Venue Site Gradient Pricing Method, System and Equipment Based on Real-Time Dynamic Data

Abstract

This application relates to the technical field of stadium management, and specifically relates to a stadium venue gradient pricing method, system and device based on real-time dynamic data. The method includes: Short-term ticket pricing adjustment step: According to the horizontal order rate in the same time period within a cycle and the vertical order rate at different times within the same day, combined with the proportion of the ticket sale time to the total sale time, dynamically adjust the benchmark price through a preset adjustment coefficient formula; Long-term ticket pricing adjustment step: Based on the median of the total sold ticket time of multiple short-term cycles and the average of the order rates, fine-tune the benchmark price through a long-term adjustment coefficient; Customer grading pricing step: Calculate the quality score according to the customer consumption frequency, consumption amount and loyalty, divide the customer levels according to the score range and match different discounts. Through short-term and long-term dynamic pricing adjustments, it is possible to quickly respond to market changes, optimize the pricing strategy for each time period, and thus maximize the revenue of the stadium.

Description

Technical Field

[0001] This application relates to the technical field of stadium management, and particularly to a stadium venue gradient pricing method, system and device based on real-time dynamic data. Background Art

[0002] In the operation of stadiums, the gradient pricing strategy directly affects profitability and resource utilization. Traditional gradient pricing methods mainly include gradient pricing based on purchase quantity, gradient pricing based on time period, gradient pricing based on customer type, and gradient pricing based on service content.

[0003] The problems existing in the above methods include: lack of data support, insufficient perception ability for obtaining the best profit-making method, and inability to obtain the optimal stepped pricing profit-making method. When applying the method, it is often based on empirical judgment, lacking detailed data analysis and support. After selecting a single profit-making method or combining them and implementing, it is impossible to quickly know whether this pricing method is a better method among all methods and whether it is a better profit and pricing situation. It is impossible to accurately evaluate the specific impacts of different pricing strategies on sales volume, passenger flow and profit. The pricing strategy is adjusted slowly and cannot respond to market changes in a timely manner. It is difficult to dynamically adjust prices according to real-time data to maximize profits. Customer segmentation is not fine enough to achieve the best profit. It is impossible to fully capture and apply the needs and willingness to pay of different customer groups. Customer data is not fully utilized for personalized pricing. Summary of the Invention

[0004] In order to solve the existing problems of stadium venue gradient pricing, especially to improve the timeliness, customization ability and sensitivity to profit fluctuations of stadium gradient pricing, the present invention provides a stadium venue gradient pricing method, system and device based on real-time dynamic data.

[0005] In the first aspect, the technical solution of the present invention provides a stadium venue gradient pricing method based on real-time dynamic data, including:

[0006] (1) Steps for short-term ticket pricing adjustment:

[0007] Calculate a short-term adjustment coefficient according to the horizontal ordering rate in the same time period within a cycle and the vertical ordering rate at different time periods within the same day, and combine the ratio of the ticket selling time to the total selling time to dynamically adjust the benchmark price through the short-term adjustment coefficient;

[0008] (2) Steps for long-term ticket pricing adjustment:

[0009] On the basis of the short-term ticket pricing adjustment, calculate a long-term adjustment coefficient based on the median of the total selling time of the sold tickets in multiple short-term cycles and the average of the ordering rates, and adjust the benchmark price;

[0010] (3)Steps for customer - tiered pricing:

[0011] Calculate the customer quality score based on the customer's consumption frequency, consumption amount, and loyalty, divide the score range, and divide the customer levels according to the score range and match differential discounts; that is, each of the said customer levels corresponds to a different level price.

[0012] As a further limitation of the technical solution of the present invention, the specific steps for short - term ticket pricing adjustment include:

[0013] S11. Every time a cycle passes, adjust each independent benchmark price once; the steps are as follows:

[0014] SS11. Select the benchmark price to be adjusted and determine whether the corresponding venue is sold;

[0015] SS12. Obtain the horizontal ordering rate, vertical ordering rate, the single - ticket selling time, and the total selling time of tickets for each time period of the venue within the current cycle;

[0016] SS13. Calculate the horizontal adjustment coefficient and the vertical adjustment coefficient according to the horizontal ordering rate and the vertical ordering rate, and calculate the comprehensive adjustment coefficient in combination with the set maximum adjustment amplitude value;

[0017] SS14. Calculate the price adjustment ratio based on the comprehensive adjustment coefficient in combination with the venue selling status;

[0018] SS15. Adjust the benchmark price based on the price adjustment ratio;

[0019] S12. After a set number of rounds of adjustment, determine the short - term optimal pricing plan.

[0020] As a further limitation of the technical solution of the present invention, in SS13, the calculation formula is as follows:

[0021] Horizontal adjustment coefficient ;

[0022] Vertical adjustment coefficient ;

[0023] Comprehensive adjustment coefficient ;

[0024] In the formula, is the horizontal ordering rate within the cycle, is the vertical ordering rate within the cycle, is the maximum adjustment amplitude value, The function refers to taking the maximum value of two numbers.

[0025] The steps of SS14 specifically include:

[0026] If the ticket is sold, calculate the price increase ratio based on the comprehensive adjustment coefficient and the proportion of the single ticket selling time; the formula is as follows:

[0027]

[0028] If the ticket is not sold, calculate the price reduction ratio based on the comprehensive adjustment coefficient; the formula is as follows:

[0029]

[0030] In the formula, is the single ticket selling time, is the total selling time of the sold tickets, is the price increase ratio, is the price reduction ratio.

[0031] The steps of SS15 specifically include:

[0032] Adjust the benchmark price based on the price increase ratio and the set minimum adjustment points; the formula is as follows:

[0033]

[0034] Adjust the benchmark price based on the price reduction ratio and the set minimum adjustment points; the formula is as follows:

[0035]

[0036] In the formula, is the minimum adjustment point, a is the time period serial number, n is the serial number within the cycle, is the current benchmark price for reserving a single venue in the a-th time period on the n-th day, is the benchmark price after short-term adjustment.

[0037] As a further limitation of the technical solution of the present invention, the specific steps for long-term ticket pricing adjustment include:

[0038] S21. Collect the total selling time of the sold tickets, the median of the total selling time of the sold tickets, the ordering rate, and the average ordering rate for multiple short-term cycles;

[0039] S22. Calculate the long-term adjustment coefficient according to the difference between the ordering rate and the average ordering rate and the median of the total selling time of the sold tickets;

[0040] S23. Fine-tune the benchmark price based on the long-term adjustment coefficient to maximize the long-term revenue.

[0041] As a further limitation of the technical solution of the present invention, the calculation formula for the long-term adjustment coefficient is as follows:

[0042]

[0043] The formula for adjusting the benchmark price is as follows:

[0044]

[0045] In the formula, is the order rate for the same venue and the same short - term period, is the average of the order rates for the same venue and the same short - term period, is for the same median of the total sold ticket times, is for the same total sold ticket times, is the long - term adjusted benchmark price, is the current benchmark price for reserving a single venue in the ath time period on the nth day.

[0046] As a further limitation of the technical solution of the present invention, the steps of customer - grading pricing include:

[0047] Obtain customer consumption frequency, consumption amount, and loyalty data;

[0048] Convert the customer consumption frequency, consumption amount, and loyalty data into rankings among all customers;

[0049] Calculate the customer quality score Q according to the following formula:

[0050]

[0051] In the formula, is the customer consumption frequency ranking, is the customer consumption amount ranking within the set period, is the customer loyalty ranking, and N is the total number of customers.

[0052] In a second aspect, the technical solution of the present invention also provides a venue - site gradient pricing system based on real - time dynamic data, including a short - term pricing adjustment module, a long - term pricing adjustment module, a customer screening and pricing module, and a data collection and analysis module;

[0053] The short - term pricing adjustment module is used to execute the short - term ticket pricing adjustment steps and dynamically adjust the benchmark price. The short - term ticket pricing adjustment steps include: calculating the short - term adjustment coefficient according to the horizontal order rate in the same time period within the cycle and the vertical order rate in different time periods within the same day, and dynamically adjusting the benchmark price through the short - term adjustment coefficient in combination with the ratio of the ticket sold time to the total sold time; the cycle is a short - term cycle, and the time length L is defined as a short - term cycle;

[0054] The long-term pricing adjustment module is used to execute long-term ticket pricing adjustment steps to optimize the long-term pricing strategy. The steps of long-term ticket pricing adjustment include: based on the short-term ticket pricing adjustment, calculating a long-term adjustment coefficient based on the median of the total sold ticket time of multiple short-term cycles and the average of the ordering rates, and adjusting the benchmark price; a set number of short-term cycles are defined as the long-term cycle;

[0055] The customer screening and pricing module is used to execute high-quality customer screening and pricing steps to achieve customer grading and differential pricing. The steps of customer grading and pricing include: calculating the customer quality score according to the customer consumption frequency, consumption amount and loyalty and dividing the score range, dividing the customer levels according to the score range and matching differential discounts; that is, each of the customer levels corresponds to a different level price;

[0056] The data collection and analysis module is used to collect ticket sales data and customer consumption data in real time and provide data support for the short-term pricing adjustment module, the long-term pricing adjustment module and the customer screening and pricing module.

[0057] As a further limitation of the technical solution of the present invention, the specific steps of the short-term ticket pricing adjustment executed by the short-term pricing adjustment module include:

[0058] S11. Every time a cycle passes, adjust each independent benchmark price once; the steps are as follows:

[0059] SS11. Select the benchmark price to be adjusted and determine whether the corresponding venue is sold;

[0060] SS12. Obtain the horizontal ordering rate, vertical ordering rate, the sold time of a single ticket and the total sold ticket time of the venue in each time period within the current cycle;

[0061] SS13. Calculate the horizontal adjustment coefficient and the vertical adjustment coefficient according to the horizontal ordering rate and the vertical ordering rate, and calculate the comprehensive adjustment coefficient in combination with the set maximum adjustment amplitude value;

[0062] SS14. Calculate the price adjustment ratio based on the comprehensive adjustment coefficient in combination with the venue sold status;

[0063] SS15. Adjust the benchmark price based on the price adjustment ratio;

[0064] S12. After a set number of rounds of adjustment, determine the short-term optimal pricing plan.

[0065] Horizontal adjustment coefficient ;

[0066] Vertical adjustment coefficient ;

[0067] Comprehensive adjustment coefficient ;

[0068] In the formula, is the horizontal ordering rate within a period, is the vertical ordering rate within a period, is the maximum adjustment amplitude value, The function refers to taking the maximum value of two numbers.

[0069] If the ticket is sold, calculate the price increase ratio based on the comprehensive adjustment coefficient and the proportion of the single ticket sale time; the formula is as follows:

[0070]

[0071] If the ticket is not sold, calculate the price reduction ratio based on the comprehensive adjustment coefficient; the formula is as follows:

[0072]

[0073] In the formula, is the single ticket sale time, is the total sale time of the sold tickets, is the price increase ratio, is the price reduction ratio.

[0074] Adjust the benchmark price based on the price increase ratio and the set minimum adjustment points; the formula is as follows:

[0075]

[0076] Adjust the benchmark price based on the price reduction ratio and the set minimum adjustment points; the formula is as follows:

[0077]

[0078] In the formula, is the minimum adjustment point, a is the time period serial number, n is the serial number within a period, is the current benchmark price for reserving a single venue in the a-th time period on the n-th day, is the benchmark price after short-term adjustment.

[0079] As a further limitation of the technical solution of the present invention, the specific steps of the long-term ticket pricing adjustment executed by the long-term pricing adjustment module include:

[0080] S21. Collect the total sale time of the sold tickets, the median of the total sale time of the sold tickets, the ordering rate, and the average ordering rate of multiple short-term periods;

[0081] S22. Calculate the long-term adjustment coefficient according to the difference between the ordering rate and the average ordering rate and the median of the total sale time of the sold tickets;

[0082] S23. Fine-tune the benchmark price based on the long-term adjustment coefficient to maximize long-term benefits.

[0083] The calculation formula for the long-term adjustment coefficient is as follows:

[0084]

[0085] The formula for adjusting the benchmark price is as follows:

[0086]

[0087] In the formula, is the order rate for the same venue and the same short-term time, is the average of the order rates for the same venue and the same short-term time, is the same median of the total sold ticket times, is the same total sold ticket times, is the benchmark price after long-term adjustment, is the current benchmark price for reserving a single venue in the a-th time period on the n-th day.

[0088] The steps for the customer screening and pricing module to perform customer hierarchical pricing include:

[0089] Obtain customer consumption frequency, consumption amount, and loyalty data;

[0090] Convert the customer consumption frequency, consumption amount, and loyalty data into rankings among all customers;

[0091] Calculate the customer quality score Q according to the following formula:

[0092]

[0093] In the formula, is the customer consumption frequency ranking, is the customer consumption amount ranking within the set period, is the customer loyalty ranking, and N is the total number of customers.

[0094] Thirdly, the present invention provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; the memory stores computer program instructions executable by the at least one processor, and the computer program instructions are executed by the at least one processor so that the at least one processor can execute the venue site gradient pricing method based on real-time dynamic data as described in the first aspect.

[0095] As can be seen from the above technical solutions, the present application has the following advantages: Through short-term and long-term dynamic pricing adjustments, it can quickly respond to market changes, optimize the pricing strategy for each time period, and thus maximize the revenue of the venue. The high-quality customer screening and pricing mechanism can accurately identify high-value customers. Through differential pricing strategies, it can increase customers' willingness to pay and loyalty, and further improve the overall profitability. Among them, the short-term ticket pricing adjustment method can quickly adapt to market supply and demand changes and reduce revenue losses caused by market fluctuations. The long-term ticket pricing adjustment method further optimizes the pricing strategy by analyzing data from multiple short-term cycles, reduces the accidental impact brought by short-term adjustments, and makes the revenue more stable. Through the high-quality customer screening and pricing mechanism, personalized preferential strategies are provided for customers of different levels, enhancing customer satisfaction and loyalty and reducing the customer churn rate. The flexible reservation and refund / modification strategies can better meet customers' needs and improve the customer experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] In order to more clearly illustrate the technical solutions of the present application, the drawings required to be used in the description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0097] Figure 1 It is a flowchart of the method provided by the embodiment of the present invention.

[0098] Figure 2 It is a block diagram of the system provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0099] The technical solution of the present invention proposes a venue site gradient pricing method, system and device based on real-time dynamic data, which solves the problems of insufficient timeliness, customization ability and sensitivity to profit fluctuations in the existing stadium gradient pricing. Briefly speaking, in the scenario of stadium gradient pricing, based on simple and easily configurable multi-dimensional parameters, through a formulaic and systematic related process, a high-revenue pricing method can be effectively, comparably and reasonably obtained. The method consists of three parts. The first part is the short-term ticket pricing adjustment method, the second part is the long-term ticket pricing adjustment method, and the third part is the high-quality customer screening and pricing mechanism. Among them, the short-term ticket pricing adjustment method aims to quickly and effectively obtain all venue pricing plans with high revenue or high profit; the long-term ticket pricing adjustment method is used for fine-tuning of long-term pricing plans, and is used in conjunction with the short-term pricing adjustment method to reduce the accidental impact brought by the short-term plan setting and timely adjust the pricing plan, so as to further make the revenue close to the highest revenue value; the high-quality customer screening and pricing mechanism is used to enhance the viscosity and payment willingness of high-quality customers and reduce the loss rate of high-quality customers. In order to make the application purpose, features and advantages of the present application more obvious and understandable, the following will use specific embodiments and drawings to clearly and completely describe the technical solution protected by the present application. Obviously, the following described embodiments are only a part of the embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0100] As Figure 1 shown, an embodiment of the present invention provides a venue site gradient pricing method based on real-time dynamic data, including:

[0101] (1) The steps of short-term ticket pricing adjustment include: S11. Adjust each independent base price once every cycle; the steps are as follows:

[0102] SS11. Select the base price to be adjusted and determine whether the corresponding venue is sold;

[0103] SS12. Obtain the horizontal order rate, vertical order rate, single ticket sale time and total sale time of tickets for each time period of the venue within the current cycle;

[0104] SS13. Calculate the horizontal adjustment coefficient and vertical adjustment coefficient according to the horizontal order rate and vertical order rate, and calculate the comprehensive adjustment coefficient in combination with the set maximum adjustment amplitude value;

[0105] SS14. Calculate the price adjustment ratio based on the comprehensive adjustment coefficient in combination with the venue sale status;

[0106] SS15. Adjust the base price based on the price adjustment ratio;

[0107] S12. After adjustment by the setting wheel, determine the short-term optimal pricing plan.

[0108] In a stadium, generally the smallest reservation unit is the usage time of a single venue, such as 1 hour for a single badminton court, 1 hour for a single table tennis court, and 1 hour for a single tennis court. There is also a small part of the way of booking by the number of people, such as the number of fitness people and the number of swimming people. In this application, a unified concept is used. For the same type of selling method, the smallest unit of order is used as the standard (such as 1 hour for a single badminton court and the number of fitness people are both the smallest units). Set the following parameters:

[0109] p is the base price (the order price for one smallest unit, for example, 30 yuan for 1 hour of a single badminton court, and this value is 30). It is a positive integer.

[0110] y is the order period (the period used for short-term adjustment calculation, for example, we set 7 days as a period, that is, the short-term period, then this value is 7, and every 7 days, the short-term adjustment method is applied for price calculation, that is, adjust the base price p). It is a positive integer.

[0111] x is the total number of time periods. It is a positive integer.

[0112] a is the time period serial number (a stadium must be divided into many bookable time periods every day. For example, 1 hour of a badminton court is regarded as a time period. If the opening hours to closing hours are 8 hours, then the time period numbers are 1 - 8, which are used as unique identifiers for horizontal calculation of the same time period within the order period). The serial number range .

[0113] n is the serial number within the period (related to x, and the range is , which is used for vertical calculation of the same time unit within the entire order period (if an order period y is 7 (unit: days), then one time unit is 1 day)). The serial number has no range limit. The example parameter table is shown in Table 1.

[0114] Table 1: Parameter Example

[0115]

[0116] Suppose Table 1 is the reservation price table of a badminton hall for a week. The hall is open for 8 hours a day and can be reserved for 1 hour at a time. Then the value of x is 8 and the value of y is 7, so represents the base price for reserving a single venue in the first hour on Monday, that is, the price. That is, it represents the base price for reserving a single venue in the a-th hour on the n-th day. Each is independent and can be set separately.

[0117] Among them, is the minimum adjustment point (when calculating the base price For use when. Every time a time period passes, the reference price needs to be adjusted. When adjusting, this adjustment point number needs to be used,). is a decimal number between.

[0118] is the maximum adjustment amplitude. How many minimum adjustment point numbers can be adjusted at most. It is a positive integer greater than 1. This parameter can prevent excessive adjustment of the price.

[0119] After each cycle of ordering, the following parameters can be obtained:

[0120] is the horizontal ordering rate within the cycle. Simply put, it is for a single , the proportion of successful sales in, which is the proportion of successful sales within the range of the row in Table 1.

[0121] is the vertical ordering rate within the cycle. Simply put, it is for a single , the proportion of successful sales in, which is the proportion of successful sales within the range of the column in Table 1.

[0122] is the selling time of a single ticket. That is, for each , the total time from ticket opening to successful sale. If it is never sold, it is recorded as -1.

[0123] is the total selling time of the sold tickets. This total is for , the total selling time of the vertically sold tickets, that is the total of the sold tickets that are successfully sold in.

[0124] is the adjustment round. That is, how many rounds of such adjustments have been made in total. is a positive integer.

[0125] Every time a cycle passes, each independent reference price will be adjusted once. The adjustment method steps for each reference price are as follows:

[0126] 1) Select the reference price to be adjusted , and determine whether the site plot has been sold. According to , the horizontal ordering rate within the cycle and the vertical ordering rate within the cycle can be obtained at the same time. Each reference price will be adjusted one by one.

[0127] 2) After selection, comprehensive calculations in the horizontal and vertical directions are first carried out. Generally, stadium tickets are available for the whole day, that is, all orders for a day are released at once. Therefore, what needs to be evaluated in the horizontal calculation is the popularity of the same time period on different dates. The vertical calculation, on the other hand, compares the popularity of different time periods within the same day. If the corresponding is -1, it means it has never been sold. Through evaluation and analysis, it can be seen that users are more sensitive to horizontal price differences. Therefore, horizontal price adjustments need to be more gentle. Vertical price adjustments can be more refined and have a larger range. So the respective adjustment methods are as follows:

[0128] Horizontal adjustment coefficient ; The function is used to smooth the adjustment range to make horizontal price adjustments more gentle.

[0129] Vertical adjustment coefficient ; is used to enhance the influence and prevent the range from being too large.

[0130] When adjusting the price, the following principles need to be followed:

[0131] For unsold situations, the higher the order rate, the lower the price adjustment range (price reduction); for sold situations, the higher the order rate, the higher the price adjustment range (price increase), and the comprehensive adjustment coefficient is obtained;

[0132] Comprehensive adjustment coefficient ;

[0133] In the formula, is the horizontal order rate within the cycle, is the vertical order rate within the cycle, is the maximum adjustment range value, The function refers to taking the maximum value of two numbers. This function ensures that the value of is between and. Furthermore

[0134]

[0135] 3) Next, the ticket price is adjusted. It is divided into two aspects, namely sold within the cycle and unsold within the cycle.

[0136] Within the cycle If the location where is sold ( ), the price increases. The price increase depends on the calculated comprehensive adjustment coefficient and the proportion of the sold time of a single ticket.

[0137] Price increase ratio

[0138] The function is limited by within and, moreover, the larger is, the smaller is. At the same time, the shape of the function will change flexibly according to Finally, the adjustment is based on the current to calculate the new (define the new as

[0139]

[0140] During the period if the location where it is located is not sold ( ), then the price is reduced. The price reduction ratio only depends on the calculated comprehensive adjustment coefficient .

[0141] The price reduction ratio

[0142] is calculated according to the price reduction ratio .

[0143] 4) Such adjustments are made for each independent . After a total of rounds of adjustments, the most suitable pricing plan in the short term can be found to complete the short-term ticket pricing adjustment.

[0144] (2) The steps of the long-term ticket pricing adjustment include: S21, collecting the total sold ticket time, the median of the total sold ticket time, the ordering rate, and the average ordering rate of multiple short-term cycles; S22, calculating the long-term adjustment coefficient according to the difference between the ordering rate and the average ordering rate and the median of the total sold ticket time; S23, making fine adjustments to the benchmark price based on the long-term adjustment coefficient to maximize the long-term revenue.

[0145] It should be noted that the long-term ticket pricing adjustment method needs to further consider the market dynamics in a longer time range on the basis of the short-term adjustment. The main goal of the long-term adjustment is to increase the ordering rate and reduce the vacancy rate, while improving the overall profitability. The long-term plan is mainly adjusted based on all the calculated data. The following are the specific steps of the long-term ticket pricing adjustment method:

[0146] The parameters are set as follows:

[0147] is the number of short-term ticket cycles passed. That is, how many short-term ticket adjustment rounds are included in one long-term round. It is a positive integer greater than or equal to 4.

[0148] is the benchmark price (the ordering price per minimum unit, for example, 30 yuan per hour for a single badminton court, and this value is 30). It is a positive integer and has the same meaning as in the short-term adjustment.

[0149] is the same is the total sold ticket time. For example , then is within these four short-term ordering cycles where the total time from ticket opening to successful sale for the four successfully sold tickets is counted. If not sold, it will also be counted as a duration, which is the duration from ticket release to ticket expiration. For example, if 3 venues are sold, 3 to are counted. For a venue where the total duration from ticket release to unable to make a reservation is , then should be added .

[0150] is the same is the median of the total sold ticket time. That is, the median of all the above . Since the time scale cannot be measured like a ratio, using the median is more reference-worthy for the application.

[0151] is the ordering rate for the same venue within the same short-term period. For example, as above, if , after 4 short-term cycles, is sold 3 times in total, then the ordering rate is 0.75. is a decimal between.

[0152] is the average of the ordering rates for the same venue within the same short-term period. That is, the average of all the . is a decimal between.

[0153] Actually, the goal of the long-term adjustment is still to adjust the of each short-term cycle. The adjustment plan is as follows:

[0154] 1) Select the benchmark price to be adjusted . The long-term adjustment amplitude is relatively small. The main influencing factor is , and the secondary influencing factor is . The primary goal is to increase profitability by increasing the ordering rate, and the secondary goal is to increase the benchmark price for venues with a high ordering rate to increase profitability.

[0155] 2) Calculate the long-term adjustment coefficient :

[0156] First can be used to reflect the impact of the selling time on the popularity of this venue, but there may be a problem of too large a value range. To make it softer without changing monotonicity and at the same time control the value range within take the following values for later use: ;

[0157] Then there is the main influencing parameter , through it can be simply obtained that >1 means a price increase, otherwise a price decrease. It is necessary to make its value within for subsequent calculations. Take the following values for later use: ;

[0158] When the above value is 0.

[0159] The long-term adjustment coefficient .

[0160] 3) The final adjustment is based on the current to calculate the new (similarly, here the new is defined as for easy distinction) and is similar to the short-term adjustment, and the function is also the same, both are to adjust the value of .

[0161]

[0162] The general formula for long-term adjustment:

[0163]

[0164] This can ensure that can affect both price increase or decrease and its range at the same time, can only affect the range to a certain extent and cannot affect price increase or decrease.

[0165] 4) For each independent such adjustments are made. There is no concept of adjustment rounds for long-term ticket price adjustment . It can be adjusted once every long-term cycle until the adjustment strategy is selected to be closed when it tends to be stable, then the long-term pricing is completed.

[0166] (3) The steps of customer tiered pricing include: calculating the customer quality score based on customer consumption frequency, consumption amount, and loyalty, dividing the score range, and dividing customer levels according to the score range and matching differential discounts; that is, each of the said customer levels corresponds to a different level price.

[0167] The screening and pricing of high-quality customers are based on three dimensions: customer consumption frequency, customer consumption amount, and customer loyalty. The data of these three dimensions are calculated through long-term retained consumption records.

[0168] Define customer consumption frequency

[0169] The total consumption amount A of the customer within a period.

[0170] Customer loyalty L is the longest continuous order cycle of the customer within a period (this cycle is the order cycle y in short-term tickets), that is, the maximum number of short-term cycles that this user continuously reserves.

[0171] Convert these three data into rankings among all customers, which are 、 、 , and these three data are used to calculate a customer quality score .

[0172] The calculation formula for the customer quality score Q is as follows:

[0173]

[0174] In the formula, is the ranking of customer consumption frequency, is the ranking of the customer's consumption amount within the set period, is the ranking of customer loyalty, and N is the total number of customers;

[0175] The range is between. The higher the score, the higher the quality of the customer.

[0176] is the VIP customer interval, enjoying a 20% discount on the benchmark price;

[0177] is the high-quality customer interval, enjoying a 10% discount;

[0178] Other users have no preferential strategies. The customer intervals remain unchanged, but the discounts can be changed.

[0179] Specifically, taking a badminton hall as an example, the venue is open for 8 hours every day, with each hour being a reservation period, for a total of 8 periods. The venue adopts short-term and long-term ticket pricing adjustment methods, combined with high-quality customer screening and pricing mechanisms, to optimize the pricing strategy and improve revenue and customer satisfaction.

[0180] (1)Implementation of short-term ticket pricing adjustment:

[0181] Set short-term ticket parameters. Include the ordering cycle, total number of time periods, minimum adjustment points, and maximum adjustment range. Collect data such as the horizontal ordering rate, vertical ordering rate, single ticket selling time, and benchmark price for each time period. For example, after one cycle, for a certain time period the horizontal ordering rate is 0.8, the vertical ordering rate is 0.6, and the single ticket selling time is 2 hours. Fill in the data to calculate the adjustment coefficient, and then adjust according to the price increase and decrease formula. Apply the adjusted benchmark price to the next ordering cycle until the short-term adjustment round is completed. Record the adjustment results for subsequent long-term adjustment analysis.

[0182] (2)Implementation of long-term ticket pricing adjustment:

[0183] Set long-term ticket parameters. Include the number of short-term ticket cycles passed, total selling time of tickets, median of total selling time of tickets, ordering rate, average ordering rate. Collect data such as the total selling time of tickets, ordering rate, and benchmark price for 4 short-term cycles. For example, the total selling times of tickets for 4 cycles are 6, 7, 5, and 8 hours respectively, and the ordering rates are 0.7, 0.8, 0.6, and 0.9 respectively. Calculate the adjustment coefficient and the new benchmark price according to the steps of the technical solution. Apply the adjusted benchmark price to the next ordering cycle. Record the adjustment results for subsequent long-term adjustment analysis.

[0184] (3)Implementation of high-quality customer screening and pricing mechanism

[0185] Collect data on customers' consumption frequency, consumption amount, and loyalty.

[0186] For example, customer A reserved the venue 10 times within 30 days, with a consumption amount of 600 yuan, and the longest consecutive ordering cycle is 5 short-term cycles.

[0187] Assuming there are 100 customers, the rankings of customer A are as follows:

[0188] Consumption frequency ranking: 30

[0189] Consumption amount ranking: 20

[0190] Loyalty ranking: 40

[0191] The calculated customer quality score is 0.7. It is concluded that customer A belongs to the high-quality customer range. Apply preferential strategies according to the quality score. Regularly evaluate the customer quality score and dynamically adjust the customer level and preferential strategies.

[0192] As Figure 2 shown, the technical solution of the present invention also provides a venue site gradient pricing system based on real-time dynamic data, including a short-term pricing adjustment module, a long-term pricing adjustment module, a customer screening and pricing module, and a data collection and analysis module;

[0193] The short-term pricing adjustment module is used to execute short-term ticket pricing adjustment steps to dynamically adjust the benchmark price. The short-term ticket pricing adjustment steps include: calculating a short-term adjustment coefficient based on the horizontal ordering rate within the same time period in the cycle and the vertical ordering rate at different time periods within the same day, and combining the proportion of the ticket sales time to the total sales time, and dynamically adjusting the benchmark price through the short-term adjustment coefficient; the cycle is a short-term cycle, and the time length L is defined as a short-term cycle;

[0194] The long-term pricing adjustment module is used to execute long-term ticket pricing adjustment steps to optimize the long-term pricing strategy. The steps of long-term ticket pricing adjustment include: on the basis of short-term ticket pricing adjustment, calculating a long-term adjustment coefficient based on the median of the total sold ticket time of multiple short-term cycles and the average of the ordering rates, and adjusting the benchmark price; a set number of short-term cycles is defined as a long-term cycle;

[0195] The customer screening and pricing module is used to execute high-quality customer screening and pricing steps to achieve customer classification and differential pricing. The steps of customer classification and pricing include: calculating the customer quality score according to the customer consumption frequency, consumption amount, and loyalty and dividing the score range, and dividing the customer levels according to the score range and matching differential discounts; that is, each of the customer levels corresponds to a different level price;

[0196] The data collection and analysis module is used to collect ticket sales data and customer consumption data in real time, and provide data support for the short-term pricing adjustment module, the long-term pricing adjustment module, and the customer screening and pricing module.

[0197] Horizontal adjustment coefficient ;

[0198] Vertical adjustment coefficient ;

[0199] Comprehensive adjustment coefficient ;

[0200] In the formula, is the horizontal ordering rate within the cycle, is the vertical ordering rate within the cycle, is the maximum adjustment amplitude value, The function refers to taking the maximum value of two numbers.

[0201] If the ticket is sold, calculate the price increase ratio based on the comprehensive adjustment coefficient and the proportion of the single ticket selling time; the formula is as follows:

[0202]

[0203] If the ticket is not sold, calculate the price reduction ratio based on the comprehensive adjustment coefficient; the formula is as follows:

[0204]

[0205] In the formula, is the single ticket selling time, is the total selling time of the sold tickets, is the price increase ratio, is the price reduction ratio.

[0206] Adjust the benchmark price based on the price increase ratio and the set minimum adjustment points; the formula is as follows:

[0207]

[0208] Adjust the benchmark price based on the price reduction ratio and the set minimum adjustment points; the formula is as follows:

[0209]

[0210] In the formula, is the minimum adjustment point, a is the time period serial number, n is the serial number within the cycle, is the current benchmark price for booking a single venue in the a-th time period on the n-th day, is the benchmark price after short-term adjustment.

[0211] In the specific steps of the long-term ticket pricing adjustment executed by the long-term pricing adjustment module, the calculation formula for the long-term adjustment coefficient is as follows:

[0212]

[0213] The formula for adjusting the benchmark price is as follows:

[0214]

[0215] In the formula, is the ordering rate for the same venue and the same short-term time, is the average of the ordering rates for the same venue and the same short-term time, is for the same median of the total selling time of the sold tickets, is for the same total selling time of the sold tickets, is the benchmark price after long-term adjustment, is the current benchmark price for reserving a single venue in the ath time period on the nth day.

[0216] Define the customer consumption frequency

[0217] The total consumption amount A of the customer within the period.

[0218] The customer loyalty L is the longest continuous ordering cycle of the customer within the period (this cycle is the ordering cycle y in short-term tickets), that is, the maximum number of short-term cycles continuously reserved by this user.

[0219] Convert these three data into rankings among all customers, which are 、 、 , and the three data are used to calculate a customer quality score .

[0220] The calculation formula for the customer quality score Q is as follows:

[0221]

[0222] In the formula, is the ranking of customer consumption frequency, is the ranking of the customer's consumption amount within the set period, is the ranking of customer loyalty, and N is the total number of customers;

[0223] The range is . The higher the score, the better the customer quality.

[0224] is the VIP customer interval, enjoying a 20% discount on the benchmark price;

[0225] is the high-quality customer interval, enjoying a 10% discount;

[0226] Other users have no preferential strategies. The customer intervals remain unchanged, but the discounts can be changed.

[0227] The system described in the embodiment of the present invention further includes a user interface module for displaying the dynamic pricing results and customer preferential information. The system realizes real-time data collection and pricing calculation through a cloud computing platform.

[0228] An embodiment of the present invention further provides an electronic device, which includes: a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus. The communication bus can be used for information transmission between the electronic device and the sensor. The processor can call the logical instructions in the memory to execute the following methods: (1) Steps for short-term ticket pricing adjustment: Calculate the short-term adjustment coefficient according to the horizontal order rate in the same time period within the cycle and the vertical order rate at different time periods within the same day, and combine the ratio of the ticket sale time to the total sale time, and dynamically adjust the benchmark price through the short-term adjustment coefficient; the cycle is a short-term cycle, and the time length L is defined as a short-term cycle; (2) Steps for long-term ticket pricing adjustment: On the basis of the short-term ticket pricing adjustment, calculate the long-term adjustment coefficient based on the median of the total sold ticket time of multiple short-term cycles and the average of the order rates, and adjust the benchmark price; a set number of short-term cycles are defined as a long-term cycle; (3) Steps for customer grading pricing: Calculate the customer quality score according to the customer consumption frequency, consumption amount, and loyalty and divide the score range, divide the customer grades according to the score range and match different discounts; that is, each of the customer grades corresponds to a different grade price.

[0229] In addition, when the logical instructions in the above-mentioned memory can be implemented in the form of software function units and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0230] An embodiment of the present invention provides a non-transitory computer-readable storage medium that stores computer instructions, which cause a computer to execute the method provided in the above method embodiment. For example, it includes: (1) Steps for short-term ticket pricing adjustment: Calculate a short-term adjustment coefficient based on the horizontal ordering rate during the same time period within a cycle and the vertical ordering rate at different time periods within the same day, and combine the ratio of the ticket sale time to the total sale time. Dynamically adjust the benchmark price through the short-term adjustment coefficient; the cycle is a short-term cycle, and the time length L is defined as a short-term cycle; (2) Steps for long-term ticket pricing adjustment: On the basis of the short-term ticket pricing adjustment, calculate a long-term adjustment coefficient based on the median of the total sold ticket times of multiple short-term cycles and the average of the ordering rates, and adjust the benchmark price; a set number of short-term cycles is defined as a long-term cycle; (3) Steps for customer grading pricing: Calculate a customer quality score based on the customer consumption frequency, consumption amount, and loyalty and divide the score range, divide the customer grades according to the score range and match different discounts; that is, each of the customer grades corresponds to a different grade price.

[0231] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A venue gradient pricing method based on real-time dynamic data, characterized in that: include: (1) Steps for short-term ticket pricing adjustments: The short-term adjustment coefficient is calculated based on the horizontal ordering rate in the same time period within the cycle and the vertical ordering rate in different time periods within the same day. The base price is dynamically adjusted through the short-term adjustment coefficient based on the proportion of ticket sales time to total sales time. (2) Steps for long-term ticket pricing adjustment: On the basis of short-term ticket pricing adjustments, the long-term adjustment coefficient is calculated based on the median of the total ticket sales time and the average of the ordering rate in multiple short-term cycles to adjust the base price; (3) Steps for customer-tiered pricing: The customer quality score is calculated based on the customer's consumption frequency, consumption amount and loyalty and divided into score intervals. The customer level is divided into different levels according to the score intervals and differentiated discounts are matched; that is, each customer level corresponds to a different level price.

2. The venue gradient pricing method based on real-time dynamic data according to claim 1 is characterized in that: Specific steps for short-term ticket pricing adjustments include: S11. After each cycle, each independent benchmark price is adjusted once; the steps are as follows: SS11. Select the base price that needs to be adjusted and determine whether the corresponding site has been sold; SS12, obtaining the horizontal ordering rate, vertical ordering rate, single ticket selling time and total ticket selling time of the venue in each time period in the current cycle; SS13. Calculate the horizontal adjustment coefficient and the vertical adjustment coefficient based on the horizontal ordering rate and the vertical ordering rate, and calculate the comprehensive adjustment coefficient in combination with the set maximum adjustment range value; SS14. Calculate the price adjustment ratio based on the comprehensive adjustment coefficient and the site sales status; SS15, adjust the base price based on the price adjustment ratio; S12. After a set round of adjustments, determine the short-term optimal pricing plan.

3. The venue gradient pricing method based on real-time dynamic data according to claim 2 is characterized in that: In SS13, the calculation formula is as follows: Horizontal adjustment factor ; Longitudinal adjustment factor ; Comprehensive adjustment coefficient ; In the formula, is the horizontal ordering rate within the period, is the vertical ordering rate within the period, is the maximum adjustment range value, The function is to find the maximum value of two numbers.

4. The venue gradient pricing method based on real-time dynamic data according to claim 3 is characterized in that: The steps of SS14 include: If the ticket is sold, the price increase ratio is calculated based on the comprehensive adjustment coefficient and the proportion of time a single ticket is sold; the formula is as follows: If the ticket is not sold, the price reduction ratio is calculated based on the comprehensive adjustment coefficient; the formula is as follows: In the formula, The time a single ticket is sold. is the total time of ticket sales, is the price increase ratio, The price reduction ratio.

5. The venue gradient pricing method based on real-time dynamic data according to claim 4 is characterized in that: The steps of SS15 include: The base price is adjusted based on the price increase ratio and the set minimum adjustment points. The formula is as follows: The base price is adjusted based on the price reduction ratio and the set minimum adjustment points. The formula is as follows: In the formula, is the minimum adjustment point, a is the time period number, n is the number within the cycle, The current base price for reserving a single site in the a-th time period on the n-th day, It is the base price after short-term adjustment.

6. The venue gradient pricing method based on real-time dynamic data according to claim 5 is characterized in that: Specific steps for long-term ticket pricing adjustments include: S21, collecting the total ticket selling time, the median of the total ticket selling time, the subscription rate and the average subscription rate for multiple short-term periods; S22. Calculate the long-term adjustment factor based on the difference between the order rate and the average order rate and the median of the total time of ticket sales; S23. Fine-tune the benchmark price based on the long-term adjustment coefficient to maximize long-term returns.

7. The venue gradient pricing method based on real-time dynamic data according to claim 6 is characterized in that: The long-term adjustment factor is calculated as follows: The formula for adjusting the base price is as follows: In the formula, is the order rate for the same venue and the same short period of time, is the average of the ordering rates for the same venue and the same short period of time, for the same The median of the total time of tickets sold, for the same The total time of ticket sales, is the long-term adjusted benchmark price. The current base price for reserving a single site for the a-th time period on the n-th day.

8. The venue gradient pricing method based on real-time dynamic data according to claim 7 is characterized in that: The steps of customer tiered pricing include: Obtain customer consumption frequency, consumption amount and loyalty data; Convert customer consumption frequency, consumption amount and loyalty data into rankings among all customers; Calculate the customer quality score Q according to the following formula; In the formula, Rank customer consumption frequency, Rank customers by spending amount within a set period of time, is the customer loyalty ranking, and N is the total number of customers.

9. A venue gradient pricing system based on real-time dynamic data, characterized in that: It includes short-term pricing adjustment module, long-term pricing adjustment module, customer screening and pricing module and data collection and analysis module; The short-term pricing adjustment module is used to execute the short-term ticket pricing adjustment step and dynamically adjust the base price. The short-term ticket pricing adjustment step includes: calculating the short-term adjustment coefficient according to the horizontal ordering rate in the same time period within the cycle and the vertical ordering rate in different time periods within the same day, and dynamically adjusting the base price through the short-term adjustment coefficient in combination with the proportion of ticket sales time to total sales time; the cycle is a short-term cycle, and the time length L is defined as a short-term cycle; The long-term pricing adjustment module is used to execute the long-term ticket pricing adjustment steps and optimize the long-term pricing strategy. The long-term ticket pricing adjustment steps include: on the basis of the short-term ticket pricing adjustment, based on the median of the sum of the ticket sales time of multiple short-term cycles and the average of the ordering rate, calculate the long-term adjustment coefficient and adjust the base price; a set number of short-term cycles is defined as a long-term cycle; The customer screening and pricing module is used to perform the steps of high-quality customer screening and pricing, and realize customer grading and differentiated pricing. The steps of customer grading pricing include: calculating the customer quality score and dividing the score interval according to the customer consumption frequency, consumption amount and loyalty, dividing the customer level according to the score interval and matching differentiated discounts; that is, each customer level corresponds to a different level price; The data collection and analysis module is used to collect ticket sales data and customer consumption data in real time, and provide data support for the short-term pricing adjustment module, long-term pricing adjustment module and customer screening and pricing module.

10. An electronic device, characterized in that: The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; the memory stores computer program instructions executable by the at least one processor, and the computer program instructions are executed by the at least one processor to enable the at least one processor to execute the venue gradient pricing method based on real-time dynamic data as described in any one of claims 1 to 8.

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