Intelligent voice telephone extension outbound system

Abstract

The invention belongs to the intelligent information technology, and discloses a voice telephone AI customer extension system and method, and the system comprises a data processing module and a voice interaction module. A speech recognition algorithm based on multi-scale feature fusion and an intelligent dialogue strategy generation algorithm based on hierarchical reinforcement learning are innovatively proposed, a multi-dimensional dynamic customer intention evaluation model is constructed, an adaptive dynamic verbal skill generation and recommendation system is developed, and a cross-industry customer extension strategy optimization method based on transfer learning is developed. The system realizes automatic outbound calling, intelligent dialogue, accurate intention evaluation and strategy dynamic optimization, effectively improves customer extension efficiency and customer conversion rate, reduces enterprise customer extension cost, is suitable for multi-industry customer extension scenes, and has high flexibility and adaptability.

Description

Technical Field

[0001] The present invention belongs to the field of artificial intelligence and intelligent marketing technology, and specifically relates to an intelligent voice telephone customer development outbound calling system. Background Art

[0002] Traditional telephone customer acquisition relies primarily on manual outbound calls, which are subject to inefficiency, high costs, and inconsistent communication results. Salespeople have a limited number of effective outbound calls per day, and these calls are easily affected by factors such as emotions and proficiency in conversational skills, resulting in a variable customer experience. With the application of artificial intelligence technology in the voice field, although some automated outbound call systems have emerged, they still have many shortcomings: First, the accuracy of voice recognition cannot meet the needs of complex scenarios, and misjudgments are prone to occur in the presence of diverse accents and background noise. Second, the dialogue logic is fixed, and the dialogue cannot be flexibly adjusted based on real-time customer feedback and personalized needs, resulting in a high rate of communication interruptions. Third, customer intention assessment is not precise enough, often relying on simple keyword matching or fixed rules, making it difficult to deeply explore potential customer needs. Fourth, there is a lack of effective strategy optimization mechanisms, making it impossible to timely adjust customer acquisition strategies based on market changes and customer feedback. Therefore, there is an urgent need for a voice telephone AI customer acquisition system with higher intelligence and greater adaptability. Summary of the Invention

[0003] Purpose of the Invention

[0004] This invention aims to provide an AI-powered customer acquisition system and method using voice telephony. Through five core innovations, it achieves automated, intelligent, and efficient customer acquisition. Specific goals include significantly improving voice recognition accuracy and conversation fluency, accurately assessing customer intent, dynamically optimizing customer acquisition strategies, reducing labor costs, and increasing customer conversion rates, providing businesses with more efficient and accurate customer acquisition solutions.

[0005] System Overview

[0006] The voice phone AI customer acquisition system of the present invention is mainly composed of the following modules:

[0007] 1. Data processing and management module: responsible for collecting, cleaning, storing and managing customer data, including basic customer information, historical communication records, industry attributes, etc., while labeling the data and building customer portraits.

[0008] 2. Voice interaction module: Integrates advanced speech recognition and speech synthesis technologies to achieve clear and natural human-computer voice interaction, and has functions such as noise cancellation and accent adaptation.

[0009] 3. Intelligent dialogue decision module: Based on deep learning and natural language processing technologies, it understands customer intent and dynamically generates optimal dialogue strategies and response content based on customer profiles and real-time dialogue scenarios.

[0010] 4. Customer Intention Assessment Module: This module uses a variety of algorithms to analyze multi-dimensional information such as voice, semantics, and emotions during conversations to accurately assess the customer's level of intention and potential value.

[0011] 5. Intelligent Strategy Optimization Module: Based on various data and customer feedback during the customer acquisition process, machine learning algorithms are used to automatically optimize outbound call strategies, script templates, follow-up plans, etc., forming a closed-loop optimization system.

[0012] 6. Monitoring and analysis module: Real-time monitoring of key indicators in the customer acquisition process, such as outbound call success rate, call duration, customer conversion rate, etc., and data analysis to provide support for decision-making.

[0013] System workflow

[0014] 1. Data preparation stage: The data processing and management module collects customer data, cleans and labels it, builds customer profiles, and provides data support for subsequent modules.

[0015] 2. Outbound call and voice interaction stage: The voice interaction module automatically calls customers according to preset tasks, uses a speech recognition algorithm based on multi-scale feature fusion to accurately identify customer voices, and responds naturally and fluently through speech synthesis technology.

[0016] 3. Intelligent dialogue stage: The intelligent dialogue decision module uses the intelligent dialogue strategy generation algorithm based on hierarchical reinforcement learning to determine the dialogue strategy based on the customer profile and real-time dialogue scenario, and generates response content to interact with the customer.

[0017] 4. Customer intention assessment stage: The customer intention assessment module analyzes the customer's voice, semantics, emotions and other information in real time, evaluates customer intention through a multi-dimensional dynamic customer intention assessment model, and marks high-intention customers.

[0018] 5. Strategy optimization and follow-up phase: The intelligent strategy optimization module uses a cross-industry customer acquisition strategy optimization method based on transfer learning to adjust outbound calling strategies and speech templates based on customer acquisition data and feedback. For high-intent customers, a follow-up plan is arranged, and further communication is carried out manually or through the system.

[0019] 6. Monitoring and analysis stage: The monitoring and analysis module monitors various indicators in the customer acquisition process in real time, conducts data analysis, and provides a basis for system optimization and decision-making.

[0020] Beneficial effects

[0021] 1. Efficient and precise customer acquisition: Automated outbound calls and intelligent dialogue significantly improve customer acquisition efficiency, and multi-dimensional customer intention assessment ensures accurate screening of target customers.

[0022] 2. High-quality customer experience: Natural and smooth voice interaction and personalized dialogue strategies enhance customer communication experience and increase customer favorability.

[0023] 3. Flexible and adaptable: Cross-industry strategy optimization and dynamic script generation enable the system to quickly adapt to different industries and customer needs.

[0024] 4. Continuous optimization capabilities: Through data feedback and machine learning, automatic optimization of customer acquisition strategies and continuous improvement of system performance are achieved.

[0025] 5. Significantly reduce costs: reduce the workload of manual outbound calls, improve resource utilization efficiency, and reduce the cost of customer acquisition for enterprises.

[0026] Description of the drawings: Figure 1 Flow chart of the working principle of the system of the present invention DETAILED DESCRIPTION

[0027] Example 1

[0028] 1: Data processing and management module implementation

[0029] In terms of data collection, customer data is obtained by connecting to the company's customer relationship management system (CRM), marketing database, and other systems. Data cleaning is performed using Python's Pandas library to remove duplicate records, missing values, and invalid data. Natural language processing tools NLTK and Jieba are used to segment, tag, and extract keywords from customer text data. Combined with basic customer information and transaction data, clustering algorithms (such as K-Means) are used for labeling to construct customer profiles. For example, for financial industry customers, labels such as "risk preference" and "investment amount" are used; for education industry customers, labels such as "learning needs" and "budget range" are used.

[0030] 2: Voice interaction module deployment

[0031] The speech recognition component uses the TensorFlow framework to build a speech recognition model based on multi-scale feature fusion. First, the speech data is preprocessed, including noise reduction and normalization. Then, short-term spectral features, LSTM long-term features, and MFCC frequency domain features are extracted, fused through an attention mechanism, and input into a convolutional neural network-recurrent neural network (CNN-RNN) structure for recognition. The speech synthesis component uses iFlytek's speech synthesis API to select the appropriate voice style and speaking rate based on the generated text content, generating natural and fluent speech. In actual applications, real-time voice quality monitoring is used to dynamically adjust recognition and synthesis parameters to ensure the stability of voice interaction.

[0032] 3: Intelligent dialogue decision module training

[0033] An intelligent dialogue strategy generation model based on hierarchical reinforcement learning was constructed using the PyTorch framework. The dialogue process is divided into multiple stages, such as the opening stage, the needs understanding stage, and the product introduction stage. At the macro-strategy level, a deep Q-network (DQN) is used to learn the optimal strategy for each stage; at the micro-speech level, a recurrent neural network (RNN) is used to generate specific speech. When setting the reward function, positive customer feedback (such as asking about product details or expressing interest) is used as a positive reward, and negative customer feedback (such as rejection or hanging up the phone) is used as a negative reward. Through training with a large amount of simulated conversation data and actual conversation data, the model parameters are continuously optimized, enabling the system to generate appropriate dialogue strategies for different customers and scenarios.

[0034] 4: Application of customer intention assessment module

[0035] In an e-commerce customer acquisition project, the customer intention assessment module collects customer voice data, text responses, and conversation behavior data in real time. Sentiment analysis libraries (such as TextBlob) are used to analyze the emotional tendencies in customer voice and text, extracting keywords and semantic information. This information is fed into a multi-dimensional dynamic customer intention assessment model by counting behavioral indicators such as the number of questions asked and the duration of a customer's speech. The model outputs a customer intention score, labeling those with a score above 80 as high-intent customers for follow-up by sales staff. After one month of implementation, the conversion rate for high-intent customers reached 35%, a 15% increase compared to traditional assessment methods.

[0036] 5: Intelligent strategy optimization module operation

[0037] The intelligent strategy optimization module collects customer acquisition data from the previous day daily, including metrics such as outbound call success rate, customer conversion rate, and average call duration. It then uses machine learning algorithms from Python's Scikit-learn library (such as random forests and gradient boosting trees) to analyze the data and identify key factors influencing customer acquisition effectiveness. For example, if the outbound call success rate is low during a certain period, the system automatically adjusts the outbound call strategy for that period, including changing the script template and adjusting the call frequency. The optimized strategy is then applied to the customer acquisition tasks for the day, and the results are continuously monitored, with adjustments and optimizations made.

[0038] 6: Monitoring and analysis module operation

[0039] The monitoring and analysis module uses Vue.js and ECharts to create a visual interface that displays key metrics from the customer acquisition process in real time. This interface allows managers to view data such as the execution status of each outbound call task, the distribution of customer interest, and conversion rates at each stage. By setting data filtering criteria and comparative analysis functions, managers can gain a deeper understanding of customer acquisition results, identify issues, and adjust strategies promptly. For example, by comparing the conversion rates of different sales pitch templates, the optimal template can be selected for promotion; by analyzing the differences in customer interest across different regions, targeted regional customer acquisition strategies can be developed.

[0040] 1. The voice interaction module adopts a speech recognition algorithm based on multi-scale feature fusion to extract and fuse the short-term feature Fs, long-term feature F1 and frequency domain feature Ff of the voice signal. The fusion formula is:

[0041] Where Wi is the dimension adaptation matrix, bi is the bias vector, and the weights of each feature are calculated and weighted fused through the attention mechanism. The intelligent dialogue decision module described above adopts an intelligent dialogue strategy generation algorithm based on hierarchical reinforcement learning, which divides the dialogue process into a macro strategy layer and a micro speech layer for strategy generation. Its reward function formula is: R = αR feedback +βR progress +γR efficiency Among them, Rfeedback is the customer feedback reward, which takes the value of 1 when the customer expresses positive intention, -1 when the customer expresses negative intention, and 0 when the customer is neutral; Rprogress is the dialogue progress reward, which takes the value of 1 when advancing to the key stage, -1 when retreating, and 0 when there is no change; Refficiency is the dialogue efficiency reward; α+β+γ=1 is the weight coefficient. The customer intention evaluation module described in constructs a multi-dimensional dynamic customer intention evaluation model, integrating voice emotion, semantic content, and dialogue behavior information for evaluation. The intelligent strategy optimization module described in adopts a cross-industry customer acquisition strategy optimization method based on transfer learning to achieve rapid adjustment of cross-industry strategies. The data processing and management module obtains customer data by connecting with external systems and uses clustering algorithms for labeling. The monitoring and analysis module provides a visual interface to display key customer acquisition indicators and supports data screening and comparative analysis. The customer acquisition method of the intelligent voice telephone customer acquisition outbound call system is characterized in that it includes the following steps:

[0042] 1. The data processing and management module processes customer data and builds profiles;

[0043] 2. The voice interaction module automatically makes outbound calls and conducts voice interaction;

[0044] 3. The intelligent dialogue decision module generates dialogue strategies and response content;

[0045] 4. Customer intention assessment module assesses customer intention;

[0046] 5. Intelligent strategy optimization module optimizes customer acquisition strategies;

[0047] In the speech interaction step, the speech recognition algorithm based on multi-scale feature fusion is used for speech recognition. In the intelligent dialogue decision step, the intelligent dialogue strategy generation algorithm based on hierarchical reinforcement learning is used to determine the dialogue strategy.

[0048] Traditional speech recognition algorithms are prone to losing key information when processing complex speech signals. The speech recognition algorithm based on multi-scale feature fusion proposed in this invention extracts features of different scales from the speech signal, including short-time features, long-time features, and frequency domain features, and then effectively fuses these features. Specifically, short-time Fourier transform is first used to extract the short-time spectral features of the speech to capture the instantaneous changes in the speech; long-term context features are then extracted through the long short-term memory network (LSTM) to understand the overall semantics of the speech; at the same time, frequency domain features such as the Mel-frequency cepstral coefficients (MFCC) of the speech are extracted. Finally, the attention mechanism is used to perform weighted fusion of features of different scales, enabling the system to focus on key speech information and significantly improve the accuracy of speech recognition in complex environments. For example, in the presence of background noise or heavy accents, the recognition accuracy is improved by more than 20% compared to traditional algorithms.

[0049] Existing dialogue systems often adopt fixed dialogue strategies and cannot flexibly respond to the diverse needs of customers. The present invention designs an intelligent dialogue strategy generation algorithm based on hierarchical reinforcement learning. The dialogue process is divided into two levels: the macro-strategy layer and the micro-speech layer. At the macro-strategy layer, the intelligent agent determines the overall direction of the dialogue, such as product introduction, demand exploration, objection handling, etc., based on the customer profile and the overall stage of the dialogue; at the micro-speech layer, specific reply dialogues are generated based on the macro-strategy and the specific circumstances of the current dialogue. By setting up a reasonable reward mechanism, such as positive rewards for active customer responses and negative rewards for customer interruptions, the intelligent agent continuously learns and optimizes strategies during the interaction with customers. As the dialogue progresses, the system can automatically adjust the dialogue strategy to achieve more natural and effective communication, thereby improving customer engagement and satisfaction.

[0050] Traditional customer intention assessment methods are too simple and one-sided, making it difficult to accurately judge the customer's true intention. The present invention constructs a multi-dimensional dynamic customer intention assessment model, which conducts a comprehensive assessment from three dimensions: voice emotion, semantic content, and dialogue behavior. In terms of voice emotion, by analyzing the tone, speaking speed, volume and other characteristics of the customer's voice, the customer's emotional state, such as excitement, hesitation, dissatisfaction, etc., is identified; in terms of semantic content, natural language processing technology is used to perform semantic understanding and keyword extraction of the customer's speech, and analyze the customer's focus and needs for products or services; in terms of dialogue behavior, behavioral indicators such as the customer's question frequency, answer time, and active topic guidance are counted. The information from these three dimensions is integrated, and a dynamic evaluation model is established using a neural network algorithm to update the customer's intention score in real time, thereby achieving accurate and dynamic evaluation of customer intentions.

[0051] In order to meet the personalized needs of different customers, the present invention has developed an adaptive dynamic speech generation and recommendation system. Based on customer portraits, real-time conversation scenarios and historical conversation data, the system uses generative adversarial networks (GANs) and language models (such as GPT-based models) to automatically generate personalized speech content. The system first analyzes the customer's characteristics and the current conversation context, and then generates multiple candidate speech from the pre-trained language model. The candidate speech is then screened and sorted through semantic matching and sentiment analysis, and the most appropriate speech is recommended for use by the intelligent dialogue decision module. At the same time, the system will continuously optimize the speech generation model based on customer feedback, so that the generated speech is more in line with customer needs and improves communication effectiveness.

[0052] There are significant differences in customer acquisition needs and customer characteristics between different industries, and traditional customer acquisition systems find it difficult to quickly adapt to new industries. The present invention proposes a cross-industry customer acquisition strategy optimization method based on transfer learning. First, a general customer acquisition strategy model is trained on existing mature industry data to learn the common characteristics of the industry and customer behavior patterns; when entering a new industry, the knowledge and parameters of the general model are transferred to the model of the new industry using transfer learning technology, and fine-tuned in combination with a small amount of data from the new industry. In this way, the system can quickly adapt to the characteristics of the new industry, reduce training time and data requirements, and maintain a high customer acquisition effect. For example, when expanding from the financial industry to the education industry, the system can adjust its strategy in a short period of time to achieve efficient customer acquisition.

Claims

1. An intelligent voice telephone customer development outbound calling system, characterized in that: include:

1. Data processing and management module, used to collect, clean, store, and label customer data to build customer profiles; 2. Voice interaction module, integrating speech recognition and speech synthesis technologies to achieve voice interaction, with noise cancellation and accent adaptation functions; 3. Intelligent dialogue decision-making module, based on deep learning and natural language processing technologies, dynamically generates dialogue strategies and response content based on customer profiles and conversation scenarios; 4. Customer intention assessment module: Analyze customer information from multiple dimensions such as voice emotion, semantic content, and conversation behavior to assess customer intention; 5. Intelligent strategy optimization module, automatically optimizes outbound call strategies, script templates, etc. based on customer acquisition data and customer feedback; 6. Monitoring and analysis module, real-time monitoring of customer acquisition indicators and data analysis.

2. The intelligent voice telephone customer acquisition outbound call system according to claim 1, wherein the voice interaction module adopts a speech recognition algorithm based on multi-scale feature fusion to extract and fuse short-term features Fs, long-term features Fl and frequency domain features Ff of the voice signal, and the fusion formula is: in, Wi is the dimension adaptation matrix, bi is the bias vector, and the weights of each feature are calculated and weighted fused through the attention mechanism.

3. The intelligent voice telephone customer acquisition outbound call system according to claim 1, wherein the intelligent dialogue decision module adopts an intelligent dialogue strategy generation algorithm based on hierarchical reinforcement learning, divides the dialogue process into a macro strategy layer and a micro speech layer for strategy generation, and its reward function formula is: R=αR feedback +βR progress +γR efficiency Among them, Rfeedback is the customer feedback reward, which takes the value of 1 when the customer expresses positive intention, -1 when the customer expresses negative intention, and 0 when the customer is neutral; Rprogress is the dialogue progress reward, which takes the value of 1 when the customer advances to a key stage, -1 when the customer retreats, and 0 when the customer remains unchanged; Refficiency is the dialogue efficiency reward; α+β+γ=1 is the weight coefficient.

4. The intelligent voice telephone customer development outbound call system according to claim 1, wherein the customer intention evaluation module constructs a multi-dimensional dynamic customer intention evaluation model, integrating voice emotion, semantic content, and dialogue behavior information for evaluation.

5. The intelligent voice telephone customer acquisition outbound call system according to claim 1, wherein the intelligent strategy optimization module adopts a cross-industry customer acquisition strategy optimization method based on transfer learning to achieve rapid adjustment of cross-industry strategies.

6. The intelligent voice telephone customer acquisition outbound call system according to claim 1, wherein the data processing and management module obtains customer data by connecting with an external system and uses a clustering algorithm to perform labeling processing.

7. The intelligent voice telephone customer acquisition outbound call system according to claim 1, wherein the monitoring and analysis module provides a visual interface, displays key customer acquisition indicators, and supports data screening and comparative analysis.

8. A customer acquisition method based on the intelligent voice telephone customer acquisition outbound call system according to any one of claims 1 to 7, characterized in that: The following steps are involved:

1. The data processing and management module processes customer data and builds profiles; 2. The voice interaction module automatically makes outbound calls and conducts voice interaction; 3. The intelligent dialogue decision module generates dialogue strategies and response content; 4. Customer intention assessment module assesses customer intention; 5. Intelligent strategy optimization module optimizes customer acquisition strategies; 6. The monitoring and analysis module monitors and analyzes customer acquisition data.

9. The customer acquisition method according to claim 8, wherein in the voice interaction step, the voice recognition algorithm based on multi-scale feature fusion according to claim 2 is used for voice recognition.

10. The customer acquisition method according to claim 8, wherein in the intelligent dialogue decision-making step, the intelligent dialogue strategy generation algorithm based on hierarchical reinforcement learning according to claim 3 is used to determine the dialogue strategy.

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