Toy sales management method based on big data analysis

Abstract

The invention discloses a toy sales management method based on big data analysis, and relates to the technical field of big data analysis, and the method specifically comprises the steps: 1, dividing the types of toys, and collecting the actual sales data of different types of toys through different sales channels; the method comprises the following steps: step 1, dividing toy types according to preferences of toy consumer groups for different toys at different age groups, step 2, according to historical sales data, setting sales value functions of different toy types for each sales channel at different time periods; the method comprises the following steps: step 1, determining the sales condition of each toy type in different sales channels in each time period and the condition that the sales value of the toys changes along with time, and step 2, determining the storage quantity of all toy types in each sales channel in different time periods according to the demand of the sales channels; according to the method, the target consumer group is positioned to divide the toy types, so that enterprises can adjust sales management methods of different toy types in time.

Description

Technical Field

[0001] The present invention belongs to the technical field of sales management, and in particular relates to a toy sales management method based on big data analysis. Background Art

[0002] In the modern market environment, companies generate a large amount of sales data every day, including sales volume, sales volume, customer feedback, market trends, etc. These data contain rich market information and consumer behavior patterns, providing companies with valuable decision-making basis.

[0003] The emergence of data technology enables enterprises to efficiently process and analyze massive amounts of data and dig out valuable information and patterns. Through big data technology, enterprises can achieve real-time monitoring, accurate prediction and intelligent decision-making of sales data, thereby optimizing sales strategies and improving sales efficiency.

[0004] It is very necessary to use big data analysis and calculation to help companies predict the sales of various toy types in different sales channels in different time periods, and determine the reasonable inventory level of each toy type based on this. Summary of the invention

[0005] The purpose of the present invention is to provide a toy sales management method based on big data analysis, which is used to solve the technical problem of determining the reasonable inventory of different toy types in the prior art.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: Step 1: Classify toys into types and collect actual sales data of different types of toys through different sales channels; classify toys into types according to the preferences of toy consumers for different toys at different age groups, and number each toy type in order of age. Toys designed for groups between a and b and between b and c are numbered and marked as type 1 toys and type 2 toys, respectively. In this way, all toy types classified by age are determined, and the toy type number set is recorded as , i is a constant, where c>b>a, and the sales channels are numbered according to different sales methods. The sales methods are specifically divided into online sales and offline sales. The online platforms that use the online sales method for sales management are classified and numbered according to different online platforms that use the offline sales method for sales management. The sales channel number set is recorded as , k is a constant, and historical sales data on all the above toy types and sales channels are collected.

[0007] Step 2. Based on the historical sales data, set up the sales value function of different toy types in different time periods for each sales channel; determine the duration T as a cycle, divide each cycle into m time periods, and the duration of each time period is t. Collect the historical sales data of different types of toys through sales channels within n cycle durations, and determine the best sales channels for each type of toys based on the historical sales data.

[0008] The historical sales data includes: historical sales damage rate, historical sales profit, historical sales popularity and historical return and exchange rate. The sales value function is established by integrating the historical sales damage rate, historical sales profit, historical sales popularity and historical return and exchange rate. The specific expression is as follows: F i k ( x ) = [ fb i k ( x )* ( fc i k ( x ) − fd i k ( x ) ) 1 + fa i k ( x ) ] ; Among them, x represents the xth period of time, i represents the i-th toy type, and k represents the k-th sales channel. represents the sales value of toy type i in the sales process of sales channel k during the x period of time, represents the sales damage rate of toy type i sold in sales channel k in time period x, represents the sales profit of toy type i in sales channel k in time period x, It indicates the sales popularity of toy type i in sales channel k during time period x. It represents the return rate of toy type i sold in sales channel k in time period x.

[0009] Among them, the sales damage rate refers to the percentage of damaged toys to the total number of toys in the process of being purchased by consumers from manufacturers through sales channels; Sales profit refers to the remainder of the total sales amount of toys minus the costs incurred during the sales process. The costs include: production costs and sales channel costs. Production costs refer to the sum of all costs incurred in the toy's non-sales process, such as copyright fees, promotion fees, and design fees. Sales channel costs refer to the sum of costs incurred in the toy's sales process, such as sales channel usage fees, transportation fees, and storage fees during the sales process. Sales heat refers to the purchase heat of toys among consumer groups within a period of time t. The sales heat is determined by combining the average sales rate and the frequency of repeated purchases of toys within a period of time t. The specific formula for sales heat is as follows: ; Where N refers to the sales volume of toy type i through sales channel k in time period x, M refers to the total number of people who purchased toy type i through sales channel k in time period x, m refers to the number of people who purchased multiple toys of toy type i through sales channel k in a single purchase in time period x, and n refers to the average number of toys purchased by people who purchased multiple toys in a single purchase during the sales process of toy type i through sales channel k in time period x; The return and exchange rate refers to the percentage of returns and exchanges due to problems with toy quality or sales process.

[0010] Through the sales value function, the sales situation of toy type i in different sales channels in each time period is determined, as well as the change in toy sales value over time during the sales process of toy type i through different sales channels.

[0011] Step 3: According to the sales channel demand, determine the storage quantity of all toy types in each sales channel in different time periods; determine the sales demand of different sales channels, which includes: multi-type sales demand and single-type sales demand. Multi-type sales demand means that the sales channel needs multiple types of toys for sale, and single-type sales demand means that the sales channel only needs one type of toy for sale; For a sales channel R with multiple types of sales demands, the range of toy types sold is determined based on the demand of R. , record this range as the sales range of R, calculate the sales value of each toy type that belongs to the sales range of R in each time period, and determine the storage quantity proportion of different toy types in this sales channel in each time period through the sales value. The specific formula is as follows: ; Among them, r represents Types of toys in the range, represents the sales value share of toy type r by sales channel R in time period x, It represents the weight coefficient of toy type r in sales channel R. The weight coefficient is determined according to the specific situation and needs, and is usually jointly formulated and confirmed by professionals or relevant stakeholders. According to the above formula, the sales value proportion of all toy types within the sales scope of sales channel R is determined, and the total toy storage quantity of sales channel R in time period x is determined as OR(x). OR(x) is multiplied by the sales value proportion of each toy type to obtain the required storage quantity of each toy type in sales channel R in time period x. The specific formula for the total toy storage quantity is OR(x) as follows: ; in, represents the weight coefficient of sales channel R for time period x, Represents the total sales quantity of toy type r through sales channel R in the x-1 time period.

[0012] For a sales channel E with a single type of sales demand, determine the toy type e sold through sales channel E, and determine the total toy storage quantity of sales channel E in time period x as OE(x). The specific formula for the total toy storage quantity OE(x) is as follows: ; The above formula is used to determine the quantity of toy type e that sales channel E should stock in each time period.

[0013] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. The present invention divides toys into types by locating target consumer groups, and constructs sales value functions of different toy types in different sales channels in different time periods based on historical sales data. The sales value function is used to determine the changing patterns of sales of each toy type over time and channels, providing a scientific basis for enterprises to formulate sales strategies; 2. The present invention establishes a sales value function by setting a sales heat function, which helps to analyze the sales of each toy type in different sales channels in different time periods, and accordingly determines the reasonable inventory level of each toy type in different sales channels, thereby reducing inventory costs, reducing out-of-stock risks, and improving capital utilization efficiency, so that enterprises can better adapt to market changes and improve market competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 A step diagram of a toy sales management method based on big data analysis is shown; Figure 2 A flow chart of a method for determining the inventory quantity of each toy type in a sales channel is shown. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] like Figure 1 , Figure 2 As shown, the toy sales management method based on big data analysis provided in this embodiment specifically includes the following steps: Step 1: Classify toys into types and collect actual sales data of different types of toys through different sales channels; classify toys into types according to the preferences of toy consumers for different toys at different age groups, and number each toy type in order of age. Toys designed for groups between a and b and between b and c are numbered and marked as type 1 toys and type 2 toys, respectively. In this way, all toy types classified by age are determined, and the toy type number set is recorded as , i is a constant, where c>b>a, and the sales channels are numbered according to different sales methods. The sales methods are specifically divided into online sales and offline sales. The sales channels are classified and numbered according to the different online platforms that use the online sales method for sales management, and the sales channels are classified and numbered according to the different offline platforms that use the offline sales method for sales management. For example, a certain type of toy is sold through online stores A and store B on different e-commerce platforms, and specialty stores C and D in different offline regions. A, B, C, and D are marked as 1, 2, 3, and 4 respectively, and the sales channel number set is recorded as , k is a constant, and historical sales data on all the above toy types and sales channels are collected.

[0018] Step 2. According to the historical sales data, set the sales value function of different types of toys in different time periods for each sales channel; determine the duration T as a cycle duration, divide each cycle duration into m time periods, and the duration of each time period is t. Collect the historical sales data of different types of toys through the sales channels within n cycle durations, and determine the best sales channel for each type of toys based on the historical sales data. In this embodiment, the cycle duration is selected as 360 days, which is divided into 12 time periods by month. The duration of each time period is 30 days, and a total of 3 cycle durations are collected.

[0019] The historical sales data includes: historical sales damage rate, historical sales profit, historical sales popularity and historical return and exchange rate. The sales value function is established by integrating the historical sales damage rate, historical sales profit, historical sales popularity and historical return and exchange rate. The specific expression is as follows: F i k ( x ) = [ fb i k ( x )* ( fc i k ( x ) − fd i k ( x ) ) 1 + fa i k ( x ) ] ; Among them, x represents the xth period of time, i represents the i-th toy type, and k represents the k-th sales channel. represents the sales value of toy type i in the sales process of sales channel k during the x period of time, represents the sales damage rate of toy type i sold in sales channel k in time period x, represents the sales profit of toy type i in sales channel k in time period x, It indicates the sales popularity of toy type i in sales channel k during time period x. It represents the return rate of toy type i sold in sales channel k in time period x.

[0020] Among them, the sales damage rate refers to the percentage of damaged toys to the total number of toys in the process of being purchased by consumers from manufacturers through sales channels; Sales profit refers to the remainder of the total sales amount of toys minus the costs incurred during the sales process. The costs include: production costs and sales channel costs. Production costs refer to the sum of all costs incurred in the toy's non-sales process, such as copyright fees, promotion fees, and design fees. Sales channel costs refer to the sum of costs incurred in the toy's sales process, such as sales channel usage fees, transportation fees, and storage fees during the sales process. Sales heat refers to the purchase heat of toys among consumer groups within a period of time t. The sales heat is determined by combining the average sales rate and the frequency of repeated purchases of toys within a period of time t. The specific formula for sales heat is as follows: ; Where N refers to the sales volume of toy type i through sales channel k in time period x, M refers to the total number of people who purchased toy type i through sales channel k in time period x, m refers to the number of people who purchased multiple toys of toy type i through sales channel k in a single purchase in time period x, and n refers to the average number of toys purchased by people who purchased multiple toys in a single purchase during the sales process of toy type i through sales channel k in time period x; The return and exchange rate refers to the percentage of returns and exchanges due to problems with toy quality or sales process.

[0021] Through the sales value function, the sales situation of toy type i in different sales channels in each time period is determined, as well as the change in toy sales value over time during the sales process of toy type i through different sales channels.

[0022] Step 3: According to the sales channel demand, determine the storage quantity of all toy types in each sales channel in different time periods; determine the sales demand of different sales channels, which includes: multi-type sales demand and single-type sales demand. Multi-type sales demand means that the sales channel needs multiple types of toys for sale, and single-type sales demand means that the sales channel only needs one type of toy for sale; For a sales channel R with multiple types of sales demands, the range of toy types sold is determined based on the demand of R. , record this range as the sales range of R, calculate the sales value of each toy type that belongs to the sales range of R in each time period, and determine the storage quantity proportion of different toy types in this sales channel in each time period through the sales value. The specific formula is as follows: ; Among them, r represents Types of toys in the range, represents the sales value share of toy type r by sales channel R in time period x, It represents the weight coefficient of toy type r in sales channel R. The weight coefficient is determined according to the specific situation and needs, and is usually jointly formulated and confirmed by professionals or relevant stakeholders. According to the above formula, the sales value proportion of all toy types within the sales scope of sales channel R is determined, and the total toy storage quantity of sales channel R in time period x is determined as OR(x). OR(x) is multiplied by the sales value proportion of each toy type to obtain the required storage quantity of each toy type in sales channel R in time period x. The specific formula for the total toy storage quantity is OR(x) as follows: ; in, represents the weight coefficient of sales channel R for time period x, Represents the total sales quantity of toy type r through sales channel R in the x-1 time period.

[0023] For a sales channel E with a single type of sales demand, determine the toy type e sold through sales channel E, and determine the total toy storage quantity of sales channel E in time period x as OE(x). The specific formula for the total toy storage quantity OE(x) is as follows: ; The above formula is used to determine the quantity of toy type e that sales channel E should stock in each time period.

[0024] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0025] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A toy sales management method based on big data analysis, characterized in that: include: Step 1: Classify toys by type and collect actual sales data of different types of toys through different sales channels; classify toys by type according to the preferences of toy consumers at different age groups for different toys; Step 2: Based on the historical sales data, set the sales value function of different toy types for each sales channel in different time periods; determine the sales of each toy type in different sales channels in each time period, and the change of toy sales value over time; Step 3. Determine the storage quantity of all toy types in each sales channel in different time periods based on sales channel demand; determine the sales demand of different sales channels, and for sales channels with different sales demand, determine the storage quantity of different toy types in each time period by calculating the sales value of each toy type within the sales scope of the sales channel in each time period.

2. A toy sales management method based on big data analysis according to claim 1, characterized in that: Toy types are divided according to the preferences of toy consumer groups for different toys at different age groups. The specific method is as follows: Toy types are divided according to the preferences of toy consumer groups for different toys at different age groups, and each toy type is numbered and marked in order of age. Toys designed for groups between a and b years old and between b and c years old are numbered and marked as type i toys and type i+1 toys respectively. The toy type number set is recorded as , i is a constant, where c>b>a.

3. A toy sales management method based on big data analysis according to claim 1, characterized in that: Set the sales value function for different toy types in different time periods for each sales channel. The specific method is: Determine the duration of historical sales data and divide it into multiple time periods according to the duration. The historical sales data includes: historical sales damage rate, historical sales profit, historical sales popularity and historical return and exchange rate. According to the historical sales damage rate, historical sales profit, historical sales popularity and historical return and exchange rate in each time period, a sales value function is established. The sales value function uses the formula Indicates, where x represents the xth period of time, i represents the i-th toy type, and k represents the k-th sales channel. represents the sales value of toy type i in the sales process of sales channel k during the x period of time, represents the sales damage rate of toy type i sold in sales channel k in time period x, represents the sales profit of toy type i in sales channel k in time period x, It indicates the sales popularity of toy type i in sales channel k during time period x. It represents the return rate of toy type i sold in sales channel k in time period x.

4. A toy sales management method based on big data analysis according to claim 3, characterized in that: Sales heat specifically includes the following: Using the formula Determine the sales heat, where N refers to the sales volume of toy type i through sales channel k in time period x, M refers to the total number of buyers of toy type i through sales channel k in time period x, m refers to the number of buyers who purchase multiple toys of toy type i at a time through sales channel k in time period x, and n refers to the average number of toys purchased by buyers who purchase multiple toys at a time during the sales process of toy type i through sales channel k in time period x.

5. The toy sales management method based on big data analysis according to claim 1 is characterized in that: Determine sales demand for different sales channels by: According to the demands of different sales channels for different types of toys, the sales demand is divided into multi-type sales demand and single-type sales demand. Multi-type sales demand means that the sales channel needs multiple types of toys to sell, and single-type sales demand means that the sales channel only needs one type of toys to sell.

6. The toy sales management method based on big data analysis according to claim 1 is characterized in that: Determine the number of toys to store for each time period by: For sales channels with multiple types of sales needs, by determining the range of toy types that the sales channel needs to sell, calculate the sales value of different toy types in each time period within the toy type range, determine the proportion of the storage quantity of different toy types in the sales channel in each time period based on the sales value, and determine the total storage quantity of toys for the sales channel with multiple types of sales needs in the current time period based on the sales quantity and sales value of different toy types in the previous time period; For a sales channel with a single type of sales demand, determine the type of toys that the sales channel needs to sell, and determine the total toy storage quantity of the sales channel with a single type of sales demand in the current time period based on the sales quantity of the toy type in the previous time period.

7. A toy sales management method based on big data analysis according to claim 6, characterized in that: The sales value is used to determine the proportion of different toy types in the sales channel in each time period. The specific method is as follows: For a sales channel R with multiple types of sales demands, the range of toy types sold is determined based on the demand of R. , record this range as the sales range of R, calculate the sales value of each toy type within the sales range of R in each time period, and use the formula Determine the proportion of different toy types in the sales channel during each time period, where r represents Types of toys in the range, represents the sales value share of toy type r by sales channel R in time period x, Represents the weight coefficient of toy type r in sales channel R.

8. A toy sales management method based on big data analysis according to claim 6, characterized in that: Determine the total toy inventory quantity for a sales channel with multiple types of sales demand in the current time period by: Determine the total number of toys stored in sales channel R during time period x as OR(x), multiply OR(x) by the sales value share of each toy type to obtain the number of toys that should be stored in sales channel R during time period x, and use the formula represents the total number of toys stored, where represents the weight coefficient of sales channel R for time period x, Represents the total sales quantity of toy type r through sales channel R in the x-1 time period.

9. A toy sales management method based on big data analysis according to claim 6, characterized in that: Determine the total toy inventory quantity of a sales channel for a single type of sales demand in the current time period by: For a sales channel E with a single type of sales demand, determine the type of toys e sold through sales channel E, using the formula Denote the total number of toys stored by sales channel E in time period x as OE(x), and determine the number of toys of type e that sales channel E should store in each time period.

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