CN116308507A - Potential customer mining method based on customer value, storage medium and equipment - Google Patents
Potential customer mining method based on customer value, storage medium and equipment Download PDFInfo
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Abstract
The invention discloses a potential customer mining method, a storage medium and equipment based on customer value, wherein the potential customer mining method acquires customer behavior data from a historical database of a transaction platform, compares the customer behavior data with log data successfully purchased in the transaction platform, and performs data cleaning to obtain customer value data; classifying all clients in the client value data according to the client consumption grade, and putting the clients conforming to the client consumption grade into corresponding potential client data sets; inquiring all commodities purchased by each customer in the potential customer data set, sorting the commodities according to the interest degree of the customer from high to low to obtain first three types of commodities interested by each customer, and forming all commodities conforming to the type on a transaction platform into the potential purchased commodity data set; the purchasing power of each potential customer is predicted through the potential purchasing commodity data set of each potential customer, the potential customer types are classified according to the purchasing power, and marketing strategies are formulated.
Description
Technical Field
The invention belongs to the technical field of consumer behavior analysis, and particularly relates to a potential customer mining method, a storage medium and equipment based on customer value.
Background
With the development of society and the progress of production technology, commodity types in daily life are becoming more and more abundant, the rigidity requirement of customers on commodity has been changed into improvement requirement, and the marketing strategy of various production and sales enterprises is changed from product oriented to customer oriented. Therefore, how to mine the value of customers becomes a critical point in the formulation of marketing strategies.
In the current customer value mining based on big data technology, the customer value is usually determined by means of an RFM model, but because the RFM model is mainly used for reflecting the current value of customers, potential customers cannot be explored, so that the advertising marketing effect is poor, and the marketing cost is further improved; meanwhile, large-scale advertising popularization brings cost pressure to digital operation of companies.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a potential customer mining method, a storage medium and equipment based on customer value, wherein the potential customer mining method extracts potential customers through customer behavior data on a transaction platform, acquires commodity sets interested by the potential customers, predicts the purchasing power of each potential customer, divides potential customer types according to the purchasing power and establishes marketing strategies, so that social commerce in digital marketing is more accurate in combination with analysis of customer value.
In order to achieve the technical purpose, the invention adopts the following technical scheme: the potential customer mining method based on the customer value specifically comprises the following steps:
step 1, obtaining customer behavior data from a historical database of a transaction platform, wherein the customer behavior data comprises the following steps: non-purchasing behavior data of the customer and purchasing behavior data of the customer;
step 2, comparing the customer behavior data with log data successfully purchased in a transaction platform, and cleaning the data to obtain customer value data;
step 3, classifying all clients in the client value data according to the client consumption grade, and placing the clients meeting the client consumption grade into corresponding potential client data sets;
step 4, inquiring all commodities purchased by each customer in the potential customer data set, sorting the commodities according to the interest degree of the customer from high to low to obtain first three types of commodities interested by each customer, and forming all commodities conforming to the type on a transaction platform into the potential purchased commodity data set;
and 5, predicting the purchasing power of each potential customer through the potential purchasing commodity data set of each potential customer, classifying the potential customer types according to the purchasing power and formulating a marketing strategy.
Further, the specific process of the step 2 is as follows: comparing the purchasing behavior data of the customer with log data of successful purchasing in a transaction platform, and filtering out the bill refreshing data and the bill returning data; comparing the non-purchasing behavior data of the customer with log data of successful purchase in the transaction platform, and filtering out data purchased again after returning the bill, and data with unchanged product and price.
Further, the interest degree of the customer in the step 4 is determined by the number of successful purchases, the amount of successful purchases and the number of times of browsing the commodity.
Further, the calculation process of the interest degree of the client is as follows:
wherein C is i Representing the customer interest level of the ith commodity, m i Representing the i-th commodity purchase success number, alpha represents the normalized weight of the i-th commodity purchase success number, n i Representing the i-th commodity purchase success amount, beta represents the normalized weight of the i-th commodity purchase success amount, l i The ith commodity browsing times are represented, gamma represents the normalized weight of the ith commodity browsing times, and I represents all purchased commodities.
Further, the prediction process of the purchasing power of the potential customer in the step 5 is as follows:
y j ＝b+f(x j ,w j )+ξ j
wherein y is j Representing the purchasing power of the jth potential customer, b representing the predicted deviation, ζ j Representing the unobservable factors of the jth potential customer, x j Representing the consumption grade, w, of the jth potential customer j A potential purchase item data set representing a jth potential customer, f (x j ,w j ) Represents x j And w j A coacting purchasing power prediction function.
Further, in step 5, purchasing power is ranked from high to low, and key clients, value clients and common clients are classified according to 30%, 40% and 30%, and for the key clients, activities related to potential purchasing goods of the key clients are pushed once a week; for value customers, push activities related to their potential purchase of goods once a month; activities related to their potential purchase of goods are pushed once a year to the average customer.
Further, the present invention also provides a computer-readable storage medium storing a computer program for causing a computer to execute the client value-based potential client mining method.
Further, the present invention also provides an electronic device, including: the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the potential customer mining method based on the customer value when executing the computer program.
Compared with the prior art, the invention has the following beneficial effects: according to the potential customer mining method based on the customer value, potential customers are extracted through the customer behavior data on the transaction platform, the commodity set interested by the potential customers is obtained, the purchasing power of each potential customer is predicted, the customer value data is more close to the actual purchasing behavior through filtering the bill and the purchasing behavior record of the return goods in the customer behavior data, the potential purchasing power mining of the customer data generating the purchasing behavior in the interior is facilitated, the advertisement promotion cost is reduced, meanwhile, the potential customer types are divided according to the purchasing power, the marketing strategy is formulated, and the social commerce in digital marketing is more accurate in combination with the analysis of the customer value.
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FIG. 1 is a flow chart of a potential customer mining method based on customer value of the present invention;
FIG. 2 is an exploded view of customer behavior data according to the present invention.
Detailed Description
The technical scheme of the invention is further explained below with reference to the accompanying drawings.
FIG. 1 is a flow chart of the customer value-based potential customer mining method of the present invention, which specifically includes the steps of:
step 1, obtaining customer behavior data from a historical database of a transaction platform, as shown in fig. 2, where the customer behavior data includes: non-purchasing behavior data of the customer and purchasing behavior data of the customer; the purchasing behavior data of the customer is data of successful transaction, including purchasing time characteristics of the customer, purchasing frequency, age bracket of the customer and purchasing commodity list; the non-purchasing behavior data of the customer is transaction unsuccessful data recorded in the transaction platform, and comprises the time characteristic of the customer ordering, ordering frequency, age bracket of the customer and commodity list of the ordering failure.
And 2, comparing the customer behavior data with log data of successful purchase in a transaction platform, and cleaning the data to obtain customer value data, wherein the customer behavior data is provided with a bill and purchase behavior record of returning goods, and in order to enable the purchase data to be more close to actual purchase behavior, the data is required to be cleaned first. Specifically, comparing the purchasing behavior data of the customer with log data of successful purchasing in a transaction platform, and filtering out the bill refreshing data and the bill returning data; comparing the non-purchasing behavior data of the customer with log data of successful purchase in the transaction platform, and filtering out data purchased again after returning the bill, and data with unchanged product and price.
And step 3, classifying all clients in the client value data according to the client consumption grade, and placing the clients meeting the client consumption grade in a corresponding potential client data set.
Step 4, inquiring all commodities purchased by each customer in the potential customer data set, sorting the commodities according to the interest degree of the customer from high to low to obtain first three types of commodities interested by each customer, and forming all commodities conforming to the type on a transaction platform into the potential purchased commodity data set; the interest degree of the clients is determined by the number of successful purchases, the amount of successful purchases and the number of times of browsing commodities, so that a reliable basis is provided for marketing strategy formulation and user mining. The calculation process of the interest degree of the client comprises the following steps:
wherein C is i Representing the customer interest level of the ith commodity, m i Representing the i-th commodity purchase success number, alpha represents the normalized weight of the i-th commodity purchase success number, n i Representing the i-th commodity purchase success amount, beta represents the normalized weight of the i-th commodity purchase success amount, l i The ith commodity browsing times are represented, gamma represents the normalized weight of the ith commodity browsing times, and I represents all purchased commodities.
And 5, predicting the purchasing power of each potential customer through the potential purchasing commodity data set of each potential customer, dividing the potential customer type according to the purchasing power and formulating a marketing strategy. Specifically, the prediction process of the purchasing power of the potential customer is:
y j ＝b+f(x j ，w j )+ξ j
wherein y is j Representing the purchasing power of the jth potential customer, b representing the predicted deviation, ζ j Representing the unobservable factors of the jth potential customer, x j Representing the consumption grade, w, of the jth potential customer j A potential purchase item data set representing a jth potential customer, f (x j ，w j ) Represents x j And w j And the purchase power prediction function of the combined action predicts the purchase power of the potential client through the consumption grade of the potential client and the potential purchase commodity of the potential client, and the potential client is mined on the premise that the trading platform has a single client unit price requirement, so that the mining result of the potential client is more accurate.
Sorting the purchasing power from high to low, and dividing key customers, value customers and common customers according to 30%, 40% and 30%, wherein for the key customers, activities related to potential purchasing commodities are pushed once a week; for value customers, push activities related to their potential purchase of goods once a month; the ordinary clients are pushed once a year for activities related to potential commodity purchase, so that potential client mining is realized, and client viscosity is enhanced.
In one aspect of the present invention, there is also provided a computer-readable storage medium storing a computer program for causing a computer to execute the customer value-based potential customer mining method.
In another aspect of the present invention, there is also provided an electronic device, including: the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the potential customer mining method based on the customer value when executing the computer program.
In the embodiments disclosed herein, a computer storage medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The computer storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a computer storage medium would include one or more wire-based electrical connections, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the invention without departing from the principles thereof are intended to be within the scope of the invention as set forth in the following claims.
Claims (8)
1. The potential customer mining method based on the customer value is characterized by comprising the following steps of:
step 1, obtaining customer behavior data from a historical database of a transaction platform, wherein the customer behavior data comprises the following steps: non-purchasing behavior data of the customer and purchasing behavior data of the customer;
step 2, comparing the customer behavior data with log data successfully purchased in a transaction platform, and cleaning the data to obtain customer value data;
step 3, classifying all clients in the client value data according to the client consumption grade, and placing the clients meeting the client consumption grade into corresponding potential client data sets;
step 4, inquiring all commodities purchased by each customer in the potential customer data set, sorting the commodities according to the interest degree of the customer from high to low to obtain first three types of commodities interested by each customer, and forming all commodities conforming to the type on a transaction platform into the potential purchased commodity data set;
and 5, predicting the purchasing power of each potential customer through the potential purchasing commodity data set of each potential customer, classifying the potential customer types according to the purchasing power and formulating a marketing strategy.
2. The method for mining potential customers based on customer value according to claim 1, wherein the specific process of step 2 is as follows: comparing the purchasing behavior data of the customer with log data of successful purchasing in a transaction platform, and filtering out the bill refreshing data and the bill returning data; comparing the non-purchasing behavior data of the customer with log data of successful purchase in the transaction platform, and filtering out data purchased again after returning the bill, and data with unchanged product and price.
3. The method of claim 1, wherein the degree of interest of the customer in step 4 is determined by a combination of the number of successful purchases, the amount of successful purchases and the number of goods browsed.
4. A method of mining potential customers based on customer value according to claim 3, wherein the calculation of the customer interest level is:
wherein C is i Representing the customer interest level of the ith commodity, m i Representing the i-th commodity purchase success number, alpha represents the normalized weight of the i-th commodity purchase success number, n i Representing the i-th commodity purchase success amount, beta represents the normalized weight of the i-th commodity purchase success amount, l i Represents the ith commodity browsing times, gamma represents the normalized weight of the ith commodity browsing times, and I represents purchasingAll commercial products purchased.
5. The method for mining potential customers based on customer value according to claim 1, wherein the predicting process of the purchasing power of the potential customers in step 5 is:
y j ＝b+f(x j ,w j )+ξ j
wherein y is j Representing the purchasing power of the jth potential customer, b representing the predicted deviation, ζ j Representing the unobservable factors of the jth potential customer, x j Representing the consumption grade, w, of the jth potential customer j A potential purchase item data set representing a jth potential customer, f (x j ,w j ) Represents x j And w j A coacting purchasing power prediction function.
6. The method of claim 1, wherein the purchasing power is sorted from high to low in step 5, and the key clients, value clients and general clients are classified according to 30%, 40% and 30%, and the key clients are pushed once a week for the activities related to their potential purchasing goods; for value customers, push activities related to their potential purchase of goods once a month; activities related to their potential purchase of goods are pushed once a year to the average customer.
7. A computer readable storage medium storing a computer program, wherein the computer program causes a computer to perform the customer value based potential customer mining method according to any one of claims 1 to 6.
8. An electronic device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, which processor, when executing the computer program, implements the customer value based potential customer mining method according to any of claims 1-6.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103714139A (en) * | 2013-12-20 | 2014-04-09 | 华南理工大学 | Parallel data mining method for identifying a mass of mobile client bases |
| CN104834720A (en) * | 2015-05-12 | 2015-08-12 | 百度在线网络技术（北京）有限公司 | O2O (Online to Offline) mode-based interest point information display method and system |
| CN106611344A (en) * | 2015-10-23 | 2017-05-03 | 北京国双科技有限公司 | Method and device for mining potential customers |
| CN109583966A (en) * | 2018-12-07 | 2019-04-05 | 平安银行股份有限公司 | A kind of high value customer recognition methods, system, equipment and storage medium |
| CN110232586A (en) * | 2019-05-05 | 2019-09-13 | 浙江华坤道威数据科技有限公司 | A kind of advertisement push system based on big data |
| CN113327134A (en) * | 2021-06-16 | 2021-08-31 | 北京百度网讯科技有限公司 | Commodity information recommendation method and device, electronic equipment and medium |
-
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- 2023-02-07 CN CN202310071489.0A patent/CN116308507A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103714139A (en) * | 2013-12-20 | 2014-04-09 | 华南理工大学 | Parallel data mining method for identifying a mass of mobile client bases |
| CN104834720A (en) * | 2015-05-12 | 2015-08-12 | 百度在线网络技术（北京）有限公司 | O2O (Online to Offline) mode-based interest point information display method and system |
| CN106611344A (en) * | 2015-10-23 | 2017-05-03 | 北京国双科技有限公司 | Method and device for mining potential customers |
| CN109583966A (en) * | 2018-12-07 | 2019-04-05 | 平安银行股份有限公司 | A kind of high value customer recognition methods, system, equipment and storage medium |
| CN110232586A (en) * | 2019-05-05 | 2019-09-13 | 浙江华坤道威数据科技有限公司 | A kind of advertisement push system based on big data |
| CN113327134A (en) * | 2021-06-16 | 2021-08-31 | 北京百度网讯科技有限公司 | Commodity information recommendation method and device, electronic equipment and medium |
Non-Patent Citations (1)
| Title |
|---|
| 孤独一抹红: "用户购买力预测分析", 《HTTPS://ZHUANLAN.ZHIHU.COM/P/368454698》, pages 1 - 12 * |
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Application publication date: 20230623 |