CN110858335A - Method and device for calculating sales promotion elasticity - Google Patents
Method and device for calculating sales promotion elasticity Download PDFInfo
- Publication number
- CN110858335A CN110858335A CN201810966513.6A CN201810966513A CN110858335A CN 110858335 A CN110858335 A CN 110858335A CN 201810966513 A CN201810966513 A CN 201810966513A CN 110858335 A CN110858335 A CN 110858335A
- Authority
- CN
- China
- Prior art keywords
- variable
- sales
- promotion
- baseline
- calculating
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Human Resources & Organizations (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Data Mining & Analysis (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method and a device for calculating promotion elasticity, and relates to the technical field of computers. One embodiment of the method comprises: fitting an optimization model based on basic data, thereby calculating to obtain a baseline sales volume of the commodity; the method comprises the steps of carrying out finest granularity segmentation on promotion duration to obtain at least one time unit, and then calculating nominal discount strength in each time unit; and calculating promotion elasticity according to the actual sales, the baseline sales and the nominal discount strength. The implementation method can solve the problem of lack of revenue promotion analysis under different discount degrees.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for calculating promotion elasticity.
Background
At present, the promotion means are mostly on a business level, and suppliers and sales negotiation give a common and frequent promotion form with acceptable discount strength, such as: pricing and promoting, and directly giving a lower price; additional value promotion, giving gift or invisible service; the sales promotion is returned in the form of order collection or rebate and the like; commemorative marketing, sales promotion under special festivals; promotion is rewarded, and the benefits are drawn in a lottery or interactive mode; sales promotion by force, starburst money, and the like; zero-margin sales promotion, such as low to five-fold, lowest overall network; season sales promotion, and sales promotion of fashion commodities. Of course, there are other types of sales promotion forms, which are not described in detail.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
the promotion forms are based on business and marketing aspects, and the previous promotion effect analysis shows that a plurality of promotion activities do not have good effects, the superposition of a plurality of promotion forms leads the actual discount strength to exceed the expectation, the promotion activities do not bring the promotion of GMV (Gross Merchandis Volume, which refers to the total Volume of trades in a certain time period), and the enterprises can suffer loss in the contrary way.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for calculating sales promotion elasticity, which can solve the problem of lack of revenue promotion analysis under different discount strengths.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a method of calculating elasticity of promotion, including:
fitting an optimization model based on basic data, thereby calculating to obtain a baseline sales volume of the commodity;
the method comprises the steps of carrying out finest granularity segmentation on promotion duration to obtain at least one time unit, and then calculating nominal discount strength in each time unit;
and calculating promotion elasticity according to the actual sales, the baseline sales and the nominal discount strength.
Optionally, fitting the optimization model based on the basic data to calculate a baseline sales volume of the commodity, including:
summarizing basic data to obtain price variables, time variables, sales promotion variables, coupon variables and inventory variables of each commodity;
taking the actual sales as a dependent variable, and taking a price variable, a time variable, a sales promotion variable, a coupon variable and a stock variable as independent variables, and performing regression analysis on the model to obtain parameters of all the variables;
and setting the parameters of the time variable, the promotion variable and the coupon variable to be zero, substituting the basic data into the model, and calculating to obtain the baseline sales volume of the commodity.
Optionally, calculating the nominal discount strength in each time unit includes:
for each time unit, the nominal discount strength within that time unit is calculated using the following formula:
nominal discount strength 1-page price x discount 1 x · · x discount N/baseline price.
Optionally, calculating the promotion elasticity according to the actual sales, the baseline sales and the nominal discount strength comprises:
according to the time unit with the finest granularity, the actual sales volume and the sales volume baseline are split, and the time sales volume and the baseline sales volume in each time unit are obtained;
for each time unit, with nominal discount strength as the independent variable and actual sales and baseline sales as dependent variables, the following regression analysis was performed:
after the regression equation is fitted, the coefficient before the nominal discount strength is the promotion elasticity.
In addition, according to another aspect of an embodiment of the present invention, there is provided an apparatus for calculating elasticity of promotion, including:
the fitting module is used for fitting the optimization model based on the basic data so as to calculate the baseline sales volume of the commodity;
the segmentation module is used for carrying out the finest granularity segmentation on the promotion duration to obtain at least one time unit, and then calculating the nominal discount strength in each time unit;
and the calculation module is used for calculating the promotion elasticity according to the actual sales volume, the baseline sales volume and the nominal discount strength.
Optionally, the fitting module is configured to:
summarizing basic data to obtain price variables, time variables, sales promotion variables, coupon variables and inventory variables of each commodity;
taking the actual sales as a dependent variable, and taking a price variable, a time variable, a sales promotion variable, a coupon variable and a stock variable as independent variables, and performing regression analysis on the model to obtain parameters of all the variables;
and setting the parameters of the time variable, the promotion variable and the coupon variable to be zero, substituting the basic data into the model, and calculating to obtain the baseline sales volume of the commodity.
Optionally, calculating the nominal discount strength in each time unit includes:
for each time unit, the nominal discount strength within that time unit is calculated using the following formula:
nominal discount strength 1-page price x discount 1 x · · x discount N/baseline price.
Optionally, the computing module is configured to:
according to the time unit with the finest granularity, the actual sales volume and the sales volume baseline are split, and the time sales volume and the baseline sales volume in each time unit are obtained;
for each time unit, with nominal discount strength as the independent variable and actual sales and baseline sales as dependent variables, the following regression analysis was performed:
after the regression equation is fitted, the coefficient before the nominal discount strength is the promotion elasticity.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any of the embodiments described above.
According to another aspect of the embodiments of the present invention, there is also provided a computer readable medium, on which a computer program is stored, which when executed by a processor implements the method of any of the above embodiments.
One embodiment of the above invention has the following advantages or benefits: because the technical means of calculating the nominal discount granularity in the time unit with the finest granularity and then calculating the promotion elasticity according to the actual sales volume, the baseline sales volume and the nominal discount strength is adopted, the technical problem of lack of income promotion analysis under different discount strengths is solved; the method carries out quantitative analysis aiming at the prior sales promotion activities, calculates the discount strength based on the base line price and the sales promotion which can be brought by the sales promotion activities under the condition of the given sales promotion activities, and then calculates the sales promotion elasticity, thereby better guiding the sales promotion and pricing work.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of the main flow of a method of calculating promotional elasticity according to an embodiment of the present invention;
FIG. 2 is a schematic view of a main flow of a method of calculating promotion elasticity according to one referential embodiment of the present invention;
FIG. 3 is a schematic diagram of the major modules of an apparatus for calculating promotional elasticity according to an embodiment of the present invention;
FIG. 4 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 5 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of a main flow of a method of calculating promotion elasticity according to an embodiment of the present invention. As shown in fig. 1, as an embodiment of the present invention, the method for calculating elasticity of promotion includes:
and step 101, fitting an optimization model based on basic data, thereby calculating the baseline sales volume of the commodity.
Optionally, the step 101 may include: summarizing basic data to obtain price variables, time variables, sales promotion variables, coupon variables and inventory variables of each commodity; taking the actual sales as a dependent variable, and taking a price variable, a time variable, a sales promotion variable, a coupon variable and a stock variable as independent variables, and performing regression analysis on the model to obtain parameters of all the variables; and setting the parameters of the time variable, the promotion variable and the coupon variable to be zero, substituting the basic data into the model, and calculating to obtain the baseline sales volume of the commodity.
The basic data is from each order, and the orders of each day are summarized according to the dimension of each commodity, and the summarized indexes comprise page price, transaction price, baseline price, total sales volume and inventory status. The stock state is a number between 0 and 1, indicating whether the goods are sufficient in each warehouse. Thus, from these base data, page prices, deal prices, baseline prices, total sales, and inventory status for each item per day can be derived.
It should be noted that, since there is more than one order volume per day, the page price, the deal price, and the baseline price are averaged to obtain the page price, the deal price, and the baseline price of each commodity per day.
The price baseline is a virtual price that refers to the price at which the consumer can purchase the item without any real special offers. It is a quantity that roughly describes the consumer's perception of the price of a commodity, and is a psychological price of the commodity in the consumer's mind. When the actual price is above the price baseline, the consumer generally does not purchase; when a given discount causes the price of a good to fall below the price baseline, the consumer is likely to purchase the good. For each commodity, there is a baseline price per day. In one embodiment of the invention, the baseline price may be calculated as follows: based on the current time, the user pushes forward for 30 days, and the average of the 30-day bargaining prices is calculated as the base-line price of the day.
Virtual variables such as holidays, types of promotional campaigns, types of coupons, and the like are then constructed based on these aggregated underlying data. Such as holidays: yuan Dan, New year, Qing Ming festival, 618, double eleven and so on; such as promotional activity type: full descent, direct descent, gift, suit, etc.; such as coupons: whether the product has a coupon issued that day.
If the day is a Yuan Dan, the virtual variable of the Yuan Dan is 1, otherwise, the virtual variable is 0; the same is true for promotions and coupons, with 1 or 0 as the virtual variable. And calculating the discount strength of various promotions and coupons, such as the discount strength of direct descending promotions and the like. In addition, a time variable is constructed that states that the day is the day of the year, week of the year.
After the virtual variables are constructed, the virtual variables are input to a model, and the model is fitted. Specifically, regression analysis is performed with the actual sales as a dependent variable and the price variable, time variable, sales promotion variable, coupon variable, and inventory variable as independent variables:
actual sales-price variable + time variable + sales promotion variable + coupon variable + inventory variable
Optionally, the model may be described as:
Q＝c+β0baseprice+β1transprice+β2pageprice+β3dayorder
+β4weekorder+βispringfestivalflag+…+βj618flag+…
+βk11.11flag+…+βl12.12flag+βn-8prometype1flag+…
+βn-7promotype2flag+βn-6promotype1rate+…
+βn-5promotype2rate+βn-4coupontype1flag+…
+βn-3coupontype2flag+βn-2coupontype1rate+…
+βn-1coupontype2rate+βnstock_state
wherein:
q is the actual sales, c is a constant, β is the parameter for each independent variable;
basepricereferred to as baseline price, transpricePage, price of the finger dealpriceIndicating the page price;
dayorderindicating the number of days, weekorderThe number of cycles;
springfestivalflagwhether the finger is spring festival 618flagWhether the finger is 618, 11.11flagWhether it is dieleven, 12.12flagWhether the indication is twenty-two or not, and the omitted part is various holidays in other festivals, which is not described again in the embodiment of the invention; these are all variables of 0 or 1;
prometype1flag、promotype2flagwhether or not to participate in a certain promotion type, coupontype1flag、coupontype2flagWhether a certain coupon type is issued; these are all variables of 0 or 1; removing deviceBesides, the promotion types and the coupon types can be added or deleted according to actual situations, and the omitted part is various other promotion types and coupon types, which are not described again in the embodiment of the invention;
promotype1rate、promotype2ratenominal discount strength, coupontype1, referring to a certain promotion typerate、coupontype2rateNominal discount strength for a certain coupon type; the ellipsis part is nominal discount strength of various other promotion types and nominal discount strength of various other coupon types, and the embodiment of the invention is not repeated;
the stock _ state is a comprehensive stock state of each warehouse and is a number between 0 and 1.
For example: for example, if a certain e-commerce company has 5 warehouses in the whole country, but only 3 warehouses are in stock in the day, the stock status is 3/5 ═ 0.6. This variable is based on the actual state of the day in the baseline prediction of sales and need not be set to 0.
And arranging the basic data of each commodity according to the ascending order of time, inputting the basic data into the model, fitting the model, and obtaining the parameters of each variable.
Since baseline sales are the basal sales per day without preferential activity. Therefore, relevant parameters such as a promotion variable, a coupon variable, a time variable and the like in the model are all set to be zero (the inventory variable does not need to be set to be 0, the actual inventory state of the day is taken as the standard, the model is substituted into the model, the model does not need to be set to be 0), basic data are substituted into the model, the predicted sales amount is the baseline sales amount, and therefore the daily baseline sales amount of each commodity is obtained.
And 102, carrying out the finest granularity segmentation on the promotion duration to obtain at least one time unit, and then calculating the nominal discount strength in each time unit.
Alternatively, for each time cell, the nominal discount strength within the time cell may be calculated using the following formula:
nominal discount strength 1-page price x discount 1 x · · x discount N/baseline price
Wherein, the page price is the page price in the time unit, and the baseline price is the baseline price in the time unit.
Assuming that there are two types of promotion, namely downscaling and full-reduction, and there is an overlap in the promotion duration, the most fine-grained promotion partitioning is required, as shown in the following table, where the two downscaling is 5%, 10%, and the two full-reductions 99-19 and 199-100, the promotion duration is partitioned into 4 time units in terms of time lines.
When the finest granularity time unit segmentation is completed, the nominal discount strength of the commodity in each time unit can be calculated based on the price baseline and the page price.
Such as: in the first time unit, the 5% dip and full minus 99-19 may be calculated as:
nominal discount strength 1-page price x (1-5%) × (1-19/99)/baseline price
And 103, calculating promotion elasticity according to the actual sales volume, the baseline sales volume and the nominal discount strength.
Since the nominal discount strength for each finest granularity time unit is calculated in step 102, the actual sales for each item in the order are aggregated and the daily sales baseline is eliminated, which is the promotion of sales.
Specifically, according to the time unit with the finest granularity, the actual sales volume and the sales volume baseline are split, and the time sales volume and the baseline sales volume in each time unit are obtained. The actual sales of the finest granularity after splitting minus the baseline sales is the improvement of the sales, corresponding to the nominal discount strength of the finest granularity. For example, when the actual sales volume of a certain product on a certain day is 480, and the time unit with the finest granularity is 12 hours, the actual sales volume of the certain product in the time unit is 240. The splitting method of the baseline sales is the same as the splitting method of the actual sales, and is not described again.
Thus, for each finest granularity time unit, with nominal discount strength as the independent variable and actual and baseline sales as dependent variables, the following regression analysis was performed:
thus, a relationship between the lift rate and the nominal discount rate is established, which is equivalent to establishing a relationship between the price and the sales. After the regression equation is fitted, the coefficient before the nominal discount strength is the promotion elasticity, and the influence of the absolute change of the nominal discount strength on the relative change of the lift rate is described.
For example, if it is known how much lift ratio is desired, a corresponding nominal discount strength may be obtained. Therefore, after nominal discount strength and price baseline are given, a reasonable page price can be set for the commodity, and the purpose of pricing is achieved.
According to the various embodiments described above, it can be seen that the technical scheme of calculating the nominal discount granularity in the time unit with the finest granularity and then calculating the promotion flexibility according to the actual sales, the baseline sales and the nominal discount strength solves the problem of lack of revenue promotion analysis under different discount strengths. The method carries out quantitative analysis on the sales promotion activities which are carried out in the past, calculates the discount strength based on the base line price and the sales promotion which can be brought by the sales promotion activities under the condition of the given sales promotion activities, and then calculates the sales promotion elasticity, thereby better guiding the sales promotion and pricing work.
Fig. 2 is a schematic diagram of a main flow of a method of calculating promotional elasticity according to one referential embodiment of the present invention, which may include:
step 201, summarizing basic data to obtain price variables, time variables, sales promotion variables, coupon variables and inventory variables of each commodity;
step 202, taking the actual sales as a dependent variable, and taking a price variable, a time variable, a sales promotion variable, a coupon variable and a stock variable as independent variables, and performing regression analysis on the model to obtain parameters of all the variables;
step 203, setting parameters of the time variable, the promotion variable and the coupon variable to be zero, substituting the basic data into the model, and calculating to obtain the baseline sales volume of the commodity;
step 204, carrying out the finest granularity segmentation on the promotion duration to obtain a plurality of time units;
step 205, calculating nominal discount strength in each time unit;
step 206, according to the time unit with the finest granularity, splitting the actual sales volume and the sales volume baseline to obtain the time sales volume and the baseline sales volume in each time unit;
and step 207, performing regression analysis on each time unit by taking the nominal discount strength as an independent variable and taking the actual sales and the baseline sales as dependent variables to obtain the promotion elasticity.
In addition, in a reference embodiment of the present invention, the detailed implementation of the method for calculating promotion elasticity is described in detail above, so that the repeated description is not repeated here.
Fig. 3 is a schematic diagram of the main modules of an apparatus for calculating promotional elasticity according to an embodiment of the present invention. As shown in FIG. 3, the apparatus 300 for calculating promotional elasticity includes a fitting module 301, a segmentation module 302, and a calculation module 303. The fitting module 301 fits an optimization model based on basic data, so as to calculate a baseline sales volume of the commodity; the segmentation module 302 performs the finest granularity segmentation on the promotion duration to obtain at least one time unit, and then calculates the nominal discount strength in each time unit; the calculation module 303 calculates the elasticity of the promotion based on the actual sales, the baseline sales, and the nominal discount rate.
Optionally, the fitting module 301 collects basic data to obtain a price variable, a time variable, a promotion variable, a coupon variable, and a stock variable of each commodity; taking the actual sales as a dependent variable, and taking a price variable, a time variable, a sales promotion variable, a coupon variable and a stock variable as independent variables, and performing regression analysis on the model to obtain parameters of all the variables; and setting the parameters of the time variable, the promotion variable and the coupon variable to be zero, substituting the basic data into the model, and calculating to obtain the baseline sales volume of the commodity.
Optionally, calculating the nominal discount strength in each time unit includes:
for each time unit, the nominal discount strength within that time unit is calculated using the following formula:
nominal discount strength 1-page price x discount 1 x · · x discount N/baseline price.
Optionally, the calculating module 303 splits the actual sales and the sales baseline according to the time unit with the finest granularity to obtain the time sales and the baseline sales in each time unit; for each time unit, with nominal discount strength as the independent variable and actual sales and baseline sales as dependent variables, the following regression analysis was performed:
after the regression equation is fitted, the coefficient before the nominal discount strength is the promotion elasticity.
According to the various embodiments described above, it can be seen that the technical scheme of calculating the nominal discount granularity in the time unit with the finest granularity and then calculating the promotion flexibility according to the actual sales, the baseline sales and the nominal discount strength solves the problem of lack of revenue promotion analysis under different discount strengths. The method carries out quantitative analysis on the sales promotion activities which are carried out in the past, calculates the discount strength based on the base line price and the sales promotion which can be brought by the sales promotion activities under the condition of the given sales promotion activities, and then calculates the sales promotion elasticity, thereby better guiding the sales promotion and pricing work.
It should be noted that, in the implementation of the apparatus for calculating elasticity for promotion of the present invention, the above method for calculating elasticity for promotion has been described in detail, and therefore, the repeated description is not repeated here.
FIG. 4 illustrates an exemplary system architecture 400 to which the method of calculating promotional elasticity or the method of calculating promotional elasticity of embodiments of the present invention may be applied.
As shown in fig. 4, the system architecture 400 may include terminal devices 401, 402, 403, a network 404, and a server 405. The network 404 serves as a medium for providing communication links between the terminal devices 401, 402, 403 and the server 405. Network 404 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.
A user may use terminal devices 401, 402, 403 to interact with a server 405 over a network 404 to receive or send messages or the like. The terminal devices 401, 402, 403 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 401, 402, 403 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 405 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the terminal devices 401, 402, 403. The background management server may analyze and process the received data such as the product information query request, and feed back a processing result (for example, target push information and product information — only an example) to the terminal device.
It should be noted that the method for calculating the promotion elasticity provided by the embodiment of the present invention is generally executed in the server 405, and accordingly, the device for calculating the promotion elasticity is generally disposed in the server 405. The method for calculating the promotion elasticity provided by the embodiment of the invention can also be executed on the terminal equipment 401, 402 and 403, and accordingly, the device for calculating the promotion elasticity is generally arranged on the terminal equipment 401, 402 and 403.
It should be understood that the number of terminal devices, networks, and servers in fig. 4 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 5, shown is a block diagram of a computer system 500 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU)501 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the system 500 are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 501.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a fitting module, a segmentation module, and a calculation module, where the names of the modules do not in some cases constitute a limitation on the modules themselves.
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: fitting an optimization model based on basic data, thereby calculating to obtain a baseline sales volume of the commodity; the method comprises the steps of carrying out finest granularity segmentation on promotion duration to obtain at least one time unit, and then calculating nominal discount strength in each time unit; and calculating promotion elasticity according to the actual sales, the baseline sales and the nominal discount strength.
According to the technical scheme of the embodiment of the invention, because the technical means of calculating the nominal discount granularity in the time unit with the finest granularity and then calculating the promotion elasticity according to the actual sales volume, the baseline sales volume and the nominal discount strength is adopted, the technical problem of lack of income promotion analysis under different discount strengths is solved; the method carries out quantitative analysis aiming at the prior sales promotion activities, calculates the discount strength based on the base line price and the sales promotion which can be brought by the sales promotion activities under the condition of the given sales promotion activities, and then calculates the sales promotion elasticity, thereby better guiding the sales promotion and pricing work.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for calculating elasticity of promotions, comprising:
fitting an optimization model based on basic data, thereby calculating to obtain a baseline sales volume of the commodity;
the method comprises the steps of carrying out finest granularity segmentation on promotion duration to obtain at least one time unit, and then calculating nominal discount strength in each time unit;
and calculating promotion elasticity according to the actual sales, the baseline sales and the nominal discount strength.
2. The method of claim 1, wherein fitting the optimization model based on the base data to calculate a baseline sales volume for the commodity comprises:
summarizing basic data to obtain price variables, time variables, sales promotion variables, coupon variables and inventory variables of each commodity;
taking the actual sales as a dependent variable, and taking a price variable, a time variable, a sales promotion variable, a coupon variable and a stock variable as independent variables, and performing regression analysis on the model to obtain parameters of all the variables;
and setting the parameters of the time variable, the promotion variable and the coupon variable to be zero, substituting the basic data into the model, and calculating to obtain the baseline sales volume of the commodity.
3. The method of claim 1, wherein calculating a nominal discount strength for each time unit comprises:
for each time unit, the nominal discount strength within that time unit is calculated using the following formula:
nominal discount strength 1-page price x discount 1 x · · x discount N/baseline price.
4. The method of claim 1, wherein calculating promotional elasticity based on actual sales, baseline sales, and nominal discount strength comprises:
according to the time unit with the finest granularity, the actual sales volume and the sales volume baseline are split, and the time sales volume and the baseline sales volume in each time unit are obtained;
for each time unit, with nominal discount strength as the independent variable and actual sales and baseline sales as dependent variables, the following regression analysis was performed:
after the regression equation is fitted, the coefficient before the nominal discount strength is the promotion elasticity.
5. An apparatus for calculating elasticity of promotions, comprising:
the fitting module is used for fitting the optimization model based on the basic data so as to calculate the baseline sales volume of the commodity;
the segmentation module is used for carrying out the finest granularity segmentation on the promotion duration to obtain at least one time unit, and then calculating the nominal discount strength in each time unit;
and the calculation module is used for calculating the promotion elasticity according to the actual sales volume, the baseline sales volume and the nominal discount strength.
6. The apparatus of claim 5, wherein the fitting module is configured to:
summarizing basic data to obtain price variables, time variables, sales promotion variables, coupon variables and inventory variables of each commodity;
taking the actual sales as a dependent variable, and taking a price variable, a time variable, a sales promotion variable, a coupon variable and a stock variable as independent variables, and performing regression analysis on the model to obtain parameters of all the variables;
and setting the parameters of the time variable, the promotion variable and the coupon variable to be zero, substituting the basic data into the model, and calculating to obtain the baseline sales volume of the commodity.
7. The apparatus of claim 5, wherein calculating a nominal discount strength for each time unit comprises:
for each time unit, the nominal discount strength within that time unit is calculated using the following formula:
nominal discount strength 1-page price x discount 1 x · · x discount N/baseline price.
8. The apparatus of claim 5, wherein the computing module is configured to:
according to the time unit with the finest granularity, the actual sales volume and the sales volume baseline are split, and the time sales volume and the baseline sales volume in each time unit are obtained;
for each time unit, with nominal discount strength as the independent variable and actual sales and baseline sales as dependent variables, the following regression analysis was performed:
after the regression equation is fitted, the coefficient before the nominal discount strength is the promotion elasticity.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-4.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810966513.6A CN110858335A (en) | 2018-08-23 | 2018-08-23 | Method and device for calculating sales promotion elasticity |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810966513.6A CN110858335A (en) | 2018-08-23 | 2018-08-23 | Method and device for calculating sales promotion elasticity |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110858335A true CN110858335A (en) | 2020-03-03 |
Family
ID=69635144
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810966513.6A Pending CN110858335A (en) | 2018-08-23 | 2018-08-23 | Method and device for calculating sales promotion elasticity |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110858335A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113570467A (en) * | 2021-06-09 | 2021-10-29 | 上海淇玥信息技术有限公司 | Resource-specific information pushing method and device and electronic equipment |
| CN116029762A (en) * | 2023-03-03 | 2023-04-28 | 广州飞狮数字科技有限公司 | Method and device for determining commodity discount based on reinforcement learning |
-
2018
- 2018-08-23 CN CN201810966513.6A patent/CN110858335A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113570467A (en) * | 2021-06-09 | 2021-10-29 | 上海淇玥信息技术有限公司 | Resource-specific information pushing method and device and electronic equipment |
| CN113570467B (en) * | 2021-06-09 | 2024-04-26 | 上海淇玥信息技术有限公司 | Method and device for pushing special resource sharing information and electronic equipment |
| CN116029762A (en) * | 2023-03-03 | 2023-04-28 | 广州飞狮数字科技有限公司 | Method and device for determining commodity discount based on reinforcement learning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110348894B (en) | Method and device for displaying resource advertisement and electronic equipment | |
| CN110324414B (en) | Resource bit flow distribution method and device and electronic equipment | |
| CN109961306B (en) | Method and device for distributing inventory of articles | |
| CN107274209A (en) | The method and apparatus for predicting advertising campaign sales data | |
| CN109146533B (en) | Information pushing method and device | |
| CN109961299A (en) | The method and apparatus of data analysis | |
| CN112884405A (en) | Inquiry system and scheduling method thereof | |
| US20140214621A1 (en) | Method and device for pushing information | |
| CN110348921B (en) | Method and device for selecting store articles | |
| CN113379511A (en) | Method and apparatus for outputting information | |
| CN110858335A (en) | Method and device for calculating sales promotion elasticity | |
| WO2019169990A1 (en) | Method and apparatus for providing coupons to user | |
| CN112118546B (en) | Message pushing method, message pushing device, computer equipment and medium | |
| CN113434754A (en) | Method and device for determining recommended API (application program interface) service, electronic equipment and storage medium | |
| CN113269575A (en) | Method and device for calculating time sequence queue | |
| CN110046920B (en) | Method and device for calculating life cycle length of commodity | |
| CN115544152A (en) | Data processing method and device | |
| CN110956478A (en) | Method and device for determining goods input quantity | |
| CN110838019A (en) | Method and device for determining trial supply distribution crowd | |
| CN110766431A (en) | Method and device for judging whether user is sensitive to coupon | |
| CN114445128A (en) | Card ticket management method and device, electronic equipment and computer readable medium | |
| CN109872211A (en) | A kind of method and apparatus of object recommendation | |
| CN111125514B (en) | Method, device, electronic equipment and storage medium for analyzing user behaviors | |
| CN108805593A (en) | A kind of method of data processing, system, electronic equipment and readable storage medium storing program for executing | |
| CN113379433A (en) | Advertisement putting method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |