CN112884550A - Commodity recommendation method and device based on customer purchasing ability - Google Patents
Commodity recommendation method and device based on customer purchasing ability Download PDFInfo
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Abstract
The invention discloses a commodity recommendation method and device based on customer purchasing ability, comprising the following steps: acquiring current call information of a plurality of clients; wherein, the current call information includes: the method comprises the steps of receiving call time interval information, call duration information, response waiting duration information and call type information; the call type information comprises a client telephone incoming call and a client telephone answering; inputting the current call information of each customer into a preset customer purchasing ability evaluation model so that the customer purchasing ability evaluation model evaluates the purchasing ability of each customer to obtain a purchasing ability score of each customer; the client purchasing ability model is constructed according to the historical call information of the client and the historical consumption amount of the client; extracting key customers from a plurality of customers according to the purchasing power scores, and then recommending the key customers; wherein the key customers are customers with purchasing ability scores exceeding a preset threshold value. The commodity recommendation efficiency can be improved by implementing the invention.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a commodity recommendation method and device based on customer purchasing ability.
Background
Different customers have different purchasing abilities, in the existing telemarketing process, in order to improve the sales conversion rate and the sales performance, a marketing person generally needs to manually evaluate the purchasing ability of each customer according to the communication condition with the customer and the data of the customer, then manually select the customers with stronger purchasing intentions and purchasing abilities from a plurality of customers, and recommend commodities to the customers. However, manual selection is time-consuming and labor-consuming, and further commodity recommendation efficiency is low.
Disclosure of Invention
The embodiment of the invention provides a commodity recommendation method and device based on customer purchasing power, which can improve commodity recommendation efficiency.
An embodiment of the present invention provides a commodity recommendation method based on a customer purchasing power, including:
acquiring current call information of a plurality of clients; wherein the current call information includes: the method comprises the steps of receiving call time interval information, call duration information, response waiting duration information and call type information; the call type information comprises a client call incoming ratio and a client call answering ratio;
inputting the current call information of each customer into a preset customer purchasing ability evaluation model so that the customer purchasing ability evaluation model evaluates the purchasing ability of each customer to obtain a purchasing ability score of each customer; the client purchasing ability model is constructed according to the historical call information of the client and the historical consumption amount of the client;
extracting key customers from the plurality of customers according to the purchasing power scores, and then recommending commodities to the key customers; wherein the key customers are customers with purchasing ability scores exceeding a preset threshold value.
Further, the customer purchasing ability model is constructed according to the historical call information of the customer and the historical consumption amount of the customer, and specifically comprises the following steps:
determining the purchasing power score of the customer according to the historical consumption amount of the customer; wherein the affordance score positively correlates with the historical spending amount;
and establishing the customer purchasing ability model based on the XGboost algorithm by taking the historical call information of the customer as input and the purchasing ability score of the customer as output.
Further, the recommending of the goods by the key customers specifically includes:
judging whether the key customer is an old customer, if so, acquiring historical consumption commodity information of the key customer, recommending commodities which are consistent with the historical consumption commodity in type and have price difference within a preset range to the key customer, or recommending commodities which are completely consistent with the historical consumption commodity to the key customer;
if not, selecting an old customer with the same purchasing ability score according to the purchasing ability score of the key customer, recommending commodities with the same type as the second historical consumed commodity and the price difference within a preset range to the key customer or recommending commodities completely consistent with the second historical consumed commodity to the key customer according to the second historical consumed commodity information of the old customer.
On the basis of the above method item embodiments, the present invention correspondingly provides apparatus item embodiments;
the embodiment of the invention provides a commodity recommending device based on the purchasing ability of a customer, which comprises: the system comprises an information acquisition module, a purchasing ability evaluation module and a commodity recommendation module;
the information acquisition module is used for acquiring current call information of a plurality of clients; wherein the current call information includes: the method comprises the steps of receiving call time interval information, call duration information, response waiting duration information and call type information; the call type information comprises a client call incoming ratio and a client call answering ratio;
the purchasing ability evaluation module is used for inputting the current call information of each customer into a preset customer purchasing ability evaluation model so that the customer purchasing ability evaluation model evaluates the purchasing ability of each customer to obtain the purchasing ability score of each customer; the client purchasing ability model is constructed according to the historical call information of the client and the historical consumption amount of the client;
the commodity recommendation module is used for extracting key customers from the plurality of customers according to the purchasing power scores and then recommending commodities to the key customers; wherein the key customers are customers with purchasing ability scores exceeding a preset threshold value.
Further, the system also comprises a model building module; the model building module is used for determining the purchasing power score of the customer according to the historical consumption amount of the customer; wherein the affordance score positively correlates with the historical spending amount; and establishing the customer purchasing ability model based on the XGboost algorithm by taking the historical call information of the customer as input and the purchasing ability score of the customer as output.
Further, the commodity recommendation module recommends the important customer to the commodity, specifically including:
judging whether the key customer is an old customer, if so, acquiring historical consumption commodity information of the key customer, recommending commodities which are consistent with the historical consumption commodity in type and have price difference within a preset range to the key customer, or recommending commodities which are completely consistent with the historical consumption commodity to the key customer;
if not, selecting an old customer with the same purchasing ability score according to the purchasing ability score of the key customer, recommending commodities with the same type as the second historical consumed commodity and the price difference within a preset range to the key customer or recommending commodities completely consistent with the second historical consumed commodity to the key customer according to the second historical consumed commodity information of the old customer.
By implementing the embodiment of the invention, the following beneficial effects are achieved:
the embodiment of the invention provides a commodity recommendation method and device based on customer purchasing ability. Compared with the prior art, the invention can automatically select the customers with stronger purchasing ability from a plurality of customers and then automatically recommend commodities for the customers, thereby improving the commodity recommendation efficiency and reducing the manpower loss.
Drawings
Fig. 1 is a flowchart illustrating a method for recommending a commodity based on a purchasing power of a customer according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a product recommendation device based on a customer purchasing power according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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 invention.
As shown in fig. 1, an embodiment of the present invention provides a method for recommending a product based on a purchasing power of a customer, including:
step S101: acquiring current call information of a plurality of clients; wherein the current call information includes: the method comprises the steps of receiving call time interval information, call duration information, response waiting duration information and call type information; the call type information comprises a client call incoming ratio and a client call answering ratio;
step S102: inputting the current call information of each customer into a preset customer purchasing ability evaluation model so that the customer purchasing ability evaluation model evaluates the purchasing ability of each customer to obtain a purchasing ability score of each customer; the client purchasing ability model is constructed according to the historical call information of the client and the historical consumption amount of the client;
step S103: extracting key customers from the plurality of customers according to the purchasing power scores, and then recommending commodities to the key customers; wherein the key customers are customers with purchasing ability scores exceeding a preset threshold value.
And S101, counting the call information of each client of the clients, wherein the counting dimensions mainly comprise: a call period, a call duration, a response waiting duration, and a call type.
The conversation time interval refers to the time interval when the client makes a conversation with the marketer, preferably, the conversation made at 8 hours-11 hours is defined as the morning conversation time interval, the conversation made at 11 hours-13 hours is defined as the noon conversation time interval, the conversation made at 13 hours-17 hours is defined as the noon conversation time interval, and the conversation made at 17 hours-22 hours is defined as the evening conversation time interval; the conversation time length refers to the time length of conversation between the client and the marketing personnel; the response waiting time refers to the time in the process of waiting for the customer to answer when the marketing staff makes a call to the customer, and the call types are divided into two types, wherein one type is that the customer actively makes a call to the marketing staff, the call type is the incoming call of the customer, and the other type is that the marketing staff initiates a call to the customer, and the call type is the answering call of the customer;
for step S102, the construction of the model is described first, and in a preferred embodiment, the construction of the customer purchasing power model according to the historical call information of the customer and the historical expense of the customer specifically includes: determining the purchasing power score of the customer according to the historical consumption amount of the customer; wherein the affordance score positively correlates with the historical spending amount; and establishing the customer purchasing ability model based on the XGboost algorithm by taking the historical call information of the customer as input and the purchasing ability score of the customer as output.
According to the method, the purchasing power of a customer is represented by the consumption amount of the customer, the consumption amount is in positive correlation with the purchasing power of the customer, the higher the consumption cutoff amount is, the stronger the purchasing power of the customer is, the consumption amount is converted into a purchasing power score of the customer according to a certain proportion, a large number of training samples are obtained by collecting the historical call information of each customer and the corresponding consumption amount, then model training is carried out through an XGboost algorithm, and a customer purchasing power model is constructed; after the model is obtained, the current call information of each current customer is input into the model, and the purchasing ability scores of the current customers are evaluated by the model.
For step S103, in a preferred embodiment, the recommending the goods by the key customer specifically includes: judging whether the key customer is an old customer, if so, acquiring historical consumption commodity information of the key customer, recommending commodities which are consistent with the historical consumption commodity in type and have price difference within a preset range to the key customer, or recommending commodities which are completely consistent with the historical consumption commodity to the key customer; if not, selecting an old customer with the same purchasing ability score according to the purchasing ability score of the key customer, recommending commodities with the same type as the second historical consumed commodity and the price difference within a preset range to the key customer or recommending commodities completely consistent with the second historical consumed commodity to the key customer according to the second historical consumed commodity information of the old customer.
After obtaining the purchasing power scores of the customers, taking the customers with the purchasing power scores exceeding a preset threshold (for example, 50 scores) as the key customers, then recommending the commodities, judging the current customers when recommending the commodities, obtaining the current customer names, judging the current key customers to be old customers if the customers with the same names can be found in the database, obtaining the historical consumption commodity information of the current key customers, recommending commodities with the same types or the same types and the prices within a preset error range, judging the current key customers to be new customers if the customers with the same names cannot be found in the database, selecting the old customers with the consistent scores as the similar customers according to the purchasing power scores, and recommending according to the historical consumption commodity information of the similar customers; the same type means the same type of goods, for example, the user is recommended to "treadmill" if he bought "treadmill" before.
On the basis of the above method item embodiments, the present invention correspondingly provides apparatus item embodiments;
as shown in fig. 2, an embodiment of the present invention provides a product recommendation apparatus based on a customer purchasing power, including: the system comprises an information acquisition module, a purchasing ability evaluation module and a commodity recommendation module;
the information acquisition module is used for acquiring current call information of a plurality of clients; wherein the current call information includes: the method comprises the steps of receiving call time interval information, call duration information, response waiting duration information and call type information; the call type information comprises a client call incoming ratio and a client call answering ratio;
the purchasing ability evaluation module is used for inputting the current call information of each customer into a preset customer purchasing ability evaluation model so that the customer purchasing ability evaluation model evaluates the purchasing ability of each customer to obtain the purchasing ability score of each customer; the client purchasing ability model is constructed according to the historical call information of the client and the historical consumption amount of the client;
the commodity recommendation module is used for extracting key customers from the plurality of customers according to the purchasing power scores and then recommending commodities to the key customers; wherein the key customers are customers with purchasing ability scores exceeding a preset threshold value.
In a preferred embodiment, the system further comprises a model building module; the model building module is used for determining the purchasing power score of the customer according to the historical consumption amount of the customer; wherein the affordance score positively correlates with the historical spending amount; and establishing the customer purchasing ability model based on the XGboost algorithm by taking the historical call information of the customer as input and the purchasing ability score of the customer as output.
In a preferred embodiment, the recommending module of goods recommends goods for the key customers, specifically including: judging whether the key customer is an old customer, if so, acquiring historical consumption commodity information of the key customer, recommending commodities which are consistent with the historical consumption commodity in type and have price difference within a preset range to the key customer, or recommending commodities which are completely consistent with the historical consumption commodity to the key customer; if not, selecting an old customer with the same purchasing ability score according to the purchasing ability score of the key customer, recommending commodities with the same type as the second historical consumed commodity and the price difference within a preset range to the key customer or recommending commodities completely consistent with the second historical consumed commodity to the key customer according to the second historical consumed commodity information of the old customer.
It should be noted that the above device item embodiments correspond to the method item embodiments of the present invention, and can implement the product recommendation method based on the customer purchasing ability according to any item of the above embodiments of the present invention. In addition, the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may also be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
By implementing the embodiment of the invention, the invention can automatically select the customers with stronger purchasing ability from a plurality of customers and then automatically recommend commodities for the customers, thereby improving the commodity recommendation efficiency and reducing the manpower loss.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (6)
1. A commodity recommendation method based on customer purchasing ability is characterized by comprising the following steps:
acquiring current call information of a plurality of clients; wherein the current call information includes: the method comprises the steps of receiving call time interval information, call duration information, response waiting duration information and call type information; the call type information comprises a client telephone incoming call and a client telephone answering;
inputting the current call information of each customer into a preset customer purchasing ability evaluation model so that the customer purchasing ability evaluation model evaluates the purchasing ability of each customer to obtain a purchasing ability score of each customer; the client purchasing ability model is constructed according to the historical call information of the client and the historical consumption amount of the client;
extracting key customers from the plurality of customers according to the purchasing power scores, and then recommending commodities to the key customers; wherein the key customers are customers with purchasing ability scores exceeding a preset threshold value.
2. The commodity recommendation method based on customer purchasing power as claimed in claim 1, wherein said customer purchasing power model is constructed according to the historical call information of the customer and the historical spending amount of the customer, and specifically comprises:
determining the purchasing power score of the customer according to the historical consumption amount of the customer; wherein the affordance score positively correlates with the historical spending amount;
and establishing the customer purchasing ability model based on the XGboost algorithm by taking the historical call information of the customer as input and the purchasing ability score of the customer as output.
3. The method for recommending commodities for customer purchasing ability according to claim 1, wherein said key customer makes commodity recommendations, specifically comprising:
judging whether the key customer is an old customer, if so, acquiring historical consumption commodity information of the key customer, recommending commodities which are consistent with the historical consumption commodity in type and have price difference within a preset range to the key customer, or recommending commodities which are completely consistent with the historical consumption commodity to the key customer;
if not, selecting an old customer with the same purchasing ability score according to the purchasing ability score of the key customer, recommending commodities with the same type as the second historical consumed commodity and the price difference within a preset range to the key customer or recommending commodities completely consistent with the second historical consumed commodity to the key customer according to the second historical consumed commodity information of the old customer.
4. An article recommendation device based on a customer purchase ability, comprising: the system comprises an information acquisition module, a purchasing ability evaluation module and a commodity recommendation module;
the information acquisition module is used for acquiring current call information of a plurality of clients; wherein the current call information includes: the method comprises the steps of receiving call time interval information, call duration information, response waiting duration information and call type information; the call type information comprises a client call incoming ratio and a client call answering ratio;
the purchasing ability evaluation module is used for inputting the current call information of each customer into a preset customer purchasing ability evaluation model so that the customer purchasing ability evaluation model evaluates the purchasing ability of each customer to obtain the purchasing ability score of each customer; the client purchasing ability model is constructed according to the historical call information of the client and the historical consumption amount of the client;
the commodity recommendation module is used for extracting key customers from the plurality of customers according to the purchasing power scores and then recommending commodities to the key customers; wherein the key customers are customers with purchasing ability scores exceeding a preset threshold value.
5. The commodity recommendation device based on customer purchase ability according to claim 4, further comprising a model construction module;
the model building module is used for determining the purchasing power score of the customer according to the historical consumption amount of the customer; wherein the affordance score positively correlates with the historical spending amount; and establishing the customer purchasing ability model based on the XGboost algorithm by taking the historical call information of the customer as input and the purchasing ability score of the customer as output.
6. The commodity recommendation device based on the customer purchasing ability according to claim 4, wherein the commodity recommendation module recommends the key customer commodities by specifically comprising:
judging whether the key customer is an old customer, if so, acquiring historical consumption commodity information of the key customer, recommending commodities which are consistent with the historical consumption commodity in type and have price difference within a preset range to the key customer, or recommending commodities which are completely consistent with the historical consumption commodity to the key customer;
if not, selecting an old customer with the same purchasing ability score according to the purchasing ability score of the key customer, recommending commodities with the same type as the second historical consumed commodity and the price difference within a preset range to the key customer or recommending commodities completely consistent with the second historical consumed commodity to the key customer according to the second historical consumed commodity information of the old customer.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113379516A (en) * | 2021-08-12 | 2021-09-10 | 永正信息技术(南京)有限公司 | Recommended product determination method and device |
| CN114493712A (en) * | 2022-01-30 | 2022-05-13 | 上海烈熊网络技术有限公司 | Digital marketing method and marketing platform |
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| CN111784449A (en) * | 2020-06-29 | 2020-10-16 | 中国平安财产保险股份有限公司 | Data pushing method, data pushing equipment, storage medium and device |
| CN111914169A (en) * | 2020-07-16 | 2020-11-10 | 中信银行股份有限公司 | Product recommendation method and device, electronic equipment and computer-readable storage medium |
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- 2021-02-07 CN CN202110169165.1A patent/CN112884550A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111784449A (en) * | 2020-06-29 | 2020-10-16 | 中国平安财产保险股份有限公司 | Data pushing method, data pushing equipment, storage medium and device |
| CN111914169A (en) * | 2020-07-16 | 2020-11-10 | 中信银行股份有限公司 | Product recommendation method and device, electronic equipment and computer-readable storage medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113379516A (en) * | 2021-08-12 | 2021-09-10 | 永正信息技术(南京)有限公司 | Recommended product determination method and device |
| CN114493712A (en) * | 2022-01-30 | 2022-05-13 | 上海烈熊网络技术有限公司 | Digital marketing method and marketing platform |
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