Scientific product selection method for searching blue sea demand based on market performance

Abstract

The invention discloses a scientific product selection method for searching blue sea demands based on market performance, and the method specifically comprises the following steps: clue analysis: setting a screening condition of various cluster information to generate a screening rule, and carrying out the filtering and screening of the various cluster information according to the screening rule, and obtaining a plurality of preliminary clues as product selection clues; market analysis: analyzing and selecting commodities corresponding to a plurality of commodity selection clues as primarily selected commodities according to the collected product data; product judgment: determining a preset target operation range, obtaining various target source data in the target operation range, and analyzing according to the target source data to obtain a first keyword library for describing user attention and a second keyword library for describing commodities; and matching the primarily-selected commodities with the first keyword library and the second keyword library, and taking the primarily-selected commodities higher than a preset matching threshold as to-be-selected commodities.

Description

Technical Field

[0001] The present invention relates to the technical field of product selection, and in particular to a scientific product selection method for finding blue ocean demand based on market performance. Background Art

[0002] The so-called blue ocean refers to an unknown market space. If a company wants to start and maintain profitable growth, it must surpass industry competition and create a brand new market. This includes breakthrough growth business (new products or new models in old markets) and strategic new business development (creating new markets, new industry segments, or even brand new industries). Compared to the blue ocean, which refers to an unknown market space, the red ocean refers to a known market space. Generally, the choice faced by entering the market is to open up a new path in the blue ocean or to fight a bloody path in the red ocean, which is a metaphor for the choice of survival in the market space. In a highly competitive market environment, companies need to constantly look for new market demands to gain a competitive advantage. Traditional market research methods are often limited to analyzing existing market demands and are difficult to discover potential blue ocean demands. Therefore, a scientific method is needed to find blue ocean demands to guide product selection and market development. Summary of the invention

[0003] The purpose of this invention is to provide a scientific product selection method for finding blue ocean demand based on market performance, which has the advantages of accurately finding blue ocean demand, guiding product selection and market development, and improving the competitiveness and market share of enterprises.

[0004] The above technical objectives of the present invention are achieved through the following technical solutions: A scientific product selection method for finding blue ocean demand based on market performance, characterized by comprising the following steps: Clue analysis: Filter and screen various types of cluster information according to preset screening rules to obtain preliminary data, and determine preliminary clues based on the preliminary data; based on the preliminary clues, mine various types of data information related to the preliminary clues through multi-platform channels; analyze the preliminary clues and their corresponding various types of data information, and determine several of the preliminary clues as product selection clues; Market analysis: Determine the category to which the product corresponding to the product selection clue belongs, and collect product data of different products with the best sales and the highest weight within the preset ranking order of multiple platforms, levels and market categories on a daily basis. Select products corresponding to multiple product selection clues as preliminary products based on the collected product data analysis; Product judgment: Determine the preset target business scope, obtain multiple target source data within the target business scope, and analyze these target source data to obtain a first keyword library for describing user attention and a second keyword library for describing products; Match the preliminary selected products with the first keyword library and the second keyword library, and select the preliminary selected products that are higher than the preset matching threshold as the selected products; Internal adaptation analysis: Obtain all relevant data of the selected product again, and obtain the attribute curve required for the development of the product based on the relevant data analysis. The attribute curve reflects the proportion of factors required for the development and sales of the product; obtain the relevant development data of the target operator, generate a development curve based on the development data, fit the development curve and the attribute curve, and if the fitting degree is greater than the preset fitting threshold, it is selected as the target product; Product implementation: Determine the life cycle status of the target product based on the preset product evolution curve, and formulate a development strategy plan corresponding to the target product based on the specific life cycle status.

[0005] Further configuration: the selecting of products corresponding to multiple product selection clues as preliminary selected products according to the collected product data analysis specifically includes the following steps: Summarize and classify the various data according to the categories of each level and each market, and filter the sorted data to obtain the representative data of the market of the corresponding category on that day; Analyze the representative data of the market of the category on that day to obtain a summary curve of the representative data of the market of the category on that day; And based on the summary curve analysis, select the products corresponding to multiple product selection clues as the initial products.

[0006] Further setting: the representative data specifically includes: the number of brands, the number of sellers, the proportion of new products, the sales volume of new products, the number of new product evaluations, the highest sales volume, and the lowest sales volume. The representative data reflects the development trend of the market.

[0007] Further configuration: the summary curve reflects the life cycle of the product corresponding to the product selection clue and the development trend in the corresponding market.

[0008] Further configuration: the target source data specifically includes consumer portrait data within the target business scope, attribute data of the preliminary selected commodities, and information data related to the preliminary selected commodities within the target business scope.

[0009] Further setup: After completing the product implementation steps, the following steps are also included: Feedback adjustment: The target product is initially launched on the market according to the development strategy plan, and relevant listing data of the target product during the initial listing process is collected. The listing data specifically includes sales data, market share data, user evaluation data, etc. A feedback adjustment strategy is generated based on the analysis and comparison of the listing data, and the development strategy plan is modified according to the feedback adjustment strategy to obtain a new development strategy plan.

[0010] Further configuration: the selecting of products corresponding to multiple product selection clues as preliminary selected products according to the collected product data analysis specifically includes the following contents: Determine whether the product corresponding to the product selection clue is anti-giant based on the market share, market growth rate and user growth rate of the product analyzed by product data; If the market share, market growth rate and user growth rate are greater than the preset growth threshold, the product is judged to be a giant and the product corresponding to the product selection clue is used as the preliminary product.

[0011] Further configuration: the matching of the preliminary selected products with the first keyword library and the second keyword library specifically includes the following steps: Substituting the product data of the preliminary selected products into the growth model of the preset basic category cycle for analysis to obtain the cycle status of the preliminary selected products, generating a number of product keywords corresponding to the preliminary selected products according to the cycle status and the product data, and matching the several product keywords with the second keyword library to obtain a second matching rate; Generating a plurality of user keywords corresponding to the preliminary selected product according to the target source data and the periodic state corresponding to the product, and matching the plurality of user keywords with the first keyword library to obtain a first matching rate; The sum of the first matching rate and the second matching rate is compared with a preset matching threshold, and if the sum is higher than the preset matching threshold, the initially selected product is determined as a product to be selected.

[0012] In summary, the present invention has the following beneficial effects: through clue analysis, market analysis, product judgment and other steps, it is possible to accurately find blue ocean demands, guide product selection and market development, and improve the competitiveness and market share of the enterprise. Through internal adaptation analysis, it is judged whether the target operator's own operating status and its own advantages are in line with the development trend of the target blue ocean product, and it is possible to screen out products that are more compatible with the enterprise, ensuring that the screened products can gain greater competitiveness with the help of the enterprise's own advantages. The set product implementation steps and feedback adjustment steps select a promotion method that is more suitable for its commodity cycle and market according to the commodity cycle of the blue ocean product itself, conduct market verification and adjustment, and adjust the product development strategy according to the actual feedback of the market situation to obtain better market development. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a schematic diagram of the overall structure of the embodiment. DETAILED DESCRIPTION

[0014] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0015] Example: like Figure 1 As shown in the figure, the scientific product selection method for finding blue ocean demand based on market performance includes the following steps: Clue analysis: Set the screening conditions for various types of cluster information to generate screening rules, filter and screen various types of cluster information according to the screening rules to obtain preliminary data, and determine preliminary clues based on the preliminary data; based on the preliminary clues, dig out various types of data information related to the preliminary clues through multiple channels; analyze the preliminary clues and the corresponding various types of data information, and determine several of the preliminary clues as product selection clues; Market analysis: Determine the category to which the product corresponding to the product selection clue belongs, collect product data of the products with the best sales and the highest weight in the category within the preset ranking order on multiple platforms, levels and markets on a daily basis, and select products corresponding to multiple product selection clues as preliminary products based on the collected product data analysis; The preset ranking order range may be, for example, the top 50 products.

[0016] Product judgment: Determine the preset target business scope, obtain multiple target source data within the target business scope, and analyze these target source data to obtain a first keyword library for describing user attention and a second keyword library for describing products; Match the preliminary selected products with the first keyword library and the second keyword library, and select the preliminary selected products that are higher than the preset matching threshold as the selected products; The matching of the preliminary selected products with the first keyword library and the second keyword library specifically includes the following steps: The product data of the preliminary selected commodities are substituted into the growth model of the preset basic category cycle for analysis to obtain the cycle status of the preliminary selected commodities, and a number of commodity keywords corresponding to the preliminary selected commodities are generated according to the cycle status and the product data. The several commodity keywords are matched with the second keyword library to obtain a second matching rate.

[0017] Internal adaptation analysis: Obtain all relevant data of the selected product again, and obtain the attribute curve required for the development of the product based on the relevant data analysis. The attribute curve reflects the proportion of factors required for the development and sales of the product; obtain the relevant development data of the target operator, generate a development curve based on the development data, fit the development curve and the attribute curve, and if the fitting degree is greater than the preset fitting threshold, it is selected as the target product; The factor proportions mentioned specifically include factors such as publicity proportion, competitor proportion, financial capacity proportion and other related factors affecting commodity sales. The intensity of some product publicity has a great impact on sales, so the publicity proportion of this product will be higher. The attribute curve reflects the proportion of each factor in the product development and sales process, and the various data of the development curve of the target operator corresponds to the attribute curve one by one. For example, the proportion of the ability value of the publicity department of the target operator in the overall company strength corresponds to the publicity proportion in the attribute curve.

[0018] Product implementation: Determine the life cycle status of the target product based on the preset product evolution curve, and formulate the corresponding development strategy plan for the target product according to the specific life cycle status. For example, if the product is in the bonus period of the category, that is, the supply is less than the demand, there are more buyers than sellers, then it is necessary to adopt the method of opening more stores and laying out more price bands to seize the market in accordance with this cycle state. If the product is in the innovation bonus period, that is, the supply exceeds the demand, the market is saturated, and there are many brands, then the product needs to adopt product innovation and find a development strategy for more segmented markets.

[0019] Since different products are in different stages of their life cycles, different development strategies should be adopted for different products to achieve better market results.

[0020] Feedback adjustment: The target product is initially launched on the market according to the development strategy plan, and relevant listing data of the target product during the initial listing process is collected. The listing data specifically includes sales data, market share data, user evaluation data, etc. A feedback adjustment strategy is generated based on the analysis and comparison of the listing data, and the development strategy plan is modified according to the feedback adjustment strategy to obtain a new development strategy plan.

[0021] The above-mentioned selecting products corresponding to multiple product selection clues as preliminary selected products according to the collected product data analysis specifically includes the following steps: Summarize and classify the various data according to the categories of each level and each market, and filter the sorted data to obtain the representative data of the market of the corresponding category on that day; The representative data of the category market on that day are analyzed to obtain a summary curve of each representative data dimension of the category market on that day.

[0022] And based on the summary curve analysis, select the products corresponding to multiple product selection clues as the initial products.

[0023] The representative data specifically include: the number of brands, the number of sellers, the proportion of new products, the sales volume of new products, the number of new product reviews, the highest sales volume, and the lowest sales volume. The representative data reflects the development trend of the market.

[0024] The summary curve reflects the life cycle of the product corresponding to the product selection clue and its development trend in the corresponding market.

[0025] The target source data specifically includes consumer portrait data within the target business scope, attribute data of the preliminary selected commodities, and information data related to the preliminary selected commodities within the target business scope.

[0026] The selecting of products corresponding to multiple product selection clues as preliminary selected products according to the collected product data analysis specifically includes the following contents: Determine whether the product corresponding to the product selection clue is anti-giant based on the market share, market growth rate and user growth rate of the product analyzed by product data; If the market share, market growth rate and user growth rate are greater than the preset growth threshold, the product is judged to be a giant and the product corresponding to the product selection clue is used as the preliminary product.

[0027] To determine whether a product is anti-monopoly based on its market data, the following aspects can be considered: Market share: Observe the product’s share of the market. If the product’s market share is gradually growing and surpassing its main competitors, it may indicate that the product is fighting against the giants.

[0028] Market growth rate: Analyze the growth rate of the market in which the product is located. If the market growth rate of the product is higher than that of the main competitors, it may indicate that the product is fighting against giants.

[0029] User growth: Observe whether the number of users of the product is growing continuously. If the number of users of the product gradually increases and exceeds that of major competitors, it may indicate that the product is resisting the giants.

[0030] User satisfaction: Understand user satisfaction with the product through market research or user feedback. If the user satisfaction of the product is higher than that of the main competitors, it may indicate that the product is fighting against the giants.

[0031] Profit growth: Observe the profit growth of the product. If the profit of the product increases gradually and exceeds that of the main competitors, it may indicate that the product is anti-giant.

[0032] Innovation capability: Assess the product's innovation capability and technical strength. If the product can continuously launch competitive new products or features, it may indicate that the product is fighting against giants.

[0033] Generate a number of user keywords corresponding to the preliminary selected product according to the target source data and the periodic state corresponding to the product, and match the number of user keywords with the first keyword to obtain a first matching rate; The sum of the first matching rate and the second matching rate is compared with a preset matching threshold, and if the sum is higher than the preset matching threshold, the initially selected product is determined as a product to be selected.

[0034] Regarding the internal adaptation analysis step, although the selected products have been screened out, and the selected products also meet the needs of blue ocean products and the needs of various markets within the target business scope, due to the different product life cycles of each selected product and the different conditions of the target operator, not every selected product is in line with the current situation of the enterprise. For example, the market promotion of some selected products requires a large amount of financial resources, and the current financial situation of the enterprise itself is unstable and cannot provide financial resources for promotion. In this case, the selected product does not meet the current status of the enterprise, and the combination of the two cannot bring greater advantages. For example, some selected products require stronger publicity and promotion capabilities of the enterprise. If the enterprise lacks this ability, then the product does not meet the current situation of the enterprise. Therefore, it is necessary to select products that are more in line with the current situation of the target operator from the selected products through the internal adaptation analysis step to achieve the effect of one plus one greater than two, thereby achieving the purpose of anti-giant and seeking opportunities for the development of the enterprise.

[0035] Through clue analysis, market analysis, product judgment and other steps, it is possible to accurately find blue ocean needs, guide product selection and market development, and improve the company's competitiveness and market share. Through internal adaptation analysis, it is judged whether the target operator's own operating status and its own advantages are in line with the development trend of the target blue ocean product, and it is possible to screen out products that are more compatible with the company, ensuring that the screened products can gain greater competitiveness with the help of the company's own advantages. The set product implementation steps and feedback adjustment steps are based on the commodity cycle of the blue ocean product itself to select a promotion method that is more suitable for its commodity cycle and market, conduct market verification and adjustment, and adjust the product development strategy based on the actual feedback of the market situation to obtain better market development.

[0036] The above-described implementation methods do not constitute a limitation on the protection scope of the technical solution. Any modification, equivalent replacement and improvement made within the spirit and principle of the above-described implementation methods shall be included in the protection scope of the technical solution.

Claims

1. A scientific product selection method based on market performance to find blue ocean demand, characterized by: The specific steps include: Clue analysis: Filter and screen various types of cluster information according to preset screening rules to obtain preliminary data, and determine preliminary clues based on the preliminary data; based on the preliminary clues, mine various types of data information related to the preliminary clues through multi-platform channels; Analyze the preliminary clues and their corresponding data information, and determine several preliminary clues as product selection clues; Market analysis: Determine the category to which the product corresponding to the product selection clue belongs, and collect product data of different products with the best sales and the highest weight within the preset ranking order of multiple platforms, levels and market categories on a daily basis. Select products corresponding to multiple product selection clues as preliminary products based on the collected product data analysis; Product judgment: Determine the preset target business scope, obtain multiple target source data within the target business scope, and analyze these target source data to obtain a first keyword library for describing user attention and a second keyword library for describing products; Match the preliminary selected products with the first keyword library and the second keyword library, and select the preliminary selected products that are higher than the preset matching threshold as the selected products; Internal adaptation analysis: Obtain all relevant data of the selected product again, and obtain the attribute curve required for the development of the product based on the relevant data analysis. The attribute curve reflects the proportion of factors required for the development and sales of the product; obtain the relevant development data of the target operator, generate a development curve based on the development data, fit the development curve and the attribute curve, and if the fitting degree is greater than the preset fitting threshold, it is selected as the target product; Product implementation: Determine the life cycle status of the target product based on the preset product evolution curve, and formulate a development strategy plan corresponding to the target product based on the specific life cycle status.

2. According to claim 1, a scientific product selection method for finding blue ocean demand based on market performance is characterized in that: The method of selecting products corresponding to multiple product selection clues as preliminary selected products according to the collected product data analysis specifically includes the following steps: Summarize and classify the various data according to the categories of each level and each market, and filter the sorted data to obtain the representative data of the market of the corresponding category on that day; Analyze the representative data of the market of the category on that day to obtain a summary curve of the representative data of the market of the category on that day; And based on the summary curve analysis, select the products corresponding to multiple product selection clues as the initial products.

3. A scientific product selection method for finding blue ocean demand based on market performance according to claim 2, characterized in that: The representative data specifically include: the number of brands, the number of sellers, the proportion of new products, the sales volume of new products, the number of new product reviews, the highest sales volume, and the lowest sales volume. The representative data reflects the development trend of the market.

4. A scientific product selection method for finding blue ocean demand based on market performance according to claim 2, characterized in that: The summary curve reflects the life cycle of the product corresponding to the product selection clue and its development trend in the corresponding market.

5. According to claim 1, a scientific product selection method for finding blue ocean demand based on market performance is characterized in that: The target source data specifically includes consumer portrait data within the target business scope, attribute data of the preliminary selected commodities, and information data related to the preliminary selected commodities within the target business scope.

6. A scientific product selection method for finding blue ocean demand based on market performance according to claim 1, characterized in that: After completing the product implementation steps, the following steps are also included: Feedback adjustment: The target product is initially launched on the market according to the development strategy plan, and relevant listing data of the target product during the initial listing process is collected. The listing data specifically includes sales data, market share data, user evaluation data, etc. A feedback adjustment strategy is generated based on the analysis and comparison of the listing data, and the development strategy plan is modified according to the feedback adjustment strategy to obtain a new development strategy plan.

7. According to claim 1, a scientific product selection method for finding blue ocean demand based on market performance is characterized in that: The selecting of products corresponding to multiple product selection clues as preliminary selected products according to the collected product data analysis specifically includes the following contents: Determine whether the product corresponding to the product selection clue is anti-giant based on the market share, market growth rate and user growth rate of the product analyzed by product data; If the market share, market growth rate and user growth rate are greater than the preset growth threshold, the product is judged to be a giant and the product corresponding to the product selection clue is used as the preliminary product.

8. According to claim 1, a scientific product selection method for finding blue ocean demand based on market performance is characterized in that: The matching of the preliminary selected products with the first keyword library and the second keyword library specifically includes the following steps: Substituting the product data of the preliminary selected products into the growth model of the preset basic category cycle for analysis to obtain the cycle status of the preliminary selected products, generating a number of product keywords corresponding to the preliminary selected products according to the cycle status and the product data, and matching the several product keywords with the second keyword library to obtain a second matching rate; Generating a plurality of user keywords corresponding to the preliminary selected product according to the target source data and the periodic status corresponding to the product, and matching the plurality of user keywords with the first keyword library to obtain a first matching rate; The sum of the first matching rate and the second matching rate is compared with a preset matching threshold, and if the sum is higher than the preset matching threshold, the initially selected product is determined as a product to be selected.

Images