CN111091832A - Intention assessment method and system based on voice recognition - Google Patents
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Abstract
An intent evaluation method, system, and computer-readable storage medium based on speech recognition, wherein the method comprises: acquiring conversation voice information of a conversation between an employee and a client; converting the dialogue voice information into dialogue text information through voice recognition, evaluating the dialogue text information according to a purchase intention evaluation model, and determining a purchase intention evaluation result; and outputting the purchase intention evaluation result. According to the embodiment of the application, the purchase intention evaluation result can be obtained based on the voice recognition and purchase intention evaluation model, and the employee can introduce the product with high purchase intention to the customer according to the purchase intention evaluation result, so that the order rate is improved.
Description
Technical Field
The present disclosure relates to the field of management technologies, and more particularly, to a method, system, and computer-readable storage medium for intent evaluation based on speech recognition.
Background
With the rapid development of economy, the purchasing power of people is increased day by day, and simultaneously, higher requirements are put on under-line sales personnel. In a sales team of an enterprise, the purchasing intention of a customer is generally required to be accurately judged, and the real intention of the customer is grasped, so that sales personnel can follow up more in planning and direction, and the later sales volume is increased.
Conventionally, the purchasing intention of a customer can only be subjectively judged by experienced sales personnel without supporting objective data, so that effective information cannot be well collected, and the difference of subjective judgment performed manually is large and the accuracy is low.
Disclosure of Invention
Provided are an intention evaluation method, system, and computer-readable storage medium based on voice recognition to automatically analyze and evaluate a purchase intention of a customer.
The embodiment of the application provides an intention assessment method based on voice recognition, which comprises the following steps:
acquiring conversation voice information of a conversation between an employee and a client;
converting the dialogue voice information into dialogue text information through voice recognition, evaluating the dialogue text information according to a purchase intention evaluation model, and determining a purchase intention evaluation result;
and outputting the purchase intention evaluation result.
In one embodiment, before converting the dialogue voice information into dialogue text information through voice recognition, the method further comprises:
and carrying out identity marking on employee voice information and client voice information in the conversation voice information according to the voiceprint characteristics of the conversation voice information.
In one embodiment, before converting the dialogue voice information into dialogue text information through voice recognition, the method further comprises:
and determining whether the voice information is invalid voice information according to the voice duration of the dialogue voice information, and if so, discarding the invalid voice information.
In one embodiment, the converting the dialogue voice information into dialogue text information through voice recognition, evaluating the dialogue text information according to a purchase intention evaluation model, and determining a purchase intention evaluation result includes:
converting the dialogue voice information into dialogue text information through voice recognition, analyzing the dialogue text information, and recognizing a product corresponding to the dialogue text information;
according to the multi-dimensional evaluation parameters in the purchase intention evaluation model, counting the evaluation parameter information of the product;
and determining the purchase intention evaluation result of the product according to the evaluation parameter information and the corresponding weight.
In an embodiment, the analyzing the dialog text information and identifying a product corresponding to the dialog text information includes:
and extracting keywords from the dialog text information, matching the keywords with preset keywords, and determining a corresponding product according to a matching result.
In one embodiment, each evaluation parameter is divided into a plurality of levels, and the statistics of the evaluation parameter information of the product according to the evaluation parameters in multiple dimensions in the purchase intention evaluation model includes:
and inquiring the dialogue text information according to the multi-dimensional evaluation parameters in the purchase intention evaluation model, and counting the grade information corresponding to each evaluation parameter of the product.
In one embodiment, the evaluation of the purchase intention includes an intention value, and the determining of the evaluation of the purchase intention of the product based on the evaluation parameter information and the corresponding weight includes:
and determining corresponding weight according to the grade information corresponding to each evaluation parameter of the product, and carrying out standardized calculation on the weight to obtain the intention value of the product.
An intention evaluation system based on speech recognition is also provided in an embodiment of the present application, including:
the voice acquisition module is used for acquiring conversation voice information of the conversation between the employee and the client;
the voice analysis module is used for converting the dialogue voice information into dialogue text information through voice recognition, evaluating the dialogue text information according to the purchase intention evaluation model and determining a purchase intention evaluation result;
and the output module is used for outputting the purchase intention assessment result.
An intention evaluation system based on speech recognition is also provided in an embodiment of the present application, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the speech recognition based intent evaluation method when executing the program.
Embodiments of the present application also provide a computer-readable storage medium storing computer-executable instructions for performing the intent evaluation method based on speech recognition.
Compared with the related art, the method comprises the following steps: acquiring conversation voice information of a conversation between an employee and a client; converting the dialogue voice information into dialogue text information through voice recognition, evaluating the dialogue text information according to a purchase intention evaluation model, and determining a purchase intention evaluation result; and outputting the purchase intention evaluation result. According to the embodiment of the application, the purchase intention evaluation result can be obtained based on the voice recognition and purchase intention evaluation model, and the employee can introduce the product with high purchase intention to the customer according to the purchase intention evaluation result, so that the order rate is improved.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the application. Other advantages of the application may be realized and attained by the instrumentalities and combinations particularly pointed out in the specification, claims, and drawings.
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The accompanying drawings are included to provide an understanding of the present disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the examples serve to explain the principles of the disclosure and not to limit the disclosure.
FIG. 1 is a flow chart of an intent evaluation method based on speech recognition according to an embodiment of the present application;
FIG. 2 is a flowchart of step 102 according to an embodiment of the present application;
FIG. 3 is a diagram illustrating a system for intent evaluation based on speech recognition according to an embodiment of the present application.
Detailed Description
The present application describes embodiments, but the description is illustrative rather than limiting and it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible within the scope of the embodiments described herein. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are possible. Any feature or element of any embodiment may be used in combination with or instead of any other feature or element in any other embodiment, unless expressly limited otherwise.
The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive concept as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive aspects to form yet another unique inventive aspect, as defined by the claims. Thus, it should be understood that any of the features shown and/or discussed in this application may be implemented alone or in any suitable combination. Accordingly, the embodiments are not limited except as by the appended claims and their equivalents. Furthermore, various modifications and changes may be made within the scope of the appended claims.
Further, in describing representative embodiments, the specification may have presented the method and/or process as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described. Other orders of steps are possible as will be understood by those of ordinary skill in the art. Therefore, the particular order of the steps set forth in the specification should not be construed as limitations on the claims. Further, the claims directed to the method and/or process should not be limited to the performance of their steps in the order written, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the spirit and scope of the embodiments of the present application.
As shown in fig. 1, an intention assessment method based on speech recognition provided by an embodiment of the present application includes:
The staff can be a shopping guide staff and the like which communicate with the client through voice.
The voice acquisition module can be worn on the staff, and voice information of conversation between the staff and the customer can be acquired in real time through operation of the staff. For example, when the shopping guide staff needs to analyze the client intention, the recording button is pressed, so that the voice acquisition module is started to enter the recording mode.
And 102, converting the dialogue voice information into dialogue text information through voice recognition, evaluating the dialogue text information according to the purchase intention evaluation model, and determining a purchase intention evaluation result.
In an embodiment, before step 102, the method may further include:
and carrying out identity marking on employee voice information and client voice information in the conversation voice information according to the voiceprint characteristics of the conversation voice information.
The voiceprint of the employee can be modeled in advance to obtain a voiceprint model of the employee. And extracting the features of the dialogue voice information, comparing and matching the obtained voiceprint features with the voiceprint models of the employees, marking the voice information which is successfully matched in the dialogue voice information as the voice information of the employees according to the matching result, and marking the voice information which is unsuccessfully matched in the dialogue voice information as the voice information of the clients.
The dialogue voice information comprises 2 paths of sound tracks, wherein the voiceprint characteristics can be matched with the voiceprint model, and then the sound tracks are added with marks of the staff; if it cannot match, add its track to the client's tag.
In an embodiment, before step 102, the method may further include:
and determining whether the voice information is invalid voice information according to the voice duration of the dialogue voice information, and if so, discarding the invalid voice information.
A speech duration threshold may be set, for example, 3 seconds, and when less than the speech duration threshold, the conversational speech information is discarded directly.
For example, when a customer enters a store, the shopper asks: is help required?
Customer: not used, i see at will.
The voice time collected under the scene is short, and for the customers without shopping guide service, intention analysis can not be performed through voice recognition, and the data of the dialogue voice information can be directly abandoned, so that the data volume of invalid information in the memory of the server is reduced.
As shown in fig. 2, step 102 may include:
According to the embodiment of the application, the product name can be matched quickly according to the dialogue text information.
If the product name is mentioned in the dialog text message, the corresponding product can be determined directly.
In one embodiment, keywords are extracted from the dialog text information, the keywords are matched with preset keywords, and corresponding products are determined according to matching results.
By marking the speech sound information as described above, the employee text information and the customer text information of the dialogue text information can be distinguished.
In an actual shopping guide conversation occasion, a specific product name cannot be directly mentioned sometimes, so that keywords can be extracted from the introduction of the employee to the product, and the product name can be analyzed; keywords can also be extracted from the question words of the customer actively asking for a certain product, and the product name can be analyzed. For example, the shopper says: the cordyceps sinensis foundation liquid is fine and smooth in powder quality and high in moistening degree, and has a skin nourishing effect. Then, according to the keywords 'Chinese caterpillar fungus', 'foundation liquid', 'fine' and 'skin care', the product name can be quickly analyzed and positioned: brand powder base solution.
The multidimensional evaluation parameters may include, but are not limited to, conversation turns, negative word frequency, positive word frequency, and price inquiry. Other dimensions or more may be used depending on the actual scenario. And the conversation turns can be analyzed by carrying out identity marking on the speech information in the steps so as to distinguish the staff from the clients.
In an embodiment, each evaluation parameter is divided into a plurality of grades, the dialog text information is inquired according to the multi-dimensional evaluation parameters in the purchase intention evaluation model, and the grade information corresponding to each evaluation parameter of the product is counted.
The evaluation parameters for each dimension may be divided into several levels, for example, the dialog turn is divided into: level 1-3, negative word frequency: level 1-3, positive word frequency: level 1-3, price inquiry case: 1-3 grades.
According to the embodiment of the application, effective data support can be provided for customer maintenance and follow-up in the later period through the constructed purchase intention evaluation model.
In an embodiment, the purchase intention assessment result includes an intention value, a corresponding weight is determined according to the grade information corresponding to each assessment parameter of the product, and the weight is subjected to standardized calculation to obtain the intention value of the product.
Wherein, different weight values (for example, 0.4, 0, 3, 0.2 and 0.1) are set for evaluation parameters of each dimension of the conversation turns, the negative word frequency, the positive word frequency and the price inquiry condition; and then carrying out standardized calculation on the weight, wherein the finally obtained intention value is between 0 and 1.
For example, the shopper makes product introductions: your good, this product, product characteristic is … …, and product efficiency is … …. (analysis of product name: A)
Customer: is this suitable for my elderly?
The shopping guide personnel: the product is very suitable for … … people.
Customer: that you see what color number i fit in.
The shopping guide personnel: good, you look at this color number and try to see if you like.
Customer: this is good.
In the above dialogue voice information, if three rounds of dialogue have been performed with respect to product a, the dialogue rounds can be evaluated as: and 3, the word frequency of the positive word is as follows: level 1, negative word frequency is: 3 levels (the highest level corresponding to the negative word does not appear), and the price inquiring condition is as follows: level 1 (no price inquiry corresponds to the lowest level).
And determining corresponding weight according to the grade information, and then determining an intention value.
The intention value may be expressed in other ways, for example, in an intention level, and the intention level is divided into a plurality of levels according to the level of the purchase intention.
And 103, outputting the purchase intention assessment result.
Wherein the purchase intention evaluation result may be output in a displayed manner.
In an embodiment, the method may further comprise: and storing the information such as the conversation voice information, the conversation text information, the intention evaluation result and the like into a server.
According to the embodiment of the application, the purchase intention evaluation result can be obtained based on the voice recognition and purchase intention evaluation model, and the employee can introduce the product with high purchase intention to the customer according to the purchase intention evaluation result, so that the order rate is improved.
As shown in fig. 3, an intention evaluation system based on speech recognition is further provided in an embodiment of the present application, including:
the voice acquisition module 31 is used for acquiring conversation voice information of the staff in conversation with the client;
the voice analysis module 32 is used for converting the dialogue voice information into dialogue text information through voice recognition, evaluating the dialogue text information according to the purchase intention evaluation model and determining a purchase intention evaluation result;
an output module 33, configured to output the purchase intention assessment result.
The voice acquisition module 31 can be worn on the staff, and voice information of conversation between the staff and the customer can be acquired in real time through the operation of the staff. For example, when the shopping guide staff needs to analyze the client's intention, the recording button is pressed to turn on the voice collecting module 31 and enter the recording mode.
The voice analysis module 32 can identify the voice message, translate and identify the product name, and calculate the corresponding intention value of the product according to the intention scoring model.
In one embodiment, the speech analysis module 32 is configured to:
and carrying out identity marking on the employee voice information and the client voice information in the dialogue voice information according to the voiceprint characteristics of the dialogue voice information.
The voiceprint of the employee can be modeled in advance to obtain a voiceprint model of the employee. And extracting the features of the dialogue voice information, comparing and matching the obtained voiceprint features with the voiceprint models of the employees, marking the voice information which is successfully matched in the dialogue voice information as the voice information of the employees according to the matching result, and marking the voice information which is unsuccessfully matched in the dialogue voice information as the voice information of the clients.
The dialogue voice information comprises 2 paths of sound tracks, wherein the voiceprint characteristics can be matched with the voiceprint model, and then the sound tracks are added with marks of the staff; if it cannot match, add its track to the client's tag.
In an embodiment, the speech analysis module 32 is further configured to:
and determining whether the voice information is invalid voice information according to the voice duration of the dialogue voice information, and if so, discarding the invalid voice information.
A speech duration threshold may be set, for example, 3 seconds, and when less than the speech duration threshold, the conversational speech information is discarded directly.
The voice analysis module 32 may establish a purchasing intention scoring model of the product by analyzing the product name, the conversation round, the word frequency of the negative word, the word frequency of the positive word, and the price inquiry condition in the conversation voice information, thereby obtaining an intention value of a certain product.
In one embodiment, the speech analysis module 32 is configured to:
converting the dialogue voice information into dialogue text information through voice recognition, analyzing the dialogue text information, and recognizing a product corresponding to the dialogue text information; according to the multi-dimensional evaluation parameters in the purchase intention evaluation model, counting the evaluation parameter information of the product; and determining the purchase intention evaluation result of the product according to the evaluation parameter information and the corresponding weight.
If the product name is mentioned in the dialog text message, the speech analysis module 32 may directly determine the corresponding product.
In one embodiment, the speech analysis module 32 is configured to: and extracting keywords from the dialog text information, matching the keywords with preset keywords, and determining a corresponding product according to a matching result.
By marking the speech sound information as described above, the employee text information and the customer text information of the dialogue text information can be distinguished. When the product name is not mentioned, the product name can be determined by means of identifying a keyword.
The multidimensional evaluation parameters may include, but are not limited to, conversation turns, negative word frequency, positive word frequency, and price inquiry. Other dimensions or more may be used depending on the actual scenario. The conversation turns can be distinguished from employees and clients by carrying out identity marking on the conversation voice information, so that the conversation turns can be analyzed.
In one embodiment, each of the evaluation parameters is classified into a plurality of classes, and the speech analysis module 32 is configured to: and inquiring the dialogue text information according to the multi-dimensional evaluation parameters in the purchase intention evaluation model, and counting the grade information corresponding to each evaluation parameter of the product.
The evaluation parameters for each dimension may be divided into several levels, for example, the dialog turn is divided into: level 1-3, negative word frequency: level 1-3, positive word frequency: level 1-3, price inquiry case: 1-3 grades.
In one embodiment, the evaluation result of the purchase intention includes an intention value, and the voice analysis module 32 is configured to: and determining corresponding weight according to the grade information corresponding to each evaluation parameter of the product, and carrying out standardized calculation on the weight to obtain the intention value of the product.
Wherein, different weight values (for example, 0.4, 0, 3, 0.2 and 0.1) are set for evaluation parameters of each dimension of the conversation turns, the negative word frequency, the positive word frequency and the price inquiry condition; and then carrying out standardized calculation on the weight, wherein the finally obtained intention value is between 0 and 1.
The output module 33 may be a display device such as a bracelet or a smart card, and acquires the purchase intention evaluation result of the voice analysis module, and outputs the purchase intention evaluation result in a display manner.
For example, a practical process may be:
when a customer enters a store, a shopping guide person goes up to service, presses a recording key on the voice acquisition module to start recording, automatically starts a recording function of the voice acquisition equipment by recognizing keywords such as 'you good', 'good morning' and the like, and acquires conversation voice information.
The voice acquisition module can be in a recording mode all the time, and when a shopping guide person is in conversation with a customer, the shopping guide person can directly press an intention key if the shopping guide person wants to know which kind of product the customer pays more attention to at present, and the voice analysis module analyzes conversation voice information in the time from the start of service of the customer to the pressing of the key to obtain the current intention of the customer; the shopping guide personnel can inquire the current intention of the customer for many times in the conversation process with the customer, the intention result inquired each time is a voice analysis result in the time from the beginning of the conversation to the pressing of the intention key, namely, the scoring calculation is finished once every time the intention key is pressed, and the calculation result is fed back in real time; the same product may correspond to different scores in multiple feedback results.
For example, a shopping guide person can inquire the following display results through a display screen (a bracelet, an intelligent card, and the like):
the product is as follows: intention value A: is divided into
The product is as follows: intention value B: is divided into
The product is as follows: c intention value: is divided into
According to the intention scoring result, the shopping guide personnel can introduce products with high intention values and further explain the use effect, after-sale related contents and the like of the products to customers.
After a customer is served, the shopping guide personnel can stop recording by pressing a recording ending button and automatically store the dialogue voice information collected in the period from recording starting to recording ending into the background server.
An intention evaluation system based on speech recognition is also provided in an embodiment of the present application, including: memory, processor and computer program stored on the memory and executable on the processor, wherein the processor implements the speech recognition based intent evaluation method when executing the program.
Embodiments of the present application also provide a computer-readable storage medium storing computer-executable instructions for performing the intent evaluation method based on speech recognition.
In this embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
Claims (10)
1. An intention assessment method based on speech recognition, comprising:
acquiring conversation voice information of a conversation between an employee and a client;
converting the dialogue voice information into dialogue text information through voice recognition, evaluating the dialogue text information according to a purchase intention evaluation model, and determining a purchase intention evaluation result;
and outputting the purchase intention evaluation result.
2. The intent evaluation method based on speech recognition according to claim 1, wherein before converting the dialogue speech information into dialogue text information by speech recognition, the method further comprises:
and carrying out identity marking on employee voice information and client voice information in the conversation voice information according to the voiceprint characteristics of the conversation voice information.
3. The intent evaluation method based on speech recognition according to claim 1, wherein before converting the dialogue speech information into dialogue text information by speech recognition, the method further comprises:
and determining whether the voice information is invalid voice information according to the voice duration of the dialogue voice information, and if so, discarding the invalid voice information.
4. The intention evaluation method based on speech recognition according to claim 1, wherein the converting the dialogue speech information into dialogue text information through speech recognition, evaluating the dialogue text information according to a purchase intention evaluation model, and determining a purchase intention evaluation result comprises:
converting the dialogue voice information into dialogue text information through voice recognition, analyzing the dialogue text information, and recognizing a product corresponding to the dialogue text information;
according to the multi-dimensional evaluation parameters in the purchase intention evaluation model, counting the evaluation parameter information of the product;
and determining the purchase intention evaluation result of the product according to the evaluation parameter information and the corresponding weight.
5. The intent evaluation method based on speech recognition according to claim 4, wherein the analyzing the dialog text message to identify the product corresponding to the dialog text message comprises:
and extracting keywords from the dialog text information, matching the keywords with preset keywords, and determining a corresponding product according to a matching result.
6. The intention evaluation method based on speech recognition according to claim 4, wherein each evaluation parameter is divided into a plurality of levels, and the statistics of the evaluation parameter information of the product according to the evaluation parameters in a plurality of dimensions in the purchase intention evaluation model includes:
and inquiring the dialogue text information according to the multi-dimensional evaluation parameters in the purchase intention evaluation model, and counting the grade information corresponding to each evaluation parameter of the product.
7. The speech recognition-based intention assessment method according to claim 6, wherein said purchase intention assessment result includes an intention value, said determining a purchase intention assessment result of said product based on said assessment parameter information and corresponding weights comprises:
and determining corresponding weight according to the grade information corresponding to each evaluation parameter of the product, and carrying out standardized calculation on the weight to obtain the intention value of the product.
8. An intent evaluation system based on speech recognition, comprising:
the voice acquisition module is used for acquiring conversation voice information of the conversation between the employee and the client;
the voice analysis module is used for converting the dialogue voice information into dialogue text information through voice recognition, evaluating the dialogue text information according to the purchase intention evaluation model and determining a purchase intention evaluation result;
and the output module is used for outputting the purchase intention assessment result.
9. An intent evaluation system based on speech recognition, comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 7 when executing the program.
10. A computer-readable storage medium storing computer-executable instructions for performing the method of any one of claims 1-7.
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| CN111949776A (en) * | 2020-07-17 | 2020-11-17 | 上海淇馥信息技术有限公司 | Method and device for evaluating user tag and electronic equipment |
| CN112308387A (en) * | 2020-10-20 | 2021-02-02 | 深圳思为科技有限公司 | Client intention degree evaluation method and device and cloud server |
| CN112633992A (en) * | 2021-01-11 | 2021-04-09 | 上海明略人工智能(集团)有限公司 | Sales management method and system based on voice recognition |
| CN112734467A (en) * | 2020-12-31 | 2021-04-30 | 北京明略软件系统有限公司 | Passenger flow prediction method and system for offline service scene |
| CN113139059A (en) * | 2021-05-13 | 2021-07-20 | 八维(杭州)科技有限公司 | Intention grading method based on man-machine conversation |
| CN114331572A (en) * | 2022-03-14 | 2022-04-12 | 北京明略软件系统有限公司 | Potential customer determination method and device, electronic equipment and storage medium |
| CN116303978A (en) * | 2023-05-17 | 2023-06-23 | 福建博士通信息股份有限公司 | Potential user mining method based on voice analysis |
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| CN114331572A (en) * | 2022-03-14 | 2022-04-12 | 北京明略软件系统有限公司 | Potential customer determination method and device, electronic equipment and storage medium |
| CN116303978B (en) * | 2023-05-17 | 2023-08-15 | 福建博士通信息股份有限公司 | Potential user mining method based on voice analysis |
| CN116303978A (en) * | 2023-05-17 | 2023-06-23 | 福建博士通信息股份有限公司 | Potential user mining method based on voice analysis |
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