CN111090799A - Network marketing effect analysis and suggestion system - Google Patents
Network marketing effect analysis and suggestion system Download PDFInfo
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- CN111090799A CN111090799A CN201911252792.0A CN201911252792A CN111090799A CN 111090799 A CN111090799 A CN 111090799A CN 201911252792 A CN201911252792 A CN 201911252792A CN 111090799 A CN111090799 A CN 111090799A
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Abstract
The invention relates to a network marketing effect analysis and suggestion system, which comprises the following steps: step S1: constructing a marketing platform database; step S2: inputting information; step S3: setting marketing effect evaluation weight for the marketing platform according to the input information and the information in the database; step S4: capturing marketing information data; step S5: obtaining a marketing effect pre-evaluation value of each marketing platform according to the information in the database in the step S1 and the data captured in the step S3, performing weighted operation on the marketing effect pre-evaluation value of each marketing platform according to the weight set in the step S2 to obtain a marketing effect evaluation value, and exporting a network marketing effect analysis report; step S6: obtaining an optimization scheme according to the marketing effect evaluation obtained in the step S5, and deriving a network marketing optimization suggestion report; the invention enables an enterprise decision maker to directly see the marketing effect of each marketing platform by directly capturing the data of each marketing platform and matching with the processing rule.
Description
Technical Field
The invention relates to the field of e-commerce, in particular to a network marketing effect analysis and suggestion system.
Background
With the development of information technology, various network forums and e-commerce platforms become a main way for consumers to acquire product and service information, so that not only enterprises pay attention to network marketing and hire special teams to carry out network marketing, but also the core of network marketing is to guide consumers to buy service or product through marketing information such as advertisements and soft texts;
many small and medium-sized enterprises understand the importance of network marketing and can actively find marketing companies to make and put relevant marketing information, but enterprise managers are difficult to confirm whether the network marketing effect is good or not, and are more difficult to know a platform with poor network marketing effect, and make adjustment of marketing strategies, so a system capable of analyzing the network marketing effect is urgently needed at present, further a suggestion system capable of making suggestions according to the marketing effect, and a suggestion tool capable of reducing decision burden of the enterprise managers is also needed at present.
Disclosure of Invention
The invention provides a network marketing effect analysis and suggestion system for solving the problems, which comprises the following steps:
step S1: constructing a marketing platform database;
step S2: inputting enterprise information and product information;
step S3: setting different marketing effect evaluation weights for each marketing platform according to the input information in the step S2 and the information in the database in the step S1;
step S4: capturing marketing information data from each marketing platform;
step S5: obtaining a marketing effect pre-evaluation value of each marketing platform according to the information in the database in the step S1 and the information data captured in the step S4, performing weighted operation on the marketing effect pre-evaluation value of each marketing platform according to the weight set in the step S2 to obtain a marketing effect evaluation value, and exporting a network marketing effect analysis report;
step S6: and obtaining an optimization scheme according to the marketing effect evaluation obtained in the step S5, and deriving a network marketing optimization suggestion report.
Preferably, the information in the database in step S1 includes basic information of each marketing platform, and the marketing platform is labeled with a classification label according to a classification basis of a main content medium of the marketing platform, with a weight label in different fields according to an enterprise industry field or a product field, and is labeled with a pre-evaluation label according to a data type of the marketing platform.
Preferably, the method for crawling marketing information in step S4 is called by a web crawler or api.
Preferably, the process of deriving the marketing effect pre-evaluation value in step S5 includes: and fitting the captured information with pre-evaluation labels in a database, and selecting a score value with the highest fitting degree.
Preferably, the process of setting the marketing effect evaluation weight in step S3 includes: and fitting the input information with the weight label, and selecting the weight value with the highest fitting degree.
Preferably, the step S5 is to derive a network marketing effect analysis report, where the network marketing effect analysis report includes: and classifying and collecting the marketing effect evaluation values according to the classification labels of the marketing platform.
Preferably, the optimization scheme in step S6 includes the following steps:
step S61: setting an evaluation reference line;
step S62: comparing the marketing effect evaluation value obtained in the step S5 with an evaluation reference line;
step S61: making an optimization suggestion for reducing marketing input for a marketing platform with a marketing effect evaluation value higher than an evaluation reference line; and making an optimization suggestion for increasing marketing input for the marketing platform with the marketing effect evaluation value lower than the evaluation reference line.
The invention has the beneficial effects that: by directly capturing the data of each marketing platform and matching with the processing rules, an enterprise decision maker can directly see the marketing effect of each marketing platform, and meanwhile, an optimization scheme based on the marketing effect can be provided in a matching way, so that the decision burden of the enterprise decision maker is reduced, and the decision maker can make a more scientific marketing scheme which better meets the actual situation.
Detailed Description
The invention is further illustrated below:
step S1: constructing a marketing platform database;
the information in the database in the step S1 includes the basic information of each marketing platform, and a classification label is marked on the marketing platform according to the main content medium of the marketing platform as a classification basis;
according to the enterprise industry field or the product field, weight labels in different fields are marked on the marketing platform, such as: the weight value of the platform A in the field B is X, and the weight value of the platform A in the field C is Y
And marking a pre-evaluation label on the marketing platform according to the data type of the marketing platform, wherein if the score value corresponding to the data D is G, the score value corresponding to the data E is F.
Step S2: inputting enterprise information and product information;
the entered enterprise information comprises the name, the industry field and the geographic position of the enterprise; the input product information comprises product names, product industry fields and popularization information keywords.
Step S3: setting different marketing effect evaluation weights for each marketing platform according to the input information in the step S2 and the information in the database in the step S1;
the process of setting the marketing effect evaluation weight includes: and fitting the information recorded in the step S2 with the weight label, and selecting the weight value with the highest fitting degree as the evaluation weight.
Step S4: capturing marketing information from each marketing platform;
the method for grabbing each platform is different, some platforms have api interfaces to be called, some platforms can only grab by using web crawlers, or the two are combined.
Step S5: obtaining a marketing effect pre-evaluation value of each marketing platform according to the information in the database in the step S1 and the information captured in the step S4, performing weighted operation on the marketing effect pre-evaluation value of each marketing platform according to the weight set in the step S2 to obtain a marketing effect evaluation value, and exporting a network marketing effect analysis report;
the process of deriving the marketing effect pre-evaluation value comprises the following steps: and fitting the information captured in the step S4 with the pre-evaluation labels in the database, and selecting the evaluation value with the highest fitting degree as the pre-evaluation value of the marketing effect.
The process of the network marketing effect analysis report comprises the following steps: classifying and collecting marketing effect evaluation values according to classification labels of marketing platforms
Step S6: and obtaining an optimization scheme according to the marketing effect evaluation obtained in the step S5, and deriving a network marketing optimization suggestion report.
The obtained optimization scheme comprises the following steps:
step S61: setting an evaluation reference line;
step S62: comparing the marketing effect evaluation value obtained in the step S5 with an evaluation reference line;
step S61: making an optimization suggestion for reducing marketing input for a marketing platform with a marketing effect evaluation value higher than an evaluation reference line; and making an optimization suggestion for increasing marketing input for the marketing platform with the marketing effect evaluation value lower than the evaluation reference line.
Step S7: continuously repeating the steps S4-S6 with a month as a period
The above-described embodiments are merely preferred examples of the present invention, and not intended to limit the scope of the invention, so that equivalent changes or modifications in the structure, features and principles of the invention described in the claims should be included in the claims.
Claims (9)
1. A network marketing effect analysis and suggestion system is characterized by comprising the following steps:
step S1: constructing a marketing platform database;
step S2: inputting enterprise information and product information;
s3, setting different marketing effect evaluation weights for each marketing platform;
step S4: capturing marketing information data from each marketing platform;
step S5: obtaining a marketing effect pre-evaluation value of each marketing platform according to the information in the database in the step S1 and the information data captured in the step S4, performing weighted operation on the marketing effect pre-evaluation value of each marketing platform according to the weight set in the step S2 to obtain a marketing effect evaluation value, and exporting a network marketing effect analysis report;
step S6: and obtaining an optimization scheme according to the marketing effect evaluation obtained in the step S5, and deriving a network marketing optimization suggestion report.
2. The system for analyzing and suggesting a marketing effect on network of claim 1, wherein the information in the database in step S1 includes basic information of each marketing platform, and the marketing platform is labeled with classification labels based on the main content media of the marketing platform, with weight labels of different fields for the marketing platform according to the industry field of the enterprise or the product field, and with pre-evaluation labels for the marketing platform according to the data type of the marketing platform.
3. The system for analyzing and suggesting the marketing effect of the network of claim 1, wherein the method of crawling marketing information in step S4 is called by web crawler or api.
4. The system for analyzing and suggesting a marketing effect on network of claim 1, wherein the process of setting the marketing effect evaluation weight for the marketing platform in step S3 further comprises: refer to the information of the database in step S1.
5. The system for analyzing and suggesting a marketing effect on internet according to claim 2, wherein the step S5 of deriving a pre-evaluation value of marketing effect comprises: and fitting the captured information with pre-evaluation labels in a database, and selecting a score value with the highest fitting degree.
6. The system for analyzing and suggesting a marketing effect on internet according to claim 4, wherein the step S3 of setting the evaluation weight of the marketing effect comprises: and fitting the input information with the weight label, and selecting the weight value with the highest fitting degree.
7. The system for analyzing and suggesting a cyber marketing effect according to claim 2, wherein the step S5 is executed to derive a cyber marketing effect analysis report, wherein the cyber marketing effect analysis report includes: and classifying and collecting the marketing effect evaluation values according to the classification labels of the marketing platform.
8. The system for analyzing and suggesting network marketing effect of claim 1, wherein the optimization scheme of step S6 comprises the following steps:
step S61: setting an evaluation reference line;
step S62: comparing the marketing effect evaluation value obtained in the step S5 with an evaluation reference line;
step S61: making an optimization suggestion for reducing marketing input for a marketing platform with a marketing effect evaluation value higher than an evaluation reference line; and making an optimization suggestion for increasing marketing input for the marketing platform with the marketing effect evaluation value lower than the evaluation reference line.
9. The system for analyzing and suggesting network marketing effect of claim 1, further comprising:
step S7: and continuously repeating the steps S4-S6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911252792.0A CN111090799A (en) | 2019-12-09 | 2019-12-09 | Network marketing effect analysis and suggestion system |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911252792.0A CN111090799A (en) | 2019-12-09 | 2019-12-09 | Network marketing effect analysis and suggestion system |
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| CN111090799A true CN111090799A (en) | 2020-05-01 |
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| CN201911252792.0A Pending CN111090799A (en) | 2019-12-09 | 2019-12-09 | Network marketing effect analysis and suggestion system |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112214596A (en) * | 2020-09-01 | 2021-01-12 | 武汉策微信息科技有限公司 | Marketing operation interaction system integrated with clue management function |
| CN116109165A (en) * | 2022-11-28 | 2023-05-12 | 河南九域腾龙信息工程有限公司 | Network operation capability analysis method and device |
-
2019
- 2019-12-09 CN CN201911252792.0A patent/CN111090799A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112214596A (en) * | 2020-09-01 | 2021-01-12 | 武汉策微信息科技有限公司 | Marketing operation interaction system integrated with clue management function |
| CN116109165A (en) * | 2022-11-28 | 2023-05-12 | 河南九域腾龙信息工程有限公司 | Network operation capability analysis method and device |
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Application publication date: 20200501 |