Product competition analysis method and device, electronic equipment and storage medium

Abstract

The invention provides a product competition analysis method and device, electronic equipment and a storage medium, and the method comprises the steps: determining the attention ratio of each user to each product based on the behavior data of each user for each product; wherein the attention proportion is used for indicating the attention proportion or mental proportion of the user to the product of the specified type under the target brand in the target industry; determining a first attention index of the target product based on the attention proportion, and determining a second attention index of a user who pays attention to the target product for the competitive product; the first attention index is used for indicating a purchase intention of a user who pays attention to the target product to the target product, and the second attention index is used for a purchase intention of the user to the competitive product; and determining a competition index of the target product based on the first attention index, determining a competition index of the competitive product based on the second attention index, and performing competition analysis on the target product according to the competition index.

Description

Technical Field

[0001] The present disclosure relates to the technical field of computers, and in particular, to a product competition analysis method, device, electronic device, and storage medium. Background Art

[0002] The user's purchase behavior is a decision-making process. For example, the purchase of important or expensive products is a long-term decision-making process. During this process, users are exposed to a large amount of offline and online information, repeatedly comparing and analyzing this information before deciding on the product to purchase. In this case, analyzing competing products becomes a core issue for advertisers.

[0003] In existing technologies, it is possible to estimate the competitive distance and situation of products through survey data. Existing technologies can conduct competitive analysis of products through single indicator analysis (for example, price or product positioning) or sample surveys. However, the single indicator analysis method reflects a single dimension and cannot provide a more comprehensive competitive analysis. Therefore, the competitive analysis results obtained by existing technologies have large deviations and cross-robustness, and are easily affected by abnormal data, making it difficult to intuitively and quantitatively reflect the competitive distance and situation between products. Summary of the Invention

[0004] The embodiments of the present disclosure at least provide a product competition analysis method, device, electronic device, and storage medium.

[0005] In a first aspect, an embodiment of the present disclosure provides a product competition analysis method, comprising:

[0006] Determine the percentage of attention each user pays to each product based on the behavioral data of each user for each product; wherein the percentage of attention each user pays to each product indicates the percentage of attention or mindshare of the user on a specific product model under a target brand within a target industry;

[0007] Determining a first attention index of the target product based on the attention ratio, and determining a second attention index of users who follow the target product for competing products; the first attention index is used to indicate the purchase intention of users who follow the target product for the target product, and the second attention index is used to indicate the purchase intention of the users for the competing product;

[0008] The competition index of the target product is determined based on the first attention index, and the competition index of the competitor product is determined based on the second attention index, and a competition analysis is performed on the target product according to the competition index.

[0009] In an optional implementation, determining the competition index of the target product based on the first attention index includes:

[0010] Performing distributed processing on the first attention index of each target product to obtain multiple attention proportion intervals;

[0011] Determine an interval weight for each of the attention ratio intervals; wherein the interval weight is determined based on an actual retention conversion rate of users corresponding to each of the attention ratio intervals;

[0012] Based on each of the attention share intervals and the interval weights, a competition index of each of the target products is determined.

[0013] In an optional implementation, determining the competition index of each target product based on each attention share interval and the interval weight includes:

[0014] Counting the number of users corresponding to each of the attention ratio intervals to obtain a target number;

[0015] Summing the products of the attention indexes, the number of targets, and the interval weight in each of the attention proportion intervals to obtain a summation result;

[0016] Based on the sum of all the summation results, the competition index of the target product is determined.

[0017] In an optional embodiment, before performing competition analysis on the target product according to the competition index, the method further includes:

[0018] Determining validity verification indicators for a specified product; wherein the validity verification indicators include the matching between the ranking information of the competition index of the specified product and the actual competition landscape, and / or the change in the ranking information of the competition index of the specified product in the target scenario;

[0019] When the validity of the competition index is verified based on the validity verification indicator, a competition analysis is performed on each of the target products according to the competition index.

[0020] In an optional embodiment, determining the effectiveness verification index of the specified product includes at least one of the following:

[0021] Determining the compatibility between ranking information of a competition index of a first designated product during a target verification period and an actual competitive landscape of the first designated product; wherein the first designated product is a product carrying a verification feature;

[0022] Determine changes in the ranking information of the competition index of the second designated product after a special event occurs;

[0023] After delivering online materials to users corresponding to competing products of the third designated product, a change in ranking information of the competition index of the third designated product is determined.

[0024] In an optional embodiment, determining the effectiveness verification index of the specified product includes:

[0025] Determining a competing product of the designated product; wherein the attention index of the designated product and the attention index of the competing product have the same user;

[0026] Sort the competition indexes of the competing products to obtain a positive ranking of the competing products of the designated product;

[0027] The ranking of the competition index of the designated product is determined in the competitor positive ranking of the competitor product, and the ranking and the competitor positive ranking of the designated product are determined as ranking information of the competition index of the designated product.

[0028] In an optional implementation, determining the attention percentage of each user to each product based on the behavioral data of each user for each product includes:

[0029] Performing weighted summation on the various behavior data of each user for each of the products according to preset behavior weights to obtain a first result;

[0030] Performing weighted summation on the multiple comprehensive behavior data according to the preset behavior weights to obtain a second result; wherein each type of comprehensive behavior data is the sum of the same behavior data of each user for all products;

[0031] The user's attention ratio for the product is determined based on the ratio between the first result and the second result.

[0032] In a second aspect, an embodiment of the present disclosure further provides a product competition analysis device, comprising:

[0033] A first determining unit is configured to determine, based on each user's behavioral data regarding each product, a percentage of attention each user pays to each product; wherein the percentage of attention each user pays to, or a percentage of their mindset towards, a specific product model under a target brand within a target industry;

[0034] a second determining unit, configured to determine a first attention index of the target product based on the attention ratio, and to determine a second attention index of users who pay attention to the target product for a competing product; the first attention index is used to indicate a purchase intention of users who pay attention to the target product for the target product, and the second attention index is used to indicate a purchase intention of the users for the competing product;

[0035] A competition analysis unit is used to determine the competition index of the target product based on the first attention index, determine the competition index of the competitor based on the second attention index, and perform competition analysis on the target product according to the competition index.

[0036] In a third aspect, an embodiment of the present disclosure further provides an electronic device comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the above-mentioned first aspect or any possible implementation of the first aspect are performed.

[0037] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned first aspect or any possible implementation of the first aspect are executed.

[0038] The embodiments of the present disclosure provide a product competition analysis method, device, electronic device, and storage medium. In the embodiments of the present disclosure, first, based on each user's behavioral data for each product, the attention ratio of each user to each product is determined; wherein the attention ratio is used to indicate the user's attention ratio or mental ratio to the target product of a specified model under the target brand in the target industry; then, based on the attention ratio, the attention index of the target product is determined, and the second attention index of the users who pay attention to the target product to the competing product is determined; wherein the first attention index is used to indicate the purchase intention of the users who pay attention to the target product to the target product, and the second attention index is used for the purchase intention of the users to the competing product; next, the competition index of the target product is determined based on the first attention index, and the competition index of the competing product is determined based on the second attention index; finally, if the competition index validity test passes, competition analysis is performed on each target product according to the competition index.

[0039] In the above implementation, by analyzing the user's behavioral data for each product, it is possible to obtain a true share of attention that reflects the user's purchasing intention, thereby providing a more reasonable data basis for the competitive analysis of the target product. By determining the purchase intention (i.e., attention share) of a single user by the share of the behavioral data of a single user for each target product, and then aggregating the purchase intention to obtain the competitive index of each target product and competing products, it is possible to accurately predict the competitive index of the target product and competing products, and quantify the competitive relationship between the target products, thereby more intuitively and quantitatively reflecting the competitive distance and situation between the target products.

[0040] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments. The drawings herein are incorporated into and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and, together with the specification, are used to illustrate the technical solutions of the present disclosure. It should be understood that the following drawings only illustrate certain embodiments of the present disclosure and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without inventive effort.

[0042] Figure 1 A flowchart of a product competition analysis method provided by an embodiment of the present disclosure is shown;

[0043] Figure 2 A schematic diagram showing a flow chart of another product competition analysis method provided by an embodiment of the present disclosure;

[0044] Figure 3 A schematic diagram of a product competition analysis device provided by an embodiment of the present disclosure is shown;

[0045] Figure 4 A schematic diagram of an electronic device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. The components of the embodiments of the present disclosure generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present disclosure provided in the drawings is not intended to limit the scope of the disclosure for which protection is sought, but merely represents selected embodiments of the present disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present disclosure.

[0047] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0048] The term "and / or" herein simply describes an association relationship, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, and the existence of B alone. In addition, the term "at least one" herein refers to any combination of at least two of any one or more of a plurality of items. For example, "at least one of A, B, and C" can represent any one or more elements selected from the set consisting of A, B, and C.

[0049] In relevant industry media, early identification of users' actual purchasing intentions has always been a technical challenge. Current methods, such as those specified by brand advertisers, differ significantly from the actual competitive products in users' minds. User purchasing behavior is a decision-making process. For example, purchasing important or valuable products is a long-term decision-making process. During this process, users are exposed to a large amount of offline and online information, repeatedly comparing and analyzing this information before deciding on a product. Users' decision-making intentions are ever-changing, and their level of interest and mindset towards products fluctuate. This makes competitive product analysis for each product challenging. Therefore, competitive product analysis for a particular product has become a core issue for advertisers.

[0050] In existing technologies, it is possible to estimate a product's competitive distance and situation through survey data. Existing technologies can conduct competitive analysis on products through single indicator analysis (for example, price or product positioning) or sample surveys. However, single-indicator analysis methods reflect a single dimension and cannot provide a more comprehensive competitive analysis. As a result, the competitive analysis results obtained by existing technologies have large deviations and lack robustness, and are easily affected by abnormal data, making it difficult to intuitively and quantitatively reflect the competitive distance and situation between products.

[0051] Based on the above research, the technical solution of the present disclosure provides a product competition analysis method, device, electronic device and storage medium. In the embodiment of the present disclosure, first, based on each user's behavioral data for each product, the attention ratio of each user to each product is determined; wherein, the attention ratio is used to indicate the user's attention ratio or mental ratio to the target product of a specified model under the target brand in the target industry; then, based on the attention ratio, the attention index of the target product is determined, and the second attention index of the users who pay attention to the target product to the competing product is determined; wherein, the first attention index is used to indicate the purchase intention of the users who pay attention to the target product to the target product, and the second attention index is used for the purchase intention of the users to the competing product; next, the competition index of the target product is determined based on the first attention index, and the competition index of the competing product is determined based on the second attention index; finally, if the validity test of the competition index is passed, a competition analysis is performed on each target product according to the competition index.

[0052] In the above implementation, by analyzing the user's behavioral data for each product, it is possible to obtain a true share of attention that reflects the user's purchasing intention, thereby providing a more reasonable data basis for the competitive analysis of the target product. By determining the purchase intention (i.e., attention share) of a single user by the share of the behavioral data of a single user for each target product, and then aggregating the purchase intention to obtain the competitive index of each target product and competing products, it is possible to accurately predict the competitive index of the target product and competing products, and quantify the competitive relationship between the target products, thereby more intuitively and quantitatively reflecting the competitive distance and situation between the target products.

[0053] To facilitate understanding of this embodiment, we first provide a detailed introduction to a product competition analysis method disclosed in this embodiment. The method provided in this embodiment is generally executed by an electronic device with certain computing capabilities. In some possible implementations, this product competition analysis method can be implemented by a processor invoking computer-readable instructions stored in a memory.

[0054] See also Figure 1 FIG. 1 is a flowchart of a product competition analysis method provided by an embodiment of the present disclosure, wherein the method includes steps S101 to S103, wherein:

[0055] S101: Determine the attention ratio of each user to each product based on the behavioral data of each user for each product; wherein the attention ratio is used to indicate the user's attention ratio or mental ratio to the product of a specified model under a target brand in a target industry.

[0056] In the disclosed embodiment, the behavioral data is data that can reflect the user's intended products and that matches the purchase behavior data to meet the requirements. The purchase behavior data can be the user's store visit behavior and / or the information retention behavior. In addition, it can also be other behavioral data that can reflect the user's purchase actions. Products can be various types of products such as car series, home appliances, and electronic devices. The store visit behavior can be store visit behavior for offline stores, and can also be store entry behavior for online stores. The information retention behavior can be information retention behavior for offline stores, and can also be reservation behavior for online stores and similar behaviors.

[0057] S102: Determine a first attention index of the target product based on the attention ratio, and determine a second attention index of users who follow the target product for competing products; the first attention index is used to indicate the purchase intention of users who follow the target product for the target product, and the second attention index is used to indicate the purchase intention of the users for the competing product.

[0058] In the embodiment of the present disclosure, the attention percentage of users who follow the target product can be determined from the total attention percentage. Then, the attention percentage of all users who follow the target product can be determined as the attention index of the target product, which is recorded as the first attention index. Therefore, the first attention index includes at least one attention percentage.

[0059] Similarly, the attention ratio of users who pay attention to the target product to the competitor of the target product can also be determined in the total attention ratio, and then the attention index of the competitor is determined based on the attention ratio, which is recorded as the second attention index.

[0060] S103: Determine the competitive index of the target product based on the first attention index, determine the competitive index of the competing product based on the second attention index, and perform a competitive analysis on the target product based on the competitive indices. For example, the competitive analysis of the target product can be performed based on the competitive index of the target product and the competitive index of the competing product. Here, the competitive index of the target product can be obtained by weighted summing the attention shares in the first attention index of the target product; and the competitive index of the competing product can be obtained by weighted summing the attention shares in the second attention index of the competing product. For example, a weight can be pre-determined for each attention share in the first attention index, and then the weighted sum can be performed based on the weight. The weights of the attention shares corresponding to different users who follow the target product can be the same or different; for example, similar attention shares can be assigned the same weight. In the disclosed technical solution, based on the attention share of a single user for a single target product, the competitive index of each target product can be obtained by aggregating the attention shares. This competitive index can be used to identify the core competitive product portfolio of each target product and quantify the competitive distance between the target product and its core competitors, thereby quantifying the core competitive product portfolio and the competitive distance.

[0061] As can be seen from the above description, the disclosed technical solution determines the competitive index of each target product and its competitors based on massive user behavioral data. This competitive index is then used to conduct competitive analysis on the target product, thereby identifying each target product's actual core competitors and the competitive distance. While clearly determining the user's level of interest in a single target product, the disclosed technical solution also accurately reflects the market's competitive landscape and the competitive environment for individual target products by fitting the proportion of attention a group of users has for multiple target products.

[0062] The above steps will be described in detail below in conjunction with specific implementation methods.

[0063] In the embodiment of the present disclosure, the above step S101 determines the attention ratio of each user to each product based on the behavior data of each user for each product, and specifically includes the following steps:

[0064] Step S11: performing weighted summation on the various behavior data of each user for each product according to preset behavior weights to obtain a first result;

[0065] Step S12: performing weighted summation on the multiple comprehensive behavior data according to the preset behavior weights to obtain a second result; wherein each type of comprehensive behavior data is the sum of the same behavior data of each user for all products;

[0066] Step S13: Determine the user's attention ratio for the product based on the ratio between the first result and the second result.

[0067] In the embodiment of the present disclosure, a preset behavior weight for each behavior data can be predetermined, and each behavior data can be de-dimensionalized; then, each de-dimensionalized behavior data of each user for each product and its corresponding preset behavior weight are weighted and summed to obtain a first result, for example, the formula Indicates that, f(x k ) series-act represents the kth dimensionless behavior data of each product, C k Represents the preset behavior weight of the k-th behavior data, and n is the number of behavior data.

[0068] Next, the de-dimensionalized behavior data of all products of the same type can be summed up to obtain a variety of comprehensive behavior data, where f(x k ) all-act The kth comprehensive behavior data is obtained by summing up the kth de-dimensionalized behavior data of all products. Each de-dimensionalized behavior data corresponds to a comprehensive behavior data. At this time, the preset behavior weight and the comprehensive behavior data can be weighted and summed to obtain the second result. The second result can be obtained by the formula express.

[0069] Finally, the ratio of the first result to the second result can be used to determine the user's attention ratio for each product. The specific formula is:

[0070] Among them, SOA is the proportion of users' attention to the product. The attention proportion is used to indicate the proportion of users' attention or mind share on products of a specified model under the target brand in the target industry. The SOA is defined as the ratio of a user's comprehensive behavior towards a certain product (for example, the sum of multiple valid behavior data for a certain car series) to the user's comprehensive behavior towards all products (for example, the sum of multiple valid behavior data for all car series).

[0071] By combining behavioral data with behavioral weights to construct an intention index model, it is possible to quantify the degree of user intention for each product, thereby more intuitively and accurately reflecting the user's car purchase intention. At the same time, the disclosed technical solution can cover a wide range of user groups, thereby further meeting the service needs of more users, and can also effectively help advertisers identify high-intention car purchase users earlier.

[0072] In the embodiment of the present disclosure, the above step S103 determines the competition index of the target product based on the first attention index, and specifically includes the following steps:

[0073] Step S21: performing distributed processing on the first attention index of each target product to obtain multiple attention proportion intervals;

[0074] Step S22: determining an interval weight for each of the attention ratio intervals; wherein the interval weight is determined based on the actual retention conversion rate of the user corresponding to each of the attention ratio intervals;

[0075] Step S23: Determine the competition index of each target product based on each of the attention share intervals and the interval weights.

[0076] In the embodiment of the present disclosure, first, the first attention index of each target product can be distributedly processed to obtain multiple attention ratio intervals, for example, the attention ratio intervals (a1%-b1%), (a2%-b2%), ..., (a n %-b n %).

[0077] In a specific implementation, the attention proportions of each of the first attention indexes can be sorted from largest to smallest, and then the sorting results can be distributed to obtain multiple attention proportion intervals. For example, the sorting results can be evenly distributed; or non-uniformly distributed according to preset rules.

[0078] Here, the distributed processing methods for the first attention index of each target product can be the same or different. For example, the distributed processing methods and their corresponding target product features can be pre-determined. For each target product, the product features of the target product can be determined, and then the matching distributed processing method can be determined based on the product features. Each distributed processing method can correspond to multiple product features. For example, the product features that have the most hits for the target product can be determined, and the first attention index can be distributed processed according to the distributed processing method corresponding to the product features.

[0079] For each attention ratio interval, a corresponding interval weight can be determined; wherein, the interval weight is determined according to the actual retention conversion rate of users corresponding to each attention ratio interval.

[0080] For example, users whose attention ratios fall within various attention ratio intervals can be screened out from a large number of users. For these users, after obtaining their permissions, the actual retention conversion rates of the users can be obtained, and then the interval weights can be determined based on the actual retention conversion rates.

[0081] Here, a mapping table between actual retention conversion rates and interval weights can be pre-set, and the interval weights for each attention share interval can be determined based on the mapping table. Furthermore, a mapping function between actual retention conversion rates and interval weights can be pre-set, and the interval weights for each attention share interval can be determined based on this mapping function. Finally, the target product's competitive index can be determined based on the interval weights and attention share intervals.

[0082] In the embodiment of the present application, the process of determining the competitive index of the competitor based on the second attention index is the same as the process of determining the competitive index of the target product based on the first attention index, and will not be described in detail here. Specifically, the second attention index of the competitor can be distributed to obtain multiple attention share intervals; the interval weight of each attention share interval is determined; wherein the interval weight is determined based on the actual retention conversion rate of users corresponding to each attention share interval; based on each attention share interval and the interval weight, the competitive index of the competitor is determined.

[0083] In the embodiment of the present disclosure, the above steps of determining the competition index of each target product based on each attention share interval and the interval weight specifically include:

[0084] First, count the number of users corresponding to each attention ratio interval to obtain the target number;

[0085] Secondly, summing the products of each attention index, the number of targets and the interval weight in each attention proportion interval to obtain a summation result;

[0086] Finally, based on the sum of all the summation results, the competition index of the target product is determined.

[0087] In the embodiment of the present disclosure, the number of users corresponding to each attention ratio in each attention ratio interval can be determined to obtain the target number. Then, the product between the target number and the corresponding interval weight is calculated to obtain the weight described in the above embodiment. Next, each attention index in each attention ratio interval is multiplied by the product to obtain the operation result; then, the sum of all operation results is calculated to obtain the sum result. For each attention ratio interval, a sum result can be calculated. Finally, all the sum results can be summed to obtain the competition index of the target product.

[0088] In general, the calculation formula of the competition index of each target product can be described as:

[0089] SOA series-id =Sum{SOA(a%~b%)*N*C}. (a%~b%) represents the attention span, N represents the number of users whose attention span on the target product falls within (a%~b%), and C represents the interval weight based on the conversion efficiency mapping corresponding to the attention span between a% and b%.

[0090] For example, assume that the attention share intervals include: interval 1 (a1%-b1%), interval 2 (a2%-b2%), interval 3 (a3%-b3%); among them, the interval weight of interval 1 is C1, the interval weight of interval 2 is C2, and the interval weight of interval 3 is C3; the number of users corresponding to interval 1 is N1, the number of users corresponding to interval 2 is N2, and the number of users corresponding to interval 3 is N3.

[0091] At this point, you can calculate the product of each attention index in interval 1 and N1*C1 to obtain operation result 1, then sum all operation results 1 to obtain summation result 1. Calculate the product of each attention index in interval 2 and N2*C2 to obtain operation result 2, then sum all operation results 2 to obtain summation result 2. Calculate the product of each attention index in interval 3 and N3*C3 to obtain operation result 3, then sum all operation results 3 to obtain summation result 3. Finally, summation operation is performed on summation result 1, summation result 2, and summation result 3 to obtain the competition index of the target product.

[0092] Since the conversion rates of attention shares in different percentiles of different target products are different, the interval weights of the corresponding attention share intervals are determined by the actual user retention conversion rate, and the competition index is determined by the interval weights, target number and attention share intervals, which can improve the accuracy and reliability of the competition index.

[0093] In an embodiment of the present disclosure, before performing competition analysis on the target product according to the competition index, the method further includes the following steps:

[0094] First, determining validity verification indicators for a specified product; wherein the validity verification indicators include the match between the ranking information of the specified product's competition index and the actual competitive landscape, and / or the change in the ranking information of the specified product's competition index in the target scenario;

[0095] Secondly, when the validity of the competition index is verified based on the validity verification indicator, a competition analysis is performed on each of the target products according to the competition index.

[0096] In an embodiment of the present disclosure, before conducting a competition analysis on each target product based on the competition index, the competition index can be tested for effectiveness, wherein if the effectiveness test of the competition index passes, a competition analysis can be conducted on each target product based on the competition index. If the effectiveness test of the competition index fails, the competition index needs to be re-determined. For example, the attention ratio of each user to the target product can be re-determined according to the newly selected behavioral data, thereby re-determining the competition index based on the new attention ratio. In addition, the interval weight of the attention ratio interval can be adjusted, so that the attention indexes in the attention ratio interval are weighted and summed according to the adjusted interval weight until a competition index that passes the effectiveness test is obtained.

[0097] Here, a validity verification indicator for a specific product can be determined. If the validity of the competition index is verified based on the validity verification indicator, then the validity verification of the competition index is determined to have passed. For example, if it is determined that the ranking information matches the actual competitive landscape, then the validity verification is determined to have passed. Alternatively, if the change in the ranking information of the competition index of the specific product in the target scenario is consistent with expectations, then the validity verification is determined to have passed.

[0098] Through the effectiveness testing scheme described above, the competition model can be verified and tested from multiple perspectives, thereby further ensuring the accuracy and effectiveness of the competition model.

[0099] In the embodiment of the present disclosure, the validity verification index of a specified product can be determined by the following methods, specifically including:

[0100] Method 1:

[0101] Determine the match between ranking information of a competition index of a first designated product within a target verification period and an actual competitive landscape of the first designated product; wherein the first designated product is a product carrying a verification feature.

[0102] Here, the first designated product may be a popular product, a special product, a classic product, or other product carrying a verification feature, wherein the verification feature is a pre-set product feature that satisfies validity verification.

[0103] During specific implementation, the ranking information of the competition index of the first designated product within the target verification cycle can be determined, wherein the ranking information includes the positive ranking of competing products and the reverse ranking of competing products.

[0104] The positive ranking of each product's competitors is used to indicate: for all users who follow this product, the SOA of non-products series-id Index, the result of ranking from high to low.

[0105] The competitor's reverse ranking of each product is used to indicate the product's position in the competitor's positive ranking of other products.

[0106] If it is determined that the ranking information calculated by the competition index matches the actual competition distance of the product indicated by the actual competition landscape, then the validity verification of the competition index is determined to be passed.

[0107] In specific implementation, the competition index can be used to determine the positive ranking and negative ranking of a product's competitors. Then, the positive ranking and negative ranking of the competitors can be determined to see whether they match the actual competitive landscape. If a match is found, the validity of the competition index is determined to have passed.

[0108] Method 2:

[0109] Determine changes in ranking information of the competition index after a special event occurs for the second designated product.

[0110] Here, the first designated product and the second designated product can be the same product or different products, and there is no specific limitation here, whichever is feasible. Special events can include listings, negative public opinion, auto shows, etc.

[0111] In an embodiment of the present disclosure, it is possible to determine whether the change in the ranking information of the competition index after a special event occurs in the second designated product is in line with expectations. For example, the change in the positive ranking and the reverse ranking of the competitor can be determined, and then the validity of the competition index can be verified based on the change.

[0112] For example, negative public opinion can cause a change in a product's competitive index ranking, such as a drop in ranking. In this case, it can be determined whether the product's positive and negative rankings of competitors have changed accordingly, and whether the changes are in line with expectations. If so, the validity of the competitive index has been verified; otherwise, the verification has failed.

[0113] Method 3:

[0114] After delivering online materials to users corresponding to competing products of the third designated product, a change in ranking information of the competition index of the third designated product is determined.

[0115] Here, the first designated product, the second designated product, and the third designated product can be the same product or different products, and there is no specific limitation here, whichever is feasible. Special events can include listings, negative public opinion, auto shows, etc.

[0116] In a competitor interception scenario, online materials, such as advertising materials, can be delivered to users of competing products of a third designated product. Subsequently, changes in the ranking information of the third designated product's competition index can be determined to see if they meet expectations. For example, changes in the positive and negative rankings of competing products can be determined, and the validity of the competition index can be verified based on these changes. If the changes meet expectations, the validity verification of the competition index passes; otherwise, the verification fails.

[0117] Through the above implementation, the effectiveness of the competition index can be verified from multiple dimensions, thereby obtaining a more reasonable competition index and improving the accuracy and credibility of the competition index.

[0118] In the embodiment of the present disclosure, determining the validity verification index of a specified product specifically includes the following steps:

[0119] First, a competitor product of the designated product is determined; wherein the attention index of the designated product and the attention index of the competitor product have the same user;

[0120] Secondly, the competition index of the competing products is sorted to obtain the positive ranking of the competing products of the designated product;

[0121] Finally, the ranking of the competition index of the designated product is determined in the competitor positive ranking of the competitor product, and the ranking and the competitor positive ranking of the designated product are determined as the ranking information of the competition index of the designated product.

[0122] In the embodiment of the present disclosure, users who follow a specified product can be identified, and other products that such users follow can be identified, and these other products can be identified as competing products of the specified product. Next, the competition index of the competing products is sorted to obtain a positive ranking of the competing products.

[0123] For competing products, the same method can be used to determine the competitive product's positive ranking. Then, the ranking of the specified product within the competitive product's positive ranking can be determined. Finally, the ranking indicated by the competitive product's positive ranking and negative ranking can be determined as the ranking information of the specified product's competition index.

[0124] In an embodiment of the present disclosure, if the validity of the competition index is verified based on the validity verification indicator, a competition analysis can be performed on each target product based on the competition index, specifically including the following analysis scenarios:

[0125] (1) Help advertisers formulate competitive strategies in a timely manner based on real core competitors.

[0126] Here, competitive strategies include content strategy, audience targeting strategy, and media selection strategy.

[0127] In the embodiment of the present disclosure, the competition index may be statistically analyzed from a specific dimension, so that a matching competition strategy may be formulated according to the statistical analysis results.

[0128] (2) Recommendation of user-related products.

[0129] Here, based on the competition index of each target product, products of interest and core competitive products of the products of interest can be recommended to users, thereby achieving user-level recommendations of products of interest and content, thereby enhancing user experience.

[0130] (3) Determine the effectiveness of competitor interception of commercial products.

[0131] After delivering online materials to users, it is necessary to re-determine the ranking of the competition index to determine whether the change in ranking is in line with expectations, and then determine the delivery effect of the online materials based on the expected results to achieve the effectiveness of intercepting commercial products.

[0132] The following combination Figure 2 The above process is described in general. In this embodiment, the target product is described using a car series as an example. Specifically, the process includes the following modules:

[0133] A pre-study module for user attention share. This module is used to determine users' purchase intentions for each car series.

[0134] In this module, the formula Determine the user's attention ratio for the car series (that is, the car purchase intention), where SOA is the user's attention ratio for the car series. SOA is defined as the ratio of a user's comprehensive behavior for a certain car series (the sum of multiple valid behavior data for a certain car series) to the user's comprehensive behavior for all car series (the sum of multiple valid behavior data for all car series).

[0135] This module processes basic data on the vehicle series. This module processes basic data on the attention percentages of all users of this vehicle series, as well as the attention percentages of all users of competing vehicle series. For example, this attention percentage can be used to determine a first attention index for this vehicle series, and a second attention index for competing vehicle series among users of this vehicle series.

[0136] A module for developing a vehicle series competition index. This module is used to determine the competition index of a vehicle series based on the attention share interval after distributed processing. Specifically, this module determines the competition index of the vehicle series itself and the competition index of its competitors. The competition index of a vehicle series can be further fitted based on the attention share of users within the vehicle series itself; and the competition index of its competitors can be fitted based on the attention share of users who pay attention to the vehicle series itself.

[0137] In the embodiment of the present disclosure, the module can be used to perform distributed processing on the attention index of each main vehicle system (i.e., the first attention index mentioned above), thereby obtaining multiple attention ratio intervals, for example, the attention ratio intervals (a1%-b1%), (a2%-b2%), ..., (a n %-b n %). In specific implementations, the attention percentages in the attention index can be sorted from largest to smallest, and the sorted results can then be distributed to obtain multiple attention percentage intervals. For example, the sorted results can be evenly distributed, or unevenly distributed according to preset rules. Subsequently, a weight can be determined for each attention percentage interval, and based on each attention percentage interval and weight, the competitive index for each major vehicle series can be determined.

[0138] Validity test module: This module conducts validity testing on the competitive index of the vehicle series through test schemes 1, 2, and 3.

[0139] Test Plan 1: Conduct an effectiveness test based on the match between the ranking information of the competition index of the first designated vehicle series during the target verification cycle and the actual competitive landscape of the first designated vehicle series.

[0140] Test plan two: Conduct effectiveness testing based on the changes in the ranking information of the competition index after a special event occurs in the second designated vehicle series.

[0141] Test plan three: After delivering online materials to users corresponding to the competing car series of the third designated car series, conduct an effectiveness test based on the changes in the ranking information of the competition index of the third designated car series.

[0142] If the effectiveness of the competition index is passed, the competition index can be applied to the following scenarios:

[0143] (1) Vehicle series competition scenario.

[0144] In this scenario, the competition landscape description and periodic analysis of competitiveness of different tracks, different car series levels, and different brands can be achieved based on the car series competition index.

[0145] In the embodiment of the present disclosure, the competition index may be statistically analyzed from a specific dimension, so that a matching competition strategy may be formulated according to the statistical analysis results.

[0146] (2) Recommendations of relevant car series by users.

[0147] Here, based on the competition index of each car series, the car series that the user is interested in, as well as the core competing car series of the car series of interest, can be recommended to the user, thereby achieving user-level recommendations of car series of interest and content, thereby enhancing the user experience.

[0148] (3) Determine the effectiveness of competitor interception of commercial products.

[0149] By determining the ranking of the competition index for each product launch scenario, we can determine the effectiveness of competing product interception scenarios. For example, we can determine whether the ranking changes meet expectations, and then determine the effectiveness of the network material launch based on the expected results, thereby determining the effectiveness of the interception product.

[0150] From the above description, it can be seen that the disclosed technical solution uses the competitive index to identify the real competitive product combination and distance of automobile brands. This method fully considers the depth and frequency of user behavioral data, is suitable for the durable consumer goods industry with a long decision-making cycle, can cover a wide range of user groups, can clearly quantify the distance and degree of competition, and is highly representative and indicative of the real competitive product combination of car series. At the same time, the disclosed technical solution can also effectively identify the core competitive products of this car series in the competitive product interception scenario, thereby providing a data basis for the formulation of competitive strategies for this car series. At the same time, it can also effectively intercept users who are wavering between this car series and competing car series, identify the impact of wavering users, and efficiently determine the efficiency and effectiveness of advertising material delivery.

[0151] Those skilled in the art will understand that in the above-mentioned method of the specific implementation method, the writing order of each step does not mean a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0152] Based on the same inventive concept, the embodiment of the present disclosure also provides a product competition analysis device corresponding to the product competition analysis method. Since the principle of solving the problem by the device in the embodiment of the present disclosure is similar to the above-mentioned product competition analysis method in the embodiment of the present disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0153] Reference Figure 3 FIG. 1 is a schematic diagram of a product competition analysis device provided by an embodiment of the present disclosure, wherein the device includes: a first determination unit 10, a second determination unit 20, and a competition analysis unit 30; wherein,

[0154] A first determining unit is configured to determine, based on each user's behavioral data regarding each product, a percentage of attention each user pays to each product; wherein the percentage of attention each user pays to, or a percentage of their mindset towards, a specific product model under a target brand within a target industry;

[0155] a second determining unit, configured to determine a first attention index of the target product based on the attention ratio, and to determine a second attention index of users who pay attention to the target product for a competing product; the first attention index is used to indicate a purchase intention of users who pay attention to the target product for the target product, and the second attention index is used to indicate a purchase intention of the users for the competing product;

[0156] A competition analysis unit is used to determine the competition index of the target product based on the first attention index, determine the competition index of the competitor based on the second attention index, and perform competition analysis on the target product according to the competition index.

[0157] In the above implementation, by analyzing the user's behavioral data for each product, it is possible to obtain a true share of attention that reflects the user's purchasing intention, thereby providing a more reasonable data basis for the competitive analysis of the target product. By determining the purchase intention (i.e., attention share) of a single user by the share of the behavioral data of a single user for each target product, and then aggregating the purchase intention to obtain the competitive index of each target product and competing products, it is possible to accurately predict the competitive index of the target product and competing products, and quantify the competitive relationship between the target products, thereby more intuitively and quantitatively reflecting the competitive distance and situation between the target products.

[0158] In a possible implementation manner, the second determining unit is further configured to:

[0159] Performing distributed processing on the first attention index of each target product to obtain multiple attention proportion intervals;

[0160] Determine an interval weight for each of the attention ratio intervals; wherein the interval weight is determined based on an actual retention conversion rate of users corresponding to each of the attention ratio intervals;

[0161] Based on each of the attention share intervals and the interval weights, a competition index of each of the target products is determined.

[0162] In a possible implementation manner, the second determining unit is further configured to:

[0163] Counting the number of users corresponding to each of the attention ratio intervals to obtain a target number;

[0164] Summing the products of the attention indexes, the number of targets, and the interval weight in each of the attention proportion intervals to obtain a summation result;

[0165] Based on the sum of all the summation results, the competition index of the target product is determined.

[0166] In a possible implementation manner, the device is further used to:

[0167] Before conducting a competitive analysis on each target product based on the competition index, determining a validity verification indicator for the designated product; wherein the validity verification indicator includes the matching between the ranking information of the competition index of the designated product and the actual competition landscape, and / or the change in the ranking information of the competition index of the designated product in the target scenario;

[0168] When the validity of the competition index is verified based on the validity verification indicator, a competition analysis is performed on each of the target products according to the competition index.

[0169] In one possible implementation, the device is further configured to determine a validity verification indicator for a specified product by at least one of the following methods:

[0170] Determining the compatibility between ranking information of a competition index of a first designated product during a target verification period and an actual competitive landscape of the first designated product; wherein the first designated product is a product carrying a verification feature;

[0171] Determine changes in the ranking information of the competition index of the second designated product after a special event occurs;

[0172] After delivering online materials to users corresponding to competing products of the third designated product, a change in ranking information of the competition index of the third designated product is determined.

[0173] In a possible implementation manner, the device is further used to:

[0174] Determining a competing product of the designated product; wherein the attention index of the designated product and the attention index of the competing product have the same user;

[0175] Sort the competition indexes of the competing products to obtain a positive ranking of the competing products of the designated product;

[0176] The ranking of the competition index of the designated product is determined in the competitor positive ranking of the competitor product, and the ranking and the competitor positive ranking of the designated product are determined as ranking information of the competition index of the designated product.

[0177] In a possible implementation manner, the first determining unit is further configured to:

[0178] Performing weighted summation on the various behavior data of each user for each of the products according to preset behavior weights to obtain a first result;

[0179] Performing weighted summation on the multiple comprehensive behavior data according to the preset behavior weights to obtain a second result; wherein each type of comprehensive behavior data is the sum of the same behavior data of each user for all products;

[0180] The user's attention ratio for the product is determined based on the ratio between the first result and the second result.

[0181] For descriptions of the processing flow of each module in the device and the interaction flow between each module, reference can be made to the relevant descriptions in the above method embodiment, which will not be described in detail here.

[0182] Corresponding to Figure 1 In one possible implementation method, the present disclosure also provides an electronic device 400, such as Figure 4 FIG. 4 is a schematic diagram of the structure of an electronic device 400 provided in an embodiment of the present disclosure, including:

[0183] Processor 41, memory 42, and bus 43; memory 42 is used to store execution instructions, including internal memory 421 and external memory 422; memory 421 herein, also referred to as internal memory, is used to temporarily store operation data in processor 41, as well as data exchanged with external memory 422 such as a hard disk. Processor 41 exchanges data with external memory 422 via internal memory 421. When electronic device 400 is running, processor 41 communicates with memory 42 via bus 43, causing processor 41 to execute the following instructions:

[0184] Determine the percentage of attention each user pays to each product based on the behavioral data of each user for each product; wherein the percentage of attention each user pays to each product indicates the percentage of attention or mindshare of the user on a specific product model under a target brand within a target industry;

[0185] Determining a first attention index of the target product based on the attention ratio, and determining a second attention index of users who follow the target product for competing products; the first attention index is used to indicate the purchase intention of users who follow the target product for the target product, and the second attention index is used to indicate the purchase intention of the users for the competing product;

[0186] The competition index of the target product is determined based on the first attention index, and the competition index of the competitor product is determined based on the second attention index, and a competition analysis is performed on the target product according to the competition index.

[0187] The present disclosure also provides a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program executes the steps of the product competition analysis method described in the above method embodiment. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0188] The embodiments of the present disclosure also provide a computer program product, which carries program code. The instructions included in the program code can be used to execute the steps of the product competition analysis method described in the above method embodiment. For details, please refer to the above method embodiment and will not be repeated here.

[0189] The computer program product may be implemented in hardware, software, or a combination thereof. In one embodiment, the computer program product is implemented as a computer storage medium. In another embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0190] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems and devices described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. In the several embodiments provided in the present disclosure, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0191] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0192] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0193] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0194] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present disclosure, which are used to illustrate the technical solutions of the present disclosure, rather than to limit them. The scope of protection of the present disclosure is not limited thereto. Although the present disclosure has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed in the present disclosure, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure shall be subject to the scope of protection of the claims.

Claims

1. A product competition analysis method, characterized in that: include: Determine the percentage of attention each user pays to each product based on the behavioral data of each user for each product; wherein the percentage of attention each user pays to each product indicates the percentage of attention or mindshare of the user on a specific product model under a target brand within a target industry; Determining a first attention index of the target product based on the attention ratio, and determining a second attention index of users who follow the target product for competing products; the first attention index is used to indicate the purchase intention of users who follow the target product for the target product, and the second attention index is used to indicate the purchase intention of the users for the competing product; The competition index of the target product is determined based on the first attention index, and the competition index of the competitor product is determined based on the second attention index, and a competition analysis is performed on the target product according to the competition index.

2. The method according to claim 1, characterized in that The determining the competition index of the target product based on the first attention index includes: Performing distributed processing on the first attention index of each target product to obtain multiple attention proportion intervals; Determine an interval weight for each of the attention ratio intervals; wherein the interval weight is determined based on an actual retention conversion rate of users corresponding to each of the attention ratio intervals; Based on each of the attention share intervals and the interval weights, a competition index of each of the target products is determined.

3. The method according to claim 2, characterized in that Determining the competition index of each target product based on each attention share interval and the interval weight includes: Counting the number of users corresponding to each of the attention ratio intervals to obtain a target number; Summing the products of the attention indexes, the number of targets, and the interval weight in each of the attention proportion intervals to obtain a summation result; Based on the sum of all the summation results, the competition index of the target product is determined.

4. The method according to claim 1, wherein Before performing a competition analysis on the target product according to the competition index, the method further includes: Determining validity verification indicators for a specified product; wherein the validity verification indicators include the matching between the ranking information of the competition index of the specified product and the actual competition landscape, and / or the change in the ranking information of the competition index of the specified product in the target scenario; When the validity of the competition index is verified based on the validity verification indicator, a competition analysis is performed on each of the target products according to the competition index.

5. The method according to claim 4, characterized in that The validity verification index of the specified product is determined, including at least one of the following: Determining the compatibility between ranking information of a competition index of a first designated product during a target verification period and an actual competitive landscape of the first designated product; wherein the first designated product is a product carrying a verification feature; Determine changes in the ranking information of the competition index of the second designated product after a special event occurs; After delivering online materials to users corresponding to competing products of the third designated product, a change in ranking information of the competition index of the third designated product is determined.

6. The method according to claim 4, characterized in that The effectiveness verification indicators for the specified product are determined as follows: Determining a competing product of the designated product; wherein the attention index of the designated product and the attention index of the competing product have the same user; Sort the competition indexes of the competing products to obtain a positive ranking of the competing products of the designated product; The ranking of the competition index of the designated product is determined in the competitor positive ranking of the competitor product, and the ranking and the competitor positive ranking of the designated product are determined as ranking information of the competition index of the designated product.

7. The method according to claim 1, characterized in that Determining the attention ratio of each user to each product based on the behavior data of each user for each product includes: Performing weighted summation on the various behavior data of each user for each of the products according to preset behavior weights to obtain a first result; Performing weighted summation on the multiple comprehensive behavior data according to the preset behavior weights to obtain a second result; wherein each type of comprehensive behavior data is the sum of the same behavior data of each user for all products; The user's attention ratio for the product is determined based on the ratio between the first result and the second result.

8. A product competition analysis device, characterized in that: include: A first determining unit is configured to determine, based on each user's behavioral data regarding each product, a percentage of attention each user pays to each product; wherein the percentage of attention each user pays to, or a percentage of their mindset towards, a specific product model under a target brand within a target industry; a second determining unit, configured to determine a first attention index of the target product based on the attention ratio, and to determine a second attention index of users who pay attention to the target product for a competing product; the first attention index is used to indicate a purchase intention of users who pay attention to the target product for the target product, and the second attention index is used to indicate a purchase intention of the users for the competing product; A competition analysis unit is used to determine the competition index of the target product based on the first attention index, determine the competition index of the competitor based on the second attention index, and perform competition analysis on the target product according to the competition index.

9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the product competition analysis method as described in any one of claims 1 to 7 are performed.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, executes the steps of the product competition analysis method according to any one of claims 1 to 7.

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