Offline store commodity sales volume prediction method

Abstract

The invention discloses an offline store commodity sales volume prediction method, which comprises the following steps: A, widely and accurately extracting sales, inventory and member data of at least one year from a store system, comprehensively collecting external data, and carefully cleaning abnormal values and missing values in the sales data by using an intelligent algorithm to ensure the accuracy and integrity of the data, building a foundation firmly for subsequent analysis; b, feature construction: extracting sales time, commodity attributes and associated features, combining various features, screening effective combinations, and unifying dimension processing data; the system is accurate in data processing and model construction, achieves accurate prediction of sales volume, helps stores to reduce cost and increase benefits, optimizes operation, improves benefits and efficiency, guarantees supply, excavates commodity features, positions customers, enhances competitiveness, adapts the model to market changes, improves market shares, guarantees safety and reputation based on prediction and control of cost, profit and data encryption, promotes stable development of the stores, and has a good market prospect. And the comprehensive strength is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of offline store sales forecasting, and in particular to an offline store commodity sales forecasting method. Background Art

[0002] An offline store refers to a commercial place set up in a physical space for displaying, selling goods or providing services. It is an important carrier of traditional commercial activities, corresponding to the online e-commerce platform. For example, street clothing stores, supermarkets, restaurants, barber shops, etc. all fall into the category of offline stores.

[0003] At present, offline stores are unable to accurately predict product sales, which is crucial to their operations. Traditionally, they rely on experience and simple statistics, which has many problems. On the one hand, there are many factors that affect sales, such as weather, holidays, competition, economic conditions, and changes in consumer preferences. Simple analysis of historical sales data alone cannot fully consider these factors. On the other hand, traditional data recording and analysis methods are inefficient and inaccurate, making it difficult to process large-scale multi-dimensional data and unable to explore potential patterns. In addition, for emerging stores or fast-changing industries, traditional methods are limited in their effectiveness due to the lack of historical data. To this end, we propose a method for predicting product sales in offline stores. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a method for predicting the sales volume of goods in offline stores, which has the advantage of accurate prediction and solves the problem that offline stores are currently unable to accurately predict the sales volume of goods, which is crucial to their operations. Traditionally, they rely on experience and simple statistics, and there are many problems. On the one hand, there are many factors affecting sales volume, such as weather, holidays, competition, economic situation, and changes in consumer preferences, which cannot be fully considered by simple analysis of historical sales data. On the other hand, traditional data recording and analysis methods are inefficient and inaccurate, making it difficult to process large-scale multi-dimensional data and unable to explore potential rules. In addition, for emerging stores or fast-changing industries, the effectiveness of traditional methods is limited due to the lack of historical data.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for predicting the sales volume of offline store products, comprising the following steps: A. We extensively and accurately extract at least one year of sales, inventory, and membership data from the store system. We also comprehensively collect external data and use intelligent algorithms to carefully clean up outliers and missing values ​​in the sales data to ensure the accuracy and completeness of the data, laying a solid foundation for subsequent analysis. B. Feature construction: extract sales time, product attributes and related features, combine various features and select effective combinations, and unify the dimensions to process data; C. Model training: Carefully select ARIMA or appropriate machine learning models based on data characteristics, reasonably divide the training set and validation set to conduct rigorous training and fine-tune parameters, and skillfully use regularization and other advanced technologies to prevent model overfitting and improve model stability and generalization ability; D. Model evaluation: Use professional indicators such as MSE and MAE to accurately evaluate the performance of the model on the validation set, use visual charts to intuitively present data features and model prediction results, and use multiple rounds of cross-validation to deeply explore the improvement direction of the model and continuously optimize the model effect; E. Forecasting and decision-making: Use the trained and optimized model to accurately make sales forecasts for 1-4 weeks and 1 year. Based on the forecast results, formulate detailed inventory management, employee scheduling, marketing strategies and procurement plans in close combination with the actual store situation to improve store operating efficiency in all aspects.

[0006] Preferably, in the data collection and cleaning step, for the promotional activity information in the sales data, the type of promotion, start and end time, intensity and participating products are recorded in detail, and the impact on sales is subsequently analyzed.

[0007] Preferably, in the feature construction step, the applicable scenarios and target customer group characteristics of the product are further subdivided according to the product attribute characteristics, and incorporated into the feature combination.

[0008] Preferably, in the model training step, a cross-validation method is used to select the optimal model hyperparameter combination to improve the generalization ability of the model.

[0009] Preferably, in the forecasting decision step, differentiated inventory replenishment strategies are formulated based on sales forecast results and inventory turnover rates of different commodities.

[0010] Preferably, a regular data update and model retraining mechanism is established to adapt to market changes and ensure the accuracy of predictions.

[0011] Preferably, in the forecasting decision step, a flexible procurement plan is formulated based on long-term sales forecasts and supplier cooperation to reduce procurement costs.

[0012] Preferably, during the entire sales forecasting process, the data is encrypted to ensure data security and privacy.

[0013] Compared with the prior art, the present invention provides a method for predicting the sales volume of offline store products, which has the following beneficial effects: The data processing and model building of the present invention are accurate, and accurate sales forecasts can be achieved, helping stores reduce costs and increase efficiency, optimize operations, improve benefits and efficiency, ensure supply, explore product features, locate customers, enhance competitiveness, and adapt models to market changes, increase market share, control costs and ensure profits based on forecasts, and use data encryption to ensure security and reputation, thereby promoting stable development of stores and improving their overall strength. DETAILED DESCRIPTION

[0014] The following will be combined with 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.

[0015] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0016] The present invention provides a method for predicting sales volume of offline store commodities, comprising the following steps: A. We extensively and accurately extract at least one year of sales, inventory, and membership data from the store system. We also comprehensively collect external data and use intelligent algorithms to carefully clean up outliers and missing values ​​in the sales data to ensure the accuracy and completeness of the data, laying a solid foundation for subsequent analysis. B. Feature construction: extract sales time, product attributes and related features, combine various features and select effective combinations, and unify the dimensions to process data; C. Model training: Carefully select ARIMA or appropriate machine learning models based on data characteristics, reasonably divide the training set and validation set to conduct rigorous training and fine-tune parameters, and skillfully use regularization and other advanced technologies to prevent model overfitting and improve model stability and generalization ability; D. Model evaluation: Use professional indicators such as MSE and MAE to accurately evaluate the performance of the model on the validation set, use visual charts to intuitively present data features and model prediction results, and use multiple rounds of cross-validation to deeply explore the improvement direction of the model and continuously optimize the model effect; E. Forecasting and decision-making: Use the trained and optimized model to accurately make sales forecasts for 1-4 weeks and 1 year. Based on the forecast results, formulate detailed inventory management, employee scheduling, marketing strategies and procurement plans in close combination with the actual store situation to improve store operating efficiency in all aspects.

[0017] In the data collection and cleaning steps, for the promotional information in the sales data, record the type, start and end time, intensity and participating products of the promotion in detail, and then analyze its impact on sales.

[0018] In the feature construction step, the applicable scenarios and target customer group characteristics of the product are further subdivided according to the product attribute characteristics and incorporated into the feature combination.

[0019] In the model training step, the cross-validation method is used to select the optimal combination of model hyperparameters to improve the generalization ability of the model.

[0020] In the forecasting decision-making step, differentiated inventory replenishment strategies are formulated based on the sales forecast results and inventory turnover rates of different commodities.

[0021] Establish a regular data update and model retraining mechanism to adapt to market changes and ensure the accuracy of forecasts.

[0022] In the forecasting and decision-making step, flexible procurement plans are developed based on long-term sales forecasts and supplier cooperation to reduce procurement costs.

[0023] During the entire sales forecasting process, data is encrypted to ensure data security and privacy.

[0024] Importantly, it should be noted that the construction and arrangement of the present application shown in a number of different exemplary embodiments are only exemplary. Although only a few embodiments are described in detail in this disclosure, it should be readily understood by those who refer to this disclosure that many modifications are possible (e.g., the size, scale, structure, shape and proportion of various elements, and parameter values ​​(e.g., temperature, pressure, etc.), mounting arrangements, use of materials, color, changes in orientation, etc.) without substantially departing from the novel teachings and advantages of the subject matter described in the application. For example, an element shown as integrally formed may be composed of multiple parts or elements, the position of the element may be inverted or otherwise changed, and the nature or number or position of the discrete element may be altered or changed. Therefore, all such modifications are intended to be included within the scope of the present invention. The order or sequence of any process or method steps may be changed or reordered according to an alternative embodiment. In the claims, any "device plus function" clause is intended to cover the structure described herein that performs the function, and is not only structurally equivalent but also equivalent structure. Other substitutions, modifications, changes and omissions may be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present invention. Therefore, the invention is not limited to a specific embodiment, but extends to numerous modifications still falling within the scope of the appended claims.

[0025] Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment may not be described (ie, those features that are not relevant to the best mode presently contemplated for carrying out the invention or those features that are not relevant to implementing the invention).

[0026] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the essence and scope of the technical solution of the present invention.

Claims

1. A method for predicting sales volume of offline store products, characterized in that: The following steps are involved: A. Data collection and cleaning: We will widely and accurately extract at least one year of sales, inventory and membership data from the store system, and comprehensively collect external data. We will use intelligent algorithms to carefully clean up outliers and missing values ​​in the sales data to ensure the accuracy and completeness of the data, laying a solid foundation for subsequent analysis. B. Feature construction: Deeply explore the periodicity of sales time and the characteristics of special time periods, carefully analyze product attributes and expand the dimensions of related features, carefully combine various features and use scientific methods to select the most valuable effective combination, use standardized means to unify the dimensions of data processing, and make data features more expressive and predictive; C. Model training: Carefully select ARIMA or appropriate machine learning models based on data characteristics, reasonably divide the training set and validation set to conduct rigorous training and fine-tune parameters, and skillfully use regularization and other advanced technologies to prevent model overfitting and improve model stability and generalization ability; D. Model evaluation: Use professional indicators such as MSE and MAE to accurately evaluate the performance of the model on the validation set, use visual charts to intuitively present data features and model prediction results, and use multiple rounds of cross-validation to deeply explore the improvement direction of the model and continuously optimize the model effect; E. Forecasting and decision-making: Use the trained and optimized model to accurately make sales forecasts for 1-4 weeks and 1 year. Based on the forecast results, formulate detailed inventory management, employee scheduling, marketing strategies and procurement plans in close combination with the actual store situation to improve store operating efficiency in all aspects.

2. The offline store commodity sales prediction method according to claim 1, characterized in that: In the data collection and cleaning steps, for the promotional activity information in the sales data, the type, start and end time, intensity and participating products of the promotion are recorded in detail, and the impact on sales is subsequently analyzed.

3. The offline store commodity sales prediction method according to claim 1, characterized in that: In the feature construction step, the applicable scenarios and target customer group characteristics of the product are further subdivided according to the product attribute characteristics, and incorporated into the feature combination.

4. The offline store product sales prediction method according to claim 1, characterized in that: In the model training step, a cross-validation method is used to select the optimal model hyperparameter combination to improve the generalization ability of the model.

5. The offline store commodity sales prediction method according to claim 1, characterized in that: In the forecasting decision step, differentiated inventory replenishment strategies are formulated based on sales forecast results and inventory turnover rates of different commodities.

6. The offline store product sales prediction method according to claim 1, characterized in that: The aforementioned mechanism of establishing regular data updates and model retraining can adapt to market changes and ensure the accuracy of forecasts.

7. The offline store commodity sales prediction method according to claim 1, characterized in that: In the forecasting and decision-making step, a flexible procurement plan is formulated based on long-term sales forecasts and supplier cooperation to reduce procurement costs.

8. The offline store commodity sales prediction method according to claim 1, characterized in that: During the entire sales forecasting process, the data is encrypted to ensure data security and privacy.