CN103258279A

CN103258279A - Commodity dynamic pricing method based on statistical model

Info

Publication number: CN103258279A
Application number: CN 201210036587
Authority: CN
Inventors: 蒋中华; 徐静
Original assignee: Individual
Current assignee: Individual
Priority date: 2012-02-19
Filing date: 2012-02-19
Publication date: 2013-08-21

Abstract

Provided is a commodity dynamic pricing method based on a statistical model. According to the commodity dynamic pricing method based on the statistical model, in a certain time period which is limited in advance, a commodity price or the number of coupons or rebates is decided according to the number of commodities purchased by all customers or the number of users who carry out purchase in a dynamic mode, a linear or nonlinear price reduction model is built through construction of a proper mathematical model. According to the commodity dynamic pricing method based on the statistical model, due to the rule, in a time period, the number of persons in promissory purchase is in direct proportion to discounts, the larger the number of the persons in purchase is, the larger a discount is, and the cheaper a commodity is, a discounted price mathematical model is built, for example, start prices of mobile phones of one type are 2012, an appointed time is a week, when 100 thousand persons purchase the mobile phones, discounted prices are 1900, and a price model of linear dynamic gradual reduction or nonlinear dynamic gradual reduction such as a quadratic curve is built between 0 and the 100 thousand purchasers.

Description

Commodity Dynamic Pricing method based on statistical model

Technical field

E-commerce field or traditional shopping field, commodity purchasing etc.

Background technology

At present, known e-commerce website commodity price all is to take static pricing method, can dynamically not adjust preferential according to user's quantity purchase.

Summary of the invention

All be to take static pricing method in order to overcome existing e-commerce website commodity price, the present invention proposes a kind of based on statistical model commodity Dynamic Pricing method, and can buy commodity amount (perhaps buying number of users) according to all users dynamically and carry out the Dynamic Pricing mode, perhaps determine to return profit according to model, return what of certificate, it is more low that all users buy commodity amount, and commodity price is more low, preferential dynamics is more big.

The technical solution adopted for the present invention to solve the technical problems is: in time statistics a period of time, the user buys commodity amount (perhaps buying number of users) and determines commodity price dynamically or return certificate, return sharp what, by making up the appropriate mathematical statistical model, set up linearity or nonlinear reduced-order model or return certificate, return sharp method.

The invention has the beneficial effects as follows, can buy commodity amount more for a long time at a large number of users, for the user wins maximum preferential discount commodity price, make the commodity shipment amount increase, reduce cost, make consumer user obtain the price of material benefit more flexibly.

Our Dynamic Pricing mode is better than the static pricing method of present ecommerce commodity, and part progressively replaces existing static pricing method in the future, is popular in each big e-commerce website, B2C/B2B/C2C electronic business transaction website, electronic emporium, market etc.

Description of drawings

The present invention is further described below in conjunction with drawings and Examples.

Stipulate in the time period that agreement purchase number and discount are directly proportional, the people who buys is more many, and discount is more big, more cheap, formulate a discounted cost mathematical model, such as a mobile phone, arrange in the week, initial price is 2012, if user's commodity purchasing quantity is 100,000, discounted cost is 1900, between 0 and 100,000 buyers, set up linear (or nonlinear so, as quafric curve) price model that dynamically successively decreases, be 50,000 people as commodity purchasing quantity, price is so

2012-(2012-1900)*(50000/100000)=2012-112*(1/2)=1900+56=1956,

Wherein 2012 is benchmark price, and 112 is price difference, and (1/2) is linear model discount ratio.

Y=b-ax=2012-(112/100000) x wherein x is quantity purchase

Perhaps set up other nonlinear reduced-order model: Nonlinear Dynamic reduced-order models such as hyperbolic curve or quafric curve

As nonlinear hyperbolic dynamic commodity pricing model:

y=a/(x+b)=(1900*100000)/[x+(1900*100000/2012)]

Nonlinear quadratic curve dynamic commodity pricing model:

y=b-ax ²=2012-[112/(10000) ²]*x ²

Can also set up the reduced-order model of multistage, it is different that each section is bought commodity amount range of price decrease, and initial price is 2012, if user's commodity purchasing quantity is 100,000, discounted cost is 1900, and when user's commodity purchasing quantity is 200,000, discounted cost is 1800,

The linear reduced-order model that can set up multistage is

First section 1 ~ 10000:y ₁=b ₁-a ₁X=2012-(112/100000) x

Second section 100001 ~ 200000:y ₂=b ₂-a ₂X=2000-(100/100000) x

Fig. 1 is linear dynamic merchandise valuation synoptic diagram.

Fig. 2 is non-Linear Double curve dynamic commodity price synoptic diagram.

Fig. 3 is nonlinear quadratic curve dynamic commodity price synoptic diagram.

Fig. 4 is multistage linearity curve dynamic commodity price synoptic diagram

Claims

1. buy commodity amount (perhaps buying number of users) and determine commodity price dynamically or return certificate according to all users of statistics, return sharp what, by making up the appropriate mathematical model, set up linear or nonlinear dynamic pricing models method.

2. the non-linear model that successively decreases that can set up other here according to claim 1, as conic model, perhaps hyperbolic model also can reach same effect.

3. according to claim 1, the model that we set up is a continuous type model, several bodies of purchaser are individual based on the natural integer model, also can simulate the same effect of our model and method by discrete each integer of instantiation, and are so too complicated certainly, can reduce discrete instantiation number, increase instantiation integer interbody spacer, from integer 1 to 100,1000, or 10000 etc., this also is that we belong to part integral point in our continuous model.

4. according to claim 1, we have only enumerated a price segment, can be divided into a plurality of price segments in the reality, are that 1900,20 ten thousand people purchase is 1800 as initial price 2012,10 ten thousand people's purchasing prices.

5. time that is limiting according to claim 1; the quantity of buying commodity by the statistics user determines commodity price dynamically; perhaps buy commodity and return profit, commodity return certificate (reward voucher) and also belong to covert dynamic decision commodity price method, also belong to this method protection right.

6. according to claim 1 can the statistics bought number of users and replaced adding up the user and buy commodity amount and dynamically determine commodity price, because may exist a user to buy the situation of a plurality of commodity.

7. can take non-timely transaction, can use monthly payment plan to mode, the user at first only need place an order, the deposit of payment some, and left fund can be paid off before businessman's delivery.

8. can take a kind of trade mode timely, this timely pattern is the limiting time section not, add up current commodity purchasing quantity, the dynamic calculation price, in time set price, the transaction commodity are after the user buys commodity, need to upgrade commodity price, the electronic commerce procedure of the compatible current popular of this timely trade mode.