TWI826823B - Analytical device to estimate customer needs based on region - Google Patents

Abstract

An analysis device for estimating customer needs based on regions includes an object database, a customer activity database, a data collection module, a processing module, and a transmission module. The processing module calculates the customer corresponding to the customer code based on the geographical location of the objects in the plurality of activity contents to which the customer code belongs, and at the same time, based on the overlap between the characteristics of the objects and the weight distribution rate of the objects. Preference requirements are sent to the client device through the sending module.

Description

Analytical device to estimate customer needs based on region

The present disclosure relates to a display processing device, and in particular to an analysis device for estimating customer needs based on region.

According to surveys on home purchase intentions, people care about the convenience around their homes. The property requirements that prospective home buyers will consider include: proximity to consumer shopping districts, proximity to parks and green spaces, proximity to MRT/high-speed rail/stations, etc. Housing objects with good properties can increase the convenience of life for the residents. Nowadays, the Internet is widespread, so people are accustomed to searching for the information they want online. Regarding the supply of real estate objects, some real estate object providers will present photos, layouts and housing information of the objects on their websites. Some real estate property providers will use maps to display the surrounding school districts, hospitals and other objects around the property. When customers see an object of interest on the website, they will proactively contact the real estate industry, and the industry will correspondingly assign a broker to provide services.

However, under the existing model, most of the brokers who go to serve customers are completely ignorant of customer needs, forcing brokers to always find ways to adapt to the situation without doing their homework in advance, making it difficult to improve service quality.

In order to solve the problem of unclear customer needs, one purpose disclosed by the present invention is to provide an analysis method for estimating customer needs based on region. Analytical devices and methods. An analysis device for estimating customer needs based on regions includes an object database, a customer activity database, a data collection module, a processing module, and a transmission module. The processing module calculates the relative position of the customer code based on the geographical locations of the objects in the plurality of activity contents to which the customer code belongs, and at the same time, based on the overlap between the characteristics of the objects and a weight distribution rate of the objects. A customer preference requirement is sent to the client device through the transmission module.

10:Object system

111, 112, 113, 114: Objects

111A, 112A, 113A, 114A: actual distance

111P, 112P, 113P, 114P: Object characteristics

121:Additional objects

121A: Actual distance

121P: Additional object features

131: Recommended objects

131A: Actual distance

131P: Recommended object characteristics

15:Customer code

15P: Customer’s usual location

20:Display system

21:Data processing device

211:Object database

212: Processing module

2121:Search component

2122:Map generator component

2123: Recommended components

213:Data collection module

22:Client device

221:Display screen

26:Database group

28: Analysis device to estimate customer needs based on region

31:Teleport module

91:User

D111, D112, D113, D114: object data

D111A, D112A, D113A, D114A: activity range data unit

D111A1, D112A1, D113A1, D114A1: attribute words

D111A2, D112A2, D113A2, D114A2: quantity string

D111A3, D112A3, D113A3, D114A3: interactive content words

D111H, D112H, D113H, D114H: Object feature category indicator

D111P, D112P, D113P, D114P: location information

D121: Additional object data

D121A: Additional activity scope data unit

D121A1: Additional attribute words

D121A2: Extra quantity string

D121A3: Additional interactive content words

D121H: Additional object feature category indicator

D121P: Additional location information

D131: Recommended object information

D131A: Actual distance data unit

D131A1: Recommended attribute words

D131A2: Recommended quantity string

D131A3: Recommended words for interactive content

D131H: Recommended object feature category indicator

D131P: Recommended location information

D15: Real estate information unit

D15H: Real estate category indicator

D15P: Real estate location information

D5: Geographical location

D51: Icon data

D52: Map data block

D53: Multiple map data blocks

D54: The third map data block

D55: The fourth map data block

D61: Object attribute image data block

D62: Object attribute image data block

DA1: Multiple object data

DHA1, DHA2: object characteristic category indicator

DL1: activity range value

H111, H112, H113, H114: Object feature categories

HA1, HA2: Object feature category

HB1: Additional object feature categories

HC1: Recommended object feature categories

HD1: Real estate category

K111, K112, K113, K114: icon

KA2: Plural icons

M111P, M112P, M113P, M114P: mark position

M2: Multiple maps

M3: The third map

M4: The fourth map

M5: The fifth map

MA1: Multiple marked positions

MA2: Multiple marked positions

R1: Range of activity

S11: Multiple activity contents

S12: Multiple activity contents

S13: Third activity content

S14: The fourth activity content

U1: picture

300:Service requirements

The present disclosure can be further understood through the detailed description of the following figures: Figure 1: a schematic diagram of a display system in various embodiments of the present disclosure.

Figure 2: A schematic diagram of an object system in various embodiments of the present disclosure.

Figure 3: Schematic structural diagram of an object database in Figure 1.

Figure 4: A schematic diagram of a map in various embodiments of the present disclosure.

Figures 5A~5B: schematic diagrams of requirement analysis in various embodiments of the present disclosure.

See Figure 1, Figure 2 and Figure 3. Figure 1 is a schematic diagram of a display system 20 in various embodiments of the present disclosure. Figure 2 is a schematic diagram of an object system 10 in various embodiments of the present disclosure. Figure 3 is a schematic structural diagram of the object database in a database group 26 in Figure 1 . As shown in Figure 1, the display system 20 includes a data processing device 21. And a client device 22 coupled to the data processing device 21. The data processing device 21 includes an object database 211 , a processing module 212 coupled to the data unit 211 , and a data collection module 213 coupled to the processing module 212 . The object database 211 includes the database group 26, and the database group 26 is further divided into an object database and a customer activity database. The client device 22 includes a display screen 221. For example, there is a transmission module 31 between the data collection module 213 and the client device 22, and the transmission module 31 is coupled therebetween.

As shown in FIG. 2 , the object system 10 includes a plurality of objects 111 , 112 , 113 and 114 . For example, the object system 10 may further include at least one additional object 121 , a recommended object 131 and a customer code 15 . There are a plurality of interactive relationships 111A, 112A, 113A and 114A respectively between the client code 15 and the objects 111, 112, 113 and 114. The objects 111, 112, 113, and 114 respectively have a plurality of object characteristics 111P, 112P, 113P, and 114P. The additional object 121 has an additional range of motion and an additional object characteristic 121P. The recommended object 131 has an actual distance 131A and a recommended object feature 131P. The customer code 15 has a real estate customer's usual location 15P. The processing module 212 calculates an actual distance between the objects falling within the plurality of sent activity contents and the customer's usual location.

The objects 111, 112, 113 and 114 respectively belong to a plurality of object feature categories H111, H112, H113 and H114. Each category of the object feature categories H111, H112, H113 and H114 is a plurality of object feature categories HA1 and HA2. one of them. This additional object belongs to 121 In an additional object feature category HB1, the additional object feature category HB1 is different from any one of the object feature categories HA1 and HA2. The recommended object 131 belongs to a recommended object feature category HC1. The customer code 15 belongs to a real estate category HD1.

For example, the object feature categories HA1 and HA2 are environmental categories and living function categories respectively, and the additional object feature category HB1 is a medical category. For example, two objects 112 and 113 respectively belong to the environment category and the living function category, so the actual distance 112A of the object 112 and the actual distance 113A of the object 113 are closest to 10 meters and 100 meters respectively.

In Figure 1, the data collection module 213 receives a plurality of activity contents S11 of a plurality of client activities from the broker's user 91 or a plurality of activity contents S12 from the client's user 91. The plurality of activity contents S11 include the customer code and a plurality of service requirements 300 of the customer code, and the service requirements 300 are mainly issued for the objects 111, 112, 113 and 114. The service requirements 300 are mainly issued for the objects. Issued, a plurality of activity contents indicate the customer code, and a plurality of geographical locations of the customer code. The above-mentioned service requirement 300 may be a service requirement 300 for a broker to search or bring items to be viewed, or a customer's active conditional subscription, search condition summary, or online house viewing summary. Active subscription is similar to search conditions, except that after completing the subscription, the system will prompt the customer through a predetermined communication channel when it finds an object that meets the subscription conditions.

To put it simply, in this case, the processing module 212 is based on the geographical locations of the objects in the plurality of activity contents to which the customer code belongs, At the same time, the relative customer preference requirements of the customer code are calculated based on the overlap between the characteristics of the object and the weight distribution rate of the objects. In this way, the broker or counterparty can determine whether to spend time testing and contacting based on the customer preference requirements relative to the customer code.

The price information in the object characteristics is collected from a plurality of activity contents S11 or a plurality of activity contents S12 from the customer's users 91, and represents the same object characteristics of different object characteristic categories obtained under different service requirements 300. information. If the processing module 212 detects an overlap between these object features, it represents a higher degree of confidence. If the object characteristic is of numeric type, the predetermined interval range is a numerical range. If the object characteristic is of text type, the predetermined interval range is a synonym or abbreviation of the text.

As shown in Figure 5A, for example, if the object characteristics are numeric, and the processing module 212 finds that the object characteristics belonging to price overlap in the range of 21 to 22 million, then it will consider that 21 to 22 million (A interval) is the customer’s customer preference demand. The principles of inductive analysis of the remaining object features are roughly the same, so that complete customer preference needs can be discovered from different object feature categories.

As shown in Figure 5B, in addition to finding the customer preference needs based on the above, you can also find the common preference needs of customers in each region based on the geographical location of the objects in the plurality of activity contents to which the customer code belongs. For example, in Taipei City's Wenshan District, $1,300 can buy a family, while in Da'an District, $1,300 can only buy a suite, but only for singles or newlyweds.

To be able to view the customer preferences relative to that customer code According to the customer preference requirement relative to the customer code, the processing module 212 causes the client device 22 to display the customer preference requirement in the personal message relative to the customer code using a predetermined display means.

In order to achieve the above purpose, the system needs to first create an object database, and each object data in the object database has an object index code and a plurality of objects to which the object index code belongs. Among them, the object includes the abbreviation, price, community name, address, floor, building registration area, land registration area, unit price per unit area, type, layout, house age, parking space, seating orientation, elevator, Management fees, layout map, and living functions.

The object feature category database 211 stores a plurality of object feature category data. Each object feature category data has an object index code and a plurality of objects to which the object; index code belongs. Each object is mainly characterized by a single object feature or multiple objects. defined by the characteristics of an object.

The processing module 212 will use a weight calculation formula to obtain the main influencing objects and their relative object characteristic categories for the object characteristics of the objects in the plurality of activity contents to which the customer code belongs, and then based on the object characteristic category The content of the object defined by a single object feature or multiple object features is used to find multiple real estate information that meet the conditions, and generate customer preference requirements that include a specific number of multiple real estate information.

In order to collect more information about user 91's activities, the system can also place special devices in specific physical locations to discover the existence of the devices used by user 91. For example, once a mobile phone is detected The WIFI signal is recorded immediately, which means that the user 91 may also send out multiple activity contents S12 in a passive state. The plurality of activity contents S12 indicates the customer code, and the plurality of geographical locations of the customer code (that is, the location of the above-mentioned special device).

In some embodiments, the object system 10 further includes an activity range R1. The activity range R1 is centered on the client code 15 and has an activity range radius L1 calculated from the client code 15 as the center. The activity range radius L1 indicates the activity range R1 related to the client code 15.

In addition, an actual distance between the client code 15 and the objects 111, 112, 113 and 114 can also be represented by the total walking distance based on the distance information of the streets and alleys.

As shown in Figures 1 and 3, the object database in the database group 26 includes a geographical location D5, and a plurality of object data D111, D112, D113 and 114 representing the objects 111, 112, 113 and 114 respectively. D114 may further include at least one additional object data D121 representing the at least one additional object 121, may further include a recommended object data D131 representing the recommended object 131, and may further include a real estate data unit D15 representing the customer code 15. .

In Figure 3, the object data D111, D112, D113 and D114 respectively include a plurality of position information D111P, D112P, D113P and D114P representing the characteristics of the object 111P, 112P, 113P and 114P, respectively indicating the objects. Multiple object feature category indicators D111H, D112H, of feature categories H111, H112, H113 and H114 D113H and D114H, and a plurality of activity scope data units D111A, D112A, D113A and D114A respectively represent the interactive relationships 111A, 112A, 113A and 114A.

The additional object data D121 includes an additional location information D121P representing the additional object characteristic 121P, an additional object characteristic category indicator D121H indicating the additional object characteristic category HB1, and an additional activity range data unit D121A representing the additional activity range. . The recommended object data D131 includes a recommended location information D131P representing the recommended object feature 131P, a recommended object feature category indicator D131H indicating the recommended object feature category HC1, and an actual distance data unit D131A representing the actual distance 131A. The real estate data unit D15 includes a real estate location information D15P representing the frequent location 15P of the real estate customer, and a real estate category indicator D15H indicating the real estate category HD1.

As shown in Figures 2 and 3, the interactive relationships 111A, 112A, 113A, and 114A in Figure 2 are respectively represented by a plurality of attribute words D111A1, D112A1, D113A1, and D114A1, and are respectively formed as A plurality of quantities respectively corresponding to the interactive relationships 111A, 112A, 113A and 114A, and a plurality of interactive contents respectively corresponding to the interactive relationships 111A, 112A, 113A and 114A.

The customer activity database in the database group 26 is used to store multiple customer activity data. Each customer activity data mainly includes the customer code, the customer's frequent location and multiple activity contents S11 or multiple activity contents. S12, in which multiple activity contents S11 include the customer code and multiple service requirements of the customer code 300, and the service demand 300 is mainly issued for the object. The plurality of activity contents S12 indicates the customer code and the plurality of geographical locations of the customer code. At the same time, the customer code is received through the communication module for the object. Give the customer an evaluation, and store the customer evaluation in the customer activity database. The weight distribution rate of these objects is determined based on the frequency of interaction between these objects and the customer code. The higher the interaction frequency, the greater the impact of the object on the customer preference demand of the customer code.

The data collection module 213 receives a plurality of activity contents S11 and a plurality of activity contents S12 at different times, and transmits a plurality of map data blocks D53 and the third map data block D54 to the client device 22 at different times. , so that the client device 22 displays a plurality of maps M2 and the third map M3 on the display screen 221 at different times. For example, the data collection module 213 is coupled to the client device 22 via the transmission module 31 .

In some embodiments, the user 91 has issued ordering records (ie, multiple activity contents) for objects 111 and 114, and at the same time, the user 91 has passed by special devices near objects 112 and 113, and can discover where the user 91 is. The existence of the device used (that is, multiple activity contents), and through the above-mentioned geographical locations, the activity range R1 can be surrounded on the map. In other words, if user 91 makes a request for purchasing a house in the future and the object he is interested in falls within the activity range R1, the system will mark the user 91 as a local guest. However, the system can flexibly expand the scope of activities covered by R1 to allow local customers to make more accurate judgments, because it is possible that user 91 just did not leave enough behavioral trajectory information in the system. leading to misjudgment by the system. Correspondingly, if the object that the user 91 is interested in falls within the activity range R1, the system will mark the user 91 as a non-local guest.

However, sometimes the user 91 only does this occasionally and runs to an area where he does not frequently move. In this regard, the processing module 212 first removes the object characteristics of the objects in the plurality of active contents S11 to which the customer code belongs or Only when the plurality of geographical locations in the plurality of activity contents S12 exceeds the predetermined deviation value, the relative activity range of the customer code is calculated. On the other hand, if the amount of data is too small, the system may misjudge and a threshold needs to be set. Therefore, the processing module 212 first confirms the object characteristics of the objects in the plurality of activity contents S11 to which the customer code belongs or Only when the plurality of geographical locations in the plurality of activity contents S12 exceeds the predetermined number, the relative activity range of the customer code is calculated.

In addition, the processing module 212 can display relevant information on the objects that the user 91 has interacted with on the electronic map, and transmit the activity range and multiple maps to the client device through the transmission module 31 twenty two. Specifically, the processing module 212 will also filter out the objects falling within the plurality of sent activity contents, and then generate a plurality of map data blocks representing a plurality of maps, wherein the plurality of maps are in the map. The marked locations on the map respectively present a map of a plurality of icons, and the icons respectively mark the objects.

Please refer to Figure 4. A plurality of active contents S12 indicates a plurality of icons KA2 (such as icon K112) selected among the icons K111, K112, K113 and K114. The plurality of icons KA2 are located in a plurality of maps M2. A plurality of marking positions MA1 (such as marking position M112P), and Corresponding to the object data D111, D112, D113 and D114, the plurality of maps M2 have a plurality of marked positions MA2 corresponding to a plurality of marked positions MA1.

The system can selectively display relevant information between the user 91 and the object based on the user's selection. Please refer to Figures 1, 3, and 4. The client device 22 is operated by the user 91 to generate interactive operations at a plurality of times and at a plurality of times after the plurality of times, and to perform interactive operations at different times. The interactive operation is transmitted to the data processing device 21 . For example, the data processing device 21 can calculate an actual distance between the objects and the customer's usual location. By receiving a user input from the user 91, when the icons KA2 displayed on the display screen 221 are selected, the actual distances in the object data are presented on the icons KA2. nearby.

The numerous variations and other embodiments of the present disclosure set forth herein will be appreciated by those skilled in the art with the benefit of the teachings presented in the above description and associated drawings. Therefore, it should be understood that the present disclosure is not limited to the specific embodiments disclosed, and modifications and other embodiments are intended to be included within the scope of the following claims.

20:Display system

21:Data processing device

211:Object database

212: Processing module

2121:Search component

2122:Map generator component

2123: Recommended components

213:Data collection module

22:Client device

221:Display screen

26:Database group

28: Analysis device to estimate customer needs based on region

31:Teleport module

91:User

D111, D112, D113, D114: object data

D121: Additional object data

D131: Recommended object information

D15: Real estate information unit

D1A: Activity scope data unit

D5:Geographic information

D53: Multiple map data blocks

D54: The third map data block

D55: The fourth map data block

D61: Object attribute image data block

D62: Object attribute image data block

DA1: Multiple object data

DHA1, DHA2: object characteristic category indicator

DL1: activity range value

M2: Multiple maps

M3: The third map

M4: The fourth map

M5:The fifth map

S11: Multiple activity contents

S12: Multiple activity contents

S13: Third activity content

S14: The fourth activity content

U1: picture

Claims (5)

An analysis device for estimating customer needs based on region, used to transmit information to a client device, including: an object database, the object database includes a geographical location of a plurality of objects, and a location representing the plurality of objects respectively. A plurality of object data, each of which has an object characteristic; a customer activity database, which is used to store a plurality of customer activity data. Each customer activity data mainly includes a customer code and a plurality of activity contents. , and the characteristics of the object relative to a plurality of activity contents and a customer evaluation, wherein the plurality of activity contents include the customer code, and a plurality of service requirements of the customer code, and the service requirements are mainly issued for the object; 1. A data collection module is coupled to the customer activity database, receives a plurality of activity contents from the client device through a communication module, and stores the plurality of activity contents to the customer activity database; a processing module is coupled to the The data collection module, the object database, and the customer activity database are based on the geographical locations of the objects in the plurality of activity contents to which the customer code belongs, and at the same time, the overlap between the characteristics of the objects and the A weight distribution rate of the object calculates a customer preference requirement relative to the customer code. The customer preference requirement is composed of a plurality of objects and each service requirement has a predetermined interval range for the object characteristics; and a transmission module, coupled The data collection module, the object database, and the customer activity database are used to transmit the customer preference requirements to the client. A device; wherein the object characteristic is a numeric type, the predetermined interval range is a numerical range, the object characteristic is a text type, and the predetermined interval range is a synonym or abbreviation of the text. As described in claim 1, the analysis device for estimating customer needs based on regional areas, wherein the plurality of service needs can be broker service exploration, to-be-viewed object summary, active condition subscription, search condition summary, and online house viewing summary. The analysis device for estimating customer needs based on region as described in claim 1, wherein the processing module calculates a distance between the objects falling within the plurality of sent activity contents and a customer's frequent location. Actual distance; wherein, when a plurality of icons displayed on a display screen are selected, the actual distance is presented near the plurality of icons. The analysis device for estimating customer needs based on region as described in claim 1, wherein the processing module first eliminates the locations of the objects in the plurality of activity contents to which the customer code belongs that exceed a predetermined deviation value. Calculate an activity range surrounded by multiple objects relative to the client code. The analysis device for estimating customer needs based on region as described in claim 1, wherein the weight distribution rate of the objects is determined based on an interaction frequency between the objects and the customer code. The higher the interaction frequency, the higher the interaction frequency. The greater the object's impact on the customer's preference requirements for the customer's code.

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