Market segmentation scheme generation assistance method, device, and electronic device

Abstract

Embodiments of the present application disclose a market segmentation scheme generation auxiliary method and device and an electronic device. The method comprises: receiving a request for generating a market segmentation scheme for a target user group under a target category; providing guidance information for selecting value tags step by step according to a preset hierarchical architecture according to a value tag tree corresponding to the target category; in the process of receiving value tag selection results step by step, dynamically generating decision reference information and displaying the decision reference information in an interface for making a next decision, wherein the dynamically generated decision reference information comprises: a plurality of value tag combinations composed of selected value tags and value tags to be selected next, and market performance related index data of a second commodity set determined by each value tag combination and available in the target user group. Through the embodiments of the present application, a user can help to make a more objective and reasonable market segmentation scheme.

Description

Technical Field

[0001] This application relates to the field of information processing technology, and in particular to methods, apparatus and electronic devices for assisting in the generation of solutions for niche markets. Background Technology

[0002] The consumer goods market is the sum of commodity exchange relationships. Due to the existence of heterogeneous consumer needs and the advantages that companies possess in different aspects, the market can be segmented, known as "market segments." The criteria for segmenting the consumer goods market can include various methods, one important one being segmentation by consumer groups. For example, multiple groups can be divided according to purchasing power, occupation, etc., and different groups can correspond to different market segments. Each market segment can correspond to a set of products, also known as a "product group." For example, the drinking water category includes sub-markets such as purified water and mineral water, and so on. Therefore, the process of market segmentation involves generating a subset of products from a large set of products for a specific target group, designating these products as the "mental products" of that target group, and then targeting them with these products. Mental products are those that consumers easily think of first when they have relevant shopping needs, or those that easily elicit positive user feedback (clicks or purchases). For example, when people with higher purchasing power need to buy groceries, they might first think of organic fresh vegetables, and so on.

[0003] However, since products typically have many information dimensions, various product subsets can be obtained when divided according to different dimensions or combinations of dimensions. How to generate or divide product subsets, and which product subset is more suitable for distribution to which group, so as to achieve the effect that the distributed product subset truly belongs to the "mental product" of the corresponding group and thus achieve the expected market performance, involves the issue of developing a market segmentation plan. The purpose is to more effectively match the target audience with the product group.

[0004] In existing technologies, market segmentation plans are typically developed by market procurement and other relevant personnel based on their own experience. The quality of these plans depends on their accumulated experience, their ability to collect internal and external data, and their capacity to identify and interpret that data. Therefore, the final plans often have a high degree of subjectivity.

[0005] Therefore, how to help users develop more objective and reasonable market segmentation plans has become a technical problem that needs to be solved by those skilled in the art. Summary of the Invention

[0006] This application provides a method, apparatus, and electronic device for generating market segmentation solutions, which can help users develop more objective and reasonable market segmentation solutions.

[0007] This application provides the following solution:

[0008] A method for generating segmentation solutions, comprising:

[0009] Receive requests to generate market segmentation plans for the target user group under the target category;

[0010] Based on the value tag tree corresponding to the target category, guidance information is provided to select value tags step by step according to a preset hierarchical structure; the value tags are used to add value tag attributes to products that meet the conditions in the first product set corresponding to the target category;

[0011] During the process of receiving the value tag selection results step by step, decision reference information is dynamically generated and displayed in the interface for making the next decision. The dynamically generated decision reference information includes: multiple value tag combinations consisting of the selected value tags and the next value tags to be selected, and market performance-related indicator data of the second set of goods determined by each value tag combination that can be obtained in the target user group.

[0012] A segmentation solution generation auxiliary device, comprising:

[0013] The request receiving unit is used to receive requests for generating market segmentation plans for the target user group under the target category.

[0014] The guidance information providing unit is used to provide guidance information for selecting value tags step by step according to a preset hierarchical structure based on the value tag tree corresponding to the target category; the value tags are also used to add value tag attributes to products that meet the conditions in the first product set corresponding to the target category;

[0015] The decision reference information providing unit is used to dynamically generate decision reference information during the process of receiving value tag selection results at each level, and to display it on the interface for making the next decision. The dynamically generated decision reference information includes: multiple value tag combinations consisting of selected value tags and next-next-select value tags, and market performance-related indicator data of the second set of goods determined by each value tag combination that can be obtained in the target user group.

[0016] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of any of the preceding methods.

[0017] An electronic device, comprising:

[0018] One or more processors; and

[0019] A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method described in any of the preceding descriptions.

[0020] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0021] Through the embodiments of this application, a value tag tree for a specific category can be constructed in advance based on the user's product selection needs under that category. This value tag can then be used to add value tag attributes to specific eligible products, allowing each specific value tag to be associated with a corresponding subset of products. This enables users to formulate market segmentation strategies based on this value tag tree. During the formulation of a market segmentation strategy, the interface provides guidance for selecting value tags at each level according to a hierarchical architecture, along with dynamically changing decision reference information. This allows users to determine the basic architecture at a higher level before selecting specific segmented value tags, thus enabling a more professional approach to market segmentation strategy formulation. Furthermore, by displaying specific market performance-related indicator data, each step of the user's decision is supported by objective data, facilitating the generation of higher-quality market segmentation strategies.

[0022] Of course, any product implementing this application does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the system architecture provided in the embodiments of this application;

[0025] Figure 2 This is a flowchart of the method provided in the embodiments of this application;

[0026] Figure 3 This is a schematic diagram of the first interface provided in an embodiment of this application;

[0027] Figure 4-1 , 4-2 This is a schematic diagram of the guide information in the interface provided in the embodiments of this application;

[0028] Figure 5-1 , 5-2This is a schematic diagram of decision reference information in the interface provided in the embodiments of this application;

[0029] Figure 6-1 , 6-2 This is a schematic diagram of decision reference information after combining multiple levels of value points, provided in an embodiment of this application.

[0030] Figures 7-1 to 7-3 This is a schematic diagram illustrating the gradual change of decision reference information in rows and columns provided in the embodiments of this application;

[0031] Figure 8 This is a schematic diagram of the device provided in the embodiments of this application;

[0032] Figure 9 This is a schematic diagram of the electronic device provided in the embodiments of this application. Detailed Implementation

[0033] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0034] First, it should be noted that the inventors of this application discovered during the development of this application that each specific product often possesses its own value proposition (also known as a selling point), which becomes the reason why consumers choose a particular product. For example, if a product's main value proposition is "green and organic," and a consumer happens to be particularly interested in this value proposition, then this value proposition will become the reason why that consumer chooses the product, and so on. Correspondingly, there may be multiple products with the same value proposition, and multiple users interested in the same value proposition. Therefore, if products can be selected based on value propositions, the resulting market segment may gain a relatively broad or potentially large market space among the corresponding population.

[0035] For specific products, the title and other descriptive information typically express the key value points through words and phrases. However, since the ways in which these value points are expressed in product descriptions vary, this embodiment of the application, in order to facilitate market segmentation based on value points, firstly analyzes and digitally represents users' product selection needs for a specific category, generating a value tag tree corresponding to that category. This tree can include multi-level value tags. For example, firstly, a first set of products corresponding to a category can be identified (this can be obtained from various data sources; subsequent product selection and market segmentation strategies are based on this set). Then, by analyzing the titles and other descriptive information of each product in this first set, as well as user historical behavior data (e.g., purchase behavior, click behavior), descriptive words that are likely to elicit positive user feedback are extracted. These descriptive words are then clustered, and value tags are summarized based on the clustering results. These value tags can be further clustered to obtain higher-level value tags, thus forming a tree structure of value tags for a category. For example, in a specific embodiment, the drinking water category may include two levels of value labels. The parent value label includes category, quality, function, packaging, etc. The category may also include child value labels such as sparkling water, baby water, and purified water. The quality may also include child value labels such as premium and safe, etc. The aforementioned parent and child value labels can be summarized according to the method provided in the embodiments of this application.

[0036] In other words, in this embodiment of the application, the specific value tag can be a word or phrase summarized from the product's descriptive words. Relatively speaking, since descriptive words are words that actually exist in the product's title and other descriptive information, they are more specific words, while value tags are more abstract words. Such a value tag may not appear directly in the product's specific descriptive information, but there may be a descriptive word in the product's specific descriptive information used to express the value tag.

[0037] After generating the value tag tree corresponding to a specific category, the generated value tags can be used to tag specific products in the first product set of that category, allowing multiple specific products to be attached to a single value tag. Thus, when segmenting a target user group, the system first selects a category, and then provides selectable value tags at each level according to the specific value tag tree. By selecting value tags at each level, value tag combinations can be obtained. Since each specific value tag is attached to a specific subset of products, a particular product subset can be calculated by processing logical relationships such as intersection and union between these subsets. This product subset corresponds to a specific market segment, and users can select the market segment by choosing value tag combinations.

[0038] However, since the number of nodes in the value tag tree may be quite large, if all the value tags at each level are listed and displayed directly, it will still be quite difficult for users to select and combine them from among the many value tags, and they may even still make choices based on subjective understanding.

[0039] Therefore, in this embodiment, guidance information for selecting value tags can be provided step-by-step according to a pre-defined hierarchical architecture. Furthermore, during the process of receiving value tag selection results step-by-step, decision reference information can be dynamically generated, including multiple value tag combinations assembled from previously selected value tags and candidate value tags in the next level. That is, as value tags are selected, the specific combinations of value tags continuously change, and correspondingly, the market is gradually refined. Additionally, market performance-related indicator data available within the target user group for each value tag combination's corresponding second product set can be provided. This allows users to complete the final market segment selection through a step-by-step exploration process, ensuring that each step is based on objective evidence. When providing specific indicator data, in addition to basic indicators such as GMV (Gross Merchandise Volume), data on indicators that aid in further decision-making can also be provided, such as GMV growth rate, TU (target user group) GMV share, TU GMV growth rate, etc. Furthermore, as the number of selected value tags increases, indicating a more segmented market, the number of indicators provided on the interface can also gradually increase, offering more reference information. These increased indicators can also include extended indicators calculated based on the aforementioned basic tables or decision indicators. For example, they could include indicators for characterizing the size of the existing market, indicators for characterizing the potential for incremental opportunities, indicators for characterizing the potential for consumption upgrades, and so on.

[0040] In addition, after completing the selection of value tags at each level, an interface for market segmentation can be provided. This interface can display multiple value tag combinations based on the selection results at each level, as well as market performance-related indicator data for the second set of products defined by these value tag combinations within the target user group. This indicator data may include data on metrics used to estimate the market potential of the second set of products within the target user group, and so on.

[0041] To facilitate understanding, the following clarifies several concepts related to product sets mentioned above:

[0042] First product set: Corresponding to the category, each category can have its own first product set. The creation of the value tag tree and the generation of the second product set corresponding to the subsequent market segment can all be based on this first product set.

[0043] A product subset, corresponding to a value tag, is a subset of the first product set. Each value tag can have a batch of products attached. The product subset corresponding to a parent value tag can be the union of the product subsets corresponding to its multiple child value tags.

[0044] The second product set, corresponding to a specific value tag combination, can be a set of products obtained by calculating the intersection and / or union of product subsets corresponding to multiple value tags. This second product set can correspond to a specific granular market segment. As the number of value tags in the combination increases, the scope of the second product set shrinks, and the market becomes more finely segmented.

[0045] Through the hierarchical selection of value tags guided by the above-mentioned layered architecture, and the dynamically changing decision reference information, users can be more professional in formulating market segmentation plans. They can determine the basic architecture first and then select specific segmented value tags. Furthermore, by displaying specific market performance-related indicator data, users' decisions at each step can be supported by objective data, which is conducive to generating better market segmentation plans.

[0046] From a system architecture perspective, embodiments of this application can provide a segmented market solution generation auxiliary system, see [link to relevant documentation]. Figure 1The system can obtain the first set of products for each category. Then, based on the product sets under each category, it can obtain descriptive information such as product titles, as well as user historical behavior data (e.g., historical order records, historical browsing records, etc.). From this, it can extract user product selection needs and digitally represent them as a value tag tree. Specific value tags can be added to specific products, so that each value tag corresponds to a product subset. Thus, when segmenting the target user group within a target category, it can provide hierarchical guidance on value tag selection and dynamically generate decision reference information as it receives the selection results. This decision reference information can exist in tabular form, with each row representing a segmented market formed by value tag groups composed of value tags at different levels, and the vertical axis representing the values ​​of each segmented market across multiple indicators. The so-called dynamic generation of decision reference information means that, in the row direction, the assembled value tag combinations continuously change as the value tag selection results are obtained at each level. Additionally, optionally, more indicators can be gradually provided in the column direction for users to refer to during the decision-making process. Specific indicators and corresponding data can be obtained by querying the indicator management system, or by defining and registering new indicators within the system. After selecting value tags across multiple levels, specific market segments can be identified by choosing from the assembled value tag combinations. The selection results can be submitted to the manager for comprehensive evaluation.

[0047] The specific implementation schemes provided in the embodiments of this application will be described in detail below.

[0048] First, this application provides a product selection assistance method. In the data preparation stage before executing the specific method, a value tag tree can be generated for multiple categories, and value tag attributes can be added to eligible products under specific categories based on the value tag tree.

[0049] There are several ways to generate a value tag tree. For example, in one approach, one can first determine a first set of products corresponding to multiple categories, and a value tag tree corresponding to each of the multiple categories. The value tag tree includes multiple levels of value tags under the same category. The value tags are generated by analyzing and digitally representing the product selection needs of users under the same category.

[0050] In practice, product information can be collected from multiple data sources. For example, it can be collected from the product information database of the current e-commerce information system, or from other e-commerce systems and other data sources. Of course, the specific category systems may differ across different data sources. For instance, the same product might be classified into different categories in different systems. Therefore, in practice, the category system of one data source can be used as the standard to align categories between different data sources. For example, if there are data sources A, B, and C, the category system of data source A can be used as the standard, and products obtained from data sources B and C can be mapped to the categories of data source A. In this way, a separate product set can be generated for each category. The subsequent market segmentation process involves selecting a specific subset of products from this product set.

[0051] After obtaining the product set for each category, in this embodiment of the application, a corresponding value tag tree can be created for each category. There are various ways to create the value tag tree. For example, it can be created entirely through manual analysis. Alternatively, in a preferred embodiment of this application, big data analysis can be performed using algorithms, and the analysis results can be provided to users for reference. Then, value tags can be abstracted based on these big data analysis results.

[0052] When conducting big data analysis, the following methods can be used: First, obtain the titles and descriptions of each product in the product collection under a specific category, as well as the user's historical behavior records. These historical behavior records can be the historical behavior records of all users within a certain period of time, including historical purchase behavior, click behavior, and so on.

[0053] Next, the text descriptions of each title can be segmented to obtain multiple descriptive words. Then, user historical behavior data can be analyzed based on the dimensions of these descriptive words to identify multiple target descriptive words that are likely to elicit positive user feedback. For example, products in a product set can be sorted according to metrics such as sales volume. Then, the descriptive words included in the descriptions of these products can be statistically analyzed to determine which descriptive words users are more likely to accept, i.e., which are more likely to elicit positive user feedback. These descriptive words can then be mined as target descriptive words. These target descriptive words can then be provided to users (mainly referring to data analysts on the platform). Users can then cluster these multiple target descriptive words to generate multiple value tags. These value tags can serve as leaf-level value tags. Subsequently, based on these leaf-level value tags, clustering can be performed upwards level by level to generate other levels of value tags. For example, for the category of drinking water, based on the obtained specific descriptive words, leaf-level value tags such as sparkling water, baby water, purified water, premium, safe, for brewing tea, and for preparing formula can be generated first. Then, sparkling water, baby water, and purified water can be further clustered into product categories; high-end and safe can be further clustered into quality; and tea brewing and formula preparation can be further clustered into higher-level value labels such as function, and so on.

[0054] After generating a specific value tag tree, the generated value tags can be used to add value tag attributes to specific products in the product collection. In other words, value tags can be used to label products, and corresponding product subsets can be attached to specific value tags.

[0055] There are several ways to tag products using value tags. For example, in one approach, since the value tag tree is generated based on target descriptive words mined from product descriptions, meaning the value tags originate from these target descriptive words, the association between specific value tags and products can be directly established using these target descriptive words. For instance, suppose a value tag A is clustered and abstracted from target descriptive words A1, A2, A3, etc. Therefore, products whose descriptions contain these target descriptive words A1, A2, or A3 can be tagged with value tag A. Of course, since value tag A may also be clustered with value tags B, C, etc., into a higher-level value tag S, products containing the aforementioned target descriptive words A1, A2, A3 can also be tagged with value tag S, and so on. In other words, in practical implementation, the same product can correspond to N value tags, where N is the number of levels in the value tag tree.

[0056] After assigning a value tag to a specific product, it's equivalent to having multiple products attached to that value tag. These products are a subset of the aforementioned product set. Specifically, for a leaf-level value tag A, its attached product subset consists of products whose descriptions contain the target descriptive terms A1, A2, or A3. For a higher-level value tag S, if it's obtained by clustering value tags A, B, C, etc., then its attached product subset can be the union of the product subsets corresponding to value tags A, B, and C.

[0057] S201: Receive a request to select market segments for the target user group under the target category.

[0058] After determining the value tag tree under a specific category and adding value tags to the products in the corresponding product set for that category, the above information can be used to provide users with auxiliary information for selecting market segments. Specifically, before selecting a market segment, information about the target user group can be entered. This user group can be pre-generated and can include a corresponding demographic identifier. By selecting this identifier, the specific target user group can be determined. That is, assuming the demographic identifier is selected as L5, it means that a market segment needs to be selected for the user group corresponding to L5.

[0059] Subsequently, an interface for selecting market segments can be provided. Initially, this interface can display multiple selectable categories. Optionally, it can also display market performance-related metrics for each category within its corresponding target user group. For example... Figure 3 As shown, assuming the "Water & Beverages" category has already been selected, further granular category selection is possible within this category, such as including water, carbonated beverages, tea beverages, fruit and vegetable juices, etc. At this point, relevant metric data for each category within the current target user group can be displayed. For example, GMV data for the current user group can be included, and comparisons of GMV data from multiple data sources can be provided. Additionally, data on metrics such as GMV share, year-on-year GMV growth rate, and GMV compound growth rate can be provided. Users can select categories based on this metric data. Since category selection is usually not difficult, the number of metrics displayed can be relatively small.

[0060] After selecting a category, you can proceed to the process of selecting a sub-market within that category. For example, if you select the "water" category, you can provide users with selectable value tags based on the value tag tree corresponding to that category. By combining these value tags, you can define a subset of products, which corresponds to a specific market segment.

[0061] S202: Based on the value tag tree corresponding to the target category, provide guidance information for selecting value tags step by step according to a preset hierarchical structure; the value tags are used to add value tag attributes to products that meet the conditions in the first product set corresponding to the target category.

[0062] In this embodiment, since the number of nodes in the value tag tree may be large, directly listing all the value tags at each level and requiring the user to choose from them could be quite difficult. Therefore, this embodiment provides a step-by-step selection method for the user of value tags at each level, and offers guidance information during the selection process. This helps the user to think about how to formulate a segmented market strategy in a structured and hierarchical manner.

[0063] In one specific implementation, suppose a value tag tree for a certain category includes two levels of value tags, specifically multiple parent value tags and multiple child value tags. In this case, when providing guidance information for selecting value tags level by level, one could provide guidance information for selecting a primary parent value tag and at least one secondary parent value tag from the multiple parent value tags, and then for selecting child value tags from the primary parent value tag and the secondary parent value tag, respectively.

[0064] For example, suppose we call the parent value tag "Mental Dimension" and the child value tag "Segmented Value Point". In the process of selecting a segmented market under the "Water" category, we can provide, for example... Figure 4-1 The shown onboarding interface guides users to select a primary mental dimension and at least one secondary mental dimension from multiple mental dimensions. Within this interface, an "editor" can be provided, offering both a display area for the selectable mental dimensions and an editing area. The display area shows the available mental dimensions for the current category, and in this case, also displays trending keywords for each mental dimension to help users understand the specific value points within each dimension. The editing area provides boxes for entering the primary and secondary mental dimensions. Users can drag the selected mental dimension from the display area into the corresponding editing box to complete their selection. For example... Figure 4-2 As shown, you can select "Category" as the primary mental dimension, and select "Brand," "Quality," and "Packaging" as additional mental dimensions, and so on.

[0065] After users have selected the primary and secondary mental dimensions, they are then guided to choose specific value points within the primary mental dimension, and then further guided to choose specific value points within the secondary mental dimensions. This approach helps users understand which mental dimensions to consider when selecting content from a category, guiding them to first establish a framework of thought before selecting specific value points within those dimensions. This method guides users to think about market segmentation strategies in a structured and hierarchical manner, and through open-ended, question-and-answer-style human-computer interaction, leads them onto a more professional data analysis path.

[0066] S203: During the process of receiving the value tag selection results step by step, decision reference information is dynamically generated and displayed in the interface for making the next decision. The dynamically generated decision reference information includes: multiple value tag combinations consisting of the selected value tags and the next value tags to be selected, and market performance-related indicator data of the second set of goods determined by each value tag combination that can be obtained in the target user group.

[0067] After guiding users to select value tags along a predetermined path, users can proceed with the selection step-by-step according to specific guidance information. During this process, embodiments of this application can also provide decision-making reference information, which is displayed on the interface used for making the next decision. This decision-making reference information may include: multiple value tag combinations assembled from the selected value tags and candidate value tags in the next level, and market performance-related indicator data available in the target user group for each value tag combination's corresponding second product set. In this way, users not only receive guidance on a more professional data analysis path but also obtain relevant indicator data to help them make the next decision, enabling them to make more professional and objective judgments based on specific data, and thus achieve more accurate next-step decisions.

[0068] The specific decision reference information is dynamically generated based on the selected value tags. That is, new decision reference information is displayed on the interface after each decision step. This decision reference information can be displayed in tabular form, where the row dimension corresponds to the specific market segment corresponding to the specific value tag combination, and the column dimension corresponds to the data under the various indicators mentioned above. The specific market segment is determined by multiple value tag combinations assembled from the selected value tags and the next level's candidate value tags. Since each decision step involves selecting more value tags, the row dimension information in the dynamically generated decision reference information will change. Specifically, as value tags are selected at each level, the granularity of the specific market segmentation gradually becomes finer. The specific market segmentation is defined through value tag combinations; that is, as value tags are selected at each level, the number of value tags in the combination increases, and correspondingly, the granularity of the market segmentation is reduced.

[0069] For example, in the aforementioned example, suppose the value tag tree under the "water" category includes two levels of value tags: mental dimension and specific value points. Users can first choose between the primary mental dimension and supplementary mental dimensions, and then select specific value points under each of these dimensions. Alternatively, suppose the primary mental dimension was previously selected as "category," and the supplementary mental dimensions as "quality," "brand," and "function." Then, users can be guided to select specific value points under the primary mental dimension. At this point, if... Figure 5-1 As shown, in the provided decision reference information, the information in the row dimension is similar to... Figure 3 Compared to the previous version, the interface has changed, now showcasing the selected primary mindset dimension—the product category—and the various market segments formed by combining this category dimension with its sub-value points. For example, it includes the mineral water market, the purified water market, and so on. Additionally, it displays market performance-related metrics for each market segment within the current target user group on the column dimensions. Furthermore, the interface provides options for selecting one or more sub-value points within the current primary mindset dimension.

[0070] After selecting the specific value points under the primary mental dimension, you can then select additional value points under the secondary mental dimension. For example, you could... Figure 5-2As shown, since the sub-value points under the primary mindset dimension have already been selected, when displaying the market segmentation information for the optional value points under the additional mindset dimension, the selected sub-value points under the primary mindset dimension can be combined with the various optional sub-value points under the additional mindset dimension to obtain multiple value tag combinations. Each value tag combination corresponds to a more granular market segment. For example, assuming the selected sub-value point under the primary mindset dimension is "purified water," and the current additional mindset dimension is "quality," with sub-value points under the "quality" dimension including "premium," "safe," "mass market," and "health," then the combined value tag combinations could include "purified water | premium," "purified water | safe," "purified water | mass market," and "purified water | health," etc. Accordingly, based on this value tag combination, a corresponding second set of products can be defined, and specific indicator data can be provided based on this second set of products.

[0071] Specifically, when determining the second set of goods based on a combination of value tags, the second set of goods corresponding to the value tag combination can be determined by considering the subsets of goods corresponding to each value tag within the same value tag combination. Specifically, for each value tag within the same value tag combination, the union of the subsets of goods corresponding to different value tags belonging to the same parent value tag is taken, and the intersection of the subsets of goods corresponding to different value tags belonging to different parent value tags is taken to determine the second set of goods corresponding to the value tag combination. For example, in the above example, for the value tag combination "Pure Water | Premium," since the parent value tag for "Pure Water" is "Category" and the parent value tag for "Premium" is "Quality," they belong to different parent value tags. Therefore, when determining the second set of goods, the intersection of the subsets of goods corresponding to "Pure Water" and "Premium" can be taken, and so on.

[0072] Furthermore, during the process of receiving value tag selection results at each level, the dynamically generated decision reference information can change dynamically not only in the row dimension (i.e., the value tag combinations and their corresponding market segment granularity change continuously) but also in the column dimension. For example, during the process of receiving value tag selection results at each level, as the number of selected value tags increases, more data on market performance-related indicators available to the second product set corresponding to the value tag combination within the target user group can be provided.

[0073] For example, through Figure 3 and Figure 5-1 The comparison between them shows that Figure 3 Primarily used for category selection, it displays a relatively small number of metrics across columns, mainly including GMV, GMV percentage, year-on-year GMV growth rate, and GMV compound annual growth rate. Meanwhile... Figure 5-1Since the primary and secondary mental dimensions have already been selected, the number of metrics displayed in the specific columns of the interface used to select sub-value points under the primary mental dimension will increase. For example, besides... Figure 3 In addition to GMV, GMV percentage, GMV year-on-year growth rate, and GMV compound growth rate, the GMV can also include "TU GMV", "TU GMV percentage", and "TU GMV year-on-year growth rate", where TU refers to the current target user group.

[0074] Furthermore, calculations can be performed based on the above indicators to provide an evaluation index for the market opportunities corresponding to specific value segments. For example, such as... Figure 5-1 As shown, specific evaluation indices can include: indices for characterizing the size of the existing market, indices for characterizing the potential for incremental opportunities, indices for characterizing the potential for consumption upgrades, and so on. The first evaluation index is primarily used to identify relatively mature and large-scale existing market segments; the second is used to identify market segments that may not be very large in scale but have rapid growth and significant development potential; and the third is primarily used to identify market segments that meet users' demands for consumption upgrades and where users have a high willingness to purchase. All three types of market segments have considerable market potential. In other words, if a market segment scores highly on a particular evaluation index, it is more likely to belong to the corresponding category of market segment. Consequently, targeting this market segment to the current target user group may yield better market performance. Therefore, this evaluation index information is very helpful for users in formulating market segmentation strategies. Of course, in practice, many other evaluation indices can be defined, which are not limited here.

[0075] Specifically, when calculating the aforementioned evaluation indices, a subset of indicators related to market performance can be selected as basic indicators. The corresponding evaluation index is then calculated by weighting the values ​​of these basic indicators. For example, for an evaluation index used to characterize the size of the existing market, the selected basic indicators could include TUGMV, TU preference index, TU sales volume, and TU GMV growth rate, with corresponding weights of 40%, 20%, 20%, and 20%, respectively, and so on. The selection of specific basic indicators and their weights can be set based on experience, or by designing relevant algorithms to select basic indicators and corresponding weights more suitable for calculating various evaluation indices, and so on. In the specific interface display, such as... Figure 5-1As shown, the calculation source of each evaluation index can also be displayed, namely the basic indicators and weights used above, so that users can understand the meaning of the specific evaluation index and thus better help users make the next decision.

[0076] After completing the selection of value tags at all levels, a third interface can be provided for selecting the market segments corresponding to the final combination of value tags. This interface can specifically display multiple value tag combinations based on the selection results of value tags at all levels. These value tag combinations can serve as candidate market segments. In addition, it can also include market performance-related indicator data that can be obtained in the target user group for the second set of products corresponding to each value tag combination.

[0077] In other words, after selecting value tags at each level, multiple value tags can be obtained. These value tags can then be combined, and each combination can correspond to a market segment. In addition, this combination of value tags can also define a specific second set of products. Therefore, based on this second set of products, indicator data corresponding to each market segment can be provided to help users select the market segment.

[0078] For example, assuming the primary mental dimension in the previous example is "category," and the secondary mental dimensions are "quality," "brand," and "function," the value points selected under the primary mental dimension of "category" include "purified water" and "mineral water." The value points selected under the secondary mental dimension of "quality" include: "mineral water" - "premium," "purified water" - "safe," and so on. To facilitate users viewing the selection results across multiple levels, an interface can be provided to display the value tag selection results at each level. For example, ... Figure 6-1 As shown in the image. This interface also provides functions such as modification and deletion, allowing users to further edit the value tag selection results.

[0079] After confirming the selected value tags at each level, the third interface displays the market segments corresponding to multiple value tag combinations. When combining value tags, you can follow the hierarchical structure and key-plus relationships outlined in the guidance information. For example, for... Figure 6-1 The selection results shown display the combinations of value tags as follows: Figure 6-2As shown, it can include "[Category] Mineral Water + [Brand] Brand A + [Quality] Premium + [Packaging] Boxed", "[Category] Mineral Water + [Brand] Brand A + [Quality] Safe + [Packaging] Bottled", etc. Furthermore, based on the combination of various value tags, a corresponding second product set can still be defined, and based on this second product set, index data related to market performance in each segment of the market within the current target user group can be generated. Moreover, the specific indicators displayed in this third interface can also include data on indicators used to predict the market space that the second product set can obtain within the target user group. For example, such as... Figure 6-2 The "Market Space Forecast" indicator shown is an example. The specific definition and scope of the market space forecast indicator can be registered in the indicator management system, through which the corresponding numerical values ​​can be obtained. Additionally, this third interface also provides information on price comparisons, product lists from various channels, and more.

[0080] Based on the above Figures 3 to 6-2 As can be seen, in this embodiment of the application, users can be guided to select value tags step by step. During the selection process, the specific combination of value tags corresponding to the segmented market can be continuously changed in the row dimension. More indicators can be gradually added in the column dimension, and even evaluation index obtained by calculation, market space prediction information, etc. can be added.

[0081] To more clearly illustrate the changes in information across the row and column dimensions, the following abstract comparison of the specific changes involved in the aforementioned examples will be used to illustrate these changes. First, after selecting the primary and secondary mental dimension, when choosing specific value points under the primary mental dimension, the decision-making reference information provided in the interface can be as follows: Figure 7-1 As shown, the row dimension can display multiple value tag combinations composed of the selected main mindset dimension and the subdivided value points under that dimension. For example, it can be represented as: [Main] Value Point A: a, [Main] Value Point A: b, etc.; the column dimension can display the data of the product subset defined by the above value point combinations on indicators such as A1, A2, and A3.

[0082] After selecting the primary mindset dimension, you will be taken to an interface where you can select sub-value points under the additional mindset dimensions. Here, you can combine the selected sub-value points under the primary mindset dimension with the candidate sub-value points under the additional mindset dimensions, and the corresponding market segments for each combination will be displayed in the row dimension. For example, ... Figure 7-2As shown, assuming the primary mental dimension is A, the selected sub-value point under this primary mental dimension is a, and an additional mental dimension is M, then when selecting a sub-value point for this additional mental dimension, the value tag combination displayed on the interface can include: [Primary] Value Point A: a + [Additional] Value Point M: x, [Primary] Value Point A: a + [Additional] Value Point M: y, [Primary] Value Point A: a + [Additional] Value Point M: z, etc.

[0083] Since there can be multiple additional mental dimensions, once the sub-value points under each additional mental dimension have been selected, it is possible to specifically display the market segments corresponding to multiple combinations of value tags. For example, Figure 7-3 As shown, the value tag combinations displayed in the row dimension can include: [Main] Value Point A: a + [Additional 1] Value Point M: x + [Additional 2] Value Point N: f, etc. In the column dimension, the specific indicators displayed can include not only A1, A2, etc., but also more indicators such as B1, B2, etc.

[0084] This open, question-and-answer-style human-computer interaction guides users towards a more professional data analysis path, enabling them to make valuable industry judgments based on continuously enriched indicator data, and ultimately produce effective segmented market decisions. This effectively lowers the operational and usage barriers for users, allowing more users to participate in the development of segmented market solutions, and leveling up the data interpretation skills of different users, effectively contributing to bottom-up innovation capabilities.

[0085] It should be noted that, using the method provided in this application embodiment, after selecting a specific market segment within a certain category for a particular target user group, the selection can be submitted within the system. The system can then forward the selection results to the relevant administrators. Administrators can aggregate market segmentation proposals submitted by multiple users, compare different market segmentation proposals from different users targeting the same user group within the same category, select the superior one, and forward it to downstream processes, etc. During this process, data on multiple metrics for each market segment can be linked, facilitating downstream users' comparison of proposals, etc.

[0086] It should also be noted that the specific indicators and the summary calculations of data for these specific indicators in this application embodiment can be supported by a dedicated indicator system. For example, the various indicators required for this application embodiment can be pre-registered in the indicator system, and the corresponding physical table implementation method can be specified (including which fields of which specific physical table to read data from, and what method to use for summarization, etc.). Then, by dynamically generating SQL (Structured Query Language) or other methods, data can be read from the corresponding physical table and summarized to obtain the data for the specific indicators.

[0087] In summary, through the embodiments of this application, a value tag tree under a specific category can be constructed. This value tag can then be used to add value tag attributes to eligible products in the first product set under that specific category. This allows each specific value tag to be associated with a corresponding subset of products. This enables users to formulate market segmentation strategies based on this value tag tree. During the formulation of a market segmentation strategy, a hierarchical architecture guides the selection of value tags at each level, along with dynamically changing decision-making reference information. This allows users to determine the basic architecture at a higher level before selecting specific segmented value tags, thus enabling a more professional approach to market segmentation strategy formulation. Furthermore, by displaying specific market performance-related indicator data, each step of the user's decision is supported by objective data, facilitating the generation of higher-quality market segmentation strategies.

[0088] It should be noted that the embodiments of this application may involve the use of user data. In practical applications, user-specific personal data may be used in the scheme described herein within the scope permitted by applicable laws and regulations, provided that it complies with the applicable laws and regulations of the country (e.g., with the user's explicit consent, with the user being properly notified, etc.).

[0089] Corresponding to the foregoing method embodiments, this application also provides a segmentation market solution generation auxiliary device, see [link to relevant documentation]. Figure 8 The device may include:

[0090] The request receiving unit 801 is used to receive a request to generate a market segmentation plan for the target user group under the target category;

[0091] The guidance information providing unit 802 is used to provide guidance information for selecting value tags step by step according to a preset hierarchical structure based on the value tag tree corresponding to the target category; the value tags are also used to add value tag attributes to products that meet the conditions in the first product set corresponding to the target category;

[0092] The decision reference information providing unit 803 is used to dynamically generate decision reference information during the process of receiving value tag selection results step by step, and display it on the interface for making the next decision. The dynamically generated decision reference information includes: multiple value tag combinations consisting of selected value tags and next-selectable value tags, and market performance-related indicator data of the second set of goods determined by each value tag combination that can be obtained in the target user group.

[0093] The value tag tree includes multiple levels of value tags under the same category. The value tags are generated by analyzing and digitally expressing users' product selection needs under the same category.

[0094] Specifically, the value tag tree can be generated in the following ways:

[0095] Get the first set of products in the same category, and the corresponding user historical behavior data;

[0096] The product description information in the first product set is segmented into multiple descriptive terms.

[0097] The user's historical behavior data is analyzed based on the dimensions of descriptive words to identify multiple target descriptive words that are easy to obtain positive user feedback. In order to generate multiple leaf-level value tags by clustering the multiple target descriptive words, and to generate other levels of value tags by clustering upwards based on the leaf-level value tags.

[0098] Specifically, product information can be obtained from multiple data sources, and the category system of one of the data sources can be used as a reference to align the products obtained from multiple data sources into categories, thereby determining multiple categories and the first set of products corresponding to each category.

[0099] Specifically, when adding value tag attributes to eligible products in the first product set, based on the clustering relationship between the target descriptor and the leaf-level value tag, and the clustering relationship between the leaf-level value tag and other higher-level value tags, corresponding leaf-level value tags and higher-level value tags are added to products whose description information contains the corresponding target descriptor.

[0100] The value tag tree includes two levels of value tags, specifically multiple parent value tags and multiple child value tags.

[0101] The guidance information providing unit can specifically be used for:

[0102] Provides guidance information for selecting a primary parent value tag and at least one secondary parent value tag from among the multiple parent value tags, and then selecting child value tags for the primary parent value tag and secondary parent value tags respectively.

[0103] Specifically, the decision reference information providing unit can be used for:

[0104] Based on the subsets of goods corresponding to each value tag in the same value tag combination, a second set of goods corresponding to the value tag combination is determined; wherein, for each value tag in the same value tag combination, the union of the subsets of goods corresponding to different value tags belonging to the same parent value tag is taken, and the intersection of the subsets of goods corresponding to different value tags belonging to different parent value tags is taken, so as to determine the second set of goods corresponding to the value tag combination.

[0105] Specifically, the decision reference information providing unit can be used for:

[0106] During the process of receiving the value tag selection results step by step, as the number of selected value tags increases, more data on market performance-related indicators available to the target user group for the second set of products corresponding to the value tag combination are provided.

[0107] The additional indicators include: an evaluation index of the market performance calculated based on multiple indicators related to market performance, and the evaluation index includes one or more of the following: an evaluation index for characterizing the size of the existing market, an evaluation index for characterizing the potential for incremental opportunities, and an evaluation index for characterizing the potential for consumption upgrading.

[0108] Additionally, the device may also include:

[0109] The third interface providing unit is used to provide a third interface for selecting the second product set after the selection of value tags at all levels is completed. The third interface is used to display multiple value tag combinations based on the selection results of value tags at all levels, as well as the market performance-related indicator data of the second product set corresponding to each value tag combination that can be obtained in the target user group.

[0110] The third interface displays indicator data including: indicator data used to estimate the market space that the second set of goods corresponding to each value tag combination can obtain in the target user group.

[0111] In addition, embodiments of this application also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in any of the foregoing method embodiments.

[0112] And an electronic device, comprising:

[0113] One or more processors; and

[0114] A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method described in any of the foregoing method embodiments.

[0115] in, Figure 9 An exemplary architecture of an electronic device is shown, which may include a processor 910, a video display adapter 911, a disk drive 912, an input / output interface 913, a network interface 914, and a memory 920. The processor 910, video display adapter 911, disk drive 912, input / output interface 913, network interface 914, and memory 920 can communicate with each other via a communication bus 930.

[0116] The processor 910 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to achieve the technical solution provided in this application.

[0117] The memory 920 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 920 can store the operating system 921 for controlling the operation of the electronic device 900, and the basic input / output system (BIOS) for controlling the low-level operations of the electronic device 900. Additionally, it can store a web browser 923, a data storage management system 924, and an auxiliary processing system 925, etc. The aforementioned auxiliary processing system 925 can be the application program that specifically implements the aforementioned steps in this embodiment. In summary, when implementing the technical solution provided in this application through software or firmware, the relevant program code is stored in the memory 920 and is called and executed by the processor 910.

[0118] Input / output interface 913 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0119] Network interface 914 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0120] Bus 930 includes a pathway for transmitting information between various components of the device, such as processor 910, video display adapter 911, disk drive 912, input / output interface 913, network interface 914, and memory 920.

[0121] It should be noted that although the above-described device only shows the processor 910, video display adapter 911, disk drive 912, input / output interface 913, network interface 914, memory 920, bus 930, etc., in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the solution of this application, and does not necessarily include all the components shown in the figures.

[0122] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0123] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0124] The above provides a detailed description of the method, apparatus, and electronic device for generating segmented market solutions provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A market segmentation scheme generation assistance method characterized by comprising: The method comprises: receiving a request for generating a market segmentation scheme for a target user group under a target category; providing guidance information for selecting value tags in a preset hierarchical architecture according to a value tag tree corresponding to the target category; the value tags are used to add value tag attributes to commodities meeting conditions in a first commodity set corresponding to the target category; wherein, when providing the guidance information for selecting value tags in a hierarchical manner, guidance information for selecting a main parent value tag and at least one additional parent value tag from a plurality of parent value tags, and selecting sub-level value tags for the main parent value tag and the additional parent value tag, respectively, is provided; in the process of receiving value tag selection results in a hierarchical manner, decision reference information is dynamically generated and displayed in an interface for making the next decision, the dynamically generated decision reference information comprises: a plurality of value tag combinations composed of selected value tags and value tags to be selected next, and market performance related index data of a second commodity set determined by each value tag combination in the target user group; the plurality of value tag combinations correspond to a plurality of market segmentation schemes, wherein, the dynamically generated decision reference information gradually provides more indicators for the user to refer to in the decision-making process as the selection results of the value tags obtained in a hierarchical manner; the indicators added each time are indicators that are helpful for making the next decision.

2. The method of claim 1, wherein: the value tag tree comprises a plurality of levels of value tags under the same category, and the value tags are generated by analyzing and digitizing the commodity selection demands of users under the same category.

3. The method of claim 2, wherein: the value tag tree is generated by: obtaining a first commodity set under the same category and corresponding user historical behavior data; performing word segmentation on the commodity description information in the first commodity set to obtain a plurality of description words; analyzing the user historical behavior data based on the dimensions of the description words to determine a plurality of target description words that are easy to obtain positive feedback from users, so as to generate a plurality of leaf-level value tags by clustering the plurality of target description words, and generate value tags of other levels by clustering upwards based on the leaf-level value tags.

4. The method of claim 3, wherein: the obtaining of the first commodity set under the same category comprises: obtaining commodity information from a plurality of data sources, and aligning the commodities obtained from the plurality of data sources according to the category system of one of the data sources to determine a plurality of categories and first commodity sets corresponding to each category.

5. The method of claim 3, wherein: In the adding of the value label attribute to the qualified commodities in the first commodity set, according to the clustering relationship between the target description word and the leaf-level value label, and the clustering relationship between the leaf-level value label and other higher-level value labels, the corresponding leaf-level value label and the higher-level value label are added to the commodity containing the corresponding target description word in the description information.

6. The method of claim 1, wherein, The dynamically generated decision reference information comprises: According to the commodity subsets corresponding to each value label in the same value label combination, a second commodity set corresponding to the value label combination is determined; wherein, for each value label in the same value label combination, the commodity subsets corresponding to different value labels belonging to the same parent value label are taken as a union set, and the commodity subsets corresponding to different value labels belonging to different parent value labels are taken as an intersection set, so as to determine the second commodity set corresponding to the value label combination.

7. The method of claim 1, wherein, The more indicators include an evaluation index of market performance calculated based on a plurality of indicators related to the market performance, and the evaluation index includes one or more of the following: an evaluation index for describing the size of the stock market, an evaluation index for describing the incremental opportunity potential, and an evaluation index for describing the consumption upgrading potential.

8. The method of claim 1, wherein, Further comprising: After the selection of the value labels at each level is completed, a third interface for selecting the second commodity set is provided, and the third interface is used to display a plurality of value label combinations combined according to the selection results of the value labels at each level, and the index data related to the market performance of the second commodity set corresponding to each value label combination available in the target user group.

9. The method of claim 8, wherein, In the third interface, the displayed index data includes index data for estimating the market space of the second commodity set corresponding to each value label combination available in the target user group.

10. A market segmentation scheme generation assistance device characterized by comprising: Further comprising: A request receiving unit configured to receive a request for generating a market segmentation scheme for a target user group under a target category; A guide information providing unit configured to provide guide information for selecting value labels in stages according to a preset hierarchical architecture, according to a value label tree corresponding to the target category; The value label is also used to add a value label attribute to the qualified commodities in the first commodity set corresponding to the target category; wherein, when the guide information for selecting value labels in stages is provided, guide information for selecting a main parent value label and at least one additional parent value label from a plurality of parent value labels, and selecting child value labels for the main parent value label and the additional parent value label is provided. The decision reference information providing unit is configured to dynamically generate decision reference information in the process of receiving the value tag selection result step by step, and display the decision reference information in an interface for making the next decision. The dynamically generated decision reference information includes: a plurality of value tag combinations composed of the selected value tag and the value tag to be selected next, and market performance related index data of a second commodity set determined by each value tag combination and available in the target user group. The plurality of value tag combinations correspond to a plurality of market segment schemes. The dynamically generated decision reference information gradually provides more indexes for the user to refer to in the decision-making process with the selection result of the value tag obtained step by step. The indexes added each time are indexes that are helpful for making the next decision.

11. A computer readable storage medium having stored thereon a computer program, characterized in that, The program, when executed by a processor, implements the steps of the method of any one of claims 1-9.

12. An electronic device, comprising: comprising: one or more processors; and a memory associated with the one or more processors, the memory configured to store program instructions that, when executed by the one or more processors, perform the steps of the method of any one of claims 1-9.

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