System and method for footwear market trend prediction

Abstract

The invention relates to the technical field of market analysis, in particular to a footwear market trend prediction system and method, and the system comprises a sales volume change analysis module, a time period influence analysis module, a promotion influence analysis module and a sales volume prediction evaluation module. According to the method, the change rate of the sales volume of the shoes can be effectively evaluated by analyzing the association between the sales volume data and the economic indexes, the specific influence of seasons and festivals on the sales volume is considered, the sales volume prediction is more suitable for the actual market condition by calculating the time period influence factors, and the sales promotion activity effect is analyzed. According to the method, the influence of the sales promotion activity on the sales volume can be evaluated, data support is provided for making an effective sales promotion strategy, the sales volume change trend is predicted through multi-dimensional analysis, the accuracy of market trend prediction is improved, resource allocation is optimized, an enterprise can more flexibly cope with market changes, and thus the market competitiveness and profitability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of market analysis, and in particular to a system and method for predicting the market trend of footwear. Background Art

[0002] The field of market analysis technology involves the use of various data analysis tools to predict market trends, evaluate consumer behavior, monitor competitor dynamics, and analyze price changes. Technologies in this field often combine statistics and big data technology to process and analyze large amounts of market information. Market analysis helps companies identify potential demand in target markets, optimize product positioning, develop market strategies, and effectively adjust marketing strategies.

[0003] Among them, the system for predicting footwear market trends is a system that analyzes and predicts the development trend of the footwear market. The system uses historical sales data, consumer preference surveys, and related market dynamics information to predict future market trends and help footwear manufacturers and retailers develop more accurate product development and marketing strategies. By applying machine learning models and data analysis tools, the system can analyze market trends for different types and styles of footwear products, predict changes in consumer demand, optimize inventory management and pricing strategies, and enhance market competitiveness.

[0004] Traditional systems focus on static data analysis and lack dynamic integration of market change factors. When dealing with seasonal changes and holiday promotions, traditional methods ignore the significant differences in consumer behavior in different time periods, such as peak consumption during holidays or promotions. This limitation of analysis will cause companies to respond to market changes in a timely or inaccurate manner, affecting the effectiveness of product strategies and inventory decisions, and affecting overall market performance and return on investment. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a system and method for predicting the market trend of footwear.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a system for predicting the market trend of footwear, the system comprising:

[0007] The sales change analysis module extracts the sales data of shoes within a period of time based on the sales data of shoes, analyzes the change in sales data, combines the changes in economic indicators and changes in consumer preferences, calculates the rate of change of shoe sales, and obtains the sales change evaluation results;

[0008] The time period impact analysis module analyzes the sales changes of shoes between the target season and the current season based on the historical sales data of the same type of shoes, evaluates the impact of the target season on shoe sales, analyzes the impact of festivals on shoe sales, calculates the time period impact factor, and obtains the target time period impact evaluation result;

[0009] The promotion impact analysis module calculates the discount impact factor based on the promotion data of the same style of shoes and the price of the shoes, evaluates the impact of the target activity discount on sales, and obtains the target discount impact evaluation result;

[0010] The sales forecast evaluation module is based on the sales change evaluation results, the target period impact evaluation results and the target discount impact evaluation results, calculates the shoe sales during the target activity period according to the current shoe sales, evaluates the degree of shoe sales change, and obtains the sales trend prediction result.

[0011] The present invention is improved in that the steps of obtaining the sales volume change evaluation result are:

[0012] Based on the sales data of the shoes, extract the sales data of the shoes within a period of time, sort the sales data in time series, mark the corresponding timestamp for each sales data, and obtain sales time series data;

[0013] Based on the sales time series data, combined with changes in economic indicators and changes in consumer preferences, the formula is:

[0014]

[0015] Calculate the sales change rate v of shoes, where s i is the sales volume on the i-th day, e i is the economic index of the i-th day, p i is the consumer preference index on the i-th day, w e is the weight of economic indicators, w p is the weight of consumer preference, n is the total number of data points, and v is the sales volume change rate;

[0016] Based on the sales volume change rate v, the degree of change in shoe sales volume is evaluated according to the positive or negative sign and size of the v value to obtain a sales volume change evaluation result.

[0017] The present invention is improved in that the method for evaluating the impact of the target season on shoe sales is:

[0018] Based on the historical sales data of the same type of shoes, extract the shoe sales data of the target season and the current season, and calculate the average of the daily sales data in the target season and the current season to obtain the seasonal average sales data;

[0019] Based on the seasonal average sales data, the formula:

[0020]

[0021] Calculate seasonal impact factor I s ,in, is the average sales volume in the target season, is the average sales volume of the current season, σ I is the standard deviation of sales volume between the two quarters, b I and d I is the adjustment coefficient, I s is the seasonal impact factor;

[0022] Based on the seasonal impact factor I s , according to I s The size of the value is used to evaluate the impact of the target season on shoe sales and obtain seasonal impact information.

[0023] The present invention is improved in that the steps of obtaining the target period impact assessment result are:

[0024] Extract the holiday sales data of the same type of shoes from the sales data, including the sales before, during and on the holiday, and calculate the average of the holiday and non-holiday sales data to obtain the holiday average sales data;

[0025] Based on the average sales data of the festival, the formula is:

[0026]

[0027] Calculate the festival impact factor I f ,in, is the average sales volume during the festival, is the average sales volume during non-holiday seasons, f is the festival impact factor, σ H is the standard deviation of the holiday sales data, C I is the weight coefficient;

[0028] Based on the festival impact factor I f and seasonal influence factor I s , through the formula:

[0029]

[0030] Calculation period impact factor I t , and obtain the impact assessment result of the target period, where λ I and E I is the adjustment coefficient, I f is the festival impact factor, I s is the seasonal influence factor, and e is the base of the natural logarithm.

[0031] The present invention is improved in that the steps of obtaining the target discount impact evaluation result are:

[0032] Based on the promotional data of the same style of shoes, the shoe sales before and after the promotional activities are extracted to obtain the activity sales data;

[0033] Based on the sales data of the activity, combined with the price changes of the activity promotion, the formula is:

[0034]

[0035] Calculate the discount impact factor D f , evaluate the impact of the target activity discount on sales, and obtain the target discount impact evaluation result, where S Δ is the sales volume change, P init is the price before the activity, P disc is the price during the promotion period, S init is the baseline sales volume before the activity, κ D and D is the adjustment coefficient, D f is the discount impact factor.

[0036] The present invention is improved in that the method for calculating the shoe sales volume during the target activity period is:

[0037] Based on the current sales data of shoes, the sales volume of shoes in the current period is obtained, and the sales volume data of the current period is obtained;

[0038] Based on the sales volume change assessment results, the target period impact assessment results and the target discount impact assessment results, combined with the current period sales volume data, the formula is:

[0039] S yc =(S dc +w v ·v·τ+w I I t )·(1+w D ·D f )

[0040] Calculate the shoe sales volume S during the target activity period yc , get sales forecast information, where S dc is the current sales volume, v is the sales volume change rate, τ represents the time distance between the target period and the current period, I t is the time period impact factor, D f is the discount impact factor, w v 、w I and w D is the weight coefficient, S yc is the sales volume of shoes during the target activity period.

[0041] The present invention is improved in that the shoe sales volume S based on the target activity period yc , through the formula:

[0042]

[0043] Calculate the sales volume change degree B, where S yc is the sales volume of shoes during the target activity period, S dc is the current sales volume, λ B is the adjustment coefficient, ρ B is the sales sensitivity coefficient, B is the degree of sales change;

[0044] Based on the sales volume change degree B, the sales volume change trend of the shoes is evaluated according to the size of the B value to obtain a sales volume trend prediction result.

[0045] A method for predicting the trend of the footwear market, wherein the method for predicting the trend of the footwear market is performed based on the system for predicting the trend of the footwear market, and comprises the following steps:

[0046] S1: Based on the sales data of shoes, analyze the change in sales data, combine the changes in economic indicators and consumer preferences, calculate the change rate of shoe sales, and obtain the sales change evaluation results;

[0047] S2: Based on the historical sales data of the same type of shoes, analyze the sales changes of shoes in the target season and the current season, evaluate the impact of the target season on shoe sales, and obtain seasonal impact information;

[0048] S3: Based on the seasonal impact information, analyze the impact of festivals on shoe sales, calculate the time period impact factor, and obtain the impact assessment result of the target time period;

[0049] S4: Based on the promotion data of the same style of shoes, calculate the discount impact factor, evaluate the impact of the target activity discount on sales, and obtain the target discount impact evaluation result;

[0050] S5: Based on the sales change evaluation results, the target period impact evaluation results and the target discount impact evaluation results, the shoe sales volume in the target activity period is calculated, the degree of shoe sales change is evaluated, and the sales trend prediction result is obtained.

[0051] Compared with the prior art, the advantages and positive effects of the present invention are:

[0052] In the present invention, by analyzing the correlation between sales data and economic indicators, the rate of change of shoe sales can be effectively evaluated. Taking into account the specific impact of seasons and festivals on sales, the sales forecast is made more in line with the actual market situation by calculating the time period impact factor. By analyzing the effectiveness of promotional activities, the impact of promotional activities on sales can be evaluated, providing data support for formulating effective promotional strategies. By predicting sales change trends through multi-dimensional analysis, the accuracy of market trend forecasts is improved, and resource allocation is optimized, enabling enterprises to respond to market changes more flexibly, thereby improving market competitiveness and profitability. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a system flow chart of the present invention;

[0054] Figure 2 A flow chart for obtaining sales volume change evaluation results for the present invention;

[0055] Figure 3 A flow chart for evaluating the impact of target season on shoe sales for the present invention;

[0056] Figure 4 A flow chart for obtaining the target period impact assessment results for the present invention;

[0057] Figure 5 A flow chart for obtaining a target discount impact assessment result for the present invention;

[0058] Figure 6 A flow chart for calculating shoe sales during a target activity period for the present invention;

[0059] Figure 7 The present invention is a flow chart for obtaining sales trend prediction results. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0061] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0062] See also Figure 1 The present invention provides a technical solution: a system for predicting the trend of the footwear market, the system comprising:

[0063] The sales change analysis module extracts the sales data of shoes within a period of time based on the sales data of shoes, analyzes the change in sales data, combines the changes in economic indicators and changes in consumer preferences, calculates the rate of change of shoe sales, and obtains the sales change evaluation results;

[0064] The time period impact analysis module analyzes the sales changes of shoes between the target season and the current season based on the historical sales data of the same type of shoes, evaluates the impact of the target season on shoe sales, analyzes the impact of festivals on shoe sales, calculates the time period impact factor, and obtains the target time period impact evaluation result;

[0065] The promotion impact analysis module calculates the discount impact factor based on the promotion data of the same style of shoes and the price of the shoes, evaluates the impact of the target activity discount on sales, and obtains the target discount impact evaluation result;

[0066] The sales forecast evaluation module calculates the sales volume of shoes in the target activity period according to the current shoe sales volume, evaluates the degree of change in shoe sales volume, and obtains the sales trend prediction result based on the sales volume change evaluation results, the target period impact evaluation results, and the target discount impact evaluation results.

[0067] The sales change evaluation results include the sales increase or decrease ratio and the corresponding shoe styles. The target period impact evaluation results include the impact of seasons on sales, the impact of holidays on sales and the degree of sales impact. The target discount impact evaluation results include the impact of differentiated discount levels on sales, the effective discount range and sales change information during the promotion period. The sales trend forecast results include the predicted shoe sales and the predicted sales fluctuation range.

[0068] See also Figure 2 , the steps to obtain the sales change evaluation results are:

[0069] Based on the sales data of shoes, extract the sales data of shoes within a period of time, sort the sales data in time series, mark the corresponding timestamp for each sales data, and obtain the sales time series data;

[0070] Based on sales time series data, combined with changes in economic indicators and changes in consumer preferences, the formula is:

[0071]

[0072] Calculate the sales change rate v of shoes, where s i is the sales volume on the i-th day, e i is the economic index of the i-th day, p i is the consumer preference index on the i-th day, w e is the weight of economic indicators, w p is the weight of consumer preference, n is the total number of data points, and v is the sales volume change rate;

[0073] Based on the sales change rate v, the degree of change in shoe sales is evaluated according to the positive or negative value and the size of the v value to obtain the sales change evaluation result.

[0074] formula:

[0075]

[0076] Parameter details and how to obtain them:

[0077] Sales i :The sales data of the i-th day can be obtained through the company's sales database, and the data is usually automatically recorded and summarized by the daily sales recording system.

[0078] Economic indicators i :The economic indicators on the i-th day, such as GDP growth rate or consumer confidence index, can be obtained from public economic research reports or official statistics released by the government. They are generally fixed values ​​and will not change significantly.

[0079] Consumer Preferences i : The consumer preference index on the i-th day, including the intensity of preference for a certain style or category of shoes, is usually obtained by market research companies through surveys. It is expressed in numerical form by counting the preference ratios of different types of shoes.

[0080] Weight w e and w p : The weight parameter is used to adjust the impact of economic indicators and consumer preferences on the rate of change of sales. The weight can be determined through statistical analysis of historical data, such as regression analysis, to ensure the accuracy of the forecast model.

[0081] Total number of data points n: This is the total number of days considered during the analysis period and is obtained directly from the dataset, i.e. the number of days for which sales were recorded.

[0082] Calculation example:

[0083] Set in a certain month (30 days), the daily sales volume has been collected i , economic indicators i , and consumer preference p i . Set the weight w e = 0.3 and w p =0.7, and the weight is obtained through regression analysis of historical data.

[0084] Select three days of data to calculate the sales volume change rate:

[0085] Day 1: s1=100, e1=0.04 (4% economic growth), p1=80 (preference index).

[0086] Day 2: s2=110, e2=0.03, p2=85.

[0087] Day 3: s3=105, e3=0.05, p3=90.

[0088] Calculation of sales volume change rate v:

[0089]

[0090] Calculate each term:

[0091] log(80)≈4.38

[0092] log(85)≈4.44

[0093]

[0094] Calculate the rate of change:

[0095]

[0096] The calculation example shows that the sales volume change rate v is 7.56, which indicates the changing trend of sales volume considering the influence of economic indicators and consumer preferences.

[0097] See also Figure 3 , the method to evaluate the impact of the target season on shoe sales is:

[0098] Based on the historical sales data of the same type of shoes, extract the shoe sales data of the target season and the current season, and calculate the average of the daily sales data in the target season and the current season to obtain the seasonal average sales data;

[0099] Based on seasonal average sales data, the formula:

[0100]

[0101] Calculate seasonal impact factor I s ,in, is the average sales volume in the target season, is the average sales volume of the current season, σ I is the standard deviation of sales volume between the two quarters, b I and d I is the adjustment coefficient, I s is the seasonal impact factor;

[0102] Based on seasonal impact factor I s , according to I s The size of the value is used to evaluate the impact of the target season on shoe sales and obtain seasonal impact information.

[0103] formula:

[0104]

[0105] Parameter details and how to obtain them:

[0106] Average sales volume and The average sales volume represents the average daily sales quantity of the target season and the current season respectively. The data is obtained by extracting all sales records of the corresponding season from the company's sales database and calculating their average.

[0107] Sales volume standard deviation σ I : A statistical parameter that measures the fluctuation of sales data. It is used to standardize sales differences to reduce the impact of other variables other than seasonality. It is obtained by calculating the standard deviation of all sales data in two seasons.

[0108] Adjustment coefficient b I and d I : The coefficients are used to adjust the effect of logarithmic and square root operations on the results, ensuring that the contribution in the formula is moderate and not too strong or too weak. The coefficients are derived from historical data analysis to optimize the model response.

[0109] Calculation example:

[0110] Set the parameters as follows:

[0111] The total sales volume of the target season is 3,000 pairs of shoes, and the total sales volume of the current season is 2,700 pairs of shoes. There are 30 days of sales records for each of the target season and the current season.

[0112] Calculation steps:

[0113] Calculate average sales volume:

[0114]

[0115] Set the standard deviation to σ I =15, adjustment coefficient b I =1.1,d I =0.15.

[0116] Calculate the seasonal impact factor using natural logarithm:

[0117]

[0118] Calculation results I s =9.2915 indicates that the target season has a greater positive impact on sales than the current season, which means that the sales performance of the target season exceeds that of the current season, indicating that the sales performance in the target season is better.

[0119] See also Figure 4 , the steps to obtain the impact assessment results during the target period are:

[0120] Extract the holiday sales data of the same type of shoes from the sales data, including the sales before, during and on the holiday, and calculate the average of the holiday and non-holiday sales data to obtain the holiday average sales data;

[0121] Based on the average holiday sales data, the formula:

[0122]

[0123] Calculate the festival impact factor I f ,in, is the average sales volume during the festival, is the average sales volume during non-holiday seasons, f is the festival impact factor, σ H is the standard deviation of the holiday sales data, C I is the weight coefficient;

[0124] Based on Festival Impact Factor I f and seasonal influence factor I s , through the formula:

[0125]

[0126] Calculation period impact factor I t , and obtain the impact assessment result of the target period, where λ I and E I is the adjustment coefficient, I f is the festival impact factor, I s is the seasonal influence factor, and e is the base of the natural logarithm.

[0127] formula:

[0128]

[0129] and

[0130]

[0131] Parameter details and how to obtain them:

[0132] Average sales volume during the festival is the average number of shoes sold during a particular holiday. This data is obtained by extracting all sales data during the holiday period from the sales database and calculating the average sales volume on each day.

[0133] Average sales during regular non-holiday periods It is the average sales volume during non-holiday periods. It is obtained by extracting and calculating the sales data during non-holiday periods.

[0134] The standard deviation of holiday sales data H : is a statistical indicator that measures the variability of holiday sales. It is obtained by calculating the standard deviation of holiday sales data and provides a measure of sales fluctuations.

[0135] C I, I and E I :C I is the weight coefficient used to adjust the influence of standard deviation in the calculation process, which is set by experts, I and E I It is an adjustment coefficient used to adjust the relative importance of festival impact factors and seasonal impact factors when calculating time period impact factors. This coefficient is determined based on regression analysis of past data.

[0136] Calculation example:

[0137] The setting data is as follows:

[0138] Holiday sales: 1,000 pairs of shoes, a total of 10 days Non-holiday sales: 8,000 pairs of shoes, a total of 100 days.

[0139] Calculate the average sales for holidays and non-holidays:

[0140]

[0141] Set the standard deviation of the holiday sales data to 15 pairs of shoes, and the coefficient C I =0.07,λ I =0.9, E I =1.1.

[0142] Calculate the festival impact factor I f and time period impact factor I t :

[0143] I f calculate:

[0144]

[0145] Setting I s =10, calculate I t :

[0146]

[0147] Calculation results I t =28.145 means that considering the combined effects of festivals and seasons, the sales volume of shoes increases by 28.145 pairs.

[0148] See also Figure 5 , the steps to obtain the target discount impact evaluation results are:

[0149] Based on the promotional data of the same style of shoes, the shoe sales before and after the promotional activities are extracted to obtain the activity sales data;

[0150] Based on the activity sales data, combined with the price changes of activity promotions, the formula is:

[0151]

[0152] Calculate the discount impact factor D f , evaluate the impact of the target activity discount on sales, and obtain the target discount impact evaluation result, where S Δ is the sales volume change, P init is the price before the activity, P disc is the price during the promotion period, S init is the baseline sales volume before the activity, κ D and D is the adjustment coefficient, D f is the discount impact factor.

[0153] formula:

[0154]

[0155] Parameter details and how to obtain them:

[0156] Sales volume change Δ : It is the difference between sales during and before the promotion. The calculation method is to extract the sales data before the promotion (for example, one month before the promotion) and during the promotion (for example, one week after the promotion) from the company's sales records, and then subtract the sales before the promotion from the sales during the promotion.

[0157] Price before event init : is the product price before the promotion begins. Get the pre-promotion price directly from the sales data.

[0158] Promotional period price P disc : This is the product price during the promotion. Also get the list price during the promotion from the sales data.

[0159] Baseline Sales Volume S init : This is the baseline sales volume before the event, providing a reference standard for evaluating the effectiveness of the promotion. It is obtained by analyzing the sales data of the same period before the event.

[0160] Adjustment coefficient κ D and D : Coefficients are used to adjust the weights of the factors that influence the price before and after the event. These coefficients are usually set based on historical data and market response to ensure that the calculation reflects actual market conditions.

[0161] Calculation

[0162] Set the following parameters:

[0163] Sales volume in the month before the event init The sales volume during the activity is 650 units per week. The price before the activity is P initis 100, and the price during the promotion period is P disc is 80, and the adjustment coefficient κ D =1.2 and ξ D =0.8.

[0164] Calculate sales volume change S Δ :

[0165] S Δ =650-500=150

[0166] Calculate the discount impact factor:

[0167]

[0168] Calculation result D f =0.168 means that the promotion increased sales by 16.8%. This figure helps companies understand the specific effects of promotion strategies so that they can adjust future sales strategies to increase efficiency and profitability.

[0169] See also Figure 6 , the method to calculate the shoe sales during the target activity period is:

[0170] Based on the current sales data of shoes, the sales volume of shoes in the current period is obtained, and the sales volume data of the current period is obtained;

[0171] Based on the sales volume change assessment results, the target period impact assessment results, and the target discount impact assessment results, combined with the current period sales volume data, the formula is:

[0172] S yc =(S dc +w v ·v·τ+w I I t )·(1+w D ·D f )

[0173] Calculate the shoe sales volume S during the target activity period yc , get sales forecast information, where S dc is the current sales volume, v is the sales volume change rate, τ represents the time distance between the target period and the current period, I t is the time period impact factor, D f is the discount impact factor, w v 、w I and w D is the weight coefficient, S yc is the sales volume of shoes during the target activity period.

[0174] formula:

[0175] S yc =(Sdc +w v ·v·τ+w I I t )·(1+w D ·D f )

[0176] Parameter details and how to obtain them:

[0177] Current Sales Volume dc :The average sales volume in the most recent period directly obtained from the company's sales system. It provides the baseline data for calculating future sales forecasts.

[0178] v: sales volume change rate, calculated in the previous step.

[0179] Time length τ: The time distance from the current period to the target period, in months. It is used to calculate the change in total sales during the forecast period.

[0180] I t : Time period impact factor, calculated through the previous steps.

[0181] D f : Discount impact factor, calculated in the previous step.

[0182] Weight coefficient w v , w I , w D : The weight coefficient is determined based on historical data and market analysis and is used to adjust the contribution of each factor in the total sales forecast. The weight coefficient helps to accurately adjust the impact of each variable on the final sales forecast.

[0183] Calculation example:

[0184] Set the following parameters:

[0185] Current Sales Volume dc is 1000 units, the sales volume change rate v is 50 units / month, the time length τ is 3 months, and the time period impact factor I t For 100 units, the discount impact factor is D f is 0.1, and the weight coefficient w v =0.5, w I =0.8, w D =1.0.

[0186] Calculate expected sales during the target event period:

[0187] S yc =(S dc +w v ·v·τ+w I I t )·(1+wD ·D f )

[0188] =(1000+0.5·50·3+0.8·100)·(1+1.0·0.1)

[0189] =(1000+75+80)·1.1

[0190] =1155·1.1

[0191] =1270.5

[0192] Calculation result S yc = 1270.5 units, indicating that considering the current sales volume, sales growth trend, additional impact of a specific period, and discount promotions, the sales volume in the target period is expected to increase to approximately 1270 units. This calculation helps companies predict future sales trends and provides data support for production planning and market strategies.

[0193] See also Figure 7 , the steps to obtain the sales trend forecast results are:

[0194] Shoe sales based on target activity period S yc , through the formula:

[0195]

[0196] Calculate the sales volume change degree B, where S yc is the sales volume of shoes during the target activity period, S dc is the current sales volume, λ B is the adjustment coefficient, ρ B is the sales sensitivity coefficient, B is the degree of sales change;

[0197] Based on the sales volume change degree B, according to the size of the B value, the sales volume change trend of the shoes is evaluated to obtain the sales volume trend prediction result.

[0198] formula:

[0199]

[0200] Parameter details and how to obtain them

[0201] Current Sales Volume dc :It is the average sales data of the most recent period directly extracted from the company's sales system. It provides a benchmark point for evaluating sales changes.

[0202] Target sales volume yc : Calculated by the previous steps.

[0203] Adjustment coefficient λ B: It is a coefficient derived from historical data analysis, which is used to adjust the sensitivity of current sales to the rate of change to adapt to different sales bases.

[0204] Sales sensitivity coefficient ρ B : Through in-depth analysis of past sales data, it helps to adjust the impact of changes in sales base on forecast results.

[0205] Calculation example:

[0206] Set the following parameters:

[0207] Current Sales Volume dc is 1000 units, and the predicted sales volume for the target period is S yc is 1200 units, adjustment factor λ B =0.05, sales sensitivity coefficient ρ B =0.02.

[0208] Calculate the percentage change in sales volume B:

[0209] Calculate the base sales change ratio:

[0210]

[0211] Calculate the logarithmic adjustment factor:

[0212] 1+λ B ·log(1+ρ B ·S dc )

[0213] =1+0.05·log(1+0.02·1000

[0214] =1+0.05·log(21)

[0215] =1+0.05·1.322=1.0661

[0216] Calculate B:

[0217] B=0.2·1.0661·100%

[0218] =21.322%

[0219] The calculation result B = 21.322%, which means that during the target activity period, sales are expected to increase by about 21.322% compared to the current level. The growth rate is adjusted through a logarithmic function to make the forecast more in line with actual market dynamics, providing companies with detailed forecasts of sales changes, which will help optimize future market strategies and inventory management.

[0220] The method for predicting the footwear market trend comprises the following steps:

[0221] S1: Based on the sales data of shoes, analyze the change in sales data, combine the changes in economic indicators and consumer preferences, calculate the change rate of shoe sales, and obtain the sales change evaluation results;

[0222] S2: Based on the historical sales data of the same type of shoes, analyze the sales changes of shoes in the target season and the current season, evaluate the impact of the target season on shoe sales, and obtain seasonal impact information;

[0223] S3: Based on the seasonal impact information, analyze the impact of festivals on shoe sales, calculate the time period impact factor, and obtain the impact evaluation result of the target time period;

[0224] S4: Based on the promotion data of the same style of shoes, calculate the discount impact factor, evaluate the impact of the target activity discount on sales, and obtain the target discount impact evaluation result;

[0225] S5: Based on the sales change evaluation results, the target period impact evaluation results and the target discount impact evaluation results, the shoe sales volume in the target activity period is calculated, the degree of change in shoe sales volume is evaluated, and the sales trend prediction results are obtained.

[0226] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A system for predicting footwear market trends, characterized in that: The system comprises: The sales change analysis module extracts the sales data of shoes within a period of time based on the sales data of shoes, analyzes the change in sales data, combines the changes in economic indicators and changes in consumer preferences, calculates the rate of change of shoe sales, and obtains the sales change evaluation results; The time period impact analysis module analyzes the sales changes of shoes between the target season and the current season based on the historical sales data of the same type of shoes, evaluates the impact of the target season on shoe sales, analyzes the impact of festivals on shoe sales, calculates the time period impact factor, and obtains the target time period impact evaluation result; The promotion impact analysis module calculates the discount impact factor based on the promotion data of the same style of shoes and the price of the shoes, evaluates the impact of the target activity discount on sales, and obtains the target discount impact evaluation result; The sales forecast evaluation module is based on the sales change evaluation results, the target period impact evaluation results and the target discount impact evaluation results, calculates the shoe sales during the target activity period according to the current shoe sales, evaluates the degree of shoe sales change, and obtains the sales trend prediction result.

2. The system for predicting footwear market trends according to claim 1, characterized in that: The steps for obtaining the sales volume change evaluation result are as follows: Based on the sales data of the shoes, extract the sales data of the shoes within a period of time, sort the sales data in time series, mark the corresponding timestamp for each sales data, and obtain sales time series data; Based on the sales time series data, combined with changes in economic indicators and changes in consumer preferences, the formula is: Calculate the sales change rate v of shoes, where s i is the sales volume on the i-th day, e i is the economic index of the i-th day, p i is the consumer preference index on the i-th day, w e is the weight of economic indicators, w p is the weight of consumer preference, n is the total number of data points, and v is the sales volume change rate; Based on the sales volume change rate v, the degree of change in shoe sales volume is evaluated according to the positive or negative sign and size of the v value to obtain a sales volume change evaluation result.

3. The system for predicting footwear market trends according to claim 1, characterized in that: The method for evaluating the impact of the target season on shoe sales is: Based on the historical sales data of the same type of shoes, extract the shoe sales data of the target season and the current season, and calculate the average of the daily sales data in the target season and the current season to obtain the seasonal average sales data; Based on the seasonal average sales data, the formula: Calculate seasonal impact factor I s ,in, is the average sales volume in the target season, is the average sales volume of the current season, σ I is the standard deviation of sales volume between the two quarters, b I and d I is the adjustment coefficient, I s is the seasonal impact factor; Based on the seasonal impact factor I s , according to I s The size of the value is used to evaluate the impact of the target season on shoe sales and obtain seasonal impact information.

4. The system for predicting the footwear market trend according to claim 3, characterized in that: The steps for obtaining the impact assessment results during the target period are as follows: Extract the holiday sales data of the same type of shoes from the sales data, including the sales before, during and on the holiday, and calculate the average of the holiday and non-holiday sales data to obtain the holiday average sales data; Based on the average sales data of the festival, the formula is: Calculate the festival impact factor I f ,in, is the average sales volume during the festival, is the average sales volume during non-holiday seasons, f is the festival impact factor, σ H is the standard deviation of the holiday sales data, C I is the weight coefficient; Based on the festival impact factor I f and seasonal influence factor I s , through the formula: Calculation period impact factor I t , and obtain the impact assessment result of the target period, where λ I and E I is the adjustment coefficient, I f is the festival impact factor, I s is the seasonal influence factor, and e is the base of the natural logarithm.

5. The system for predicting footwear market trends according to claim 1, characterized in that: The steps for obtaining the target discount impact evaluation result are as follows: Based on the promotional data of the same style of shoes, the shoe sales before and after the promotional activities are extracted to obtain the activity sales data; Based on the sales data of the activity, combined with the price changes of the activity promotion, the formula is: Calculate the discount impact factor D f , evaluate the impact of the target activity discount on sales, and obtain the target discount impact evaluation result, where S Δ is the sales volume change, P init is the price before the activity, P disc is the price during the promotion period, S init is the baseline sales volume before the activity, κ D and D is the adjustment coefficient, D f is the discount impact factor.

6. The system for predicting the footwear market trend according to claim 1, characterized in that: The method for calculating the shoe sales volume during the target activity period is: Based on the current sales data of shoes, the sales volume of shoes in the current period is obtained, and the sales volume data of the current period is obtained; Based on the sales volume change assessment results, the target period impact assessment results and the target discount impact assessment results, combined with the current period sales volume data, the formula is: S yc =(S dc +w v ·v·τ+w I ·I t )·(1+w D ·D f ) Calculate the shoe sales volume S during the target activity period yc , get sales forecast information, where S dc is the current sales volume, v is the sales volume change rate, τ represents the time distance between the target period and the current period, I t is the time period impact factor, D f is the discount impact factor, w v 、w I and w D is the weight coefficient, S yc is the sales volume of shoes during the target activity period.

7. The system for predicting the footwear market trend according to claim 6, characterized in that: The shoe sales volume S based on the target activity period yc , through the formula: Calculate the sales volume change degree B, where S yc is the sales volume of shoes during the target activity period, S dc is the current sales volume, λ B is the adjustment coefficient, ρ B is the sales sensitivity coefficient, B is the degree of sales change; Based on the sales volume change degree B, the sales volume change trend of the shoes is evaluated according to the size of the B value to obtain a sales volume trend prediction result.

8. A method for predicting footwear market trends, characterized in that: The system for predicting the footwear market trend according to any one of claims 1 to 7 comprises the following steps: Based on the sales data of shoes, analyze the change in sales data, combine the changes in economic indicators and consumer preferences, calculate the change rate of shoe sales, and obtain the sales change evaluation results; Based on the historical sales data of the same type of shoes, analyze the changes in shoe sales between the target season and the current season, evaluate the impact of the target season on shoe sales, and obtain seasonal impact information; Based on the seasonal impact information, analyze the impact of festivals on shoe sales, calculate the time period impact factor, and obtain the impact assessment result of the target time period; Based on the promotional data of the same style of shoes, calculate the discount impact factor, evaluate the impact of the target activity discount on sales, and obtain the target discount impact evaluation result; Based on the sales change evaluation results, the target period impact evaluation results and the target discount impact evaluation results, the shoe sales during the target activity period are calculated, the degree of shoe sales change is evaluated, and the sales trend prediction results are obtained.

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