CN113763118A - Policy recommendation method, device, equipment and storage medium - Google Patents
Policy recommendation method, device, equipment and storage medium Download PDFInfo
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- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
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Abstract
The application provides a strategy recommendation method, a strategy recommendation device, equipment and a storage medium, which can be applied to a platform server to realize a strategy recommendation function for different shops on a platform. The method comprises the following steps: the method comprises the steps of obtaining diagnosis data comprising multiple index parameters of a target store, determining at least one index parameter to be promoted of the target store, determining at least one target strategy which is used for promoting to the at least one index parameter to be promoted and accords with a current scene of the target store from a strategy set, and pushing the at least one target strategy to the target store, wherein the strategy set comprises multiple strategies used for promoting different index parameters of the target store. According to the scheme, the improvement effect of each strategy in the strategy set on each index parameter of the shop and the current scene of the target shop are comprehensively considered, at least one target strategy with high matching degree is recommended to the target shop, and the target shop is assisted to develop each activity.
Description
Technical Field
The present application relates to the field of internet technologies, and in particular, to a policy recommendation method, apparatus, device, and storage medium.
Background
In recent years, with the continuous development of internet technology, various large e-commerce platforms are developed vigorously, and the number of merchants is increased explosively. Under such a severe competitive environment, the difficulty of acquiring the user information by the merchant is continuously improved, and the difficulty of activity promotion is continuously increased.
At present, a merchant platform provides a plurality of activity strategies for merchants, and a merchant can select one or more of the activity strategies provided by the platform according to own needs, however, the activity effect brought by the merchant adopting the selected activity strategy may not meet the actual needs of the merchant, such as the browsing volume of the merchant store or the improvement of the order conversion rate is not obvious.
Aiming at the scenes, the functions of the e-commerce platform need to be upgraded, a recommendation scheme aiming at an activity strategy is developed, and the service quality of the platform is improved.
Disclosure of Invention
The embodiment of the application provides a strategy recommendation method, a strategy recommendation device, equipment and a storage medium, and aims to recommend an activity strategy for thousands of people and thousands of faces to a merchant.
A first aspect of an embodiment of the present application provides a policy recommendation method, including:
the method comprises the steps of obtaining diagnosis data of a target shop, wherein the diagnosis data comprise a plurality of index parameters of the target shop;
determining at least one index parameter to be promoted of the target shop according to the diagnosis data;
determining at least one target strategy which is used for improving the at least one index parameter to be improved and accords with the current scene of the target store from a strategy set, wherein the strategy set comprises a plurality of strategies for improving different index parameters of the store;
pushing the at least one targeted policy to the targeted store.
In one embodiment of the present application, the plurality of target parameters of the targeted store include at least two of:
browsing amount of stores, conversion rate, vermicelli activity, member activity and customer unit price.
In an embodiment of the present application, the determining at least one index parameter to be promoted of the target store according to the diagnosis data includes:
determining an average value of each index parameter of all shops with the same shop scale and/or shop type as the target shop;
and determining at least one index parameter to be promoted of the target shop according to the average value of the index parameters of all shops and the index parameters of the target shop.
In an embodiment of the application, the determining, according to an average value of index parameters of all stores and index parameters of the target store, at least one index parameter to be promoted of the target store includes:
if the first index parameter of the target store is smaller than the average value of the first index parameter in all stores, taking the first index parameter as an index parameter to be promoted of the target store; the first index parameter is any one of a plurality of index parameters.
In one embodiment of the present application, the policy set further includes an effectiveness score of each policy on each index parameter, where the effectiveness score is preset by an operator or determined according to historical policy effectiveness data and/or store feedback data of the platform store.
In an embodiment of the application, if the target to-be-promoted index parameter of the target store includes a first index parameter, the determining, from the policy set, at least one target policy for promoting the at least one to-be-promoted index parameter and conforming to the current scene of the target store includes:
obtaining a plurality of strategies of which the effect scores for improving the first index parameters are larger than a first preset score from the strategy set;
and determining at least one target strategy which accords with the current scene of the target store from a plurality of strategies of which the effect score for improving the first index parameter is greater than a first preset score.
In an embodiment of the present application, if the index parameter to be promoted of the target store includes a first index parameter and a second index parameter; the determining at least one target strategy used for improving the at least one index parameter to be improved and conforming to the current scene of the target store from the strategy set comprises the following steps:
determining a comprehensive effect score of each strategy in the strategy set on improving the first index parameter and the second index parameter;
obtaining a plurality of strategies with the comprehensive effect scores larger than a second preset score from the strategy set;
and determining at least one target strategy which accords with the current scene of the target store from a plurality of strategies of which the comprehensive effect score is larger than a second preset score.
In an embodiment of the application, the determining, from a set of policies, at least one target policy for promoting the at least one target parameter to be promoted and conforming to a current scene of the target store includes:
and determining at least one target strategy which is used for improving the at least one index parameter to be improved and accords with the current scene of the target store, the scale of the store and the type of the store from a strategy set.
A second aspect of an embodiment of the present application provides a policy recommendation apparatus, including:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring diagnosis data of a target store, and the diagnosis data comprises a plurality of index parameters of the target store;
the processing module is used for determining at least one item of index parameter to be promoted of the target shop according to the diagnosis data;
determining at least one target strategy which is used for improving the at least one index parameter to be improved and accords with the current scene of the target store from a strategy set, wherein the strategy set comprises a plurality of strategies for improving different index parameters of the store;
a push module to push the at least one targeting policy to the targeted store.
In one embodiment of the present application, the plurality of target parameters of the targeted store include at least two of:
browsing amount of stores, conversion rate, vermicelli activity, member activity and customer unit price.
In an embodiment of the present application, the processing module is specifically configured to:
determining an average value of each index parameter of all shops with the same shop scale and/or shop type as the target shop;
and determining at least one index parameter to be promoted of the target shop according to the average value of the index parameters of all shops and the index parameters of the target shop.
In an embodiment of the present application, the processing module is specifically configured to:
and if the first index parameter of the target store is smaller than the average value of the first index parameter in all stores, taking the first index parameter as an index parameter to be promoted of the target store.
In one embodiment of the present application, the policy set further includes an effectiveness score of each policy on each index parameter, where the effectiveness score is preset by an operator or determined according to historical policy effectiveness data and/or store feedback data of the platform store.
In an embodiment of the application, if the index parameter to be promoted of the target store includes a first index parameter, the processing module is specifically configured to:
obtaining a plurality of strategies of which the effect scores for improving the first index parameters are larger than a first preset score from the strategy set;
and determining at least one target strategy which accords with the current scene of the target store from a plurality of strategies of which the effect score for improving the first index parameter is greater than a first preset score.
In an embodiment of the present application, if the index parameter to be promoted of the target store includes a first index parameter and a second index parameter; the processing module is specifically configured to:
determining a comprehensive effect score of each strategy in the strategy set on improving the first index parameter and the second index parameter;
obtaining a plurality of strategies with the comprehensive effect scores larger than a second preset score from the strategy set;
and determining at least one target strategy which accords with the current scene of the target store from a plurality of strategies of which the comprehensive effect score is larger than a second preset score.
In an embodiment of the present application, the processing module is specifically configured to:
and determining at least one target strategy which is used for improving the at least one index parameter to be improved and accords with the current scene of the target store, the scale of the store and the type of the store from a strategy set.
A third aspect of embodiments of the present application provides an electronic device, including:
a memory, a processor, and a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of any one of the first aspects of the present application.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium having stored thereon a computer program for execution by a processor to perform the method according to any one of the first aspect of the present application.
A fifth aspect of embodiments of the present application provides a computer program product comprising a computer program that, when executed by a processor, performs the method according to any one of the first aspect of the present application.
The embodiment of the application provides a strategy recommendation method, a strategy recommendation device, equipment and a storage medium, which can be applied to a platform server to realize a strategy recommendation function for different shops on a platform. The method comprises the following steps: the method comprises the steps of obtaining diagnosis data comprising multiple index parameters of a target store, determining at least one index parameter to be promoted of the target store, determining at least one target strategy which is used for promoting to the at least one index parameter to be promoted and accords with a current scene of the target store from a strategy set, and pushing the at least one target strategy to the target store, wherein the strategy set comprises multiple strategies used for promoting different index parameters of the target store. According to the scheme, the improvement effect of each strategy in the strategy set on each index parameter of the shop and the current scene of the target shop are comprehensively considered, at least one target strategy with high matching degree is recommended to the target shop, and the target shop is assisted to develop each activity.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a scene schematic diagram of a policy recommendation method provided in an embodiment of the present application;
fig. 2 is a schematic flowchart of a policy recommendation method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a policy recommendation device according to an embodiment of the present application;
fig. 4 is a hardware structure diagram of an electronic device according to an embodiment of the present application.
With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It will be understood that the terms "comprises" and "comprising," and any variations thereof, as used herein, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
With the continuous development of e-commerce platforms, in order to improve the service quality of merchants, e-commerce platforms currently provide a structured activity strategy system to meet the requirement that different merchants select one or more activity strategies according to their own needs, and improve the activity effects of the merchants on the platform, such as exposure, user quantity, order quantity, and the like.
However, in the actual promotion process of the event, the campaign strategies selected by the merchants may not achieve the expected effect, and the reasons may be various, for example, some campaign strategies are applicable to medium-sized and small merchants, some campaign strategies are applicable to popular merchants, and for example, the campaign effects of the same campaign strategy at different periods (such as traditional holidays, early months, late months, etc.) may also have great differences. In addition, for the merchants who just live in the platform or most of the small and medium-sized merchants on the platform, no professional personnel is used for activity planning and maintenance, and the expected activity effect is difficult to achieve. During large-scale activities (such as 618 and 11), the rhythm and the strategy of the provided activities are complex, all merchants cannot be reached, and most of small and medium-sized merchants cannot use the existing activity strategy system to keep up with the rhythm to do related activity promotion.
In view of the above problems, an embodiment of the present application provides a policy recommendation method, which can be applied to an e-commerce platform, and is mainly used for optimizing functions of an activity policy system provided by the e-commerce platform and increasing policy recommendation services to meet activity promotion requirements of different merchants. The technical concept is as follows:
aiming at different merchants, data of merchant stores can be acquired through data acquisition, so that the scale, type and various index parameters of the merchant stores can be acquired, wherein the index parameters comprise store browsing amount, conversion rate, bean vermicelli liveness, member liveness, guest unit price and the like, the weak index parameters of the stores are determined through comparison of the index parameters of the stores in the same industry, and matched activity strategies are recommended to the merchants so as to improve the weak index parameters of the stores. The platform provides a plurality of activity strategies for merchants, and the activity effect of each activity strategy in different dimensions is different. For example, in the dimension of the merchant, the same activity strategy has obvious activity effect on medium and small merchants and has general activity effect on large merchants. For another example, in the index parameter dimension, the same activity strategy obviously improves the browsing amount of the stores, and generally improves the unit price of the stores. For another example, in different periods of the rhythm of the platform activity, the activity effect of the same activity strategy in a certain season is obvious, and the activity effect in other seasons is general. Therefore, when the activity strategy is recommended to the merchant, the factors in all the aspects can be comprehensively considered, the activity strategy recommendation accuracy is improved, and the merchant is assisted to improve various index parameters of the shop.
Fig. 1 is a scene schematic diagram of a policy recommendation method provided in an embodiment of the present application, as shown in fig. 1, the policy recommendation scene includes a terminal device 11 and a server 12, and the terminal device 11 is in communication connection with the server 12 through a wireless network.
The terminal device 11 may be various electronic devices having a display screen, including but not limited to a smart phone, a tablet computer, a notebook computer, a desktop computer, and the like. The server 12 may be a server that provides various network information, such as an e-commerce platform server, which may provide a variety of campaign strategies for the merchant.
Specifically, a merchant accesses the server 12 through the terminal device 11, may autonomously select one or more activity strategies according to its own needs, and is used to improve one or more index parameters of the merchant's store, and may also send a strategy recommendation request to the server 12 through the terminal device 11, requesting the server 12 to recommend a suitable activity strategy for the merchant.
The technical solution of the present application will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 2 is a schematic flowchart of a policy recommendation method according to an embodiment of the present application. The execution subject of this embodiment may be a platform server, and various activity policies are pre-configured in the platform server. As shown in fig. 2, the policy recommendation method specifically includes the following steps:
step 101, obtaining diagnosis data of a target store, wherein the diagnosis data comprises a plurality of index parameters of the target store.
In an embodiment of the application, the platform server may obtain the diagnosis data of the target store after receiving a policy recommendation request sent by the terminal device, where the policy recommendation request includes an identifier of the target store.
In one embodiment of the present application, the platform server may periodically push the policy recommendation information for all stores or some stores on the platform, for example, at the beginning of each month, personalized policy recommendation information is recommended to all stores, or some stores subscribed to the policy recommendation function. Therefore, the target store in the present embodiment may be any store on the platform, or may be a store subscribing to the policy recommendation function on the platform.
Optionally, the multiple index parameters of the target store include at least two of the following: browsing amount of stores, conversion rate, vermicelli activity, member activity and customer unit price. The conversion rate includes a search conversion rate, a conversion rate of an order of UV (simple notation, which means a person who accesses and browses a shop page through the internet), and the like. The customer price refers to the amount of money that the user of each purchased product of the store purchases the product on average.
Optionally, the index parameters of the target store may further include: various indexes of products in the target shop (such as the number of products which are bought and sold in 30 days, the number of new products, the number of explosive products, the product attention amount, the product purchase rate, the product conversion rate and the like); the total number of fans or members in the target store, and the user image of the fans or members; reserve purchasing power of the targeted store (e.g., 90 or 150 day user repeat purchase rate, near 360 day user reserve rate, etc.); the target stores are guided by the conditions (including the number of stores which are accessed through different channels such as search, activity, content, paid advertisement, live broadcast and the like).
It should be noted that the index parameters of the target store include, but are not limited to, the above-listed related parameters, and any other parameters for evaluating the operation condition of the store belong to the scope protected by the present embodiment.
Here, the browsing amount of the store refers to the number of users who enter the store per unit time (for example, hourly, daily, weekly, or monthly). The conversion rate of the store is a ratio of the number of users who enter the store and have a purchasing behavior per unit time to the number of users who enter the store. The fan vitality of the stores mainly refers to the number of fan replies, the number of fans forwarded and the number of fans purchased, which can be caused by a merchant after a fan group or a recommendation platform releases a new product or service of the stores. The member activity of the store refers to the overall activity of all members of the store, and for a single member, the member can be divided into one of active, inactive, pre-lost and lost according to the occurrence time of the latest consumption behavior of the member. The customer order of the store refers to the amount of money that each user of the store purchases the product or service on average.
It should be noted that, in this embodiment, all the index parameters of the store are not exhaustive, and as long as the index parameters capable of representing the operation state of the store all belong to the protection scope of this embodiment.
And 102, determining at least one index parameter to be promoted of the target shop according to the diagnosis data.
In an embodiment of the application, whether each index parameter of the target store is an index parameter to be promoted or not can be determined according to each index parameter of the target store and a preset threshold value of each index parameter. Wherein, the preset threshold value of each index parameter can be pre-configured by the platform operator.
For example, taking the conversion rate of the store as an example, assuming that the preset threshold value of the conversion rate of the store preconfigured by the platform is 50%, if the conversion rate of the target store is 45%, it is determined that the conversion rate index of the target store needs to be increased, and the platform may recommend a policy for increasing the conversion rate for the target store.
In one embodiment of the application, whether each index parameter of the target store is an index parameter to be promoted or not can be determined according to each index parameter of the target store and an average value of each index parameter of the stores in the same industry.
In an alternative embodiment, the diagnosis data of all stores which are the same as the store scale and/or the store type of the target store are obtained, then the average value of each index parameter of all stores which are the same as the store scale and/or the store type of the target store is determined, and then whether each index parameter of the target store is the index parameter to be promoted or not is determined by comparing the magnitude relation between each index parameter of the target store and the average value of each index parameter of all stores. Taking the first index parameter as an example, if the first index parameter of the target store is smaller than the average value of the first index parameter in all stores, the first index parameter is taken as an index parameter to be promoted of the target store.
For example, taking the browsing volume of a store as an example, it is assumed that the average browsing volume per day of the store in the same industry of the target store is found to be 300 people times through big data analysis, however, the average browsing volume per day of the target store is only 50 people times and is far lower than the average level, so that the browsing volume index of the target store needs to be increased, and the platform can recommend a strategy for increasing the browsing volume for the target store.
In the second embodiment, through big data analysis, the target store and each index parameter of the store in the same industry are transversely compared, so that the weak index parameter of the target store is determined.
And 103, determining at least one target strategy which is used for promoting to at least one index parameter to be promoted and accords with the current scene of the target store from the strategy set.
In this embodiment, the policy set includes a plurality of policies for promoting different index parameters of the store. Illustratively, the set of policies includes policy 1, policy 2, policy 3, policy 4, and policy 5, for example, policy 1 and policy 3 may be used to promote browsing volume of the store, policy 2 and policy 5 may be used to promote member liveness of the store, and policy 4 may be used to promote customer price of the store. For another example, policy 1 and policy 3 may be used to increase the browsing volume and conversion rate of the store, and policy 2, policy 4 and policy 5 may be used to increase the member liveness and unit price of the store. That is, each policy in the set of policies may be used to promote at least one index parameter of the store.
For example, the policies in the policy set may specifically be: the balance of money is reduced (for example, 300 is reduced by 15, 50 is reduced by 5), the sum of money is reduced (for example, 600 is full to send a gift or an electronic ticket), the shop promotion of a platform main page, the shop shares to send the electronic ticket, the product or service five-star good comment electronic ticket and the like, and the payment is made after the use.
Optionally, in some embodiments, the set of policies further includes an effectiveness score of each policy on each index parameter. It will be appreciated that each strategy is primarily directed to a different index parameter, and thus each strategy will have a different effect score on the different index parameters. Assuming that the numerical range of the effect score is 0-1, the effect score is closer to 1, which indicates that the strategy has better promotion effect on a certain index parameter.
For example, taking a certain policy in the policy set as an example, it is assumed that the effect scores of the policy on the browsing volume, conversion rate, fan activity, member activity, and unit price of the store are 0.8, 0.9, 0.4, 0.3, and 0.2, respectively.
The effect score of each strategy in the strategy set on each index parameter can be preset by an operator, or can be determined according to historical strategy effect data and/or store feedback data of the platform store.
The historical policy effect data may be obtained as follows: the method comprises the steps of obtaining diagnosis data of a shop for activity promotion by using a certain strategy provided by a platform, and comparing the change situation of the diagnosis data before and after the shop uses the strategy to determine the effect score of the strategy on each index parameter of the shop.
The store feedback data may be obtained as follows: the platform provides a feedback entrance of the strategy for the merchant, and the merchant can feed back information according to the use condition of the strategy. For example, whether the effect of the feedback strategy is expected or whether the feedback likes a certain strategy. For another example, more detailed information may be fed back, and the merchant may score the effect for a policy to continuously update the effect scores for the policies in the set of policies.
In an embodiment of the present application, if the target parameter to be promoted of the target store includes a first index parameter, determining at least one target policy for promoting to at least one target parameter to be promoted and conforming to a current scene of the target store from the policy set, includes:
obtaining a plurality of strategies of which the effect scores for improving the first index parameters are larger than a first preset score from the strategy set; and determining at least one target strategy according with the current scene of the target store from a plurality of strategies of which the effect score for improving the first index parameter is greater than the first preset score.
The above embodiment shows that when the target store has one index parameter to be promoted, at least one policy with an obvious promotion effect on the index parameter can be selected from the policy set. Specifically, a strategy for improving the effect score of the index parameter to be larger than a first preset score is selected from the strategy set through the set first preset score.
It should be noted that, in the present embodiment, the current scene of the target store is also considered. The scenes of the stores include a platform operation scene and scenes of the stores themselves, and can be understood as follows: if a certain shop participates in the platform activity, the current scene of the shop is a platform operation scene, and if the shop does not participate in the platform activity, the current scene of the shop is the scene of the shop. It should be understood that the platform will usually release platform activities (i.e. related activities released according to the rhythm of the platform activities) to the merchant according to the holidays, seasons, themes, etc., and if the merchant participates in the platform, the platform may recommend strategies corresponding to the platform activities to the merchant, so as to assist the merchant in promoting various indexes. The strategies preconfigured in the strategy set may be applicable to all scenes, and some strategies may be only applicable to platform operation scenes or scenes of the stores themselves, so that the current scene of the target store needs to be considered, and the strategy suitable for the store is further screened from the multiple strategies selected in the above manner.
In an embodiment of the present application, if the target parameter to be promoted of the target store includes a first index parameter and a second index parameter, determining at least one target policy, which is used for promoting to at least one target parameter to be promoted and conforms to a current scene of the target store, from the policy set, includes:
determining the comprehensive effect score of each strategy in the strategy set for improving the first index parameter and the second index parameter, acquiring a plurality of strategies with the comprehensive effect scores larger than a second preset score from the strategy set, and determining at least one target strategy according with the current scene of the target store from the plurality of strategies with the comprehensive effect scores larger than the second preset score.
The second preset score may be the same as or different from the first preset score, and may be specifically set according to actual requirements, which is not limited in this embodiment.
In an alternative embodiment, the total effect score of each strategy for improving the first index parameter and the second index parameter is obtained by averaging the effect score of each strategy for improving the first index parameter and the effect score for improving the second index parameter.
For example, taking a certain policy in the policy set as an example, the first index parameter is the browsing volume of the store, the second index parameter is the fan activity of the store, and assuming that the effect scores of the policy on the browsing volume, the conversion rate, the fan activity, the member activity and the unit price of the store are 0.8, 0.9, 0.4, 0.3 and 0.2, respectively, the combined effect score of the policy on the promotion of the browsing volume of the store and the fan activity can be determined to be (0.8+0.4)/2 to 0.6.
In another alternative embodiment, the total effect score of each strategy for improving the first index parameter and the second index parameter is obtained by weighted summation of the effect score of each strategy for improving the first index parameter and the effect score for improving the second index parameter. Specifically, the comprehensive effect score of each strategy for improving the first index parameter and the second index parameter can be determined according to the weight distribution proportion of the first index parameter and the second index parameter to be improved, which are fed back by the merchant.
For example, assuming that the target parameters to be promoted of a certain store include the conversion rate of the store and the price of customers, the weight distribution ratio fed back by the merchant to the two target parameters to be promoted is obtained, for example, the conversion rate is 0.8, and the price of customers is 0.2, that is, the merchant pays more attention to the promotion of the conversion rate. Assuming that the effect scores of a certain policy on the browsing amount, conversion rate, fan activity, member activity and guest unit price of a store are 0.8, 0.9, 0.4, 0.3 and 0.2, respectively, it can be determined that the combined effect score of the policy on the improvement of the conversion rate of the store and the guest unit price is 0.9 × 0.8+0.2 × 0.2 ═ 0.76.
Optionally, in some embodiments, a plurality of strategies that meet the current scene of the target store may be screened from the strategy set, and then at least one target strategy for promoting to at least one index parameter to be promoted may be determined from the plurality of strategies.
And 104, pushing at least one target strategy to the target shop.
The strategy recommendation method provided by the embodiment determines at least one index parameter to be promoted of a target store by acquiring diagnostic data including a plurality of index parameters of the target store, determines at least one target strategy which is used for promoting to the at least one index parameter to be promoted and accords with the current scene of the target store from a strategy set, and pushes the at least one target strategy to the target store, wherein the strategy set comprises a plurality of strategies for promoting different index parameters of the store. According to the scheme, the improvement effect of each strategy in the strategy set on each index parameter of the shop and the current scene of the target shop are comprehensively considered, at least one target strategy with high matching degree is recommended to the target shop, the improvement effect of each target strategy on the index parameter to be improved of the target shop is obvious, the target strategies are suitable for the current scene of the target shop, the target shop is assisted to develop each activity, and each weak index parameter of the target shop is accurately improved.
On the basis of the foregoing embodiment, optionally, the determining, by the platform server, at least one target policy that is used for promoting to at least one index parameter to be promoted and that conforms to the current scene of the target store from the policy set specifically includes:
and determining at least one target strategy which is used for promoting to at least one index parameter to be promoted and accords with the current scene, the scale and the type of the target store from the strategy set.
In this embodiment, after determining a plurality of strategies for promoting at least one index parameter to be promoted, not only the current scene of the target store but also the store scale and the store type of the target store need to be considered. It should be understood that some of the set of policies are applicable only to small and medium sized stores, some are applicable only to large stores, and different policies may be for different types of stores. Therefore, in some scenarios, the scale of the target store and the type of the target store need to be considered, and multiple strategies selected for promoting to at least one index parameter to be promoted are further screened, so that the accuracy of strategy recommendation is improved.
On the basis of the above embodiment, optionally, the platform server provides a policy activity effect data billboard for the merchant in addition to the policy recommendation function and the recommended policy feedback function, and presents the change condition of the data from the perspective of each index parameter of the policy. By collecting data fed back by merchants and strategy activity effect data, the execution effect of each strategy in the strategy set is further updated through data analysis, strategies with poor effects are eliminated, and the strategy recommendation result of the platform server is continuously optimized.
In the embodiment of the present application, the policy recommendation device may be divided into functional modules according to the method embodiment, for example, each functional module may be divided according to each function, or two or more functions may be integrated into one processing module. The integrated module can be realized in a form of hardware or a form of a software functional module. It should be noted that, in the embodiment of the present application, the division of the module is schematic, and is only one logic function division, and there may be another division manner in actual implementation. The following description will be given by taking an example in which each functional module is divided by using a corresponding function.
Fig. 3 is a schematic structural diagram of a policy recommendation device according to an embodiment of the present application. As shown in fig. 3, the policy recommendation apparatus 200 provided in this embodiment includes: an acquisition module 201, a processing module 202 and a push module 203.
An obtaining module 201, configured to obtain diagnostic data of a target store, where the diagnostic data includes multiple index parameters of the target store;
the processing module 202 is configured to determine at least one to-be-promoted index parameter of the target store according to the diagnostic data;
determining at least one target strategy which is used for improving the at least one index parameter to be improved and accords with the current scene of the target store from a strategy set, wherein the strategy set comprises a plurality of strategies for improving different index parameters of the store;
a pushing module 203 for pushing the at least one targeting policy to the targeted store.
In one embodiment of the present application, the plurality of target parameters of the targeted store include at least two of:
browsing amount of stores, conversion rate, vermicelli activity, member activity and customer unit price.
In an embodiment of the present application, the processing module 202 is specifically configured to:
determining an average value of each index parameter of all shops with the same shop scale and/or shop type as the target shop;
and determining at least one index parameter to be promoted of the target shop according to the average value of the index parameters of all shops and the index parameters of the target shop.
In an embodiment of the present application, the processing module 202 is specifically configured to:
and if the first index parameter of the target store is smaller than the average value of the first index parameter in all stores, taking the first index parameter as an index parameter to be promoted of the target store.
In one embodiment of the present application, the policy set further includes an effectiveness score of each policy on each index parameter, where the effectiveness score is preset by an operator or determined according to historical policy effectiveness data and/or store feedback data of the platform store.
In an embodiment of the application, if the index parameter to be promoted of the target store includes a first index parameter, the processing module 202 is specifically configured to:
obtaining a plurality of strategies of which the effect scores for improving the first index parameters are larger than a first preset score from the strategy set;
and determining at least one target strategy which accords with the current scene of the target store from a plurality of strategies of which the effect score for improving the first index parameter is greater than a first preset score.
In an embodiment of the present application, if the index parameter to be promoted of the target store includes a first index parameter and a second index parameter; the processing module 202 is specifically configured to:
determining a comprehensive effect score of each strategy in the strategy set on improving the first index parameter and the second index parameter;
obtaining a plurality of strategies with the comprehensive effect scores larger than a second preset score from the strategy set;
and determining at least one target strategy which accords with the current scene of the target store from a plurality of strategies of which the comprehensive effect score is larger than a second preset score.
In an embodiment of the present application, the processing module 202 is specifically configured to:
and determining at least one target strategy which is used for improving the at least one index parameter to be improved and accords with the current scene of the target store, the scale of the store and the type of the store from a strategy set.
The policy recommendation device provided in this embodiment may implement the technical solution of any of the above method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.
Fig. 4 is a hardware structure diagram of an electronic device according to an embodiment of the present application, and as shown in fig. 4, an electronic device 300 according to the embodiment includes:
a memory 301;
a processor 302; and
a computer program;
the computer program is stored in the memory 301 and configured to be executed by the processor 302 to implement the technical solution of any one of the above method embodiments, and the implementation principle and the technical effect are similar and will not be described herein again.
Alternatively, the memory 301 may be separate or integrated with the processor 302. When the memory 301 is a separate device from the processor 302, the electronic device 300 further comprises: a bus 303 for connecting the memory 301 and the processor 302.
The embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by the processor 302 to implement the technical solution in any of the foregoing method embodiments.
The present application provides a computer program product, including a computer program, which when executed by a processor implements the technical solutions in any of the foregoing method embodiments.
An embodiment of the present application further provides a chip, including: a processing module and a communication interface, wherein the processing module can execute the technical scheme in any one of the method embodiments.
Further, the chip further includes a storage module (e.g., a memory), where the storage module is configured to store instructions, and the processing module is configured to execute the instructions stored in the storage module, and the execution of the instructions stored in the storage module causes the processing module to execute the technical solution in any one of the foregoing method embodiments.
It should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.
The memory may comprise a high-speed RAM memory, and may further comprise a non-volatile storage NVM, such as at least one disk memory, and may also be a usb disk, a removable hard disk, a read-only memory, a magnetic or optical disk, etc.
The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.
The storage medium may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuits (ASIC). Of course, the processor and the storage medium may reside as discrete components in an electronic device.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications or substitutions do not depart from the spirit and scope of the present disclosure as defined by the appended claims.
Claims (12)
1. A method for policy recommendation, comprising:
the method comprises the steps of obtaining diagnosis data of a target shop, wherein the diagnosis data comprise a plurality of index parameters of the target shop;
determining at least one index parameter to be promoted of the target shop according to the diagnosis data;
determining at least one target strategy which is used for improving the at least one index parameter to be improved and accords with the current scene of the target store from a strategy set, wherein the strategy set comprises a plurality of strategies for improving different index parameters of the store;
pushing the at least one targeted policy to the targeted store.
2. The method of claim 1, wherein the plurality of target store metric parameters includes at least two of:
browsing amount of stores, conversion rate, vermicelli activity, member activity and customer unit price.
3. The method of claim 1, wherein determining at least one target store to be promoted parameter from the diagnostic data comprises:
determining an average value of each index parameter of all shops with the same shop scale and/or shop type as the target shop;
and determining at least one index parameter to be promoted of the target shop according to the average value of the index parameters of all shops and the index parameters of the target shop.
4. The method as claimed in claim 3, wherein the determining at least one index parameter to be promoted of the target store according to the average value of the index parameters of all stores and the index parameters of the target store comprises:
and if the first index parameter of the target store is smaller than the average value of the first index parameter in all stores, taking the first index parameter as an index parameter to be promoted of the target store.
5. The method of claim 1, wherein the strategy set further comprises an effectiveness score of each strategy on each index parameter, wherein the effectiveness score is preset by an operator or is determined according to historical strategy effect data and/or store feedback data of the platform store.
6. The method according to any one of claims 1 to 5, wherein if the target store target parameters to be promoted include a first index parameter, the determining at least one target strategy for promoting the at least one target parameter to be promoted and conforming to the current scene of the target store from the strategy set comprises:
obtaining a plurality of strategies of which the effect scores for improving the first index parameters are larger than a first preset score from the strategy set;
and determining at least one target strategy which accords with the current scene of the target store from a plurality of strategies of which the effect score for improving the first index parameter is greater than a first preset score.
7. The method according to any one of claims 1 to 5, wherein if the target store's index parameter to be promoted comprises a first index parameter and a second index parameter; the determining at least one target strategy used for improving the at least one index parameter to be improved and conforming to the current scene of the target store from the strategy set comprises the following steps:
determining a comprehensive effect score of each strategy in the strategy set on improving the first index parameter and the second index parameter;
obtaining a plurality of strategies with the comprehensive effect scores larger than a second preset score from the strategy set;
and determining at least one target strategy which accords with the current scene of the target store from a plurality of strategies of which the comprehensive effect score is larger than a second preset score.
8. The method according to any one of claims 1-5, wherein the determining at least one target strategy for promoting the at least one target parameter to be promoted and conforming to the current scene of the target store from a strategy set comprises:
and determining at least one target strategy which is used for improving the at least one index parameter to be improved and accords with the current scene of the target store, the scale of the store and the type of the store from a strategy set.
9. A policy recommendation apparatus, comprising:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring diagnosis data of a target store, and the diagnosis data comprises a plurality of index parameters of the target store;
the processing module is used for determining at least one item of index parameter to be promoted of the target shop according to the diagnosis data;
determining at least one target strategy which is used for improving the at least one index parameter to be improved and accords with the current scene of the target store from a strategy set, wherein the strategy set comprises a plurality of strategies for improving different index parameters of the store;
a push module to push the at least one targeting policy to the targeted store.
10. An electronic device, comprising:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of any one of claims 1-8.
11. A computer-readable storage medium, on which a computer program is stored, which computer program is executable by a processor to implement the method according to any one of claims 1-8.
12. A computer program product, characterized in that it comprises a computer program which, when being executed by a processor, carries out the method of any one of claims 1-8.
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