CN111414542A - Real estate customer group identification and marketing method - Google Patents
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Abstract
The invention belongs to the technical field of real estate sales, and particularly discloses a real estate customer group identification and marketing method, which comprises the following steps: the method comprises the following steps: getting through a house purchasing and mall data interface of a client, and acquiring and sorting house purchasing data and mall data of the client; step two: predicting the transaction probability of the client by using a client transaction prediction model, and judging the house purchasing intention of the client by combining the house purchasing data acquired in the step one so as to determine whether to put an advertisement or not; step three: and predicting the client intention house source according to the client shopping mall data and house purchasing data so as to recommend the relevant house source to the client. The method can solve the problems that in the prior art, a corresponding customer group is difficult to find to implement advertisement putting, and accurate marketing cannot be achieved.
Description
Technical Field
The invention belongs to the technical field of real estate sales, and particularly relates to a real estate customer group identification and marketing method.
Background
At present, most of propaganda means in the real estate industry adopt a form of sending a leaflet on line, but the leaflet is oriented to the masses and cannot implement accurate propaganda marketing, the demands of customers are probably not matched with the content on the leaflet, and people are busy in life and may not have time to carefully watch the content on the leaflet when receiving the leaflet. Therefore, the traditional online waybill issuing mode cannot quickly find the corresponding client group to implement advertisement putting, and cannot achieve accurate marketing, which becomes a big problem.
Disclosure of Invention
The invention aims to provide a real estate customer group identification and marketing method to solve the problem that in the prior art, a corresponding customer group is difficult to find to implement advertisement putting, and accurate marketing cannot be achieved.
In order to achieve the purpose, the technical scheme of the invention is as follows: a method of real estate customer base identification and marketing comprising the steps of:
the method comprises the following steps: getting through a house purchasing and mall data interface of a client, and acquiring and sorting house purchasing data and mall data of the client;
step two: predicting the transaction probability of the client by using a client transaction prediction model, and judging the house purchasing intention of the client by combining the house purchasing data acquired in the step one so as to determine whether to put an advertisement or not;
step three: and predicting the client intention house source according to the client shopping mall data and house purchasing data so as to recommend the relevant house source to the client.
Further, in step one, the house purchasing data and the shopping mall data are associated through the telephone of the client.
Further, in step one, the mall data includes the age of the customer, the telephone number of the customer, the address of the customer, the category of the goods purchased by the customer, and the grade of the goods purchased by the customer.
Further, in step one, the house purchasing data comprises a customer telephone number, a customer transaction number, a customer purchasing amount, a customer purchasing area, a customer purchasing property type and a searched keyword.
Further, in the second step, the specific step of predicting the deal probability of the customer by using the customer deal prediction model comprises:
s21: preprocessing customer behavior data and performing characteristic engineering processing to obtain model input data, wherein the fields comprise: customer ID, visit building ID, visit days, total visit page number, total browse time, total browse times, visit building number, visit house type, visit night, average visit time per day, average click time per day, average visit page number per day, maximum click time per day, maximum browse time per day, number of days visited before and after, use number of housing loan calculator, and whether to meet or not;
s22, selecting n records of the transaction clients as positive samples, selecting records of the same number of non-transaction clients as negative samples, dividing all records into training data and testing data according to the ratio of 7:3, training and testing the model, and selecting a L ightGBM prediction model to predict the transaction probability of the clients;
s23: data of a non-transaction client is input, and the model outputs the transaction probability of the client. And if the deal probability is greater than X, determining to place the advertisement for the client.
Further, in step three, the specific step of predicting the client intention house source includes:
s31: converting the mall data and house purchasing data into client attributes, wherein the client attributes comprise client purchasing power, areas where the clients are located, order types, category preference, unit price of the clients in three months, searched brand words, searched item names and searched general words;
s32: the customer attributes are divided into three dimensions according to the mall data and house purchasing data of the customer: a crowd basic attribute dimension, a shopping behavior dimension and a keyword dimension; the basic attribute dimension of the crowd comprises the purchasing power of the customers and the areas where the customers are located; the shopping behavior dimension comprises the order category, the preference of the order category and the passenger order of nearly three months; the keyword dimension comprises searched brand words, searched item names and searched general words;
s33: obtaining information of different aspects of the house source according to the data of different dimensions; wherein the basic attribute dimension of the crowd obtains information such as traffic, business circles and the like near the house source; obtaining the size of the house type, educational facilities around the building and the like according to the shopping behavior dimension; and obtaining the name of the building according to the dimension of the keyword.
Further, in step S31, the calculation of the customer purchasing power includes the following steps:
s311: the grade of the commodity is divided, and the commodity with the commodity price in the same category which is at or above the first price is determined as a high-grade commodity; determining other commodities in the same category as non-high-grade commodities, and sequentially dividing the non-high-grade commodities into three grades from high to low according to the price: the commodities at or above the second price are inferior-grade commodities, the commodities between the third price and the second price are medium-grade commodities, and the commodities below the third price are low-grade commodities;
s312: calculating a purchasing power vector, calculating the ratio of each customer to purchase each grade of commodity, vectorizing the ratio to obtain the purchasing power vector of each customer, wherein the purchasing power vector is a four-dimensional vector, and each dimension corresponds to the grade of one commodity;
s313: and dividing purchasing power grades, and carrying out clustering operation on the purchasing power vectors to obtain four point clusters related to the purchasing power vectors of the customers, wherein each point cluster corresponds to one purchasing power grade, and the grade corresponding to the point cluster where the purchasing power vectors are located is used as the purchasing power grade of the customers.
The first price is defined as: the first price is the least expensive of the 5% of the most expensive prices of the goods in the same category.
The second price is defined as the least expensive one of the 20% most expensive ones of the non-premium commodities of the same category.
The definition of the third price is: the second price is the most expensive price in 20 percent of the cheapest price of the non-high-grade commodities of the same class.
Further, in step S31, the orders include food and drink, personal care cosmetics, mother and baby products, electric appliance digital products, household life, office products, toy intelligence, pet products, and art ornaments; and if the customer does not place an order, converting the commodity category browsed by the customer into the commodity category preference of the customer.
Further, in step S31, the searched brand word refers to the brand name of the real estate floor in the search keyword, the searched item name refers to the item name of the real estate floor in the search keyword, and the searched common word refers to the common word of the real estate floor in the search keyword; the using priority of the searched general words is higher than that of the searched item names and the searched brand words, and the using priority of the searched item names is higher than that of the searched brand words.
The beneficial effects of this technical scheme lie in: according to the mall data and the house purchasing data of the client, the related information of the intention house source of the client can be estimated, so that the corresponding house source is recommended to the client, accurate marketing is realized, and the sales efficiency is further accelerated.
Drawings
FIG. 1 is a flow chart of a method of real estate customer base identification and marketing in accordance with the present invention.
Detailed Description
The following is further detailed by way of specific embodiments:
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.
The embodiment is basically as shown in the attached figure 1: a method of real estate customer base identification and marketing comprising the steps of:
the method comprises the following steps: getting through a house purchasing and mall data interface of a client, and acquiring and sorting house purchasing data and mall data of the client; the mall data includes the age of the customer, the telephone number of the customer, the address of the customer, the category of the goods purchased by the customer and the grade of the goods purchased by the customer; the categories of commodities purchased by customers include beverage, personal care cosmetics, mother and infant articles, electrical appliance numbers, household life, office articles, toy intelligence, pet articles, artwork ornaments and the like; the house purchasing data comprises a customer telephone, customer transaction times, customer purchasing amount, customer purchasing area, customer purchasing property type and searched keywords; the house purchase data and the shopping mall data are associated through the telephone of the client.
Step two: predicting the transaction probability of the client by using a client transaction prediction model, and judging the house purchasing intention of the client by combining the house purchasing data acquired in the step one so as to determine whether to put an advertisement or not; the method comprises the following specific steps:
s21: preprocessing customer behavior data and performing characteristic engineering processing to obtain model input data, wherein the fields comprise: customer ID, visit building ID, visit days, total visit page number, total browse time, total browse times, visit building number, visit house type, visit night, average visit time per day, average click time per day, average visit page number per day, maximum click time per day, maximum browse time per day, number of days visited before and after, use number of housing loan calculator, and whether to meet or not;
s22, selecting 30000 records of transaction clients as positive samples, selecting records of equal number of non-transaction clients as negative samples, dividing all records into training data and testing data according to the ratio of 7:3, training and testing the model, and selecting L ightGBM prediction model to predict the transaction probability of the clients;
s23: data of a non-transaction client is input, and the model outputs the transaction probability of the client. And if the deal probability is more than 60%, determining to place the advertisement for the client.
Step three: predicting the intention house source of the client according to the data of the client mall, and recommending corresponding floor information to the client in the form of logistics packaging and a built-in advertisement list; the specific steps for predicting the client intention house source comprise:
s31: converting the mall data into customer attributes, wherein the customer attributes comprise customer purchasing power, the area where the customer is located, the order type, the preference of the order type, the unit price of the customer in the last three months, searched brand words, searched item names and searched general words; the next category of products comprises food and beverage, personal care cosmetics, mother and infant products, electric appliance numbers, household life, office supplies, toy intelligence, pet products and art ornaments; if the client does not order, the commodity category browsed by the client is converted into the category preference of the client;
s32: the customer attributes are divided into three dimensions according to the customer's mall data: a crowd basic attribute dimension, a shopping behavior dimension and a keyword dimension; the basic attribute dimension of the crowd comprises the purchasing power of the customers and the areas where the customers are located; the shopping behavior dimension comprises the order category, the preference of the order category and the passenger order of nearly three months; the keyword dimension comprises searched brand words, searched item names and searched general words;
s33: obtaining information of different aspects of the house source according to the mall data with different dimensions; wherein the basic attribute dimension of the crowd obtains information such as traffic, business circles and the like near the house source; obtaining the size of the house type, educational facilities around the building and the like according to the shopping behavior dimension; and obtaining the name of the building according to the dimension of the keyword. And sending the data of the three dimensions to a corresponding building, so as to perfect the customer portrait of the customer.
In step S31, the calculation of the customer purchasing power includes the steps of:
s311: the grade of the commodity is divided, and the commodity with the commodity price in the same category which is at or above the first price is determined as a high-grade commodity; determining other commodities in the same category as non-high-grade commodities, and sequentially dividing the non-high-grade commodities into three grades from high to low according to the price: the commodities at or above the second price are inferior-grade commodities, the commodities between the third price and the second price are medium-grade commodities, and the commodities below the third price are low-grade commodities; the first price is defined as: the first price is the least expensive of the 5% of the most expensive prices for goods in the same category. The second price is defined as the least expensive one of the 20% most expensive prices of the non-premium goods of the same category. The definition of the third price is: the second price is the most expensive one of the 20% least expensive price of the non-premium commodities of the same category.
S312: calculating a purchasing power vector, calculating the proportion of each grade commodity purchased by each customer, vectorizing the proportion to obtain the purchasing power vector of each customer, wherein the purchasing power vector is a four-dimensional vector, and each dimension corresponds to the grade of one commodity;
s313: the purchasing power grades are divided, the purchasing power vectors are subjected to clustering operation, four point clusters related to the purchasing power vectors of the customers are obtained, each point cluster corresponds to one purchasing power grade, the four purchasing power grades are respectively a small white collar, a high-grade white collar, a blue collar and a luxury, and the grade corresponding to the point cluster where the purchasing power vectors are located is used as the purchasing power grade of the customers.
The specific analysis of step S33 is as follows:
1. the crowd basic attribute dimension:
① customer purchase power:
the small white collar preferentially recommends the building close to the subway or the bus station, and the recommended house type is the small house type.
The high-grade white collar preferentially recommends the building next to the subway or the bus station, and the recommended house type is a small house type or a medium house type.
The blue collar preferentially recommends a building with good surrounding shopping environment, such as a building with a small business circle nearby, and recommends a house type of a medium house type or a large house type.
The local tyrant preferentially recommends a building with excellent surrounding shopping environment, such as a building with a large business circle nearby, and recommends the house type to be a large house type.
② customer location:
the area where the customer is located is obtained according to the address filled in by the customer order, the unit is selected to be a district or county level, and the floor in the area is recommended according to the customer address.
2. Shopping behavior dimension
The purchase category comprises food, beverage, mother and baby articles and toy intelligence: a building with a surrounding school and a good shopping environment (a small business circle nearby) is recommended.
The purchase category comprises personal care cosmetics, electric appliance numbers and office supplies: and recommending a building with a good shopping environment (containing a small business circle nearby) in the vicinity of a subway or a bus station.
The purchase category comprises household life and art ornaments: a building with a surrounding hospital and a good shopping environment (a small business circle nearby) is recommended.
3. Keyword dimension
The searched brand words refer to brand names of real estate floors in the search keywords of the clients, the searched item names refer to item names of the real estate floors in the search keywords of the clients, and the searched common words refer to common words of the real estate floors in the search keywords of the clients; the usage priority of the searched general words is higher than that of the searched item names and the searched brand words, and the usage priority of the searched item names is higher than that of the searched brand words.
The following is illustrated by a specific example:
the existing client predicts the transaction probability to be 70% according to the house purchasing data, and the address filled by the client is the litchi bay area in Guangzhou city in Guangdong province; purchasing commodity categories including mother and infant commodities and toy commodities; analyzing the purchasing power grade of the customer as the blue collar according to the grade of the purchased commodity; and the client search keyword contains "baolihua bay".
According to the prediction, the client is recommended to the Taiwan Baohui bay building of the Li Baohui district, the building is a medium-sized house (an advertisement leaflet of the house source is put on), the building is close to the Bowen school and the flower area business center, and subway public transportation is convenient.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
The foregoing is merely an example of the present invention, and common general knowledge in the field of known specific structures and characteristics is not described herein in any greater extent than that known in the art at the filing date or prior to the priority date of the application, so that those skilled in the art can now appreciate that all of the above-described techniques in this field and have the ability to apply routine experimentation before this date can be combined with one or more of the present teachings to complete and implement the present invention, and that certain typical known structures or known methods do not pose any impediments to the implementation of the present invention by those skilled in the art. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (9)
1. A method of real estate customer base identification and marketing characterized by: the method comprises the following steps:
the method comprises the following steps: getting through a house purchasing and mall data interface of a client, and acquiring and sorting house purchasing data and mall data of the client;
step two: predicting the transaction probability of the client by using a client transaction prediction model, and judging the house purchasing intention of the client by combining the house purchasing data acquired in the step one so as to determine whether to put an advertisement or not;
step three: and predicting the client intention house source according to the client shopping mall data and house purchasing data so as to recommend the relevant house source to the client.
2. A method of real estate customer base identification and marketing according to claim 1 wherein: in step one, the house purchasing data and the shopping mall data are correlated through the telephone of the client.
3. A method of real estate customer base identification and marketing according to claim 1 wherein: in step one, the mall data includes the age of the customer, the telephone number of the customer, the address of the customer, the category of the goods purchased by the customer and the grade of the goods purchased by the customer.
4. A method of real estate customer base identification and marketing according to claim 1 wherein: in step one, the house purchasing data comprises a customer telephone number, a customer transaction number, a customer purchasing amount, a customer purchasing area, a customer purchasing property type and a searched keyword.
5. A method of real estate customer base identification and marketing according to claim 1 wherein: in the second step, the concrete steps of predicting the deal probability of the customer by using the customer deal prediction model comprise:
s21: preprocessing customer behavior data and performing characteristic engineering processing to obtain model input data, wherein the fields comprise: customer ID, visit building ID, visit days, total visit page number, total browse time, total browse times, visit building number, visit house type, visit night, average visit time per day, average click time per day, average visit page number per day, maximum click time per day, maximum browse time per day, number of days visited before and after, use number of housing loan calculator, and whether to meet or not;
s22, selecting n records of the transaction clients as positive samples, selecting records of the same number of non-transaction clients as negative samples, dividing all records into training data and testing data according to the ratio of 7:3, training and testing the model, and selecting a L ightGBM prediction model to predict the transaction probability of the clients;
s23: data of a non-transaction client is input, and the model outputs the transaction probability of the client. And if the deal probability is greater than X, determining to place the advertisement for the client.
6. A method of real estate customer base identification and marketing according to claim 1 wherein: in step three, the specific steps of predicting the client intention house source include:
s31: converting the mall data and house purchasing data into client attributes, wherein the client attributes comprise client purchasing power, areas where the clients are located, order types, category preference, unit price of the clients in three months, searched brand words, searched item names and searched general words;
s32: the customer attributes are divided into three dimensions according to the mall data and house purchasing data of the customer: a crowd basic attribute dimension, a shopping behavior dimension and a keyword dimension; the basic attribute dimension of the crowd comprises the purchasing power of the customers and the areas where the customers are located; the shopping behavior dimension comprises the order category, the preference of the order category and the passenger order of nearly three months; the keyword dimension comprises searched brand words, searched item names and searched general words;
s33: obtaining information of different aspects of the house source according to the data of different dimensions; wherein the basic attribute dimension of the crowd obtains information such as traffic, business circles and the like near the house source; obtaining the size of the house type, educational facilities around the building and the like according to the shopping behavior dimension; and obtaining the name of the building according to the dimension of the keyword.
7. A method of real estate customer base identification and marketing according to claim 6 wherein: in step S31, the calculation of the customer purchasing power includes the steps of:
s311: the grade of the commodity is divided, and the commodity with the commodity price in the same category which is at or above the first price is determined as a high-grade commodity; determining other commodities in the same category as non-high-grade commodities, and sequentially dividing the non-high-grade commodities into three grades from high to low according to the price: the commodities at or above the second price are inferior-grade commodities, the commodities between the third price and the second price are medium-grade commodities, and the commodities below the third price are low-grade commodities;
s312: calculating a purchasing power vector, calculating the ratio of each customer to purchase each grade of commodity, vectorizing the ratio to obtain the purchasing power vector of each customer, wherein the purchasing power vector is a four-dimensional vector, and each dimension corresponds to the grade of one commodity;
s313: and dividing purchasing power grades, and carrying out clustering operation on the purchasing power vectors to obtain four point clusters related to the purchasing power vectors of the customers, wherein each point cluster corresponds to one purchasing power grade, and the grade corresponding to the point cluster where the purchasing power vectors are located is used as the purchasing power grade of the customers.
8. A method of real estate customer base identification and marketing according to claim 6 wherein: in step S31, the orders include food and drink, personal care cosmetics, mother and infant products, electric appliance digital products, household life, office supplies, toy intelligence, pet supplies, and art ornaments; and if the customer does not place an order, converting the commodity category browsed by the customer into the commodity category preference of the customer.
9. A method of real estate customer base identification and marketing according to claim 6 wherein: in step S31, the searched brand word refers to the brand name of the real estate floor in the search keyword, the searched item name refers to the item name of the real estate floor in the search keyword, and the searched common word refers to the common word of the real estate floor in the search keyword; the using priority of the searched general words is higher than that of the searched item names and the searched brand words, and the using priority of the searched item names is higher than that of the searched brand words.
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| CN112561269A (en) * | 2020-12-07 | 2021-03-26 | 深圳市思为软件技术有限公司 | Advisor recommendation method and device |
| CN112598439A (en) * | 2020-12-18 | 2021-04-02 | 深圳市思为软件技术有限公司 | Customer management method and related device |
| CN112633943A (en) * | 2020-12-31 | 2021-04-09 | 杭州冠家房地产营销策划有限公司 | Method for real estate oriented marketing |
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