Dynamic quality inspection method for verbal skill keywords based on AI technology

Abstract

The invention relates to the technical field of sales industry quality management, in particular to an AI technology-based verbal skill keyword dynamic quality inspection method, which comprises the following steps of: firstly, constructing a dynamic quality inspection standard library; collecting a communication dialogue between the shop assistant and the customer, and preprocessing the dialogue content; performing feature extraction and preliminary analysis on the preprocessed dialogue content based on an AI processor, matching the extracted features with a dynamic quality inspection standard library by adopting a matching algorithm and an evaluation algorithm, and evaluating the quality of the sales process; deep analysis is carried out based on the quality result of the sales process obtained through evaluation, personalized sales strategy recommendation of the shop assistant is generated, a sales trend prediction model is constructed, the future sales trend is predicted, and the problems that in the prior art, a quality inspection mode for sales skills and service quality of the shop assistant is time-consuming and labor-consuming and is limited by human factors, and the quality inspection efficiency is high are solved. The consistency and the accuracy of quality inspection results are difficult to guarantee.

Description

Technical Field

[0001] The present invention relates to the technical field of quality management in the sales industry, and in particular to a dynamic quality inspection method for speech keywords based on AI technology. Background Art

[0002] In the sales industry, quality inspection of sales skills and service quality of store employees is an important part of ensuring sales quality, improving customer satisfaction and brand image. However, the traditional quality inspection method mainly relies on manual monitoring of sales recordings, but there are shortcomings in the method of manual monitoring of sales recordings: first, manual monitoring of recordings is time-consuming and labor-intensive, and requires a lot of human resources. With the growth of sales business volume, the amount of recording data has also increased, making manual monitoring more and more difficult to cope with; secondly, the results of manual monitoring recordings are limited by human factors, such as the experience, judgment criteria and subjective emotions of the monitoring personnel, which makes it difficult to ensure the consistency and accuracy of the quality inspection results. Different monitoring personnel may draw different quality inspection conclusions for the same recording, which not only affects the fairness of the quality inspection, but also reduces the reliability of the quality inspection results.

[0003] In summary, the existing quality inspection methods for store clerks' sales skills and service quality are not only time-consuming and labor-intensive, but also limited by human factors, making it difficult to ensure the consistency and accuracy of the quality inspection results. Summary of the invention

[0004] The purpose of the present invention is to provide a dynamic quality inspection method for speech keywords based on AI technology, aiming to solve the technical problem that the quality inspection method of sales skills and service quality of store clerks in the prior art is not only time-consuming and labor-intensive, but also limited by human factors, and it is difficult to ensure the consistency and accuracy of the quality inspection results.

[0005] To achieve the above purpose, the present invention adopts a dynamic quality inspection method for speech keywords based on AI technology, which includes the following steps:

[0006] First, build a dynamic quality inspection standard library;

[0007] Collect the conversations between store clerks and customers and pre-process the conversation content;

[0008] The AI ​​processor extracts and preliminarily analyzes the preprocessed conversation content, and uses matching and evaluation algorithms to match the extracted features with the dynamic quality inspection standard library to evaluate the quality of the sales process.

[0009] Based on the quality results of the sales process obtained through evaluation, in-depth analysis is performed to generate personalized sales strategy recommendations for store clerks, and a sales trend prediction model is built to predict future sales trends.

[0010] The specific method of constructing a dynamic quality inspection standard library is as follows:

[0011] Collect historical quality inspection data, including samples of excellent and poor sales recordings and their corresponding quality inspection results;

[0012] Based on machine learning algorithms, quality inspection standard features are extracted from historical data to build a dynamic quality inspection standard library;

[0013] Among them, the machine learning algorithm uses cluster analysis, classification algorithm or regression algorithm;

[0014] Train the model with historical data to improve the model's prediction accuracy and generalization ability;

[0015] Continuously optimize the model, including adjusting model parameters, adding training data, introducing new features, etc., to improve the performance and adaptability of the model.

[0016] The specific method of collecting the conversation between the clerk and the customer and pre-processing the conversation content is as follows:

[0017] The sound collection device embedded in the ID card is used to collect audio data of the conversation between the store clerk and the customer, and the audio data is pre-processed.

[0018] Among them, preprocessing of audio data includes removing background noise, standardizing volume, and segmenting recordings into independent dialogue units based on silence or specific markers;

[0019] Among them, the background noise is removed by using a filter algorithm or a sample-based noise reduction algorithm;

[0020] Normalized volume uses a volume adjustment algorithm to adjust the volume level of the recording to a uniform standard;

[0021] The recording is divided into independent dialogue units based on silence or specific markers. The detect_silence function provided by the pydub processing library is used to detect the silence part and cut it according to the set silence length threshold; natural language processing technology is combined to identify specific markers for more accurate segmentation.

[0022] The specific method of extracting features and performing preliminary analysis on the preprocessed conversation content based on the AI ​​processor is as follows:

[0023] The deep learning-based automatic speech recognition system converts the recorded data into text data;

[0024] Combined with natural language processing technology, keywords, phrases and contextual information of conversations are extracted from text data, and speech feature analysis is performed using speech rate calculation algorithm, intonation analysis algorithm and pause detection algorithm. Speech features include speech rate, intonation and pause patterns.

[0025] The specific method of matching the extracted features with the dynamic quality inspection standard library and evaluating the quality of the sales process is as follows:

[0026] Matching algorithms and evaluation algorithms are used to match the extracted features with the dynamic quality inspection standard library to evaluate the quality of the sales process.

[0027] Among them, the specific method of using association rule mining or cluster analysis combined with the quality results of the sales process obtained by evaluation to conduct in-depth analysis and generate personalized sales strategy recommendations for store clerks based on collaborative filtering or content-based methods is as follows:

[0028] Conduct in-depth analysis of sales data to explore highlights and deficiencies in the sales process and identify potential sales opportunities and areas for improvement;

[0029] Based on the analysis results, personalized sales strategy recommendations are provided to store staff.

[0030] Among them, the specific method of building a sales trend prediction model and predicting future sales trends is as follows:

[0031] Using deep learning algorithms, combined with the store clerk’s current sales strategy and the personalized sales strategy provided, we build sales trend prediction models and customer preference analysis models;

[0032] Among them, the deep learning algorithm uses recurrent neural network or convolutional neural network.

[0033] Among them, after the dynamic quality inspection method for speech keywords based on AI technology is deployed, sales data is collected and analyzed in real time and combined with the sales data before deployment to analyze, and then the dynamic quality inspection method for speech keywords based on AI technology is optimized.

[0034] Among them, encryption and desensitizing measures are taken during data collection, processing, storage and transmission to protect the security and privacy of user data.

[0035] The present invention discloses a dynamic quality inspection method for keywords based on AI technology, which first builds a dynamic quality inspection standard library; collects the communication dialogues between store clerks and customers, and preprocesses the dialogue contents; extracts and preliminarily analyzes the preprocessed dialogue contents based on the AI ​​processor, and uses a matching algorithm and an evaluation algorithm to match the extracted features with the dynamic quality inspection standard library to evaluate the quality of the sales process; performs in-depth analysis based on the quality results of the sales process obtained by the evaluation, generates personalized sales strategy recommendations for store clerks, and builds a sales trend prediction model to predict future sales trends;

[0036] By integrating advanced AI technology and automated processing flow, the present invention can realize rapid and batch quality inspection of store clerks’ sales tactics and service quality; this greatly shortens the quality inspection cycle and reduces labor cost investment, allowing enterprises to manage store clerks’ performance more efficiently and improve overall service quality;

[0037] At the same time, by real-time monitoring the sales language used by store employees and their service performance during the sales process, the present invention can immediately identify potential problems and provide feedback. This dynamic quality inspection method not only helps companies to adjust sales strategies and training plans in a timely manner, but also promotes the improvement of store employees' personal abilities, thereby improving customer satisfaction and loyalty.

[0038] In addition, multi-dimensional information extraction and dynamic adjustment of quality inspection standards based on AI technology ensure the objectivity and accuracy of the quality inspection results. In this way, it solves the technical problem that the existing quality inspection method of store clerks' sales skills and service quality is not only time-consuming and labor-intensive, but also limited by human factors, making it difficult to ensure the consistency and accuracy of quality inspection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 It is a flowchart of the steps of the dynamic quality inspection method of speech keywords based on AI technology of the present invention. DETAILED DESCRIPTION

[0041] Embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0042] See also Figure 1 , Figure 1 It is a flowchart of the steps of the dynamic quality inspection method of speech keywords based on AI technology of the present invention.

[0043] The present invention provides a dynamic quality inspection method for speech keywords based on AI technology, comprising the following steps:

[0044] S100, first build a dynamic quality inspection standard library;

[0045] For this specific implementation, historical quality inspection data is collected, including excellent and poor sales recording samples and their corresponding quality inspection results;

[0046] Based on machine learning algorithms, quality inspection standard features are extracted from historical data to build a dynamic quality inspection standard library;

[0047] Among them, machine learning algorithms use clustering algorithms (such as K-means), classification algorithms (such as SVM, decision tree), and regression algorithms (such as linear regression);

[0048] Train the model with historical data to improve the model's prediction accuracy and generalization ability;

[0049] Continuously optimize the model, including adjusting model parameters, adding training data, introducing new features, etc., to improve the performance and adaptability of the model.

[0050] S200: Use a sound collection device embedded in the ID card to collect audio data of the conversation between the clerk and the customer, and pre-process the audio data.

[0051] Among them, preprocessing of audio data includes removing background noise, standardizing volume, and segmenting recordings into independent dialogue units based on silence or specific markers;

[0052] Among them, the background noise is removed by using a filter algorithm or a sample-based noise reduction algorithm;

[0053] Normalized volume uses a volume adjustment algorithm to adjust the volume level of the recording to a uniform standard;

[0054] The recording is divided into independent dialogue units based on silence or specific markers. The detect_silence function provided by the pydub processing library is used to detect the silence part and cut it according to the set silence length threshold; natural language processing technology is combined to identify specific markers for more accurate segmentation.

[0055] For this specific implementation,

[0056] S300, based on the AI ​​processor, performs feature extraction and preliminary analysis on the pre-processed conversation content, and uses matching and evaluation algorithms to match the extracted features with the dynamic quality inspection standard library to evaluate the quality of the sales process;

[0057] For this specific implementation, the deep learning-based automatic speech recognition system converts the recorded data into text data;

[0058] Combined with speech recognition algorithms: such as hidden Markov model (HMM), deep learning models (such as RNN, LSTM); natural language processing algorithms: such as keyword extraction algorithms (such as TF-IDF, TextRank), phrase recognition algorithms; speech feature analysis algorithms: such as speaking rate calculation algorithms, intonation analysis algorithms, pause detection algorithms; emotional tendency recognition algorithms: such as dictionary-based methods, machine learning-based methods (such as SVM, neural networks); extract keywords, phrases and contextual information of conversations in text data, and use speaking rate calculation algorithms, intonation analysis algorithms, and pause detection algorithms to perform speech feature analysis. Speech features include speaking rate, intonation, and pause patterns.

[0059] Matching algorithms (cosine similarity, Euclidean distance) and evaluation algorithms are used to match the extracted features with the dynamic quality inspection standard library to evaluate the quality of the sales process.

[0060] S400: Conduct in-depth analysis based on the quality results of the sales process obtained through evaluation, generate personalized sales strategy recommendations for store clerks, and build a sales trend prediction model to predict future sales trends.

[0061] For this specific implementation,

[0062] The specific method of using association rule mining or cluster analysis combined with the quality results of the sales process obtained by evaluation to conduct in-depth analysis and generate personalized sales strategy recommendations for store clerks based on collaborative filtering or content-based methods is as follows:

[0063] Conduct in-depth analysis of sales data to explore highlights and deficiencies in the sales process and identify potential sales opportunities and areas for improvement;

[0064] Based on the analysis results, personalized sales strategy recommendations are provided to store clerks. In this process, template-based methods and deep learning-based methods (such as Seq2Seq models) are used to generate personalized feedback reports. In addition, a speech example extraction algorithm is used: based on the quality inspection and evaluation results, representative speech clips are extracted from the recording data as examples.

[0065] Conduct in-depth analysis of sales data, explore highlights and deficiencies in the sales process, identify potential sales opportunities and points for improvement, provide precise guidance for store staff training, and tap sales potential;

[0066] Based on the analysis results, personalized sales strategy recommendations are provided to store staff.

[0067] Using deep learning algorithms, combined with the store clerk’s current sales strategy and the personalized sales strategy provided, we build sales trend prediction models and customer preference analysis models;

[0068] The forecasting algorithms used, such as time series analysis, machine learning models (e.g., ARIMA, LSTM), etc., are used to predict future sales trends and identify potential customer groups.

[0069] Among them, the deep learning algorithm uses recurrent neural network or convolutional neural network.

[0070] Among them, after the dynamic quality inspection method for speech keywords based on AI technology is deployed, sales data is collected and analyzed in real time and combined with the sales data before deployment to analyze, and then the dynamic quality inspection method for speech keywords based on AI technology is optimized.

[0071] Among them, encryption and desensitizing measures are taken during data collection, processing, storage and transmission to protect the security and privacy of user data.

[0072] At the same time, the quality inspection results are visualized using data in the form of charts, reports, etc. This not only helps companies quickly understand the performance of store employees, but also provides management with a scientific basis for decision-making, helping companies achieve refined management and sustainable development.

[0073] Thirdly, by centrally managing quality inspection data and results, the present invention facilitates knowledge sharing and team collaboration within the enterprise. Store clerks can learn from excellent cases and improve their personal skills; management can fully understand store clerk performance and formulate more scientific training and management strategies.

[0074] During the quality inspection process, the present invention can automatically identify and mark possible violations or risky words, helping enterprises to take timely measures to correct and prevent them. This helps enterprises comply with relevant laws, regulations and industry norms, and reduce potential legal risks and reputation losses.

[0075] The method for dynamic quality inspection of speech keywords based on AI technology of the present invention firstly builds a dynamic quality inspection standard library; collects the communication dialogues between the store clerk and the customer, and pre-processes the dialogue content; extracts and preliminarily analyzes the pre-processed dialogue content based on the AI ​​processor, and uses a matching algorithm and an evaluation algorithm to match the extracted features with the dynamic quality inspection standard library to evaluate the quality of the sales process; performs in-depth analysis based on the quality results of the sales process obtained by the evaluation, generates personalized sales strategy recommendations for the store clerk, and builds a sales trend prediction model to predict future sales trends;

[0076] By integrating advanced AI technology and automated processing flow, the present invention can realize rapid and batch quality inspection of store clerks’ sales tactics and service quality; this greatly shortens the quality inspection cycle and reduces labor cost investment, allowing enterprises to manage store clerks’ performance more efficiently and improve overall service quality;

[0077] At the same time, by real-time monitoring the sales language used by store employees and their service performance during the sales process, the present invention can immediately identify potential problems and provide feedback. This dynamic quality inspection method not only helps companies to adjust sales strategies and training plans in a timely manner, but also promotes the improvement of store employees' personal abilities, thereby improving customer satisfaction and loyalty.

[0078] In addition, the multi-dimensional information extraction and dynamic adjustment of quality inspection standards based on AI technology ensure the objectivity and accuracy of the quality inspection results. The use of AI-based keyword recognition and sentiment analysis technology can objectively and accurately capture the key information and service attitude in the salesperson's speech. Compared with the traditional manual quality inspection method, the present invention effectively avoids the inconsistency of quality inspection results and subjective bias caused by human factors, ensuring the objectivity and accuracy of the quality inspection results. Finally, by optimizing the sales skills and service quality of the store clerks, the present invention helps to improve customer satisfaction and comfort during the shopping process. This can not only enhance customer loyalty and repurchase rate, but also enhance the company's brand image and market competitiveness.

[0079] To sum up, the dynamic quality inspection method of speech keywords based on AI technology adopted by the present invention can record the communication process between store clerks and customers in real time, and through intelligent quality inspection and data analysis functions, accurately evaluate the sales skills, service attitude and customer feedback of store clerks, thereby helping stores to improve service quality, optimize sales strategies, and improve customer satisfaction and performance, thereby solving the technical problem that the quality inspection method of store clerks' sales skills and service quality in the prior art is not only time-consuming and labor-intensive, but also limited by human factors, and it is difficult to ensure the consistency and accuracy of quality inspection results.

[0080] What is disclosed above is only a preferred embodiment of the present invention, and it certainly cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. A dynamic quality inspection method for speech keywords based on AI technology, characterized in that: The steps include: First, build a dynamic quality inspection standard library; Collect the conversations between store clerks and customers and pre-process the conversation content; The AI ​​processor extracts and preliminarily analyzes the preprocessed conversation content, and uses matching and evaluation algorithms to match the extracted features with the dynamic quality inspection standard library to evaluate the quality of the sales process. Based on the quality results of the sales process obtained through evaluation, in-depth analysis is performed to generate personalized sales strategy recommendations for store clerks, and a sales trend prediction model is built to predict future sales trends.

2. The dynamic quality inspection method for speech keywords based on AI technology as claimed in claim 1 is characterized in that: The specific method of building a dynamic quality inspection standard library is as follows: Collect historical quality inspection data, including excellent and poor sales recording samples and their corresponding quality inspection results; Based on machine learning algorithms, quality inspection standard features are extracted from historical data to build a dynamic quality inspection standard library; Among them, the machine learning algorithm uses cluster analysis, classification algorithm or regression algorithm; Train the model with historical data to improve the model's prediction accuracy and generalization ability; Continuously optimize the model, including adjusting model parameters, adding training data, introducing new features, etc., to improve the performance and adaptability of the model.

3. The dynamic quality inspection method for speech keywords based on AI technology as claimed in claim 2 is characterized in that: The specific method of collecting the conversation between the clerk and the customer and pre-processing the conversation content is as follows: The sound collection device embedded in the ID card is used to collect audio data of the conversation between the store clerk and the customer, and the audio data is pre-processed.

4. The dynamic quality inspection method for speech keywords based on AI technology as claimed in claim 3 is characterized in that: Preprocessing of audio data includes removing background noise, normalizing volume, and segmenting recordings into separate dialogue units based on silence or specific markers; Among them, the background noise is removed by using a filter algorithm or a sample-based noise reduction algorithm; Normalized volume uses a volume adjustment algorithm to adjust the volume level of the recording to a uniform standard; The recording is divided into independent dialogue units based on silence or specific markers. The detect_silence function provided by the pydub processing library is used to detect the silence part and cut it according to the set silence length threshold; natural language processing technology is combined to identify specific markers for more accurate segmentation.

5. The dynamic quality inspection method for speech keywords based on AI technology as claimed in claim 4 is characterized in that: The specific method of extracting features and performing preliminary analysis on the preprocessed conversation content based on the AI ​​processor is as follows: The deep learning-based automatic speech recognition system converts the recorded data into text data; Combined with natural language processing technology, keywords, phrases and contextual information of conversations are extracted from text data, and speech feature analysis is performed using speech rate calculation algorithm, intonation analysis algorithm and pause detection algorithm. Speech features include speech rate, intonation and pause patterns.

6. The dynamic quality inspection method for speech keywords based on AI technology as claimed in claim 5 is characterized in that: The specific method of matching the extracted features with the dynamic quality inspection standard library and evaluating the quality of the sales process is as follows: Matching algorithms and evaluation algorithms are used to match the extracted features with the dynamic quality inspection standard library to evaluate the quality of the sales process.

7. The method for dynamic quality inspection of speech keywords based on AI technology as claimed in claim 6, characterized in that: The specific method of using association rule mining or cluster analysis combined with the quality results of the sales process obtained by evaluation to conduct in-depth analysis and generate personalized sales strategy recommendations for store clerks based on collaborative filtering or content-based methods is as follows: Conduct in-depth analysis of sales data to explore highlights and deficiencies in the sales process and identify potential sales opportunities and areas for improvement; Based on the analysis results, personalized sales strategy recommendations are provided to store staff.

8. The method for dynamic quality inspection of speech keywords based on AI technology as claimed in claim 7, characterized in that: Construct a sales trend forecasting model and predict future sales trends in the following ways: Using deep learning algorithms, combined with the store clerk’s current sales strategy and the personalized sales strategy provided, we build sales trend prediction models and customer preference analysis models; Among them, the deep learning algorithm uses recurrent neural network or convolutional neural network.

9. The method for dynamic quality inspection of speech keywords based on AI technology as claimed in claim 8, characterized in that: After the dynamic quality inspection method for speech keywords based on AI technology is deployed, sales data is collected and analyzed in real time and combined with the sales data before deployment to analyze the dynamic quality inspection method for speech keywords based on AI technology, thereby optimizing the dynamic quality inspection method for speech keywords based on AI technology.

10. The method for dynamic quality inspection of speech keywords based on AI technology as claimed in claim 9, characterized in that: During the process of data collection, processing, storage and transmission, encryption and desensitization measures are taken to protect the security and privacy of user data.

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