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

Abstract

This disclosure provides a product competition analysis method, apparatus, electronic device, and storage medium. The method includes: determining each user's attention percentage for each product based on behavioral data of each user towards each product; wherein the attention percentage indicates the user's attention percentage or mental share of a specific model of a product under a target brand within a target industry; determining a first attention index for the target product based on the attention percentage, and determining a second attention index for competing products by users who are interested in the target product; the first attention index indicates the user's purchase intention for the target product, and the second attention index indicates the user's purchase intention for the competing product; determining a competition index for the target product based on the first attention index, and determining a competition index for competing products based on the second attention index; and performing competitive analysis on the target product based on the competition indices.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and more specifically, to a product competition analysis method, apparatus, electronic device, and storage medium. Background Technology

[0002] A user's product 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, and repeatedly compare and analyze this information before deciding on the product to buy. At this point, competitor product analysis becomes one of the core issues that advertisers need to address.

[0003] In existing technologies, the competitive distance and situation of a product can be estimated using survey data. Existing technologies can conduct competitive analysis of products through single-indicator analysis (e.g., price or product positioning) or sample surveys. However, single-indicator analysis reflects only a limited dimension and cannot provide a more comprehensive competitive analysis. Therefore, the competitive analysis results from existing technologies are subject to significant biases and have limited robustness, making them susceptible to the influence of outlier data and difficult to intuitively and quantitatively represent the competitive distance and situation between products. Summary of the Invention

[0004] This disclosure provides at least one product competition analysis method, apparatus, electronic device, and storage medium.

[0005] In a first aspect, embodiments of this disclosure provide a product competition analysis method, including:

[0006] The attention percentage of each user towards each product is determined based on each user's behavioral data for each product; wherein, the attention percentage is used to indicate the user's attention percentage or mental percentage towards a specific model of a product under a target brand within the target industry;

[0007] Based on the attention percentage, a first attention index for the target product is determined, and a second attention index for the competing products of users who are interested in the target product is determined; the first attention index is used to indicate the purchase intention of users who are interested in the target product, and the second attention index is used to indicate the purchase intention of users of the competing products.

[0008] The competition index of the target product is determined based on the first attention index, and the competition index of the competing products is determined based on the second attention index. The competition analysis of the target product is then performed based on the competition index.

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

[0010] The first attention index of each target product is processed in a distributed manner to obtain multiple attention percentage intervals;

[0011] Determine the interval weight for each of the attention share intervals; wherein the interval weight is determined based on the actual lead generation conversion rate of the user corresponding to each of the attention share intervals;

[0012] Based on each attention share interval and the interval weight, a competition index for each target product is determined.

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

[0014] The target number is obtained by counting the number of users corresponding to each of the aforementioned attention percentage intervals;

[0015] The summation result is obtained by summing the products of each attention index, the number of targets, and the interval weight within each attention percentage interval;

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

[0017] In an optional implementation, before performing a competitive analysis of the target product based on the competition index, the method further includes:

[0018] Determine the effectiveness verification indicators for a specified product; wherein, the effectiveness verification indicators include the matching between the ranking information of the competition index of the specified product and the actual competitive landscape, and / or, the changes in the ranking information of the competition index of the specified product under the target scenario;

[0019] If the effectiveness of the competition index is verified based on the effectiveness verification indicators, a competition analysis is performed on each of the target products according to the competition index.

[0020] In one optional implementation, determining the validity verification indicators for the specified product includes at least one of the following:

[0021] Determine the matching between the ranking information of the competition index of the first designated product within the target verification period and the actual competitive landscape of the first designated product; wherein, the first designated product is a product carrying verification features;

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

[0023] After delivering network materials to users corresponding to competing products of the third designated product, the changes in the ranking information of the competition index of the third designated product are determined.

[0024] In one optional implementation, determining the effectiveness verification indicators for the specified product includes:

[0025] Identify competing products of the specified product; wherein the attention index of the specified product and the attention index of the competing products contain the same users;

[0026] The competitive indices of the competing products are sorted to obtain a positive ranking of the competitors for the specified product.

[0027] The ranking of the competition index of the specified product is determined from the positive ranking of the competitors' products, and the ranking and the positive ranking of the competitors' products are used as the ranking information of the competition index of the specified product.

[0028] In one optional implementation, determining the attention percentage of each user towards each product based on each user's behavioral data for each product includes:

[0029] The first result is obtained by weighting and summing the multiple behavioral data of each user for each product according to the preset behavioral weights.

[0030] The second result is obtained by weighting and summing multiple comprehensive behavioral data according to the preset behavioral weights; wherein, each type of comprehensive behavioral data is the sum of the same behavioral data of each user for all products;

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

[0032] Secondly, embodiments of this disclosure also provide a product competition analysis device, comprising:

[0033] The first determining unit is used to determine the attention percentage of each user towards each product based on the behavioral data of each user towards each product; wherein, the attention percentage is used to indicate the user's attention percentage or mental percentage towards a specified model of a product under a target brand within the target industry;

[0034] The second determining unit is used to determine a first attention index of the target product based on the attention ratio, and to determine a second attention index of the user who is paying attention to the target product for the competitor; the first attention index is used to indicate the user's purchase intention for the target product, and the second attention index is used for the user's purchase intention for the competitor.

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

[0036] Thirdly, embodiments of this disclosure also provide an electronic device, including: 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 communicates with the memory via the bus, and when the machine-readable instructions are executed by the processor, the steps of the first aspect above, or any possible implementation of the first aspect, are performed.

[0037] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the first aspect or any possible implementation of the first aspect.

[0038] This disclosure provides a product competition analysis method, apparatus, electronic device, and storage medium. In this disclosure, firstly, based on each user's behavioral data regarding each product, the attention percentage of each user towards each product is determined; wherein, the attention percentage indicates the user's attentional or mental share towards a specific model of a target product under a target brand within the target industry; then, based on the attention percentage, an attention index for the target product is determined, and a second attention index for competing products by users who are interested in the target product is determined; wherein, the first attention index indicates the user's purchase intention for the target product, and the second attention index indicates the user's purchase intention for the competing product; next, a competition index for the target product is determined based on the first attention index, and a competition index for the competing products is determined based on the second attention index; finally, if the competition index validity test passes, a competition analysis is performed on each target product based on the competition index.

[0039] In the above implementation, by analyzing user behavior data for each product, the attention percentage that truly reflects user purchase intention can be obtained, thus providing a more reasonable data basis for competitive analysis of target products. By determining a single user's purchase intention (i.e., attention percentage) based on the proportion of their behavior data for each target product, and then aggregating this purchase intention to obtain the competitive index of each target product and its competitors, the competitive index of target products and competitors can be accurately predicted, quantifying the competitive relationship between target products. This allows for a more intuitive and quantitative representation of the competitive distance and situation between target products.

[0040] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the embodiments will be briefly described below. These drawings are incorporated in and constitute a part of this specification. They illustrate embodiments conforming to this disclosure and, together with the specification, serve to explain the technical solutions of this disclosure. It should be understood that the following drawings only show some embodiments of this disclosure and should not be considered as limiting the scope. Those skilled in the art can obtain other related drawings based on these drawings without creative effort.

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

[0043] Figure 2 A flowchart illustrating another product competition analysis method provided by an embodiment of this disclosure is shown;

[0044] Figure 3 A schematic diagram of a product competition analysis apparatus provided in an embodiment of this disclosure is shown;

[0045] Figure 4 A schematic diagram of an electronic device provided in an embodiment of the present disclosure is shown. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0047] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0048] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0049] In relevant industry media, accurately determining users' actual purchase intentions has always been a technical challenge. Current methods, such as brand advertiser targeting, differ significantly from users' actual target competitors. A user's product purchase is a decision-making process. For example, purchasing important or expensive products involves a long decision-making process. During this process, users encounter a large amount of offline and online information, repeatedly comparing and analyzing it before deciding on a purchase. Users' decision-making intentions are constantly changing, and their level of interest and mental state regarding a product fluctuates, which increases the difficulty of competitive analysis. Therefore, competitor product analysis has become one of the core issues that advertisers need to address.

[0050] In existing technologies, a product's competitive distance and situation can be estimated using survey data. Existing technologies can conduct competitive analysis of products through single-indicator analysis (e.g., price or product positioning) or sample surveys. However, single-indicator analysis reflects only a limited dimension and cannot provide a more comprehensive competitive analysis. Therefore, the competitive analysis results obtained by existing technologies have significant biases, limited robustness, and are easily affected by outlier data, making it difficult to intuitively and quantitatively represent the competitive distance and situation between products.

[0051] Based on the above research, this disclosure provides a product competition analysis method, apparatus, electronic device, and storage medium. In this embodiment, firstly, based on each user's behavioral data regarding each product, the attention percentage of each user towards each product is determined; wherein, the attention percentage is used to indicate the user's attention percentage or mental percentage towards a specific model of a target product under a target brand within the target industry; then, based on the attention percentage, an attention index for the target product is determined, and a second attention index for competing products by users who are interested in the target product is determined; wherein, the first attention index is used to indicate the user's purchase intention for the target product, and the second attention index is used to indicate the user's purchase intention for the competing product; next, a competition index for the target product is determined based on the first attention index, and a competition index for competing products is determined based on the second attention index; finally, if the competition index validity test passes, a competition analysis is performed on each target product according to the competition index.

[0052] In the above implementation, by analyzing user behavior data for each product, the attention percentage that truly reflects user purchase intention can be obtained, thus providing a more reasonable data basis for competitive analysis of target products. By determining a single user's purchase intention (i.e., attention percentage) based on the proportion of their behavior data for each target product, and then aggregating this purchase intention to obtain the competitive index of each target product and its competitors, the competitive index of target products and competitors can be accurately predicted, quantifying the competitive relationship between target products. This allows for a more intuitive and quantitative representation of the competitive distance and situation between target products.

[0053] To facilitate understanding of this embodiment, a product competition analysis method disclosed in this disclosure will first be described in detail. The product competition analysis method provided in this disclosure is generally executed by an electronic device with a certain computing capability. In some possible implementations, this product competition analysis method can be implemented by a processor calling computer-readable instructions stored in memory.

[0054] See Figure 1 The diagram shows a flowchart of a product competition analysis method provided in this embodiment of the present disclosure. The method includes steps S101 to S103, wherein:

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

[0056] In this embodiment, behavioral data refers to data that reflects a user's intended products and whose matching with purchase behavior data meets the requirements. Purchase behavior data can include a user's in-store visits and / or contact information collection, as well as other behavioral data reflecting user purchasing actions. Products can be various types of products such as vehicles, home appliances, and electronic devices. In-store visits can be for offline stores or for online stores. Contact information collection can be for offline stores or for online stores, such as making reservations.

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

[0058] In this embodiment of the disclosure, the attention percentage of users who are interested in the target product can be determined from all attention percentages. Then, the attention percentage of all users interested in the target product is determined as the attention index of the target product, denoted as the first attention index. Therefore, the first attention index includes at least one attention percentage.

[0059] Similarly, the attention share of users who are interested in the target product to its competitors can be determined from the total attention share. Then, the attention share is used to determine the attention index of the competitor, which is denoted as the second attention index.

[0060] S103: Determine the competition index of the target product based on the first attention index, and determine the competition index of the competitors based on the second attention index, and perform a competition analysis on the target product according to the competition index. For example, perform a competition analysis on the target product based on the competition index of the target product and the competition index of the competitors. Here, the attention percentages in the first attention index of the target product can be weighted and summed to obtain the competition index of the target product; and the attention percentages in the second attention index of the competitors can be weighted and summed to obtain the competition index of the competitors. For example, the weight of each attention percentage in the first attention index can be predetermined, and then the weighted summation can be performed according to the weight. The weights of the attention percentages corresponding to different users who are paying attention to the target product can be the same or different. For example, similar attention percentages can be assigned the same weight. In the technical solution of this disclosure, based on the attention percentage of a single user for a single target product, the competition index of each target product can be obtained by aggregating the attention percentages. Through this competition index, the core competitor combination of each target product can be identified, and the competitive distance between the target product and the core competitors can be quantified, thereby realizing the quantification of the core competitor combination and the competitive distance.

[0061] As described above, this disclosed technical solution determines the competition index of each target product and its competitors based on massive user behavior data. This competition index is then used to conduct competitor analysis on the target product, thereby identifying the actual core competitors and competitive distance of each target product. While clearly determining the degree of user intent towards a single target product, this disclosed technical solution, by fitting the attention ratio of a group of users towards multiple target products, can accurately reflect the market's competitive landscape and the competitive environment of individual target products.

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

[0063] In this embodiment of the disclosure, step S101, which determines the attention percentage of each user towards each product based on each user's behavioral data for each product, specifically includes the following steps:

[0064] Step S11: Weight and sum the multiple behavioral data of each user for each product according to the preset behavioral weights to obtain a first result;

[0065] Step S12: Weight the various comprehensive behavioral data according to the preset behavioral weights to obtain a second result; wherein, each type of comprehensive behavioral data is the sum of the same behavioral data of each user for all products;

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

[0067] In this embodiment of the disclosure, a preset behavior weight for each type of behavior data can be predetermined, and each type of behavior data can be dedimensionalized. Then, the dedimensionalized behavior data of each user for each product is weighted and summed with its corresponding preset behavior weight to obtain a first result. For example, this can be achieved using a formula. It means that, where f(x) k ) series-act C represents the k-th dimensionless behavioral data for each product. k This represents the preset behavior weight for the k-th type of behavior data, where n is the number of behavior data.

[0068] Next, the dimensionless behavioral data of the same type from all products can be summed to obtain various comprehensive behavioral data, where f(x) k ) all-act This represents the k-th comprehensive behavioral data obtained by summing the k-th dimensionless behavioral data of all products. Each type of dimensionless behavioral data corresponds to a comprehensive behavioral data. At this point, the preset behavioral weights and the comprehensive behavioral data can be weighted and summed to obtain the second result. The second result can be obtained using the formula... express.

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

[0070] SOA refers to the percentage of user attention to a product. The percentage of attention is used to indicate the percentage of user attention or mental share of a specific model of a product under a target brand within a target industry. SOA is defined as the ratio of a user's total behavior towards a product (e.g., the sum of multiple effective behavioral data for a certain car series) to the user's total behavior towards all products (e.g., the sum of multiple effective behavioral data for all car series).

[0071] By constructing an intention index model by combining behavioral data with behavioral weights, it is possible to quantify users' intentions toward various products, thereby reflecting users' car purchase intentions more intuitively and accurately. At the same time, this disclosed technical solution can cover a wide range of users, thereby further meeting the service needs of more users, and can also effectively help advertisers identify high-intent car purchase users earlier.

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

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

[0074] Step S22: Determine the interval weight for each attention percentage interval; wherein the interval weight is determined based on the actual lead conversion rate of the user corresponding to each attention percentage interval;

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

[0076] In this embodiment of the disclosure, firstly, the first attention index of each target product can be processed in a distributed manner to obtain multiple attention percentage intervals. For example, attention percentage intervals (a1%-b1%), (a2%-b2%), ..., (a...) can be obtained. n %-b n %)

[0077] In practice, the attention percentages in the first attention index can be sorted from largest to smallest. Then, the sorting results can be distributed to obtain multiple attention percentage intervals. For example, the sorting results can be evenly divided; alternatively, non-uniform distribution processing can be performed 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-defined. For each target product, its product features can be determined, and then a matching distributed processing method can be determined according to the product features. For each distributed processing method, multiple product features can be corresponding to it. For example, the product feature that is most frequently matched for the target product can be determined, and the first attention index can be distributedly processed according to the distributed processing method corresponding to the product feature.

[0079] For each attention share interval, a corresponding interval weight can be determined; whereby the interval weight is determined based on the actual lead conversion rate of the user corresponding to each attention share interval.

[0080] For example, you can filter out users from a large user base whose attention share falls within various attention share intervals. For these users, after obtaining their permission, you can obtain their actual lead generation conversion rate, and then determine the interval weights based on that actual lead generation conversion rate.

[0081] Here, a mapping table between actual lead generation conversion rates and interval weights can be pre-defined, and then the interval weights for each attention share interval can be determined according to the mapping table. In addition, a mapping function between actual lead generation conversion rates and interval weights can be pre-defined, and then the interval weights for each attention share interval can be determined according to this mapping function. Finally, based on the interval weights and attention share intervals, the competitiveness index of the target product can be determined.

[0082] In this embodiment, the process of determining the competitor's competitive index based on the second attention index is the same as the process of determining the target product's competitive index based on the first attention index, and will not be described in detail here. Specifically, the second attention index of the competitor can be processed in a distributed manner 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 lead generation conversion rate of the user corresponding to each attention share interval; and the competitor's competitive index is determined based on each attention share interval and the interval weight.

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

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

[0085] Secondly, the product of each attention index, the number of targets, and the interval weight within each attention percentage interval is summed to obtain the summation result;

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

[0087] In this embodiment, the number of users corresponding to each attention percentage within each attention percentage 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 within each attention percentage interval is multiplied by this product to obtain the calculation result; then, the sum of all calculation results is calculated to obtain the summation result. A summation result can be calculated for each attention percentage interval. Finally, all summation results are summed to obtain the competition index of the target product.

[0088] In summary, the formula for calculating the competitiveness index of each target product can be described as follows:

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

[0090] For example, suppose the attention percentage intervals include: interval 1 (a1%-b1%), interval 2 (a2%-b2%), and interval 3 (a3%-b3%); where 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, we can calculate the product of each attention index in interval 1 with N1*C1, obtaining result 1. Then, we sum all results 1 to obtain summed result 1. We calculate the product of each attention index in interval 2 with N2*C2, obtaining result 2. Then, we sum all results 2 to obtain summed result 2. We calculate the product of each attention index in interval 3 with N3*C3, obtaining result 3. Then, we sum all results 3 to obtain summed result 3. Finally, we summed summed results 1, 2, and 3 to obtain the competition index of the target product.

[0092] Since the conversion rates of attention share vary for different target products, the accuracy and reliability of the competition index can be improved by determining the interval weight of the corresponding attention share interval based on the user's actual lead generation conversion rate, and then determining the competition index by using the interval weight, the number of targets, and the attention share interval.

[0093] In this embodiment of the disclosure, before performing competitive analysis on the target product based on the competition index, the method further includes the following steps:

[0094] First, determine the effectiveness verification indicators for the specified product; wherein, the effectiveness verification indicators include the matching between the ranking information of the competition index of the specified product and the actual competitive landscape, and / or, the changes in the ranking information of the competition index of the specified product under the target scenario;

[0095] Secondly, if the effectiveness of the competition index is verified based on the effectiveness verification indicators, a competition analysis is performed on each of the target products according to the competition index.

[0096] In this embodiment of the disclosure, before conducting competitive analysis on each target product based on the competition index, a validity test can be performed on the competition index. If the validity test of the competition index passes, competitive analysis can be conducted on each target product based on the competition index. If the validity test of the competition index fails, the competition index needs to be re-determined. For example, the attention share of each user towards the target product can be re-determined based on newly selected behavioral data, and the competition index can be re-determined based on the new attention share. In addition, the interval weights of the attention share intervals can be adjusted, and the attention indices within the attention share intervals can be weighted and summed according to the adjusted interval weights until a competition index that passes the validity test is obtained.

[0097] Here, validity verification metrics for a specified product can be determined. If the validity of the competition index is verified based on these metrics, then the validity verification of the competition index is deemed successful. For example, if the ranking information is determined to match the actual competitive landscape, then the validity verification is deemed successful. Alternatively, if the changes in the ranking information of the competition index for the specified product in the target scenario meet expectations, then the validity verification is deemed successful.

[0098] The effectiveness testing scheme described above can be used to verify and test the competing models from multiple perspectives, thereby further ensuring the accuracy and effectiveness of the competing models.

[0099] In this embodiment of the disclosure, the validity verification indicators of a specified product can be determined in the following ways, specifically including:

[0100] Method 1:

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

[0102] Here, the first designated product can be a popular product, a special product, or a classic product, etc., that carries verification features. The verification features are pre-set product features that meet the validity verification.

[0103] In practice, the ranking information of the competitive index of the first designated product within the target verification period can be determined. The ranking information includes the positive ranking and negative ranking of competitors.

[0104] The positive competitor rankings for each product are used to indicate: SOA for non-product users among all users interested in this product. series-id The index is the result of ranking from high to low.

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

[0106] If the ranking information calculated by the competition index is found to match the actual competitive distance of the product as indicated by the actual competitive landscape, then the validity of the competition index is verified.

[0107] In practice, the product's competitive rankings (both positive and negative) can be determined based on the competition index. Then, the positive and negative competitor rankings are checked against the actual competitive landscape. If a match is found, the validity of the competition index is verified.

[0108] Method 2:

[0109] Determine how the ranking information of the competition index changes 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; no specific limitation is made here, only what can be achieved is required. Special events can include events such as product launch, negative public opinion, and auto shows.

[0111] In this embodiment of the disclosure, it can be determined whether the change in the ranking information of the competition index after a special event occurs to the second designated product is as expected. For example, it can be determined whether the changes in the positive ranking and negative ranking of competitors are as expected, and then the validity of the competition index can be verified based on the changes.

[0112] For example, negative public opinion can cause changes in a product's competition index ranking, such as a drop in ranking. In this case, it can be determined whether there have been corresponding changes in the positive and negative rankings of the product's competitors, and whether these changes are as expected. If they are, the validity of the competition index is verified; otherwise, the verification fails.

[0113] Method 3:

[0114] After delivering network materials to users corresponding to competing products of the third designated product, the changes in the ranking information of the competition index of the third designated product are determined.

[0115] Here, the first designated product, the second designated product, and the third designated product can be the same product or different products; no specific restrictions are imposed, and the feasibility shall prevail. Special events can include events such as product launch, negative public opinion, and auto shows.

[0116] In a competitor interception scenario, online materials, such as advertising materials, can be delivered to users of competitors' products that are designated as third-party products. Then, it can be determined whether the changes in the ranking information of the competition index of the designated third-party product meet expectations. For example, changes in the competitor's positive and negative rankings 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] The above implementation methods can verify the effectiveness of the competition index from multiple dimensions, thereby obtaining a more reasonable competition index and improving its accuracy and credibility.

[0118] In this embodiment of the disclosure, determining the effectiveness verification indicators for a specified product specifically includes the following steps:

[0119] First, identify the competing products of the specified product; wherein, the attention index of the specified product and the attention index of the competing products contain the same users;

[0120] Secondly, the competition index of the competing products is sorted to obtain the positive ranking of the competitors of the specified product.

[0121] Finally, the ranking of the competition index of the specified product is determined from the positive competition ranking of the competing products, and the ranking and the positive competition ranking of the specified product are determined as the ranking information of the competition index of the specified product.

[0122] In this embodiment of the disclosure, users who are interested in a specified product can be identified, and other products that these users are interested in can be identified and identified as competing products of the specified product. Next, the competition indices of the competing products are ranked to obtain a positive ranking of the competing products.

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

[0124] In this embodiment of the 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 according to the competition index, specifically including the following analysis scenarios:

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

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

[0127] In this embodiment of the disclosure, the competition index can be statistically analyzed from specific dimensions, thereby formulating a matching competition strategy based on the statistical analysis results.

[0128] (2) Recommendations of relevant products for users.

[0129] Here, products of interest to users, as well as core competitors of those products, can be recommended based on the competitiveness index of each target product. This enables user-level recommendations of products and content of interest, thereby enhancing the user experience.

[0130] (3) Determining the effectiveness of competitor interception 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 meets expectations. Then, based on the expected results, the effectiveness of the online material delivery can be determined, thereby realizing the effectiveness of intercepting commercial products.

[0132] The following is combined Figure 2 The above process will be described as a whole. In this embodiment, the target product is illustrated using a vehicle series as an example. Specifically, the process includes the following modules:

[0133] This module is used for preliminary research on user attention share. Specifically, it is used to determine users' purchase intentions for various car models.

[0134] In this module, you can use formulas Determine the user's attention share (i.e., purchase intention) for this car series. SOA is the user's attention share for this car series. SOA is defined as the ratio of a user's comprehensive behavior for a certain car series (the sum of various effective behavioral data for a certain car series) to the user's comprehensive behavior for all car series (the sum of various effective behavioral data for all car series).

[0135] The vehicle series basic data processing module. This module performs basic data processing on the attention share of all users of this vehicle series, as well as the attention share of all users of competing vehicle series. For example, it can determine a first attention index for this vehicle series based on the attention share, and determine a second attention index for competing vehicle series among users who are interested in this vehicle series.

[0136] The module for developing a vehicle series competition index is used to determine the competition index of a vehicle series based on the attention share interval after distributed processing. Specifically, this includes determining the competition index of the current vehicle series and the competition indices of its competitors. Specifically, the competition index can be further fitted based on the attention share of users within the current vehicle series; and the competition index of the corresponding competitors can be fitted and calculated based on the attention share of users interested in the current vehicle series towards competitors.

[0137] In this embodiment of the disclosure, the module can perform distributed processing on the attention index (i.e., the aforementioned first attention index) of each main vehicle series, thereby obtaining multiple attention percentage intervals. For example, attention percentage intervals (a1%-b1%), (a2%-b2%), ..., (a...) can be obtained. n %-b n In practical implementation, the attention percentages in the attention index can be sorted from largest to smallest. Then, the sorting results are distributed to obtain multiple attention percentage intervals. For example, the sorting results can be evenly divided; alternatively, non-uniform distribution processing can be performed according to preset rules. Afterward, the interval weight of each attention percentage interval can be determined, and the competition index of each main vehicle series can be determined based on each attention percentage interval and its interval weight.

[0138] Effectiveness Testing Module. This module uses Test Plan 1, Test Plan 2, and Test Plan 3 to test the effectiveness of the vehicle series' competitiveness index.

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

[0140] Test Plan 2: Conduct an effectiveness test based on the changes in the ranking information of the competition index after a special event occurs in the second designated car series.

[0141] Test Plan 3: After delivering online materials to users of 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 competition index passes the validity test, it can be applied to the following scenarios:

[0143] (1) Competition landscape of car series.

[0144] In this scenario, the competitive landscape and the strength of competitiveness of different tracks, different vehicle series, and different brands can be described and analyzed periodically based on the vehicle series competition index.

[0145] In this embodiment of the disclosure, the competition index can be statistically analyzed from specific dimensions, thereby formulating a matching competition strategy based on the statistical analysis results.

[0146] (2) Recommendations for relevant car models for users.

[0147] Here, based on the competitiveness index of each car series, users can be recommended car series of interest, as well as the core competing car series of the car series of interest, thereby realizing user-level recommendations of car series and content of interest, and thus enhancing the user experience.

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

[0149] By determining the ranking of competition indices for specific product deployment scenarios, the effectiveness of competitor interception can be assessed. For example, it can be determined whether ranking changes align with expectations, and based on these expectations, the effectiveness of online material deployment can be determined, thus validating the interception of products.

[0150] As described above, the disclosed technical solution, through its method of identifying the true competitive product combination and distance of an automotive brand using a competitive index, fully considers the depth and frequency of user behavioral data. It is suitable for the durable goods industry with long decision-making cycles, covers a broad user base, clearly quantifies competitive distance and intensity, and has strong representativeness and indicative power for the true competitive product combination of a vehicle series. Furthermore, this disclosed technical solution can efficiently identify the core competitors of a vehicle series in competitor interception scenarios, thereby providing data support for formulating competitive strategies for that vehicle series. It can also efficiently intercept users wavering between their own vehicle series and competing vehicle series, identify the influence of these 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-described method of the specific implementation, the order in which each step is written does not imply 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, this disclosure also provides a product competition analysis device corresponding to the product competition analysis method. Since the principle of the device in this disclosure for solving the problem is similar to the product competition analysis method described above in this disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0153] Reference Figure 3 The diagram shown is a schematic representation of a product competition analysis device provided in an embodiment of this disclosure. The device includes: a first determining unit 10, a second determining unit 20, and a competition analysis unit 30; wherein,

[0154] The first determining unit is used to determine the attention percentage of each user towards each product based on the behavioral data of each user towards each product; wherein, the attention percentage is used to indicate the user's attention percentage or mental percentage towards a specified model of a product under a target brand within the target industry;

[0155] The second determining unit is used to determine a first attention index of the target product based on the attention ratio, and to determine a second attention index of the user who is paying attention to the target product for the competitor; the first attention index is used to indicate the user's purchase intention for the target product, and the second attention index is used for the user's purchase intention for the competitor.

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

[0157] In the above implementation, by analyzing user behavior data for each product, the attention percentage that truly reflects user purchase intention can be obtained, thus providing a more reasonable data basis for competitive analysis of target products. By determining a single user's purchase intention (i.e., attention percentage) based on the proportion of their behavior data for each target product, and then aggregating this purchase intention to obtain the competitive index of each target product and its competitors, the competitive index of target products and competitors can be accurately predicted, quantifying the competitive relationship between target products. This allows for a more intuitive and quantitative representation of the competitive distance and situation between target products.

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

[0159] The first attention index of each target product is processed in a distributed manner to obtain multiple attention percentage intervals;

[0160] Determine the interval weight for each of the attention share intervals; wherein the interval weight is determined based on the actual lead generation conversion rate of the user corresponding to each of the attention share intervals;

[0161] Based on each attention share interval and the interval weight, a competition index for each target product is determined.

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

[0163] The target number is obtained by counting the number of users corresponding to each of the aforementioned attention percentage intervals;

[0164] The summation result is obtained by summing the products of each attention index, the number of targets, and the interval weight within each attention percentage interval;

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

[0166] In one possible implementation, the device is further used for:

[0167] Before conducting competitive analysis on each target product based on the competition index, effectiveness verification indicators for the specified product are determined; wherein, the effectiveness verification indicators include the matching between the ranking information of the competition index of the specified product and the actual competitive landscape, and / or, the changes in the ranking information of the competition index of the specified product in the target scenario;

[0168] If the effectiveness of the competition index is verified based on the effectiveness verification indicators, 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 the validity verification indicators of a specified product by at least one of the following methods:

[0170] Determine the matching between the ranking information of the competition index of the first designated product within the target verification period and the actual competitive landscape of the first designated product; wherein, the first designated product is a product carrying verification features;

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

[0172] After delivering network materials to users corresponding to competing products of the third designated product, the changes in the ranking information of the competition index of the third designated product are determined.

[0173] In one possible implementation, the device is further used for:

[0174] Identify competing products of the specified product; wherein the attention index of the specified product and the attention index of the competing products contain the same users;

[0175] The competitive indices of the competing products are sorted to obtain a positive ranking of the competitors for the specified product.

[0176] The ranking of the competition index of the specified product is determined from the positive ranking of the competitors' products, and the ranking and the positive ranking of the competitors' products are used as the ranking information of the competition index of the specified product.

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

[0178] The first result is obtained by weighting and summing the multiple behavioral data of each user for each product according to the preset behavioral weights.

[0179] The second result is obtained by weighting and summing multiple comprehensive behavioral data according to the preset behavioral weights; wherein, each type of comprehensive behavioral data is the sum of the same behavioral data of each user for all products;

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

[0181] The processing flow of each module in the device and the interaction flow between each module can be referred to the relevant descriptions in the above method embodiments, and will not be detailed here.

[0182] Corresponding to Figure 1 In one possible implementation of the method, embodiments of this disclosure also provide an electronic device 400, such as... Figure 4 The diagram shown is a structural schematic of an electronic device 400 provided in an embodiment of this disclosure, including:

[0183] The system includes a processor 41, a memory 42, and a bus 43. The memory 42 stores execution instructions and includes main memory 421 and external memory 422. The main memory 421, also called internal memory, temporarily stores the computational data in the processor 41, as well as data exchanged with external memory such as a hard disk. The processor 41 exchanges data with the external memory 422 through the main memory 421. When the electronic device 400 is running, the processor 41 communicates with the memory 42 through the bus 43, causing the processor 41 to execute the following instructions:

[0184] The attention percentage of each user towards each product is determined based on each user's behavioral data for each product; wherein, the attention percentage is used to indicate the user's attention percentage or mental percentage towards a specific model of a product under a target brand within the target industry;

[0185] Based on the attention percentage, a first attention index for the target product is determined, and a second attention index for the competing products of users who are interested in the target product is determined; the first attention index is used to indicate the purchase intention of users who are interested in the target product, and the second attention index is used to indicate the purchase intention of users of the competing products.

[0186] The competition index of the target product is determined based on the first attention index, and the competition index of the competing products is determined based on the second attention index. The competition analysis of the target product is then performed based on the competition index.

[0187] This disclosure also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the product competition analysis method described in the above-described method embodiments. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0188] This disclosure also provides a computer program product carrying program code. The program code includes instructions that can be used to execute the steps of the product competition analysis method described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0189] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0190] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this 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 illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0191] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0192] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0193] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0194] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. A product competition analysis method characterized by, include: The attention percentage of each user towards each product is determined based on each user's behavioral data for each product; wherein, the attention percentage is used to indicate the user's attention percentage or mental percentage towards a specific model of a product under a target brand within the target industry; Based on the attention percentage, a first attention index for the target product is determined, and a second attention index for the competing products of users who are interested in the target product is determined; the first attention index is used to indicate the purchase intention of users who are interested in the target product, and the second attention index is used to indicate the purchase intention of users of the competing products. The competition index of the target product is determined based on the first attention index, and the competition index of the competing products is determined based on the second attention index. The competition analysis of the target product is then performed based on the competition index. Wherein, determining the competition index of the target product based on the first attention index includes: The first attention index of each target product is processed in a distributed manner to obtain multiple attention percentage intervals; Determine the interval weight for each of the attention share intervals; wherein the interval weight is determined based on the actual lead generation conversion rate of the user corresponding to each of the attention share intervals; Based on each attention share interval and the interval weight, a competition index for each target product is determined; The step of determining the competition index of each target product based on each attention share interval and the interval weight includes: The target number is obtained by counting the number of users corresponding to each of the aforementioned attention percentage intervals; The summation result is obtained by summing the products of each attention index, the number of targets, and the interval weight within each attention percentage interval; Based on the sum of all the summation results, the competitiveness index of the target product is determined.

2. The method of claim 1, wherein, Before conducting a competitive analysis of the target product based on the competition index, the method further includes: Determine the effectiveness verification indicators for a specified product; wherein, the effectiveness verification indicators include the matching between the ranking information of the competition index of the specified product and the actual competitive landscape, and / or, the changes in the ranking information of the competition index of the specified product under the target scenario; If the effectiveness of the competition index is verified based on the effectiveness verification indicators, a competition analysis is performed on each of the target products according to the competition index.

3. The method of claim 2, wherein, The determination of the effectiveness verification indicators for the specified product includes at least one of the following: Determine the matching between the ranking information of the competition index of the first designated product within the target verification period and the actual competitive landscape of the first designated product; wherein, the first designated product is a product carrying verification features; Determine the changes in the ranking information of the competition index after a special event occurs to the second designated product; After delivering network materials to users corresponding to competing products of the third designated product, the changes in the ranking information of the competition index of the third designated product are determined.

4. The method of claim 2, wherein, The determination of the validity verification indicators for the specified product includes: Identify competing products of the specified product; wherein the attention index of the specified product and the attention index of the competing products contain the same users; The competitive indices of the competing products are sorted to obtain a positive ranking of the competitors for the specified product. The ranking of the competition index of the specified product is determined from the positive ranking of the competitors' products, and the ranking and the positive ranking of the competitors' products are used as the ranking information of the competition index of the specified product.

5. The method of claim 1, wherein, The process of determining the attention percentage of each user for each product based on each user's behavioral data for each product includes: The first result is obtained by weighting and summing the multiple behavioral data of each user for each product according to the preset behavioral weights. The second result is obtained by weighting and summing multiple comprehensive behavioral data according to the preset behavioral weights; wherein, each type of comprehensive behavioral data is the sum of the same behavioral data of each user for all products; The user's attention percentage for the product is determined based on the ratio between the first result and the second result.

6. A product competition analysis device characterized by comprising: include: The first determining unit is used to determine the attention percentage of each user towards each product based on the behavioral data of each user towards each product; wherein, the attention percentage is used to indicate the user's attention percentage or mental percentage towards a specified model of a product under a target brand within the target industry; The second determining unit is used to determine a first attention index of the target product based on the attention ratio, and to determine a second attention index of the user who is paying attention to the target product for the competing product; the first attention index is used to indicate the user's purchase intention for the target product, and the second attention index is used to indicate the user's purchase intention for the competing product. The competition analysis unit is used to determine the competition index of the target product based on the first attention index, and to determine the competition index of the competing products based on the second attention index, and to perform competition analysis on the target product according to the competition index; The competition analysis unit is further configured to: The first attention index of each target product is processed in a distributed manner to obtain multiple attention percentage intervals; Determine the interval weight for each of the attention share intervals; wherein the interval weight is determined based on the actual lead generation conversion rate of the user corresponding to each of the attention share intervals; Based on each attention share interval and the interval weight, a competition index for each target product is determined; The competition analysis unit is further configured to: The target number is obtained by counting the number of users corresponding to each of the aforementioned attention percentage intervals; The summation result is obtained by summing the products of each attention index, the number of targets, and the interval weight within each attention percentage interval; Based on the sum of all the summation results, the competitiveness index of the target product is determined.

7. An electronic device, comprising: include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. 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 5 are performed.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the product competition analysis method as described in any one of claims 1 to 5.

Images