Multi-dimensional financial product marketing analysis method based on big data

Abstract

The invention relates to the field of financial product marketing analysis methods, and discloses a big data-based multi-dimensional financial product marketing analysis method, which comprises the steps of collecting user data, dividing users, determining customer affairs with purchase intentions, processing based on the existing user data, and rating and dividing consumer goods. The method comprises the following steps of: rating users, judging the consumption willingness level of the current user, rating the consumption willingness of the user, performing targeted marketing on the users in combination with different characteristics, behaviors and demand differences of the users, screening out the users with the consumption willingness, and catering to the market by detecting and analyzing big data of the users. The user strategy is adjusted according to the market change, and the marketing strategy and scheme are updated according to the trend, so that the market competitiveness is improved, the contact with the user is enhanced, the targeted marketing product is enhanced, and the marketing effect is improved; the multi-dimensional financial product marketing analysis method based on big data is realized.

Description

Technical Field

[0001] The present invention relates to the field of financial product marketing analysis methods, and more specifically, the present invention relates to a multi-dimensional financial product marketing analysis method based on big data. Background Art

[0002] With the rapid development of the economy, the competitive pressure in the corporate market is also increasing. In this context, companies are paying more and more attention to marketing. Traditional manual sales methods can no longer help businesses quickly and accurately find customers and occupy the market.

[0003] The financial industry is the industry that is most sensitive to the market and needs to adapt to market changes the most. However, when marketing the financial products produced by the financial industry, they mostly adopt the method of screening and differentiation, which cannot adapt well to market changes. This paper proposes a multi-dimensional financial product marketing analysis method based on big data. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the present invention provides a multi-dimensional financial product marketing analysis method based on big data, which has the advantage of more precise operation.

[0005] To achieve the above purpose, the present invention provides the following technical solution: a multi-dimensional financial product marketing analysis method based on big data, comprising the following steps: S1. Collect user data, process the collected user data, divide the users, and identify customers with purchasing intentions to provide special services; S2. Based on the existing user data, consumer goods are graded and classified. By collecting the user's consumption data, a comprehensive evaluation is conducted based on the user's consumption of consumer goods with different ratings, thereby rating the user; S3. By dividing the user's consumption categories and the proportion of the user's consumption categories, the current level of the user's consumption willingness is judged, and the user's consumption willingness is rated; S4. Design different targeted plans based on different characteristics, behaviors and needs of users, and market to users according to the plans to improve marketing effectiveness; S5. Screen out users with consumption intentions, conduct detailed analysis on this group of users, and gain in-depth understanding of their consumer behaviors and preferences, so as to better deliver accurate and relevant information during the marketing process, improve conversion rates and customer loyalty; S6. By testing and analyzing user big data, we can capture changes in the market environment, adjust strategies in real time according to changes in the market environment, cater to the market, and adjust user strategies according to market changes; S7. Update data in real time, and constantly develop data change charts based on data changes to more vividly display data change trends, so as to update marketing strategies and plans in line with the trends and improve market competitiveness; S8. Strengthen contact with users, adaptively adjust marketing strategies according to changes in user consumption habits and consumption levels, and target marketing products to improve marketing effectiveness.

[0006] As a preferred technical solution of the present invention, the user data described in S1 includes user consumption data, user social media data, user interest preferences, and user search records.

[0007] As a preferred technical solution of the present invention, the S1 processes user data to analyze customers with purchasing intentions, actively communicate with this group of users, thereby strengthening the connection and facilitating subsequent marketing.

[0008] As a preferred technical solution of the present invention, the user data is processed in S2, the business data is obtained from the business database, and the user data is compared with the business data in the business database through big data tools to perform information derivation processing to obtain the feature derived data.

[0009] As a preferred technical solution of the present invention, the user consumption willingness rating in S3 is combined with the consumer product rating and user rating in S2, and combined with historical business data to comprehensively rate and classify the user's financial consumption willingness.

[0010] As a preferred technical solution of the present invention, the different characteristics, behaviors and demand differences of users in S4 include the user's family fixed assets, expected family income level, family structure, family consumption goals and types of financial product demand.

[0011] As a preferred technical solution of the present invention, the S5 determines the user's consumption habits and financial expectations based on the user's data, behavior, and preferences, so as to better recommend financial services and convert consumption to the user.

[0012] As a preferred technical solution of the present invention, S6 judges user preferences based on user big data combined with changes in the market environment, thereby adjusting marketing strategies to cater to the market, thereby introducing new products and avoiding being eliminated by the market.

[0013] As a preferred technical solution of the present invention, the adjustment strategy in S7 includes market positioning strategy, product pricing strategy, sales promotion strategy, channel strategy, brand marketing strategy and social media marketing strategy.

[0014] As a preferred technical solution of the present invention, the S8 strengthens the contact with the user and understands the user's financial status, so as to better carry out targeted financial marketing.

[0015] Compared with the prior art, the present invention has the following beneficial effects: Collect user data, process the collected user data, divide the users, and thus determine customers with purchasing intention to provide special services; process the existing user data, rate and divide consumer goods; collect user consumption data, and rate the users through comprehensive evaluation of their consumption of consumer goods with different ratings; divide the users' consumption categories, and judge the current level of users' consumption willingness through the proportion of their consumption categories, and thus rate their consumption willingness; design different matching plans based on the different characteristics, behaviors and demand differences of users, market to users according to the plans, and thus improve marketing effectiveness; screen out users with consumption willingness, conduct detailed analysis of this group of users, and have an in-depth understanding of their consumer behaviors and preferences, so as to In the marketing process, better accurate and relevant information can be delivered to improve conversion rate and customer loyalty. By detecting and analyzing user big data, changes in the market environment can be captured sharply. According to changes in the market environment, strategies can be adjusted in real time to cater to the market, and user strategies can be adjusted according to market changes. Data can be updated in real time, and data change charts can be continuously developed according to data changes to more vividly display data change trends, so as to adapt to trends and update marketing strategies and plans, improve market competitiveness, strengthen contact with users, and adaptively adjust marketing strategies according to changes in user consumption habits and consumption levels. Targeted marketing products can improve marketing effectiveness. The above method has the characteristics of targeted financial product marketing, and can adapt well to market changes and market sensitivity, improve the ability to adapt to the market, and thus improve marketing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0017] 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.

[0018] like Figure 1 As shown, the present invention provides a multi-dimensional financial product marketing analysis method based on big data, comprising the following steps: S1. Collect user data, process the collected user data, divide the users, and identify customers with purchasing intentions to provide special services; S2. Based on the existing user data, consumer goods are graded and classified. By collecting the user's consumption data, a comprehensive evaluation is conducted based on the user's consumption of consumer goods with different ratings, thereby rating the user; S3. By dividing the user's consumption categories and the proportion of the user's consumption categories, the current level of the user's consumption willingness is judged, and the user's consumption willingness is rated; S4. Design different targeted plans based on different characteristics, behaviors and needs of users, and market to users according to the plans to improve marketing effectiveness; S5. Screen out users with consumption intentions, conduct detailed analysis on this group of users, and gain in-depth understanding of their consumer behaviors and preferences, so as to better deliver accurate and relevant information during the marketing process, improve conversion rates and customer loyalty; S6. By testing and analyzing user big data, we can capture changes in the market environment, adjust strategies in real time according to changes in the market environment, cater to the market, and adjust user strategies according to market changes; S7. Update data in real time, and constantly develop data change charts based on data changes to more vividly display data change trends, so as to update marketing strategies and plans in line with the trends and improve market competitiveness; S8. Strengthen contact with users, adaptively adjust marketing strategies according to changes in user consumption habits and consumption levels, and target marketing products to improve marketing effectiveness.

[0019] Among them, the user data described in S1 includes user consumption data, user social media data, user interest preferences, and user search records.

[0020] Among them, in S1, according to the user data processing, customers with purchasing intention are analyzed, and active communication and contact are carried out with this group of users, so as to strengthen the contact and facilitate subsequent marketing.

[0021] In S2, the user data is processed, the business data is obtained from the business database, and the user data is compared with the business data in the business database through big data tools to perform information derivation processing to obtain the feature derived data.

[0022] The user consumption willingness rating in S3 is combined with the consumer product rating and user rating in S2, and combined with historical business data to comprehensively rate and classify the user's financial consumption willingness.

[0023] The different characteristics, behaviors and demand differences of users in S4 include the user's family fixed assets, expected family income level, family structure, family consumption goals and types of financial product demand.

[0024] Among them, S5 determines the user's consumption habits and financial expectations based on the user's data, behavior, and preferences, so as to better recommend financial services and convert consumption to the user.

[0025] Among them, in S6, user preferences are judged based on user big data combined with changes in the market environment, so as to adjust the marketing strategy to cater to the market, thereby innovating and avoiding being eliminated by the market.

[0026] The adjustment strategies in S7 include market positioning strategy, product pricing strategy, sales promotion strategy, channel strategy, brand marketing strategy and social media marketing strategy.

[0027] Among them, the S8 strengthens the connection with users and understands the financial status of users, so as to better carry out targeted financial marketing.

[0028] The working principle and use process of the present invention are as follows: collecting user data, processing the collected user data, dividing the users, thereby determining customers with purchasing intentions to provide special services, processing based on existing user data, rating and dividing consumer goods, collecting user consumption data, and conducting a comprehensive evaluation of the user's consumption of consumer goods with different ratings to rate the user, dividing the user's consumption categories, and judging the current user's consumption willingness level by the proportion of the user's consumption categories, thereby rating the user's consumption willingness, combining the different characteristics, behaviors and demand differences of the users to design different matching plans, marketing the users according to the plans, thereby improving the marketing effect and screening out users with consumption willingness. , conduct a detailed analysis of this group of users, and have an in-depth understanding of the users' consumer behavior and preferences, so as to better deliver accurate and relevant information in the marketing process, improve conversion rate and customer loyalty, and detect and analyze user big data to capture changes in the market environment. According to changes in the market environment, adjust strategies in real time to cater to the market, and adjust user strategies according to market changes. Update data in real time, and constantly develop data change charts based on data changes to more vividly display data change trends, so as to adapt to trends and update marketing strategies and plans, improve market competitiveness, strengthen contact with users, and adaptively adjust marketing strategies and targeted marketing products according to changes in user consumption habits and consumption levels, thereby improving marketing effectiveness.

[0029] 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.

[0030] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-dimensional financial product marketing analysis method based on big data, characterized in that: The steps include: S1. Collect user data, process the collected user data, divide the users, and identify customers with purchasing intentions to provide special services; S2. Based on the existing user data, consumer goods are graded and classified. By collecting the user's consumption data, a comprehensive evaluation is conducted based on the user's consumption of consumer goods with different ratings, thereby rating the user; S3. By dividing the user's consumption categories and the proportion of the user's consumption categories, the current level of the user's consumption willingness is judged, and the user's consumption willingness is rated; S4. Design different targeted plans based on different characteristics, behaviors and needs of users, and market to users according to the plans to improve marketing effectiveness; S5. Screen out users with consumption intentions, conduct detailed analysis on this group of users, and gain in-depth understanding of their consumer behaviors and preferences, so as to better deliver accurate and relevant information during the marketing process, improve conversion rates and customer loyalty; S6. By testing and analyzing user big data, we can capture changes in the market environment, adjust strategies in real time according to changes in the market environment, cater to the market, and adjust user strategies according to market changes; S7. Update data in real time, and constantly develop data change charts based on data changes to more vividly display data change trends, so as to update marketing strategies and plans in line with the trends and improve market competitiveness; S8. Strengthen contact with users, adaptively adjust marketing strategies according to changes in user consumption habits and consumption levels, and target marketing products to improve marketing effectiveness.

2. The multi-dimensional financial product marketing analysis method based on big data according to claim 1, characterized in that: The user data described in S1 includes user consumption data, user social media data, user interest preferences, and user search records.

3. The multi-dimensional financial product marketing analysis method based on big data according to claim 1, characterized in that: In S1, the user data is processed to analyze the customers with purchasing intention, and active communication and contact are carried out with this group of users to strengthen the connection and facilitate subsequent marketing.

4. The multi-dimensional financial product marketing analysis method based on big data according to claim 1, characterized in that: In S2, the user data is processed, the business data is obtained from the business database, and the user data is compared with the business data in the business database through a big data tool to perform information derivation processing to obtain the feature derived data.

5. The multi-dimensional financial product marketing analysis method based on big data according to claim 1, characterized in that: The user consumption willingness rating in S3 is combined with the consumer product rating and user rating in S2, and combined with historical business data to comprehensively rate and classify the user's financial consumption willingness.

6. The multi-dimensional financial product marketing analysis method based on big data according to claim 1, characterized in that: The different characteristics, behaviors and demand differences of users in S4 include the user's family fixed assets, family expected income level, family structure, family consumption goals and types of financial product demand.

7. The multi-dimensional financial product marketing analysis method based on big data according to claim 1, characterized in that: In S5, the user's consumption habits and financial expectations are judged based on the user's data, behavior, and preferences, so as to better recommend financial services and convert consumption to the user.

8. The multi-dimensional financial product marketing analysis method based on big data according to claim 1, characterized in that: In the S6, user preferences are judged based on user big data combined with changes in the market environment, so as to adjust the marketing strategy to cater to the market, thereby introducing new products and avoiding being eliminated by the market.

9. The multi-dimensional financial product marketing analysis method based on big data according to claim 1, characterized in that: The adjustment strategies in S7 include market positioning strategy, product pricing strategy, promotion strategy, channel strategy, brand marketing strategy and social media marketing strategy.

10. The multi-dimensional financial product marketing analysis method based on big data according to claim 1, characterized in that: The S8 strengthens the connection with users and understands their financial status, so as to better carry out targeted financial marketing.

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