CN114612130A

CN114612130A - Time dimension-based shrinkage type integral data processing method

Info

Publication number: CN114612130A
Application number: CN202210160935.0A
Authority: CN
Inventors: 刘家序; 储慧; 刘力恒
Original assignee: Hangzhou Daohechuan Information Technology Co ltd
Current assignee: Hangzhou Daohechuan Information Technology Co ltd
Priority date: 2022-02-22
Filing date: 2022-02-22
Publication date: 2022-06-10
Also published as: WO2023159783A1

Abstract

The application provides a time dimension-based shrinkage integral data processing method which comprises the steps of obtaining an integral quantity to be issued correspondingly according to an issue quantity curve, and calculating the lowest target rebate total amount corresponding to a consumption period based on the obtained single integral value; if the actual rebate value of the target period is higher than or equal to the lowest target rebate total amount of the consumption period, acquiring the integral issue amount corresponding to the target consumption period according to a known issue amount curve; and if the actual rebate value of the target period is lower than the lowest target rebate total amount of the consumption period, acquiring the maximum allowable issue amount under the condition of the integral guarantee value, and calculating and acquiring the contracted integral issue amount by combining a second decrement coefficient. The high interest which can be enjoyed by the consuming user is highlighted in the early stage by controlling the issuing quantity of the credit to be gradually reduced, and meanwhile, the merchant only extracts a small part from the profit to be used as the credit for carrying out the rebate operation, so that the existing one-time large investment of a money burning subsidy type is replaced, and the financial pressure on the merchant is reduced.

Description

Time dimension-based shrinkage type integral data processing method

Technical Field

The application belongs to the field of data processing, and particularly relates to a shrinkage type integral data processing method based on time dimension.

Background

The traditional commercial scoring system is a market economic innovation tool with hundreds of years of history. The enterprise issues points to consumers or directly issues red packages, rebates and the like, and essentially aims to improve the 'repurchase rate' and promote sales. Nowadays, point systems based on various internet platforms have been popularized in various fields, but according to a certain research institution in japan, nearly half of points issued by enterprises in various industries are in a "sleeping state", and even are forgotten by consumers, and the purpose of "sales promotion" is increasingly difficult to achieve. The reason for this is that the consumers are gradually numb due to the various scores in the market, and the enterprises are involved in so-called malignant price competition, rebate competition and money burning competition.

Just because of the unreasonable behaviors, enterprises are gradually involved in the vicious competition of "money burning subsidies" and "crazy return benefits", and hope to correct the problem by feeding back more benefits to consumers. However, we judge that the short-term subsidy relationship between consumers and enterprises, which is now ubiquitous in the market, will be replaced by long-term interactive corporate relationship. Particularly, in the internet industry, "traffic" is monopolized by large internet businesses, and the money burning vicious competition mode of small and medium-sized enterprises is not sustainable. The result is likely to be only capital improvement, leading to severe mismatch in social resources.

The contracted credit based on the time dimension is the enterprise "long-term cash flow distribution in the future" to the consumer. The "future long cash flow distribution right" is essentially different from the "stock right". The profit of the equity is only from the net profit after tax. The income of 'future cash flow distribution right' is still the 'rebate subsidy and marketing cost' of the enterprise, and the income can be extracted and converged in the sales link to form a new index- 'cash flow pool' directly reflecting the development condition of the enterprise. The larger the amount of the "cash deposit pool" is, the larger the value created on behalf of the enterprise is, and the faster it is accelerated, the faster the representative enterprise develops. The future cash flow distribution right has the characteristic of long-term sustainable growth of the equity, does not need to bear the loss responsibility of enterprises, and is suitable for each common consumer. Even if some businesses would like to distribute "equity" to consumers, the risk of having consumers hold stocks across the entire population is enormous and impractical due to the extremely weak risk-bearing capacity of the general public. And the points or rebates generated by daily consumption are used as 'fragment values', and are very suitable for being used as 'fund sources' of common people.

Disclosure of Invention

The embodiment of the application provides a time dimension-based shrinkage point data processing method, which can increase the adhesion degree of consumers to the consumers based on consumption behaviors and reduce the marketing cost of merchants.

Specifically, the method for processing shrinkage type integral data based on a time dimension provided by the embodiment of the present application includes:

s1, constructing a shrinkage type integral release quantity curve corresponding to a time dimension according to a preset first decreasing coefficient;

s2, acquiring payment data of a target consumption cycle, and determining an actual rebate value corresponding to the target cycle according to a preset rebate proportion;

s3, calculating the current single integral value in real time, obtaining the corresponding integral quantity to be issued according to the issuing quantity curve according to the user payment data in the consumption period and the contraction type integral issuing quantity curve, and calculating the lowest target rebate total amount corresponding to the consumption period based on the obtained single integral value;

s4, if the actual rebate value of the target period is higher than or equal to the lowest target rebate total amount of the consumption period, acquiring the integral issue amount corresponding to the target consumption period according to the known issue amount curve, and finishing the integral sending;

s5, if the actual rebate value of the target period is lower than the lowest target rebate total of the consumption period, acquiring the maximum allowable issue quantity under the condition of integral guarantee value, and combining the second decrement coefficient to calculate and acquire the contracted integral issue quantity, thereby completing integral sending.

Optionally, the obtaining payment data of a target consumption cycle includes:

constructing a unique user ID for each consumer;

and counting the behavior data which is carried out in the whole network by each user ID in a target consumption period and is related to the sample commodity, and carrying out assignment and summarization on the behavior data based on preset weight to obtain the consumption data of the corresponding user ID in the target consumption period.

Optionally, the behavior data includes:

payment data and sharing data corresponding to the consumers; social behavior data.

Order data, sales data and commodity evaluation data of corresponding merchants.

Optionally, the S2 includes:

the actual rebate value is the payment data x the preset rebate proportion.

Optionally, the method further includes:

and S6, sending the obtained point amount to independent point management for storage, and applying for redemption by consumers for use.

Optionally, the single integral value calculated in real time includes:

the individual point value is the sum of the points stored in the independent point management, which is the sum of the released and unrecollected points.

Optionally, the S3 includes:

s31, determining the integral quantity to be issued corresponding to the ordinate of the shrinkage type integral issuing quantity curve by taking the current consumption period as the abscissa;

and S32, obtaining the lowest target total rebate of the consumption period as the single integral value calculated by the integral quantity to be issued multiplied by real time.

Optionally, the S5 includes:

in the case of the integral guarantee value, the maximum allowable issue amount is the actually determined rebate value of the current period/the integral value of the previous period.

Optionally, the function expression of the shrinkage integral issuance curve is:

the integral issue quantity is the first issue quantity multiplied by the N power of a first decreasing coefficient;

wherein, N is the serial number of the current target consumption cycle.

Has the advantages that:

by distributing the points related to the payment data to the consumers, the feedback force of the consumers on the early consumption behaviors can be improved on the premise of not increasing the financial burden of the merchant, and meanwhile, the feedback force of the consumers participating in the earlier consumption behaviors can be more prominent along with the time extension.

Drawings

In order to more clearly illustrate the technical solutions of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.

FIG. 1 is a schematic flow chart of a method for processing shrinkage type integral data based on a time dimension according to an embodiment of the present application;

FIG. 2 is a schematic diagram of a pre-constructed shrinkage-type integral yield curve according to an embodiment of the present application;

fig. 3 is a diagram illustrating an example of an integrated release amount curve for a non-achievement target rebate.

Detailed Description

To make the structure and advantages of the present application clearer, the structure of the present application will be further described with reference to the accompanying drawings.

Specifically, the method for processing shrinkage type integral data based on a time dimension, as shown in fig. 1, includes:

s3, calculating the current single integral value in real time, acquiring the integral quantity to be issued according to the issuance quantity curve according to the user payment data in the consumption period and the contraction type integral issuance quantity curve, and calculating the lowest target rebate total amount corresponding to the consumption period based on the obtained single integral value;

s4, if the actual rebate value of the target period is higher than or equal to the lowest target rebate total amount of the consumption period, acquiring the integral issue amount corresponding to the target consumption period according to the known issue amount curve, and finishing the sending of the integral;

In implementation, in order to avoid defects caused by various marketing modes in the prior art, the embodiment of the application provides a shrinkage type integral data processing method. The high interest which can be enjoyed by a consumer in the early stage is highlighted by controlling the gradual reduction of the credit issue quantity, and meanwhile, as the merchant can only extract a small part of the profit as the credit to carry out the rebate operation, the money which can be exchanged by the credit comes from the later-stage 'future-type' income of the merchant instead of the existing 'money burning subsidy' type one-time large-amount investment, the financial pressure on the merchant is greatly reduced.

Specifically, in order to achieve the above effect, step S1 is first executed to construct a curve as a reference standard of the integral issue quantity. In order to ensure that the points sent to the consumer can keep as high a value as possible, and at the same time, to issue as many points as possible for early stage customers to encourage the earlier stage consumption, the curve of the issued points proposed in the embodiment of the present application is a decreasing function, and the graph is shown in fig. 2.

A typical expression is: the integral issue amount of the current period is equal to the first issue amount x the first decrement coefficient ^ N,

for example, the first-time-release amount is 100000 × the first decrement coefficient 99% ^ N (the value of N is 50) ═ 60500. As can be seen from the curve shown in fig. 2, the payment behavior for the first consumption corresponds to the highest point issuance amount, and the later consumption behavior corresponds to a smaller point issuance amount with the passage of time, so that the characteristic of feeding back the early consumption behavior is more prominent.

In the specific implementation process, the whole consumption behavior is divided into a plurality of target consumption periods according to the preset time length, and after each target consumption period is finished, the merchant calculates the actual rebate value according to the payment data in the period and the preset rebate proportion. The calculation formula here is:

the actual rebate value is the payment data x the preset rebate proportion.

For example, the total income of the period is 100000 yuan, and the preset rebate proportion is 10%, which indicates that the actual rebate value provided by the merchant for the period is 100000 × 10% ═ 10000 yuan.

Because the contribution degree of each consumer relative to the merchant is greatly different, in order to determine the amount of the rebate to each consumer more fairly, a mode of indirectly realizing the rebate by issuing points is adopted. And in order to ensure that the part of the money amount has fair effect, the obtained point amount is sent to independent point management for storage, and the consumer applies for conversion and use. The independent points management system proposed herein is a third party organization with high credibility and non-tamper-ability, such as a block chain platform, relative to merchants and consumers.

The particular manner of determining the particular integral issue volume for the period that has ended is shown in step S3.

Firstly, determining a single integral value, wherein in a first target consumption period, the value is a preset 0.01 yuan, and in subsequent later period calculation, performing real-time calculation and determination according to a maximum allowable issue amount in the case of integral guarantee value, namely a actually determined rebate value in a current period/integral value in a previous period.

Secondly, determining a corresponding theoretical integral quantity to be issued from a constructed shrinkage type integral issuing quantity curve according to the current cycle belonging to the Nth cycle, wherein the integral issuing quantity is the first issuing quantity multiplied by the Nth power of a first decreasing coefficient; and N is the serial number of the current target consumption cycle. For example, the fiftieth value should release the integral quantity of 100000 × the first decreasing coefficient 99% ^ N (the value of N is 50) ^ 60500 according to the release quantity curve.

And determining the actual lowest target rebate total amount corresponding to the consumption period according to a calculation formula of 'the lowest target rebate total amount of the consumption period is equal to the integral amount to be issued multiplied by the single integral value according to the issuance curve'. For example: the lowest target rebate total for the 50 th consumption cycle is 60500 x 0.1 ═ 6050 dollars (assuming that the 50 th cycle credit value increases to 0.1 dollar).

After the rebate total and the theoretical rebate total corresponding to the actual payment data are obtained, judgment needs to be performed according to the size relationship between the rebate total and the theoretical rebate total, so as to determine the point issue quantity corresponding to the rebate value finally, and the specific comparison and subsequent processing procedures are shown in steps S4 and S5.

The method is divided into two cases according to different size judgment results:

1) the actual rebate value for the target period is greater than or equal to the lowest target rebate total for the consumption period

Combining the above data, that is, the calculated (100000 × 10% ═ 10000 yuan) is greater than (100000 × first decrement coefficient 99% ^ N (the value of N is 50) × 0.1) ═ 6050 yuan), this indicates that the payment data of the target consumption period is higher than expected, so the payment data can be sent as an integral according to the integral-to-be-issued integral 60500 corresponding to the integral-issued curve, and the corresponding total rebate amount is 10000 yuan. 0.1 at this time is the single integrated value calculated in real time for the 50 th consumption cycle.

2) The actual rebate value for the target period is lower than the lowest target rebate total for the consumption period in order to prevent the integrated value from falling under adjacent periods,

combining the above data, that is, the calculated (payment data 5000 × 10% ═ 500 yuan) is less than (100000 × first decrement coefficient 99% ^ N (the value of N is 50) × 0.1 ═ 6050 yuan), where 0.1 is the calculated value (also may be referred to as a real-time calculated value) of the previous cycle, this time indicates that the payment data of the target consumption cycle is lower than expected, at this time, if the payment data is continuously sent according to the integral amount corresponding to 6050 yuan, the integral is undone, at this time, a protection mechanism is triggered, and the maximum allowable issuance amount in the current cycle under the condition of the integral guarantee value is calculated based on the actual rebate value 5000 × 10% < 5000 yuan > corresponding to the actual payment data 5000 yuan:

500/single integrated value 0.1 × second decreasing coefficient 0.99 equals 4950, where 0.1 is the integrated value of the previous period.

In this state, the credit amount issued to the consumer in the target consumption cycle is 4950, the corresponding total rebate amount is 500 yuan, the rebate is significantly contracted relative to both the 60500 credit and the 100000 × 10% ═ 10000 yuan, the target sales amount cannot be reached, the value of the previous cycle is 0.1 yuan, and the second decrement coefficient is 0.99, but the value of a single credit is slightly higher than 0.1 yuan after the processing.

It should be emphasized that, as can be seen from table 1a, the second decreasing coefficient is set here in the sense that, on the premise that the protection mechanism is continuously triggered, for example, "the number of credits obtained per 1 consumption contribution value" at the 4 th week and the 5 th week can be continuously decreased, so as to make the difficulty of obtaining the credits continuously more difficult, and to continuously highlight the effect of stimulating consumption.

If there is no second decreasing coefficient, the "number of credits obtained per 1 consumption contribution value" in the 4 th week and the 5 th week in table 1b is the same value, that is, the difficulty of obtaining the credits is not changed, the consumption stimulation effect will disappear momentarily, and a systematic bug appears.

TABLE 1a

TABLE 1b

The curve of the credit issue is shown in fig. 3, wherein the dashed line represents the amount of credit to be issued and the implementation represents the actual credit issue. It can be seen that there is a significant drop in the actual credit issue for cycle 39 over the previous cycle after the protected credit handling mechanism is triggered. At this time, the following calculation formula is used:

under the condition that the total number of the points is remarkably reduced, after the points of the contracted issue quantity in the 39 th period are issued, the value of each point is increased rather than before the points are not contracted, so that the method for contracting the point issue quantity after the payment data is reduced can be clear, the stability of acquiring the point value by a consumer can be still stabilized on the premise of considering the specific income of a merchant, and the situations of the expansion of the points and the reduction of the acquired point value are avoided.

Example two

Based on the shrinkage type point data processing method provided by the previous embodiment, the embodiment of the application further provides a method for obtaining various payment data details and further determining rebate data distribution based on the details.

The method mainly comprises the following steps:

s21, obtaining payment data for a target consumption cycle.

The method specifically comprises the following steps:

s211, constructing a unique user ID for each consumer;

s212, counting behavior data which are carried out in the whole network by each user ID in a target consumption period and are related to the sample commodity, and carrying out assignment and summarization on the behavior data based on preset weight to obtain payment data of the corresponding user ID in the target consumption period.

Wherein the behavior data comprises: corresponding to the payment data and the sharing data of the consumer.

In practice, in the existing consumption environment, since the consumption behavior between the consumer and the merchant is not a single purchase, and different behaviors promoting the formation of consumption, such as approval, sharing, commenting, forwarding and the like, the payment data acquisition proposed in the embodiment of the present application, in addition to the money paid by the single consumer, may also include other behaviors promoting the formation of consumption, and can be used as reference data for calculating the amount of credit to be issued.

It should be emphasized that other actions that promote consumption in this embodiment are only references for allocating the integral amount calculated in the previous period on the premise of acquiring the payment data. The premise for the integral calculation and distribution is that payment data is acquired in each period. If there is no payment data for a certain period, i.e. there is not enough income, no credit is obtained for other actions that promote the establishment of consumption.

Different types of behavioral data are shown in table 2:

TABLE 2 different consumption behavior data List

Different weight values can be assigned to each type of data in a targeted manner according to different assignment of the merchant-oriented behavior. Therefore, all behavior data of each user corresponding to the merchant can be tracked and obtained according to the unique user ID of the user in the whole network, the payment data of the user is further gathered and obtained, and the payment data is finally used as a reference for distributing the shrinkage credit to the user after each target consumption period is finished in independent credit management.

The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for processing systolic integral data based on a time dimension, the method comprising:

s3, calculating the current single integral value in real time, acquiring the corresponding integral quantity to be issued according to the issuance quantity curve according to the user payment data in the consumption period and the contraction type integral issuance quantity curve, and calculating the lowest target rebate total amount corresponding to the consumption period based on the obtained single integral value;

2. The method for processing shrinkage point data based on time dimension of claim 1, wherein the obtaining payment data of a target consumption period comprises:

constructing a unique user ID for each consumer;

3. The method of claim 2, wherein the behavior data comprises:

corresponding to the payment data and the sharing data of the consumer.

4. The method for processing systolic integration data based on the time dimension as claimed in claim 1, wherein said S2 comprises:

the actual rebate value is the payment data x the preset rebate proportion.

5. The method of processing systolic integrated data based on the time dimension of claim 1, wherein the method further comprises:

and S6, sending the obtained point amount to an independent point management system for storage, and applying for redemption and use by consumers.

6. The method for processing the shrinkage-type integral data based on the time dimension of claim 5, wherein the real-time calculation of the single integral value comprises:

the single point value is the sum of the points stored in the independent point management system/the total amount of points not yet used.

7. The method for processing systolic integration data based on the time dimension as claimed in claim 1, wherein said S3 comprises:

8. The method for processing systolic integration data based on the time dimension as claimed in claim 1, wherein said S5 comprises:

in the case of an integrated reserve, the maximum allowed amount of credit is the actual value of the rebate determined for the current period/the integrated value of the previous period.

9. The method of any of claims 1 to 8, wherein the function expression of the systolic integral release curve is:

wherein, N is the serial number of the current target consumption cycle.