A method and system for promoting competing products, a storage medium and a terminal

Abstract

This invention discloses a method, system, storage medium, and terminal for promoting competitive products. The method for promoting competitive products is characterized by the following steps: Step S1, establishing a decision model for the promoted product; Step S2, acquiring advertising strategy data from a first company; Step S3, acquiring alternative strategy data from a second company; Step S4, inputting the advertising strategy data from the first company and the alternative strategy data from the second company into the decision model for calculation, and the decision model outputs the result data. The beneficial effects of this invention are that it provides a method, system, storage medium, and terminal for promoting competitive products. This invention can update the optimal combination strategy of the second company in real time based on the marketing strategy of the first company, achieving the optimal combination strategy even in the face of strong competitors, thereby maximizing sales and ultimately maximizing revenue.

Description

Technical Field

[0001] This invention belongs to the field of public opinion dynamics technology, and relates to the field of product promotion technology for media platforms, specifically to a competitive product promotion method and system, storage medium and terminal. Background Technology

[0002] In real life, there exists a small group of individuals who wield unique influence within their organizations or groups. Influence refers to an individual's ability to influence and change the psychology and behavior of others in their interactions. These individuals with special influence are called opinion leaders. Through their unique influence, opinion leaders can affect the views and behaviors of other members of their organizations and groups, and may even lead other members to follow and obey them.

[0003] In the process of information dissemination, opinion leaders on social media have a significant impact on consumers' purchasing decisions. On Twitter, 49% of respondents rely on products recommended by opinion leaders, and 40% of Twitter users purchase products recommended by opinion leaders. Opinion leaders can influence changes in user attitudes towards products or services, thereby affecting their purchasing decisions. Therefore, opinion leaders recommending products or services to others on social networks is a marketing strategy.

[0004] Existing research has yielded significant results regarding the dissemination of information by opinion leaders, but it still has the following limitations:

[0005] (1) In the actual process of public opinion dynamics, opinion leaders will first be influenced by the target advertisement or product, and thus form their own opinions on the advertisement or product. Therefore, the influence of advertisement or product on the formation of opinion leaders' opinions should be a key consideration.

[0006] (2) Existing research only considers the influence of opinion leaders on their followers. However, in practice, opinion leaders have a probability of disseminating targeted advertising or product information, and followers, after successfully receiving the information, will adjust their opinions according to the limited confidence rule. Therefore, the impact of the probability of successful information delivery deserves attention.

[0007] Therefore, establishing an opinion dynamics model with opinion leaders can analyze and explain how the opinions of different individuals and external public information influence the formation and evolution of group opinions, such as complex social phenomena like advertising and the spread of rumors. It can also predict, prevent, and positively guide certain social behaviors. This is particularly important for companies influencing competing products, as marketing strategies directly affect the sales and reputation of competing products. Summary of the Invention

[0008] To address the aforementioned problems in the prior art, this invention provides a method and system for promoting competitive products, a storage medium, and a terminal.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0010] A method for promoting competing products is provided, characterized by comprising the following steps:

[0011] Step S1: Establish a decision model for promotional products;

[0012] Step S2: Obtain the advertising strategy data of the first company;

[0013] Step S3: Obtain alternative strategy data from the second company;

[0014] Step S4: Input the advertising strategy data of the first company and the alternative strategy data of the second company into the decision model for calculation, and the decision model outputs the result data.

[0015] Preferably, the advertising strategy data includes information about the first company's hiring of opinion leader group A, information about advertisement P, and the influence weight of advertisement P, with opinion leader group A promoting advertisement P;

[0016] The information of opinion leader group A includes the ID of the opinion leader in opinion leader group A, the number of opinion leaders, and the probability of successful transmission of opinion leaders.

[0017] The alternative strategy data includes a contract database, which contains information about opinion leader group B and advertising Q, and opinion leader group B promotes advertising Q.

[0018] The information of opinion leader group B includes the ID of the opinion leader in opinion leader group B, the number of opinion leaders, and the probability of successful transmission of opinion leaders.

[0019] The resulting data includes the advertising influence weight of the second company, the estimated transaction volume, and the opinion leader group C.

[0020] Preferably, in step S11, there are N nodes in the preset social network, one user is simplified to one node, N2 and N3 are the number of opinion leader groups A and the number of opinion leader groups B, N1 is the number of opinion followers, and N1+N2+N3=N;

[0021] Step S12: Initialize the random network adjacency matrix [λ] ij ] N×N , λ ij =1 indicates that there is a connection between individual i and individual j, λ ij=0 indicates that there is no connection between individual i and individual j;

[0022] Step S13: Calculate the combined probability of opinion followers accepting advertisement P and advertisement Q;

[0023] Step S14: Update the opinion value of opinion leader group A;

[0024] Step S15: Update the opinion value of opinion leader group B;

[0025] Step S16: Update the opinion value of opinion followers.

[0026] Preferably, the probability that opinion followers accept the combined effect of advertisement P and advertisement Q is:

[0027]

[0028] Where, μ i θ(t) represents the probability that an opinion follower receives advertisement P, where θ i (t) represents the probability that an opinion follower receives advertisement Q, and t represents time;

[0029] Where ξ represents the probability that opinion leader group A successfully transmits the information of target advertisement P to opinion followers, 0≤ξ≤1;

[0030] Where η represents the probability that opinion leader group B successfully transmits the information of target advertisement Q to opinion followers, 0≤η≤1;

[0031] Where, ε F Indicates the confidence level of opinion follower F;

[0032] Where, x i (t) represents the viewpoint value of individual i at time t, x j (t) represents the viewpoint value of individual j at time t;

[0033] The rules for updating opinions in opinion leader group A are as follows:

[0034]

[0035] in, Bounded trust rules for opinion leader group A;

[0036] Where, ε A The confidence level of opinion leader group A;

[0037] in, This represents the number of neighbors of individual i in opinion leader group A who have exchanged opinions with him at time t;

[0038] Where, ε AThe influence weight of advertisement P, P A The target ad value for ad P;

[0039] The rules for updating opinions in opinion leader group B are as follows:

[0040]

[0041] in, Bounded trust rules for opinion leader group B;

[0042] Where, ε B The confidence level of opinion leader group B;

[0043] in, This represents the number of neighbors of individual i in opinion leader group B who exchanged views with him at time t.

[0044] Where, ω B As the influence weight of advertisement Q, P B The target ad value for ad Q;

[0045] The rules for updating the opinions of opinion followers are as follows:

[0046]

[0047] in,

[0048]

[0049] in Bounded trust rules for opinion followers;

[0050] Where, ε F It represents the confidence level of opinion followers;

[0051] Where, γ i , These represent the level of trust that opinion followers have in opinion leader group A and opinion leader group B, respectively.

[0052] Preferably, the alternative strategy data includes a pre-contract database, which contains information on opinion leader group B and information on alternative opinion leader group B1.

[0053] The information of the candidate opinion leader group B1 includes the signing cost of the candidate opinion leader group B1, the ID of the candidate opinion leader group B1 in the candidate opinion leader group B1, the number of candidate opinion leader groups B1, and the success probability of the transmission of the candidate opinion leader group B1.

[0054] The results data show the second company's signing decisions regarding the candidate opinion leader group B1.

[0055] Preferably, the signed database and the pre-signed database are input into the decision model. The revenue result corresponding to the signed database is the first estimated revenue, and the revenue result corresponding to the pre-signed database is the second estimated revenue, and the following conditions are met:

[0056] If the second estimated revenue minus the first estimated revenue is greater than the contract signing cost, the output data will show a contract signed; otherwise, the output data will show a contract not signed.

[0057] A competitive product promotion system, characterized in that it includes,

[0058] The aforementioned competitive product promotion method can be implemented.

[0059] A storage medium, characterized in that it comprises,

[0060] Used to store a specified computer program, the execution of which can implement the aforementioned competitive product promotion method.

[0061] A terminal, characterized in that it includes,

[0062] Memory used to store executable program code;

[0063] processor;

[0064] The processor is coupled to the memory;

[0065] The processor calls the executable program code stored in the memory to execute the competitive product promotion method.

[0066] Preferably, it has an input interface, an output interface, and a control interface;

[0067] The input interface is used to input data into the decision-making model. The input interface has an input terminal for the advertising strategy data of the first company and an input terminal for the alternative strategy data of the second company.

[0068] The output interface is used to output result data, and the output interface has a result data output terminal;

[0069] The control interface offers a choice of different decision-making modes.

[0070] The beneficial effects of this invention are reflected in providing a competitive product promotion method and system, storage medium, and terminal. This invention can update the optimal combination strategy of a second company in real time based on the marketing strategy of the first company, achieving optimal combination strategy even in the face of strong competitors, thus maximizing sales and revenue. By calculating and comparing decisions through a decision model to determine the most profitable decision for the company, the risk of signing top livestreamers is significantly reduced. Attached Figure Description

[0071] Figure 1 A flowchart of a competitive product promotion method;

[0072] Figure 2 Flowchart for establishing a decision-making model;

[0073] Figure 3 The parameter is ω A =0.1, ω B Experimental results of the decision model with a value of 0.1;

[0074] Figure 4 The parameter is ω A =0.3, ω B Experimental results of the decision model with a value of 0.1;

[0075] Figure 5 The parameter is ω A =0.5, ω B Experimental results of the decision model with a value of 0.1;

[0076] Figure 6 The parameter is ω A =0.7, ω B Experimental results of the decision model with a value of 0.1;

[0077] Figure 7 The parameter is ω A =0.9, ω B Experimental results of the decision model with a value of 0.1;

[0078] Figure 8 This is a schematic diagram of a promotional terminal for a competing product.

[0079] Figure 9 This is a schematic diagram of a promotional terminal interface for a competing product. Detailed Implementation

[0080] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0081] Please see Figures 1-9 As shown, the specific embodiments provided by the present invention are as follows:

[0082] Example 1:

[0083] A method for promoting a competing product, characterized by comprising the following steps:

[0084] Step S1: Establish a decision model for promotional products;

[0085] Step S2: Obtain the advertising strategy data of the first company;

[0086] Step S3: Obtain alternative strategy data from the second company;

[0087] Step S4: Input the advertising strategy data of the first company and the alternative strategy data of the second company into the decision model for calculation, and the decision model outputs the result data.

[0088] With the continuous development of the internet, online transactions have become an indispensable part of people's lives. Promotional methods such as product recommendations, live-streaming e-commerce, and brand endorsements have significantly changed product sales. For product sellers, they can only analyze and summarize past data to develop promotional strategies, but cannot provide a comprehensive evaluation and prediction of the overall promotional strategy. For competing products, judgment relies heavily on experience, which involves many uncontrollable factors, such as advertising investment, celebrity endorsements, and promotional ambassadors, posing significant risks. Therefore, providing a promotional method specifically for competing products is highly valuable.

[0089] In this embodiment, as Figure 1 As shown, a method for promoting competitive products is provided, characterized by the following steps: Step S1, establishing a decision model for the promoted product; Step S2, acquiring advertising strategy data from a first company; Step S3, acquiring alternative strategy data from a second company; Step S4, inputting the advertising strategy data from the first company and the alternative strategy data from the second company into the decision model for calculation, and the decision model outputs the result data. Wherein, the first company and the second company sell similar products and are competitors. This invention constructs a decision model where the first company and the second company are competitors. The first company's advertising and marketing strategy data and the second company's existing alternative strategy data are input into the decision model. Through calculation and analysis by the decision model, this invention derives multiple promotional strategies for the second company and finally outputs the optimal combination strategy for the second company. This invention can update the second company's optimal combination strategy in real time based on the first company's marketing strategy, achieving the optimal combination strategy even with strong competitors, maximizing sales volume, and thus maximizing revenue.

[0090] Example 2:

[0091] The advertising strategy data includes information about the opinion leader group A hired by the first company, information about advertisement P, and the influence weight of advertisement P. Opinion leader group A promotes advertisement P. The information about opinion leader group A includes the ID of the opinion leader in opinion leader group A, the number of opinion leaders, and the probability of successful transmission by the opinion leaders.

[0092] The alternative strategy data includes a contract database, which contains information about opinion leader group B and advertisement Q. Opinion leader group B promotes advertisement Q. The information about opinion leader group B includes the ID of the opinion leader in opinion leader group B, the number of opinion leaders, and the probability of successful delivery of opinion leaders.

[0093] The results data include the advertising influence weight of the second company, the estimated transaction volume, and the opinion leader group C.

[0094] In this embodiment, the advertising strategy data includes information about opinion leader group A hired by the first company, information about advertisement P, and the influence weight of advertisement P. Opinion leader group A promotes advertisement P. The information about opinion leader group A includes the IDs of opinion leaders in opinion leader group A, the number of opinion leaders, and the success rate of opinion leader delivery. Taking Douyin users as an example, the information about opinion leader group A is the information of top anchors hired by the first company, the information about advertisement P is the advertising information of product P of the first company, and the influence weight of advertisement P is the cost of advertisement P invested by the first company. The higher the cost, the higher the influence weight. In opinion leader group A, ID represents the identity identification number of the anchor in opinion leader group A, the number of opinion leaders is the number of anchors in opinion leader group A, and the success rate of opinion leader delivery is the probability that each anchor in opinion leader group A will successfully deliver the target advertisement P to opinion followers. Opinion followers are ordinary users who are not top anchors.

[0095] In this embodiment, the alternative strategy data includes a contract database containing information about opinion leader group B and advertisement Q. Opinion leader group B promotes advertisement Q. The information about opinion leader group B includes the IDs of opinion leaders in opinion leader group B, the number of opinion leaders, and the success rate of opinion leader delivery. Taking Douyin users as an example, the contract database includes a database of top anchors signed by the second company. The information about opinion leader group B is the information of top anchors signed by the second company. The information about advertisement Q is the advertisement information of product Q promoted by the second company. In opinion leader group B, ID represents the identity identification number of the anchor in opinion leader group B, the number of opinion leaders is the number of anchors in opinion leader group B, and the success rate of opinion leader delivery is the probability that each anchor in opinion leader group B will successfully deliver the target advertisement Q to its followers.

[0096] The results data include the advertising influence weight of the second company, the estimated sales volume, and the working opinion leader group C. The advertising influence weight represents the advertising expenditure of the second company on this product; the higher the cost of advertising Q, the greater the advertising influence weight of the second company, and vice versa. The closer the opinions of the opinion followers are to those of opinion leader group B, the greater the probability that the opinion followers will purchase the product promoted by opinion leader group B, thus resulting in a higher estimated sales volume for the second company. The working opinion leader C refers to the information of the anchors who need to complete the promotional work in this promotional activity.

[0097] It should be noted that social media platforms are not limited to Douyin, but can also include Weibo, WeChat, Kuaishou, and other social platforms.

[0098] Example 3:

[0099] Step S11: Presuppose there are N nodes in the social network. A user is simplified to one node. N2 and N3 are the number of opinion leader groups A and the number of opinion leader groups B, respectively. N1 is the number of opinion followers, and N1 + N2 + N3 = N.

[0100] Step S12: Initialize the random network adjacency matrix [λ] ij ] N×N , λ ij =1 indicates that there is a connection between individual i and individual j, λ ij =0 indicates that there is no connection between individual i and individual j;

[0101] Step S13: Calculate the combined probability of opinion followers accepting advertisement P and advertisement Q;

[0102] Step S14: Update the opinion value of opinion leader group A;

[0103] Step S15: Update the opinion value of opinion leader group B;

[0104] Step S16: Update the opinion value of opinion followers.

[0105] By constructing a decision-making model, this invention simulates the evolution of opinion followers' views under the influence of two advertising opinion leaders, thereby analyzing the impact of opinion leaders on the evolution of followers' opinions under different advertising intensities and information delivery probabilities. This invention explores the changing patterns of opinion followers' views, helping companies better understand the influence of opinion leaders and implement marketing strategies more effectively on social media platforms. This invention plays a crucial role in helping companies in social networks effectively manage the promotional intensity of product advertisements to enhance the effectiveness of opinion leaders' advertising.

[0106] Example 4:

[0107] The probability that opinion followers accept the combined combination of advertisement P and advertisement Q is:

[0108]

[0109] Where, μ i θ(t) represents the probability that an opinion follower receives advertisement P, where θ i (t) represents the probability that an opinion follower receives advertisement Q, and t represents time;

[0110] Where ξ represents the probability that opinion leader group A successfully transmits the information of target advertisement P to opinion followers, 0≤ξ≤1;

[0111] Where η represents the probability that opinion leader group B successfully transmits the information of target advertisement Q to opinion followers, 0≤η≤1;

[0112] Where, ε F Indicates the confidence level of opinion follower F;

[0113] Where, x i (t) represents the viewpoint value of individual i at time t, x j (t) represents the viewpoint value of individual j at time t;

[0114] The rules for updating opinions in opinion leader group A are as follows:

[0115]

[0116] in, Bounded trust rules for opinion leader group A;

[0117] Where, ε A The confidence level of opinion leader group A;

[0118] in, This represents the number of neighbors of individual i in opinion leader group A who have exchanged opinions with him at time t;

[0119] Where, ω A The influence weight of advertisement P, P A The target ad value for ad P;

[0120] The rules for updating opinions in opinion leader group B are as follows:

[0121]

[0122] in, Bounded trust rules for opinion leader group B;

[0123] Where, ε B The confidence level of opinion leader group B;

[0124] in, This represents the number of neighbors of individual i in opinion leader group B who exchanged views with him at time t.

[0125] Where, ω B As the influence weight of advertisement Q, P B The target ad value for ad Q;

[0126] The rules for updating the opinions of opinion followers are as follows:

[0127]

[0128] in,

[0129]

[0130] in Bounded trust rules for opinion followers;

[0131] Where, ε F It represents the confidence level of opinion followers;

[0132] Where, γ i , These represent the level of trust that opinion followers have in opinion leader group A and opinion leader group B, respectively.

[0133] In real life, users' opinions are influenced by others. Users often evaluate others' opinions based on the difference between their own and others' opinions before considering whether to adjust their own. The bounded trust rule model stipulates that the premise for individual opinion exchange is that the difference between their opinions must be less than or equal to a certain threshold. This specific threshold is called the trust level, confidence level, or tolerance. Opinion exchange partners selected based on this specific threshold are called neighbors.

[0134] In this embodiment, the flowchart for establishing the decision model is as follows: Figure 2 As shown, Figures 3 to 7 This diagram illustrates how the opinions of opinion leaders A, B, and followers change as the influence weight of advertisement Q changes, assuming the influence weight of advertisement P remains constant. The black line represents changes in follower opinion values, the dark gray line represents changes in opinion leader A's opinion values, and the light gray line represents changes in opinion leader B's opinion values. The horizontal axis represents time, and the vertical axis represents opinion values. A value of 0.5 indicates full support for advertisement P, a value of -0.5 indicates full support for advertisement Q, a value between (0, 0.5) indicates a stronger willingness to support advertisement P than advertisement Q, and a value between (-0.5, 0) indicates a stronger willingness to support advertisement Q than advertisement P.

[0135] specific Figure 3 Represents ω A =0.1, ω B Changes in opinion when = 0.1 Figure 4 Represents ω A =0.3, ω B Changes in opinion when = 0.1 Figure 5 Represents ω A =0.5, ω B Changes in opinion when = 0.1 Figure 6 Represents ω A =0.7, ω B Changes in opinion when = 0.1 Figure 7 Represents ω A =0.9, ω B Changes in opinion when = 0.1, from 3 to Figure 7 It can be seen that when ω B When ω remains unchanged A As the number of opinion followers increases, their support for ad P gradually increases, but when ω... A When it reaches a certain amount, then increase ω further. A Opinion followers, instead of supporting ad P, turn to support ad Q. Therefore, for multiple competing products on a social network, the advertising influence of competitors should be within an effective range; advertising influence exceeding this range will have a negative impact. The higher the probability of an opinion leader successfully conveying their message, the more conducive it is to the evolution of followers' opinions towards the target ad's message.

[0136] Through practical research, it was found that increasing the influence weight of advertisements may lead to irrational distrust of marketing messages, as the honesty of information disseminators depends on the source of the information. When opinion followers perceive opinion leaders as dishonest, they will scrutinize information more closely. Therefore, when advertisements are delivered through social networks, an excessively high influence weight compared to competitors' advertisements can cause interference attribution, leading opinion followers to distrust the advertiser. Even when opinion leaders promote advertisements containing valuable information, consumers are prone to questioning the ads when sharing them.

[0137] Therefore, companies should reasonably manage the influence weight of advertising, as there is an effective range within which advertising opinions can gain support from followers. Advertising opinions exceeding this effective range will cause consumer suspicion and lead to negative product promotion. This invention can predict the effective range of advertising influence weight based on a decision-making model, largely avoiding situations where advertising causes consumer suspicion, thereby reducing the company's marketing risks.

[0138] Example 5:

[0139] The alternative strategy data includes a pre-contracted database, which contains information on opinion leader group B and information on alternative opinion leader group B1.

[0140] The information of the candidate opinion leader group B1 includes the signing cost of the candidate opinion leader group B1, the ID of the candidate opinion leader group B1 in the candidate opinion leader group B1, the number of candidate opinion leader groups B1, and the success probability of the transmission of the candidate opinion leader group B1.

[0141] The results data show the second company's signing decisions regarding the candidate opinion leader group B1.

[0142] Input the signed database and the pre-signed database into the decision model. The revenue result corresponding to the signed database is the first estimated revenue, and the revenue result corresponding to the pre-signed database is the second estimated revenue, and the following conditions are met:

[0143] If the second estimated revenue minus the first estimated revenue is greater than the contract signing cost, the output data will show a contract signed; otherwise, the output data will show a contract not signed.

[0144] When promoting livestreamers, besides considering the revenue generated, the cost of promotion also needs to be taken into account. This is especially true for second-tier companies, who, having already acquired the marketing strategies of the first company, must carefully select their livestreamers. Signing top-tier livestreamers carries significant risks for second-tier companies. The nature of a livestreamer's fanbase is unpredictable, and there's a possibility of dissenting voices who could negatively impact the success rate of a top livestreamer's campaigns. Signing top-tier livestreamers often incurs high costs for second-tier companies, but the probability of those livestreamers generating revenue for the second company is unpredictable. Therefore, providing a method for intelligently deciding whether to sign a top-tier livestreamer is highly valuable.

[0145] In this embodiment, the alternative strategy data includes a pre-contract database containing information on opinion leader group B and alternative opinion leader group B1. The information on alternative opinion leader group B1 includes the signing cost, ID, quantity, and success probability of delivery. Taking Douyin promotion as an example, alternative opinion leader group B1 represents potential top streamers for contract signing. The signing cost of alternative opinion leader group B1 is the hiring cost of signing these top streamers over a period of time. The ID of alternative opinion leader group B1 is the ID of the potential top streamer for contract signing. The quantity of alternative opinion leader group B1 represents the quantity of potential top streamers for contract signing. The success probability of delivery by alternative opinion leader group B1 is the probability that the potential top streamer will successfully deliver the target advertisement. The result data includes the second company's contract signing decision regarding alternative opinion leader group B1.

[0146] In this embodiment, the estimated transaction volume is positively correlated with the absolute value of opinion followers following the Q advertisement, and the estimated transaction volume is positively correlated with the estimated revenue. Therefore, the opinion value of opinion followers following the Q advertisement can reflect the estimated revenue. The signed database and the pre-signed database are input into the decision model. The revenue result corresponding to the signed database is the first estimated revenue, and the revenue result corresponding to the pre-signed database is the second estimated revenue. This invention inputs information on potential top streamers into the decision model, calculates the revenue increment brought to the company after adding potential top streamers, and compares the revenue increment with the signing cost to determine whether to sign. In one embodiment, if the second estimated revenue - the first estimated revenue > the signing cost, the output data is "signed"; otherwise, the output data is "not signed." The signing decision of this invention is based on competitors' marketing data. Through the decision model calculation and comparison, the decision most beneficial to the company's profitability is obtained, which greatly reduces the risk of signing top streamers.

[0147] Example 6:

[0148] The second company's alternative strategy data is updated at intervals.

[0149] In reality, online information travels extremely fast, and opinions among followers change rapidly. For example, a single viewpoint expressed by a streamer can cause them to quickly gain or lose followers, and even a scandal can cause an internet celebrity to lose followers or even have their account suspended. Even top streamers who have signed contracts face the same problem; when a streamer's followers decrease, the probability of successfully conveying advertising information to their followers drops significantly. If the data on alternative strategies from the second company is not updated in a timely manner, the calculated results will deviate from reality, and in business competition, a single misjudgment can lead to huge losses for the company.

[0150] In this embodiment, considering the continuous changes in opinion leader information, the alternative strategy data of the second company is updated at regular intervals. This allows for the timely identification of unqualified opinion leaders and the timely replacement of their information, ensuring the accuracy of the input data to the decision-making model and thus improving its accuracy, thereby safeguarding the actual interests of the second company.

[0151] Example 7:

[0152] A competitive product promotion system, characterized in that it includes,

[0153] The aforementioned competitive product promotion method can be implemented.

[0154] A storage medium, characterized in that it comprises,

[0155] Used to store a specified computer program, the execution of which can implement the aforementioned competitive product promotion method.

[0156] A terminal, characterized in that it includes,

[0157] Memory used to store executable program code;

[0158] processor;

[0159] The processor is coupled to the memory;

[0160] The processor calls the executable program code stored in the memory to execute the competitive product promotion method.

[0161] It has an input interface, an output interface, and a control interface;

[0162] The input interface is used to input data into the decision-making model. The input interface has an input terminal for the advertising strategy data of the first company and an input terminal for the alternative strategy data of the second company.

[0163] The output interface is used to output result data, and the output interface has a result data output terminal;

[0164] The control interface offers a choice of different decision-making modes.

[0165] In this embodiment, as Figure 8 As shown, the terminal includes a processor, a memory, an input interface, an output interface, and a control interface; wherein, in one embodiment, a power supply is used to provide power. Figure 9 As shown, advertising strategy data from the first company is input into the first company's input interface, and alternative strategy data from the second company is input into the second company's input interface. A decision-making mode is selected through the control interface, and the results, calculated by the decision model, are output through the second company's output interface. The control interface includes selection modes such as minimum advertising investment, maximum estimated revenue, and maximum contractual benefit. This invention can help businesses make promotional and contractual decisions, etc.

[0166] In the description of the embodiments of the present invention, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "center", "top", "bottom", "top", "bottom", "inner", "outer", "inner side", "outer side", etc. indicate the orientation or positional relationship.

[0167] In the description of the embodiments of the present invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," "joining," and "assembly" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0168] In the description of embodiments of the present invention, specific features, structures, materials or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0169] In the description of the embodiments of the present invention, it should be understood that "-" and "~" represent a range of two numerical values, and this range includes the endpoints. For example, "AB" represents a range greater than or equal to A and less than or equal to B. "A~B" represents a range greater than or equal to A and less than or equal to B.

[0170] In the description of embodiments of the present invention, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship. Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method of promoting a competing product, characterized by, The method comprises the following steps, Step S1, establishing a decision model of the promotion product; Step S2, obtaining the advertising strategy data of the first company; Step S3, obtaining the alternative strategy data of the second company; Step S4, inputting the advertising strategy data of the first company and the alternative strategy data of the second company into the decision model for calculation, and outputting result data by the decision model; Further comprising: step S11, presetting that there are N nodes in the social network, a user is simplified as a node, N2 and N3 are the number of opinion leader group A and the number of opinion leader group B, N1 is the number of opinion followers, and N1+N2+N3=N is satisfied; Step S12, initializing a random network adjacency matrix , denotes that there is a connection between individual i and individual j, denotes that there is no connection between individual i and individual j; Step S13, calculating the synthesis probability of the opinion followers accepting the advertisement P and the advertisement Q; Step S14, updating the opinion value of the opinion leader group A; Step S15, updating the opinion value of the opinion leader group B; Step S16, updating the opinion value of the opinion followers; The alternative strategy data has a pre-signing database, the pre-signing database has the information of the opinion leader group B and the information of the alternative opinion leader group B1; the information of the alternative opinion leader group B1 has the signing cost of the alternative opinion leader group B1, the ID of the alternative opinion leader group B1 in the alternative opinion leader group B1, the number of the alternative opinion leader group B1, and the transmission success probability of the alternative opinion leader group B1; and the result data has the signing decision of the second company to the alternative opinion leader group B1. Also included is inputting a subscription database and a pre-subscription database into a decision model, the subscription database corresponding to a first estimated revenue and the pre-subscription database corresponding to a second estimated revenue, and satisfying, if , the result data outputting as a subscription, otherwise the result data outputting as a non-subscription.

2. The method according to claim 1, wherein, The advertising strategy data has the information of the opinion leader group A employed by the first company, the information of the advertisement P, and the influence weight of the advertisement P, and the opinion leader group A promotes the advertisement P; The information of the opinion leader group A has the ID of the opinion leader in the opinion leader group A, the number of the opinion leaders, and the transmission success probability of the opinion leaders; The alternative strategy data has a signing database, and the signing database has the information of the opinion leader group B and the information of the advertisement Q, and the opinion leader group B promotes the advertisement Q; The information of the opinion leader group B has the ID of the opinion leader in the opinion leader group B, the number of the opinion leaders, and the transmission success probability of the opinion leaders; The result data has the advertising influence weight of the second company, the estimated transaction volume, and the working opinion leader group C.

3. A method of promoting a competitive product according to claim 2, wherein The synthesis probability of the opinion followers accepting the advertisement P and the advertisement Q is, （1） wherein, denotes the probability that an opinion follower receives advertisement P, denotes the probability that an opinion follower receives advertisement Q, t denotes time; wherein, denotes the probability that the opinion leader group A successfully delivers the information of the target advertisement P to the opinion followers, ; wherein, represents the probability that the opinion leader group B successfully delivers the information of the target advertisement Q to the opinion followers, ; wherein, represents the confidence level of the opinion follower F; wherein, represents the opinion value of individual i at time t, represents the opinion value of individual j at time t; The opinion update rule of the opinion leader group A is, （2） wherein, A bounded trust rule for opinion leaders group A; wherein, is the confidence level of the opinion leader group A; wherein, represents the number of neighbors with which individual i in opinion leader group A has exchanged opinions at time t. wherein, is the influence weight of the advertisement P, is the target advertisement value of the advertisement P; The opinion update rule of the opinion leader group B is, （3） wherein, is the bounded trust rule for opinion leader group B; wherein, is the confidence level of the opinion leader group B; wherein, represents the number of neighbors with which individual i in opinion leader group B has exchanged opinions at time t; wherein, is the influence weight for advertisement Q, is the target advertisement value for advertisement Q; The opinion update rule of the opinion followers is, （4） wherein , ； wherein bounded trust rules for opinion followers; wherein, is the confidence level of the opinion follower; wherein, respectively represent the trust degree of opinion followers to opinion leader group A and opinion leader group B.

4. A competitive product promotion system characterized by, The method comprises, The method according to any one of claims 1-3.

5. A storage medium, characterized by The method according to any one of claims 1-3. The method according to any one of claims 1-3.

6. A terminal, characterized by comprising: The method according to any one of claims 1-3. The method according to any one of claims 1-3. ​ ​ ​ 7. The terminal of claim 6, wherein, the terminal has an input interface, an output interface and a control interface; the input interface is configured to input data to the decision model, the input interface has a first company's advertising strategy data input and a second company's alternative strategy data input; the output interface is configured to output result data, the output interface has a result data output; the control interface provides a selection of different decision modes.

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