Insurance advertisement personalized recommendation method based on big data

Abstract

The invention relates to the technical field of advertisement promotion, and discloses a big data-based insurance advertisement personalized recommendation method, which comprises the following steps of: performing advertisement putting on an internet advertisement platform, acquiring and analyzing insurance purchase information of browsing crowds, and formulating a processing strategy according to the information; if yes, sales are selected for promotion according to the first promotion strategy; if the user has the purchase record, judging whether real-time popularization is needed or not according to the purchase time period and the current time; according to the method, the insurance promotion effect and efficiency are improved through accurate user behavior data analysis and the conversion success rate of salesmen; the method comprises the specific steps of obtaining information of browsing crowds, analyzing insurance purchase information, numbering and inputting information, formulating popularization strategies for different users, and dispatching appropriate sales personnel for popularization.

Description

Technical Field

[0001] The present invention relates to the technical field of advertising promotion, and in particular to a method for personalized recommendation of insurance advertisements based on big data. Background Art

[0002] With the rapid development of the Internet and big data technology, more and more industries have begun to use big data analysis technology to improve business efficiency and precision marketing; as a service industry closely related to people's lives, the insurance industry's marketing model is also undergoing significant changes; in this context, Internet advertising platforms have become an important channel for insurance advertising; through Internet advertising platforms, insurance companies can reach potential customers widely.

[0003] Traditional insurance advertising promotion methods mainly rely on extensive user exposure and large-scale advertising, but this method has significant shortcomings; in the existing technology, there is insufficient dynamic analysis of user purchasing behavior; insurance purchasing behavior has a certain periodicity, and user needs change over time, especially when the user's existing insurance coverage expires, it is more likely to generate a need to purchase again; however, the traditional advertising delivery system lacks accurate analysis of the user's insurance coverage period and cannot remind sales personnel to follow up in real time at the right time; the existing technology cannot efficiently use the insurance coverage cycle to optimize advertising and sales strategies, resulting in a common loss of potential customers; secondly, the existing technology usually does not have the ability to monitor and optimize the conversion effect of sales personnel, resulting in unreasonable allocation of sales resources; for example, in some cases, the promotion ability of sales personnel does not match the needs of the allocated potential customers, resulting in poor promotion results; and the existing technology lacks effective means of evaluating the sales success rate, and there is no function for sales personnel to recommend specific products and customer needs, which limits the success rate of sales promotion.

[0004] To sum up, the existing technology has many shortcomings in accurately matching user needs and optimizing sales conversion rates. There is an urgent need for a personalized insurance advertising recommendation method based on big data to solve these technical pain points and improve the efficiency of advertising and the rational use of sales resources. Summary of the invention

[0005] The present invention provides a method for personalized recommendation of insurance advertisements based on big data, which helps solve the problems mentioned in the above background technology.

[0006] The present invention provides the following technical solution: a method for personalized recommendation of insurance advertisements based on big data, comprising:

[0007] Place advertisements on Internet advertising platforms;

[0008] The insurance operating organization that places the advertisement shall be recorded as the first operating organization;

[0009] Get all the people who click and browse the advertisement and record them as the selected people;

[0010] Send a request for insurance purchase information to all users in the candidate group;

[0011] The users who agree to the request for insurance purchase information in the candidate population constitute the browsing population;

[0012] Obtain insurance purchase information of all users in the browsing population;

[0013] The insurance purchase information includes the type of insurance purchased, the operating organization of the purchased insurance, the coverage period of the purchased insurance, and the number of times the insurance was purchased;

[0014] Number and enter the insurance purchase information of all users according to the insurance purchase information processing strategy;

[0015] Target every user in the browsing population;

[0016] Get the number of times the user has purchased insurance;

[0017] If the number of times the user has purchased insurance is zero, then the salesperson is dispatched to promote insurance to this user according to the first promotion strategy;

[0018] If the number of times the user has purchased insurance is not zero, determine whether it is necessary to dispatch a salesperson to conduct real-time insurance promotion for this user;

[0019] If there is no need to dispatch sales to conduct real-time insurance promotion for this user, this user will be included in the promotion list;

[0020] If it is necessary to dispatch sales to conduct real-time insurance promotion for this user, this user will be included in the promotion list;

[0021] For each user in the promotion list, when it is necessary to dispatch sales to carry out real-time insurance promotion for the user, dispatch sales to carry out insurance promotion for the user;

[0022] For each user in the promotion list, the salesperson is dispatched to conduct real-time insurance promotion to the user according to the second promotion strategy.

[0023] Optionally, the step of numbering and entering the insurance purchase information of all users according to the insurance purchase information processing strategy specifically includes:

[0024] The first group is composed of users who have purchased insurance a non-zero number of times among the browsing population;

[0025] Randomly select a user from the first group and record it as the first user;

[0026] Get all the insurances purchased by the first user;

[0027] For each insurance purchased by the first user, the type of insurance and the operating organization to which it belongs are matched to form a matching relationship, and the insurance product form is entered;

[0028] Until each insurance purchased by the first user is entered into the insurance product form;

[0029] Eliminate the first user from the first group of people;

[0030] Randomly select a user from the first group and record it as the second user;

[0031] Get all the insurances purchased by the second user;

[0032] For each insurance purchased by the second user, pair the type of insurance with the operating organization to which it belongs and enter it into the insurance product form;

[0033] Until each insurance purchased by the second user is entered into the insurance product form;

[0034] Eliminate the second user from the first group of people;

[0035] Until every insurance purchased by every user in the first group is entered into the insurance product form;

[0036] If there are multiple identical pairing relationships in the insurance product form, the identical pairing relationships refer to pairing relationships with the same insurance type and insurance operating institution;

[0037] Then only one pairing relationship among the same pairing relationships is retained;

[0038] In the insurance product form, delete the pairing relationship between the insurance operating institution and the first operating institution.

[0039] Optionally, the step of numbering and entering the insurance purchase information of all users according to the insurance purchase information processing strategy further includes:

[0040] For each pairing relationship in the insurance product table;

[0041] For the insurances in the first operating organization that are the same as the insurances in the matching relationship, enter the insurance advantages, specifically:

[0042] The insurance types in each pairing relationship in the insurance product form are numbered from 1 to N in the order of their entry time, and are recorded as the first type, the second type, and the Nth type, respectively; where N represents the total number of all pairing relationships in the insurance product form;

[0043] The user who purchases the first type of insurance is recorded as the first customer, the user who purchases the second type of insurance is recorded as the second customer, and the user who purchases the Nth type of insurance is recorded as the Nth customer.

[0044] Insurance with the same type as the first type in the first operating organization is recorded as the first target type insurance, insurance with the same type as the second type in the first operating organization is recorded as the second target type insurance... Insurance with the same type as the Nth type in the first operating organization is recorded as the Nth target type insurance;

[0045] For each sale within the first operating organization;

[0046] Get the number of customers that the salesperson has contacted with the first customer, and record it as the first number;

[0047] After the sales contacted the first customer, the number of customers who purchased the first target type of insurance among the first customers was obtained, recorded as the second number;

[0048] Calculate the conversion success rate of sales for the first type of insurance, the conversion success rate of the first type of insurance = the second quantity / the first quantity;

[0049] The conversion success rate of all sales for the first type of insurance is obtained, and the sales with the highest conversion success rate is selected to enter and store information on the first target type of insurance.

[0050] Optionally, the step of numbering and entering the insurance purchase information of all users according to the insurance purchase information processing strategy further includes:

[0051] For each sale within the first operating organization;

[0052] Get the number of customers that the salesperson has contacted with the second customer, recorded as the third number;

[0053] After the salesperson contacts the second customer, the number of second customers who purchase the second target type of insurance is obtained, recorded as the fourth number;

[0054] Calculate the conversion success rate of sales for the second type of insurance, the conversion success rate of the second type of insurance = the fourth quantity / the third quantity;

[0055] Obtain the conversion success rate of all sales for the second type of insurance, and select the sales with the highest conversion success rate to enter and store information on the second target type of insurance;

[0056] Until the information entry for the third target type insurance, the fourth target type insurance, and so on, the Nth target type insurance is completed.

[0057] Optionally, if the number of times the user has purchased insurance is zero, dispatching a salesperson to promote insurance to the user according to the first promotion strategy, specifically includes:

[0058] Get all sales within the first operating organization;

[0059] For each sale;

[0060] Get the number of users who have zero sales contacts to purchase insurance, and record it as the total number;

[0061] After obtaining the sales contact with users who have purchased insurance zero times, the number of users who purchased insurance from the first operator among the users who have purchased insurance zero times is recorded as the first time number;

[0062] Calculate the first transaction success rate for first-time insurance customers;

[0063] First transaction success rate = first quantity / total quantity;

[0064] Obtain the first-time transaction success rate of all sales, and recommend the salesperson with the highest first-time transaction success rate to promote insurance to users who have never purchased insurance.

[0065] Optionally, if the number of times the user has purchased insurance is not zero, determining whether it is necessary to dispatch a salesperson to conduct real-time insurance promotion for the user includes:

[0066] Get the current time;

[0067] Get the coverage period of all insurances purchased by the user;

[0068] For each insurance period, obtain the time period between the end time of the insurance period and the time thirty days before the end time of the insurance period, and record them as the first time period, the second time period, ... the Ath time period, where A refers to the number of all insurances purchased by the user;

[0069] If there is a time period including the current time in the first time period, the second time period, ... the Ath time period, then it is necessary to dispatch a salesperson to conduct real-time insurance promotion for this user;

[0070] And obtain the insurance type corresponding to the time period including the current time, and record it as the promotion type;

[0071] If there is no time period including the current moment in the first time period, the second time period, ... the Ath time period, there is no need to dispatch a salesperson to conduct real-time insurance promotion for this user.

[0072] Optionally, for each user in the promotion list, dispatching sales to carry out real-time insurance promotion to the user according to the second promotion strategy specifically includes:

[0073] For each user in the promotion list;

[0074] Get all the insurances purchased by the user;

[0075] Determine whether all insurance purchased by the user are insurance products of the first operating institution;

[0076] If all the insurance products purchased by the user are from the first operating institution, the sales corresponding to the last insurance purchased by the user are obtained and recorded as the target sales;

[0077] Dispatch target sales to conduct real-time insurance promotion for users who purchase all insurance products from the first operating institution.

[0078] Optionally, for each user in the promotion list, dispatching sales to carry out real-time insurance promotion to the user according to the second promotion strategy, further includes:

[0079] If all the insurance purchased by the user are not all insurance products of the first operating institution;

[0080] Get the user's promotion type;

[0081] For each promotion type of users;

[0082] Obtain the sales recorded in the information corresponding to the pairing relationship in which the category in the pairing relationship is the same as the promotion category, and record it as the target sales;

[0083] Dispatch target sales to conduct real-time insurance promotion for users who have purchased all insurance products from other operating institutions.

[0084] The present invention has the following beneficial effects:

[0085] 1. This is a personalized recommendation method for insurance advertisements based on big data. By obtaining the insurance purchase information of all users in the browsing population, insurance operating institutions can comprehensively analyze the behavioral characteristics of potential customers, conduct accurate customer segmentation and optimize advertising delivery. First, the user data of clicking and browsing advertisements is the basis for the implementation of advertising promotion strategies. By collecting this part of data, the system can screen out potential customer groups who are interested in the advertising content. This behavior-based user screening can provide real-time feedback for advertising strategies, help optimize the selection of advertising content, delivery time and promotion channels, and thus improve advertising conversion rates. Secondly, obtaining the insurance purchase information of the browsing population can provide support for subsequent big data analysis. By analyzing the user's insurance purchase information, the system can deeply explore the user's potential needs and predict their purchasing intentions. This provides sales personnel with very valuable reference information, which can help them conduct sales promotion more targetedly and improve the transaction rate.

[0086] 2. A personalized recommendation method for insurance advertisements based on big data, which selects users who have purchased insurance a non-zero number of times from the browsing population to form a first population, then obtains the insurance purchased by each user in the first population, forms a pairing relationship between the type of insurance and the operating agency to which it belongs, and enters the pairing relationship into an insurance product form, retains only one pairing relationship in the insurance product form with the same pairing relationship, and then deletes the pairing relationship between the operating agency to which the insurance belongs and the first operating agency; for each pairing relationship in the insurance product form, a suitable salesperson is dispatched to enter the insurance advantages of the first operating agency. In this way, all insurance purchased by all users in the first population can be quickly counted into the insurance product form, and the first operating agency then compares each insurance in the insurance product form with the insurance of the first operating agency, and dispatches a suitable salesperson to enter the insurance advantages of the operating agency. When promoting insurance in the later stage, even if a salesperson does not know a certain insurance very well, the insurance can be promoted based on the information in the insurance product form, so that inexperienced salespeople can also complete the promotion well, which improves the efficiency of insurance promotion to a certain extent.

[0087] 3. This method of personalized recommendation of insurance advertisements based on big data, when sending appropriate sales to enter the advantages of insurance of the first operating institution, for each type of insurance of the first operating institution with the same insurance type in the matching relationship with the insurance product form, the salesperson with the highest conversion success rate in the first operating institution is selected to enter the advantages. The higher the conversion success rate, the higher the salesperson's understanding of the corresponding insurance product. By selecting the salesperson with the highest conversion success rate, all the advantages of the insurance can be listed to the greatest extent; in this way, the overall promotion effect can be improved.

[0088] 4. This is a personalized recommendation method for insurance advertisements based on big data. For users who have not purchased insurance, the first promotion strategy is used to reasonably allocate sales resources, and the salesperson with the highest first-time transaction success rate is selected to promote insurance to users who have never purchased insurance. Since these users have not purchased any insurance products, their understanding of insurance products may be relatively limited. By sending the salesperson with the highest first-time transaction success rate to give a detailed explanation, their willingness to buy can be increased, thereby improving the overall promotion effect.

[0089] 5. A personalized recommendation method for insurance advertisements based on big data determines whether it is necessary to dispatch sales to promote insurance to the user in real time by obtaining the time period between the current moment and the end moment of the protection period and the moment thirty days before the end moment of the protection period; if there is a time period including the current moment in the first time period, the second time period ... the Ath time period, it means that the user's insurance is about to expire, and it is necessary to dispatch sales immediately to promote insurance to the user in real time; if there is no time period including the current moment in the first time period, the second time period ... the Ath time period, it means that the user's insurance is still a long time away from expiration, even if the sales is dispatched to promote insurance to the user in real time at this time, the user is very likely not to buy insurance, so it is not necessary to dispatch sales immediately to promote insurance to the user in real time, and wait until the user's insurance is about to expire, and then dispatch sales to promote insurance to the user in real time; this method can improve user satisfaction; users may feel anxious because the protection period is about to expire, and timely sales services can help them quickly solve this problem and enhance their trust in the brand; in addition, real-time promotion can also improve sales conversion rate; through big data analysis, the system can accurately determine whether the user is at a key node of purchase, help sales personnel to promote at the best time, and maximize the transaction rate.

[0090] 6. A personalized recommendation method for insurance advertisements based on big data, according to the second promotion strategy, dispatches sales to carry out real-time insurance promotion for users. If all the insurances purchased by the user are insurance products of the first operating agency, it means that the user has a high stickiness to the first operating agency and will not consider insurance products of other insurance operating agencies when purchasing insurance. Therefore, the sales corresponding to the insurance purchased by the user last is dispatched to carry out insurance promotion for the user. If not all the insurances purchased by the user are insurance products of the first operating agency, it means that the user will fully consider various factors when purchasing insurance, and thus choose insurance products of other insurance operating agencies. At this time, the user's promotion type is obtained, and for each promotion type, the sales corresponding to the pairing relationship with the same type and promotion type in the pairing relationship is obtained to carry out insurance promotion for the user. This method can help optimize the allocation of sales resources and improve the success rate of conversion; it ensures that sales resources can be reasonably allocated to where they are most needed, thereby improving the efficiency of the entire sales process. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0092] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0093] Example, see Figure 1 , a personalized recommendation method for insurance advertisements based on big data, comprising:

[0094] Place advertisements on Internet advertising platforms;

[0095] The insurance operating organization that places the advertisement shall be recorded as the first operating organization;

[0096] Get all the people who clicked and browsed the advertisement, and record them as the selected people;

[0097] Send a request for insurance purchase information to all users in the candidate group;

[0098] The users who agree to the request for insurance purchase information in the candidate population constitute the browsing population;

[0099] Obtain insurance purchase information of all users in the browsing population;

[0100] The insurance purchase information includes the type of insurance purchased, the operating organization of the purchased insurance, the coverage period of the purchased insurance, and the number of times the insurance was purchased;

[0101] This is a personalized recommendation method for insurance advertisements based on big data. By obtaining the insurance purchase information of all users in the browsing population, insurance operating institutions can comprehensively analyze the behavioral characteristics of potential customers, conduct accurate customer segmentation and optimize advertising delivery. First, the user data of clicking and browsing advertisements is the basis for the implementation of advertising promotion strategies. By collecting this part of data, the system can screen out potential customer groups who are interested in the advertising content. This behavior-based user screening can provide real-time feedback for advertising strategies, help optimize the selection of advertising content, delivery time and promotion channels, and thus improve advertising conversion rates. Secondly, obtaining the insurance purchase information of the browsing population can provide support for subsequent big data analysis. By analyzing the user's insurance purchase information, the system can deeply explore the user's potential needs and predict their purchasing intentions. This provides very valuable reference information for sales staff, which can help them conduct sales promotion more targetedly and improve the transaction rate.

[0102] Number and enter the insurance purchase information of all users according to the insurance purchase information processing strategy;

[0103] Target every user in the browsing population;

[0104] Get the number of times the user has purchased insurance;

[0105] If the number of times the user has purchased insurance is zero, then the salesperson is dispatched to promote insurance to this user according to the first promotion strategy;

[0106] If the number of times the user has purchased insurance is not zero, determine whether it is necessary to dispatch a salesperson to conduct real-time insurance promotion for this user;

[0107] If there is no need to dispatch sales to conduct real-time insurance promotion for this user, this user will be included in the promotion list;

[0108] If it is necessary to dispatch sales to conduct real-time insurance promotion for this user, this user will be included in the promotion list;

[0109] For each user in the promotion list, when it is necessary to dispatch sales to carry out real-time insurance promotion for the user, dispatch sales to carry out insurance promotion for the user;

[0110] For each user in the promotion list, the salesperson is dispatched to conduct real-time insurance promotion to the user according to the second promotion strategy.

[0111] The numbering and information entry of the insurance purchase information of all users according to the insurance purchase information processing strategy specifically includes:

[0112] The first group is composed of users who have purchased insurance a non-zero number of times among the browsing population;

[0113] Randomly select a user from the first group and record it as the first user;

[0114] Get all the insurances purchased by the first user;

[0115] For each insurance purchased by the first user, the type of insurance and the operating organization to which it belongs are matched to form a matching relationship, and the insurance product form is entered;

[0116] Until each insurance purchased by the first user is entered into the insurance product form;

[0117] Eliminate the first user from the first group of people;

[0118] Randomly select a user from the first group and record it as the second user;

[0119] Get all the insurances purchased by the second user;

[0120] For each insurance purchased by the second user, pair the type of insurance with the operating organization to which it belongs and enter it into the insurance product form;

[0121] Until each insurance purchased by the second user is entered into the insurance product form;

[0122] Eliminate the second user from the first group of people;

[0123] Until every insurance purchased by every user in the first group is entered into the insurance product form;

[0124] If there are multiple identical pairing relationships in the insurance product form, the identical pairing relationships refer to pairing relationships with the same insurance type and insurance operating institution;

[0125] Then only one pairing relationship among the same pairing relationships is retained;

[0126] In the insurance product form, delete the matching relationship between the insurance operating institution and the first operating institution;

[0127] The big data-based intelligent insurance advertising promotion method selects users who have purchased insurance a non-zero number of times from the browsing population to form a first population, then obtains the insurance purchased by each user in the first population, forms a pairing relationship between the type of insurance and the operating agency to which it belongs, and enters the pairing relationship into an insurance product form, retains only one pairing relationship in the insurance product form that is the same, and then deletes the pairing relationship between the operating agency to which the insurance belongs and the first operating agency that is the same; for each pairing relationship in the insurance product form, a suitable salesperson is dispatched to enter the insurance advantages of the first operating agency. In this way, all insurance purchased by all users in the first population can be quickly counted into the insurance product form, and the first operating agency then compares each insurance in the insurance product form with the insurance of the first operating agency, and dispatches a suitable salesperson to enter the insurance advantages of the operating agency. When promoting insurance in the later stage, even if a salesperson is not very familiar with a certain insurance, the insurance can be promoted based on the information in the insurance product form, so that inexperienced salespeople can also complete the promotion well, which improves the efficiency of insurance promotion to a certain extent.

[0128] The step of numbering and entering the insurance purchase information of all users according to the insurance purchase information processing strategy also includes:

[0129] For each pairing relationship in the insurance product table;

[0130] For the insurances in the first operating organization that are the same as the insurances in the matching relationship, enter the insurance advantages, specifically:

[0131] The advantages of the insurance refer to the advantages of a certain type of insurance in the first operating organization compared with the same type of insurance in other operating organizations, including premiums, comprehensive coverage, insured amount, and average claim settlement period;

[0132] The insurance types in each pairing relationship in the insurance product form are numbered from 1 to N in the order of their entry time, and are recorded as the first type, the second type, and the Nth type, respectively; where N represents the total number of all pairing relationships in the insurance product form;

[0133] The user who purchases the first type of insurance is recorded as the first customer, the user who purchases the second type of insurance is recorded as the second customer, and the user who purchases the Nth type of insurance is recorded as the Nth customer.

[0134] Insurance with the same type as the first type in the first operating organization is recorded as the first target type insurance, insurance with the same type as the second type in the first operating organization is recorded as the second target type insurance... Insurance with the same type as the Nth type in the first operating organization is recorded as the Nth target type insurance;

[0135] For each sale within the first operating organization;

[0136] Get the number of customers that the salesperson has contacted with the first customer, and record it as the first number;

[0137] After the sales contacted the first customer, the number of customers who purchased the first target type of insurance among the first customers was obtained, recorded as the second number;

[0138] Calculate the conversion success rate of sales for the first type of insurance, the conversion success rate of the first type of insurance = the second quantity / the first quantity;

[0139] The conversion success rate of all sales for the first type of insurance is obtained, and the sales with the highest conversion success rate is selected to enter and store information on the first target type of insurance.

[0140] The step of numbering and entering the insurance purchase information of all users according to the insurance purchase information processing strategy also includes:

[0141] For each sale within the first operating organization;

[0142] Get the number of customers that the salesperson has contacted with the second customer, recorded as the third number;

[0143] After the salesperson contacts the second customer, the number of second customers who purchase the second target type of insurance is obtained, recorded as the fourth number;

[0144] Calculate the conversion success rate of sales for the second type of insurance, the conversion success rate of the second type of insurance = the fourth quantity / the third quantity;

[0145] Obtain the conversion success rate of all sales for the second type of insurance, and select the sales with the highest conversion success rate to enter and store information on the second target type of insurance;

[0146] Until the information entry for the third target type of insurance, the fourth target type of insurance, and so on, the Nth target type of insurance is all completed;

[0147] This method of personalized recommendation of insurance advertisements based on big data, when sending a suitable salesperson to enter the advantages of the insurance of the first operating institution, for each type of insurance of the first operating institution with the same insurance type in the matching relationship with the insurance product form, the salesperson with the highest conversion success rate in the first operating institution is selected to enter the advantages. The higher the conversion success rate, the higher the salesperson's understanding of the corresponding insurance product. The salesperson with the highest conversion success rate is selected, and all the advantages of the insurance can be listed to the greatest extent; in this way, the overall promotion effect can be improved.

[0148] If the number of times the user has purchased insurance is zero, then dispatching sales to promote insurance to the user according to the first promotion strategy, specifically including:

[0149] Get all sales within the first operating organization;

[0150] For each sale;

[0151] Get the number of users who have zero sales contacts to purchase insurance, and record it as the total number;

[0152] After obtaining the sales contact with users who have purchased insurance zero times, the number of users who purchased insurance from the first operator among the users who have purchased insurance zero times is recorded as the first time number;

[0153] Calculate the first transaction success rate for first-time insurance customers;

[0154] First transaction success rate = first quantity / total quantity;

[0155] Obtain the first transaction success rate of all sales, and recommend the salesperson with the highest first transaction success rate to promote insurance to users who have never purchased insurance;

[0156] This method of personalized recommendation of insurance advertisements based on big data reasonably allocates sales resources through the first promotion strategy for users who have not purchased insurance, and selects the salesperson with the highest first-time transaction success rate to promote insurance to users who have purchased no insurance products. Since these users have not purchased any insurance products, their understanding of insurance products may be relatively limited. By sending the salesperson with the highest first-time transaction success rate to give a detailed explanation, their willingness to buy can be increased, thereby improving the overall promotion effect.

[0157] If the number of times the user has purchased insurance is not zero, it is determined whether it is necessary to dispatch a salesperson to conduct real-time insurance promotion for the user, specifically including:

[0158] Get the current time;

[0159] Get the coverage period of all insurances purchased by the user;

[0160] For each insurance period, obtain the time period between the end time of the insurance period and the time thirty days before the end time of the insurance period, and record them as the first time period, the second time period, ... the Ath time period, where A refers to the number of all insurances purchased by the user;

[0161] If there is a time period including the current time in the first time period, the second time period, ... the Ath time period, then it is necessary to dispatch a salesperson to conduct real-time insurance promotion for this user;

[0162] And obtain the insurance type corresponding to the time period including the current time, and record it as the promotion type;

[0163] If there is no time period including the current time in the first time period, the second time period, ... the Ath time period, then there is no need to dispatch a salesperson to conduct real-time insurance promotion for this user;

[0164] The method for personalized recommendation of insurance advertisements based on big data determines whether it is necessary to dispatch sales to promote insurance to the user in real time by obtaining the time period between the current moment and the end moment of the protection period and the moment thirty days before the end moment of the protection period; if there is a time period including the current moment in the first time period, the second time period ... the Ath time period, it means that the user's insurance is about to expire, and it is necessary to dispatch sales immediately to promote insurance to the user in real time; if there is no time period including the current moment in the first time period, the second time period ... the Ath time period, it means that the user's insurance is still a long time away from expiration, and even if the sales is dispatched to promote insurance to the user in real time at this time, the user is very likely not to buy insurance, so it is not necessary to dispatch sales immediately to promote insurance to the user in real time, and wait until the user's insurance is about to expire, and then dispatch sales to promote insurance to the user in real time; this method can improve user satisfaction; users may feel anxious because the protection period is about to expire, and timely sales services can help them quickly solve this problem and enhance their trust in the brand; in addition, real-time promotion can also improve sales conversion rate; through big data analysis, the system can accurately determine whether the user is at a key node of purchase, help sales personnel to promote at the best time, and maximize the transaction rate.

[0165] For each user in the promotion list, dispatching sales to carry out real-time insurance promotion to the user according to the second promotion strategy specifically includes:

[0166] For each user in the promotion list;

[0167] Get all the insurances purchased by the user;

[0168] Determine whether all insurance purchased by the user are insurance products of the first operating institution;

[0169] If all the insurance products purchased by the user are from the first operating institution, the sales corresponding to the last insurance purchased by the user are obtained and recorded as the target sales;

[0170] Dispatch target sales to conduct real-time insurance promotion for users who purchase all insurance products from the first operating institution.

[0171] The step of dispatching sales to conduct real-time insurance promotion to each user in the promotion list according to the second promotion strategy may further include:

[0172] If all the insurance purchased by the user are not all insurance products of the first operating institution;

[0173] Get the user's promotion type;

[0174] For each promotion type of users;

[0175] Obtain the sales recorded in the information corresponding to the pairing relationship in which the category in the pairing relationship is the same as the promotion category, and record it as the target sales;

[0176] Dispatch target sales to conduct real-time insurance promotion for users who have purchased insurance products from other operating institutions;

[0177] This method of personalized recommendation of insurance advertisements based on big data dispatches sales to carry out real-time insurance promotion for users according to the second promotion strategy. If all the insurances purchased by the user are insurance products of the first operating institution, it means that the user has a high stickiness to the first operating institution and will not consider insurance products of other insurance operating institutions when purchasing insurance. Therefore, the sales corresponding to the insurance purchased by the user last is dispatched to carry out insurance promotion for the user. If not all the insurances purchased by the user are insurance products of the first operating institution, it means that the user will fully consider various factors when purchasing insurance and thus choose insurance products of other insurance operating institutions. At this time, the promotion type of the user is obtained, and for each promotion type, the sales corresponding to the pairing relationship with the same type and promotion type in the pairing relationship is obtained to carry out insurance promotion for the user. This method can help optimize the allocation of sales resources and improve the success rate of conversion; it ensures that sales resources can be reasonably allocated to where they are most needed, thereby improving the efficiency of the entire sales process.

[0178] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0179] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A personalized recommendation method for insurance advertisements based on big data, characterized in that: include: Place advertisements on Internet advertising platforms; The insurance operating organization that places the advertisement shall be recorded as the first operating organization; Get all the people who clicked and browsed the advertisement, and record them as the selected people; Send a request for insurance purchase information to all users in the candidate group; The users who agree to the request for insurance purchase information in the candidate population constitute the browsing population; Obtain insurance purchase information of all users in the browsing population; The insurance purchase information includes the type of insurance purchased, the operating organization of the purchased insurance, the coverage period of the purchased insurance, and the number of times the insurance was purchased; Number and enter the insurance purchase information of all users according to the insurance purchase information processing strategy; Target every user in the browsing population; Get the number of times the user has purchased insurance; If the number of times the user has purchased insurance is zero, then the salesperson is dispatched to promote insurance to this user according to the first promotion strategy; If the number of times the user has purchased insurance is not zero, determine whether it is necessary to dispatch a salesperson to conduct real-time insurance promotion for this user; If there is no need to dispatch sales to conduct real-time insurance promotion for this user, this user will be included in the promotion list; If it is necessary to dispatch sales to conduct real-time insurance promotion for this user, this user will be included in the promotion list; For each user in the promotion list, when it is necessary to dispatch sales to carry out real-time insurance promotion for the user, dispatch sales to carry out insurance promotion for the user; For each user in the promotion list, the salesperson is dispatched to conduct real-time insurance promotion to the user according to the second promotion strategy.

2. The method for personalized recommendation of insurance advertisements based on big data according to claim 1, characterized in that: The numbering and information entry of the insurance purchase information of all users according to the insurance purchase information processing strategy specifically includes: The first group is composed of users who have purchased insurance a non-zero number of times among the browsing population; Randomly select a user from the first group and record it as the first user; Get all the insurances purchased by the first user; For each insurance purchased by the first user, the type of insurance and the operating organization to which it belongs are matched to form a matching relationship, and the insurance product form is entered; Until each insurance purchased by the first user is entered into the insurance product form; Eliminate the first user from the first group of people; Randomly select a user from the first group and record it as the second user; Get all the insurances purchased by the second user; For each insurance purchased by the second user, pair the type of insurance with the operating organization to which it belongs and enter it into the insurance product form; Until each insurance purchased by the second user is entered into the insurance product form; Eliminate the second user from the first group of people; Until every insurance purchased by every user in the first group is entered into the insurance product form; If there are multiple identical pairing relationships in the insurance product form, the identical pairing relationships refer to pairing relationships with the same insurance type and insurance operating institution; Then only one pairing relationship among the same pairing relationships is retained; In the insurance product form, delete the pairing relationship between the insurance operating institution and the first operating institution.

3. The method for personalized recommendation of insurance advertisements based on big data according to claim 2, characterized in that: The step of numbering and entering the insurance purchase information of all users according to the insurance purchase information processing strategy also includes: For each pairing relationship in the insurance product table; For the insurances in the first operating organization that are the same as the insurances in the matching relationship, enter the insurance advantages, specifically: The insurance types in each pairing relationship in the insurance product form are numbered from 1 to N in the order of their entry time, and are recorded as the first type, the second type, and the Nth type, respectively; where N represents the total number of all pairing relationships in the insurance product form; The user who purchases the first type of insurance is recorded as the first customer, the user who purchases the second type of insurance is recorded as the second customer, and the user who purchases the Nth type of insurance is recorded as the Nth customer. Insurance with the same type as the first type in the first operating organization is recorded as the first target type insurance, insurance with the same type as the second type in the first operating organization is recorded as the second target type insurance... Insurance with the same type as the Nth type in the first operating organization is recorded as the Nth target type insurance; For each sale within the first operating organization; Get the number of customers that the salesperson has contacted with the first customer, and record it as the first number; After the sales contacted the first customer, the number of customers who purchased the first target type of insurance among the first customers was obtained, recorded as the second number; Calculate the conversion success rate of sales for the first type of insurance, the conversion success rate of the first type of insurance = the second quantity / the first quantity; The conversion success rate of all sales for the first type of insurance is obtained, and the sales with the highest conversion success rate is selected to enter and store information on the first target type of insurance.

4. The method for personalized recommendation of insurance advertisements based on big data according to claim 3, characterized in that: The step of numbering and entering the insurance purchase information of all users according to the insurance purchase information processing strategy also includes: For each sale within the first operating organization; Get the number of customers that the salesperson has contacted with the second customer, recorded as the third number; After the salesperson contacts the second customer, the number of second customers who purchase the second target type of insurance is obtained, recorded as the fourth number; Calculate the conversion success rate of sales for the second type of insurance, the conversion success rate of the second type of insurance = the fourth quantity / the third quantity; Obtain the conversion success rate of all sales for the second type of insurance, and select the sales with the highest conversion success rate to enter and store information on the second target type of insurance; Until the information entry for the third target type insurance, the fourth target type insurance, and so on, the Nth target type insurance is completed.

5. The method for personalized recommendation of insurance advertisements based on big data according to claim 1, characterized in that: If the number of times the user has purchased insurance is zero, then dispatching sales to promote insurance to the user according to the first promotion strategy, specifically including: Get all sales within the first operating organization; For each sale; Get the number of users who have zero sales contacts to purchase insurance, and record it as the total number; After obtaining the sales contact with users who have purchased insurance zero times, the number of users who purchased insurance from the first operator among the users who have purchased insurance zero times is recorded as the first time number; Calculate the first transaction success rate for first-time insurance customers; First transaction success rate = first quantity / total quantity; Obtain the first-time transaction success rate of all sales, and recommend the salesperson with the highest first-time transaction success rate to promote insurance to users who have never purchased insurance.

6. The method for personalized recommendation of insurance advertisements based on big data according to claim 1, characterized in that: If the number of times the user has purchased insurance is not zero, it is determined whether it is necessary to dispatch a salesperson to conduct real-time insurance promotion for the user, specifically including: Get the current time; Get the coverage period of all insurances purchased by the user; For each insurance period, obtain the time period between the end time of the insurance period and the time thirty days before the end time of the insurance period, and record them as the first time period, the second time period, ... the Ath time period, where A refers to the number of all insurances purchased by the user; If there is a time period including the current time in the first time period, the second time period, ... the Ath time period, then it is necessary to dispatch a salesperson to conduct real-time insurance promotion for this user; And obtain the insurance type corresponding to the time period including the current time, and record it as the promotion type; If there is no time period including the current moment in the first time period, the second time period, ... the Ath time period, there is no need to dispatch a salesperson to conduct real-time insurance promotion for this user.

7. The method for personalized recommendation of insurance advertisements based on big data according to claim 1, characterized in that: For each user in the promotion list, dispatching sales to carry out real-time insurance promotion to the user according to the second promotion strategy specifically includes: For each user in the promotion list; Get all the insurances purchased by the user; Determine whether all insurance purchased by the user are insurance products of the first operating institution; If all the insurance products purchased by the user are from the first operating institution, the sales corresponding to the last insurance purchased by the user are obtained and recorded as the target sales; Dispatch target sales to conduct real-time insurance promotion for users who purchase all insurance products from the first operating institution.

8. The method for personalized recommendation of insurance advertisements based on big data according to claim 7, characterized in that: The step of dispatching sales to conduct real-time insurance promotion to each user in the promotion list according to the second promotion strategy may further include: If all the insurance purchased by the user are not all insurance products of the first operating institution; Get the user's promotion type; For each promotion type of users; Obtain the sales recorded in the information corresponding to the pairing relationship in which the category in the pairing relationship is the same as the promotion category, and record it as the target sales; Dispatch target sales to conduct real-time insurance promotion for users who have purchased all insurance products from other operating institutions.

Images