value AD3, and obtaining an inverse U-shape index U index 2 of the variables according to the total variation TV and the third difference value AD3, where U index 2 = TV/AD3; and screening out variables with U-shape features on the basis of preset positive U-shape index threshold and inverse U-shape index threshold.

5. The method for intelligent marketing according to Claim 4, characterized in that the monotonicity index threshold, the positive U-shape index threshold and the inverse U-shape index threshold are in the range of [1, 1.51.

6. The method for intelligent marketing according to Claim 4, characterized in that when the maximum value of the variable trend curve except for the left and right two endpoints is smaller than values of the left and right two endpoints simultaneously, the inverse U-shape index U index 2 is not calculated;
when the minimum value of the variable trend curve except for the left and right two endpoints Date Recue/Date Received 2022-03-01 is greater than values of the left and right two endpoints simultaneously, the positive U-shape index U index 1 is not calculated.

7. The method for intelligent marketing according to Claim 1, characterized in that the step of employing the variables with monotonicity features or U-shape features to train an intelligent marketing model, and simultaneously employing a stepwise regression operation to screen and obtain one or more target variable(s) adapted to the intelligent marketing model includes:
data-preprocessing the variables with monotonicity features or U-shape features;
screening important variables out of the preprocessed variables; and inputting the important variables in the intelligent marketing model, and simultaneously employing a stepwise regression operation to screen and obtain one or more target variable(s) adapted to the intelligent marketing model.

8. The method for intelligent marketing according to Claim 7, characterized in that the data-preprocessing includes filling missing values, processing abnormal values, and one-hot coding with respect to categorical variables.

9. The method for intelligent marketing according to Claim 7, characterized in that the step of screening important variables out of the preprocessed variables includes:
calculating IV values and PSI values of the variables respectively; and screening out important variables whose IV values are greater than an IV
threshold and whose PSI values are smaller than a PSI threshold.

10. A system for intelligent marketing, characterized in comprising a first variable screening module, a second variable screening module, and a marketing pushing module, of which:
the first variable screening module is employed for obtaining variables and drawing a variable trend curve, and obtaining variables with monotonicity features or U-shape features on the basis of the variable trend curve and a preset trend recognizing rule;
the second variable screening module is employed for employing the variables with monotonicity Date Recue/Date Received 2022-03-01 features or U-shape features to train an intelligent marketing model, and simultaneously employing a stepwise regression operation to screen and obtain one or more target variable(s) adapted to the intelligent marketing model; and the marketing pushing module is employed for employing the target variable(s) and the intelligent marketing model to obtain a target customer, and pushing a commodity to the target customer.

Date Recue/Date Received 2022-03-01