2 thoughts on “How to use new media marketing to increase customer stickiness”
Dominick
1. Consider the needs of the customer and experience 2. Interactive should be timely, the language style should be close to the customer 3. Online activities must have characteristics, should be viewed from time to time, and integrate customer information collection into it.
1. Targeted marketing It big data can provide certain enterprise transaction characteristics and funding demand characteristics. It can help business departments analyze and screen the capital needs of the enterprise, provide cash management products, and help enterprises solve liquidity problems. Big data can help the credit card center track hot information, provide precise marketing products for specific people, and increase new card users, such as hot film, entertainment activities, catering group purchases, etc. Banks have launched customized wealth management products and insurance products for specific groups. 2. Social marketing people’s social behavior has generated huge data. Using social platforms and combined with big data analysis, the financial industry can carry out lower cost social marketing. With the help of open Internet platforms, According to a large number of customer needs data, product and channel promotion. Through the massive data returned by the Internet social platform, the stage results of the marketing plan are evaluated, and marketing can be adjusted in real time. It uses word -of -mouth MLM and viral communication to help the financial industry quickly carry out product promotion, brand promotion, and channel publicity. 3, Data Platform How to achieve precision marketing, thereby increasing customer stickiness. This is undoubtedly a powerful data platform to be backed up, relying on big data platforms, similar to cloudy data. Such a data platform is a fulcrum. The guiding role of customer needs and continuously strengthened the practical application of the Internet to achieve quickly obtaining customer purchase desires and purchasing needs from big data. 4. Credit risk assessment Ber banks can use big data to increase credit risk input latitude, increase the level of credit risk management, and dynamically manage the risks of enterprises and individual customers. Establishing big data -based credit risk assessment models and methods will increase banks’ financial support for SMEs and individuals. The establishment of personal credit score standards will help banks lead the leading era of credit consumption. The dynamic credit risk management mechanism based on big data will help banks to predict high -risk credit default time in advance, intervene in time, reduce the probability of default, and prevent credit fraud at the same time. 5. Fraud risk management Credit card companies can use big data to predict and discover malicious fraud in time, even if measures are taken to reduce the risk of credit fraud. Banks can establish anti -fraud monitoring systems based on big data, dynamically manage fraud incidents of online banking, POS machines, ATM and other channels. Big data provides multi -latitude monitoring indicators and linkage methods, which can make up for and improve the lack of current anti -fraud monitoring methods. Essence Especially in terms of identifying customer behavior trends, big data has great advantages. 6. Enhancement of customer experience Ber banks can provide custom services and greetings to customers who enter the outlets according to big data analysis, provide customized services for customers on holidays, predict corporate customers in the future of funds, make appointments in advance, and make appointments in advance. Improve customer experience. Private banks can help customers invest in financial market products based on big data analysis reports, earn excess profits, form a competitive advantage, and improve customer experience. The insurance business can provide customers with effective services in advance based on big data prediction, improve customer experience, and increase business opportunities. The wealth management business can use large number analysis to quickly launch industry reports and market trend reports to help investors understand hot spots in time and improve customer satisfaction. 7. Demand analysis and product innovation Big data provides overall data. Banks can use the overall sample data to screen from them. You can classify the products from various aspects of customer occupation, age, income, residence, habit, assets, assets, credit, etc., and customize the customer’s needs based on other data input latitudes. Banks can also predict the development characteristics of the industry based on the company’s transaction data and provide financial product services for corporate customers. 8. Increased operating efficiency Big data can show the actual income and cost of different product lines, helping bank product management. At the same time, big data provides comprehensive reports for management, revealing the efficiency of internal operation management, and strong internal efficiency improvement. Big data can help market departments effectively monitor marketing programs and marketing situations, improve marketing accuracy, and reduce marketing costs. Big data can show risk view to control credit risk while speeding up credit approval. Big data can help the insurance industry with insurance solutions quickly, improve efficiency, and reduce costs. Wealth management products can also use big data dynamics to provide industry reports to quickly help investors. 9 . Decision support Big data can help financial enterprises, provide data support for the upcoming decision -making decisions, and can also summarize the laws according to big data analysis to further interpret new decisions. Decision tree models based on big data and artificial intelligence technology will effectively help the financial industry analyze credit risks and provide strong support for business decisions. Before the financial industry or new service is pushed to the market, it can be tested in local areas. Big data technology can analyze accurate marketing collected data, and provides decision -making support for the marketing of new products through statistical analysis reports.
1. Consider the needs of the customer and experience
2. Interactive should be timely, the language style should be close to the customer
3. Online activities must have characteristics, should be viewed from time to time, and integrate customer information collection into it.
1. Targeted marketing
It big data can provide certain enterprise transaction characteristics and funding demand characteristics. It can help business departments analyze and screen the capital needs of the enterprise, provide cash management products, and help enterprises solve liquidity problems. Big data can help the credit card center track hot information, provide precise marketing products for specific people, and increase new card users, such as hot film, entertainment activities, catering group purchases, etc. Banks have launched customized wealth management products and insurance products for specific groups.
2. Social marketing
people’s social behavior has generated huge data. Using social platforms and combined with big data analysis, the financial industry can carry out lower cost social marketing. With the help of open Internet platforms, According to a large number of customer needs data, product and channel promotion. Through the massive data returned by the Internet social platform, the stage results of the marketing plan are evaluated, and marketing can be adjusted in real time. It uses word -of -mouth MLM and viral communication to help the financial industry quickly carry out product promotion, brand promotion, and channel publicity.
3, Data Platform
How to achieve precision marketing, thereby increasing customer stickiness. This is undoubtedly a powerful data platform to be backed up, relying on big data platforms, similar to cloudy data. Such a data platform is a fulcrum. The guiding role of customer needs and continuously strengthened the practical application of the Internet to achieve quickly obtaining customer purchase desires and purchasing needs from big data.
4. Credit risk assessment
Ber banks can use big data to increase credit risk input latitude, increase the level of credit risk management, and dynamically manage the risks of enterprises and individual customers. Establishing big data -based credit risk assessment models and methods will increase banks’ financial support for SMEs and individuals. The establishment of personal credit score standards will help banks lead the leading era of credit consumption. The dynamic credit risk management mechanism based on big data will help banks to predict high -risk credit default time in advance, intervene in time, reduce the probability of default, and prevent credit fraud at the same time.
5. Fraud risk management
Credit card companies can use big data to predict and discover malicious fraud in time, even if measures are taken to reduce the risk of credit fraud. Banks can establish anti -fraud monitoring systems based on big data, dynamically manage fraud incidents of online banking, POS machines, ATM and other channels. Big data provides multi -latitude monitoring indicators and linkage methods, which can make up for and improve the lack of current anti -fraud monitoring methods. Essence Especially in terms of identifying customer behavior trends, big data has great advantages.
6. Enhancement of customer experience
Ber banks can provide custom services and greetings to customers who enter the outlets according to big data analysis, provide customized services for customers on holidays, predict corporate customers in the future of funds, make appointments in advance, and make appointments in advance. Improve customer experience. Private banks can help customers invest in financial market products based on big data analysis reports, earn excess profits, form a competitive advantage, and improve customer experience. The insurance business can provide customers with effective services in advance based on big data prediction, improve customer experience, and increase business opportunities. The wealth management business can use large number analysis to quickly launch industry reports and market trend reports to help investors understand hot spots in time and improve customer satisfaction.
7. Demand analysis and product innovation
Big data provides overall data. Banks can use the overall sample data to screen from them. You can classify the products from various aspects of customer occupation, age, income, residence, habit, assets, assets, credit, etc., and customize the customer’s needs based on other data input latitudes. Banks can also predict the development characteristics of the industry based on the company’s transaction data and provide financial product services for corporate customers.
8. Increased operating efficiency
Big data can show the actual income and cost of different product lines, helping bank product management. At the same time, big data provides comprehensive reports for management, revealing the efficiency of internal operation management, and strong internal efficiency improvement. Big data can help market departments effectively monitor marketing programs and marketing situations, improve marketing accuracy, and reduce marketing costs. Big data can show risk view to control credit risk while speeding up credit approval. Big data can help the insurance industry with insurance solutions quickly, improve efficiency, and reduce costs. Wealth management products can also use big data dynamics to provide industry reports to quickly help investors.
9
. Decision support
Big data can help financial enterprises, provide data support for the upcoming decision -making decisions, and can also summarize the laws according to big data analysis to further interpret new decisions. Decision tree models based on big data and artificial intelligence technology will effectively help the financial industry analyze credit risks and provide strong support for business decisions. Before the financial industry or new service is pushed to the market, it can be tested in local areas. Big data technology can analyze accurate marketing collected data, and provides decision -making support for the marketing of new products through statistical analysis reports.