Lead follow-up method, lead follow-up device, electronic device, and storage medium

Abstract

The embodiment of the application provides a clue follow-up method, a clue follow-up device, an electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring a business operation clue and a clue scoring model; scoring the business operation clue by using the clue scoring model to obtain a target clue score; sorting the business operation clue according to the target clue score to obtain a clue sorting order; classifying the business operation clue according to the clue sorting order to obtain qualified clues and unqualified clues; following up the qualified clues according to the clue sorting order; acquiring a clue follow-up intention of the unqualified clues; and following up the unqualified clues according to the clue follow-up intention, so that the efficiency of the clue follow-up can be improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, is applicable to the field of financial technology technology, and particularly relates to a lead following method, a lead following device, an electronic device, and a storage medium. Background Art

[0002] Customer leads refer to leads of users or potential customers who are interested in purchasing a product. Related technologies manually review and sort individual customer leads, then follow up on each one individually. This process is time-consuming. Faced with a massive volume of customer leads, it's difficult to follow up on them in a timely manner, resulting in low efficiency. Summary of the Invention

[0003] The main purpose of the embodiments of the present application is to provide a clue following method, a clue following device, an electronic device and a storage medium, aiming to improve the efficiency of clue following.

[0004] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application provides a thread following method, the method comprising:

[0005] Obtain business leads and lead scoring models;

[0006] Scoring the business operation leads using the lead scoring model to obtain a target lead score;

[0007] Sorting the business operation leads according to the target lead scores to obtain a lead ranking order;

[0008] Classifying the business operation leads according to the lead sorting order to obtain qualified leads and unqualified leads;

[0009] Following up on the qualified leads according to the lead ranking order;

[0010] Obtain the lead follow-up intention of the unqualified lead, and follow up the unqualified lead according to the lead follow-up intention.

[0011] In some embodiments, the clue scoring model is trained according to the following steps:

[0012] Obtain sample business leads and sample lead scores;

[0013] updating the model parameters of the first preset scoring model according to the sample business leads and the sample lead scores to obtain a first scoring sub-model;

[0014] updating the model parameters of a second preset scoring model according to the sample business leads and the first scoring sub-model to obtain a second scoring sub-model;

[0015] The clue scoring model is determined according to the first scoring sub-model and the second scoring sub-model.

[0016] In some embodiments, updating the model parameters of the second preset scoring model based on the sample business leads and the first scoring sub-model to obtain the second scoring sub-model includes:

[0017] Obtaining the model gradient of the first scoring sub-model to obtain model residual data;

[0018] Scoring the sample business leads using the second preset scoring model to obtain a first predicted lead score;

[0019] Performing loss calculation based on the model residual data and the first prediction clue score to obtain first loss data;

[0020] The model parameters of the second preset scoring model are updated according to the first loss data to obtain the second scoring sub-model.

[0021] In some embodiments, determining the clue scoring model based on the first scoring sub-model and the second scoring sub-model includes:

[0022] Obtaining model weight data of the second scoring sub-model;

[0023] Performing model weighting on the second scoring sub-model according to the model weight data to obtain a reference scoring sub-model;

[0024] Model aggregation is performed on the first scoring sub-model and the reference scoring sub-model to obtain the clue scoring model.

[0025] In some embodiments, obtaining model weight data of the second scoring sub-model includes:

[0026] Performing a lead score on the sample business lead using the first scoring sub-model to obtain a second predicted lead score;

[0027] Calculating the loss based on the second predicted clue score and the sample clue score to obtain second loss data;

[0028] Performing a lead score on the sample business lead using the second scoring sub-model to obtain a third predicted lead score;

[0029] determining third loss data based on the second loss data, the third prediction clue score, and a preset model weight;

[0030] The preset model weight is updated according to the third loss data to obtain the model weight data.

[0031] In some embodiments, performing model aggregation on the first scoring sub-model and the reference scoring sub-model to obtain the clue scoring model includes:

[0032] Performing model aggregation on the first scoring sub-model and the reference scoring sub-model to obtain a candidate scoring model;

[0033] Obtaining the model scoring accuracy of the candidate scoring model;

[0034] The candidate scoring models are screened according to the model scoring accuracy to obtain the clue scoring model.

[0035] In some embodiments, updating the model parameters of the first preset scoring model according to the sample business leads and the sample lead scores to obtain a first scoring sub-model includes:

[0036] Scoring the sample business leads using the first preset scoring model to obtain a fourth predicted lead score;

[0037] performing loss calculation based on the fourth predicted clue score and the sample clue score to obtain fourth loss data;

[0038] The model parameters of the first preset scoring model are updated according to the fourth loss data to obtain the first scoring sub-model.

[0039] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides a clue following device, the device comprising:

[0040] Acquisition module, used to obtain business leads and lead scoring models;

[0041] A scoring module, configured to score the business operation leads using the lead scoring model to obtain a target lead score;

[0042] A sorting module, configured to sort the business operation leads according to the target lead scores to obtain a lead sorting order;

[0043] A classification module, configured to classify the business operation leads according to the lead sorting order to obtain qualified leads and unqualified leads;

[0044] A first follow-up module, configured to follow up the qualified leads according to the lead sorting order;

[0045] The second follow-up module is configured to obtain the lead follow-up intention of the unqualified lead and follow up the unqualified lead according to the lead follow-up intention.

[0046] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.

[0047] To achieve the above-mentioned purpose, the fourth aspect of the embodiments of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the method described in the first aspect.

[0048] The lead following method, lead following device, electronic device and storage medium proposed in the embodiment of the present application obtain business operation leads and lead scoring models, score the business operation leads using the lead scoring model, and obtain a target lead score, so as to use the target lead score to determine the probability that the business operation leads can be converted into business orders. In order to obtain the lead priority of the business operation leads, the business operation leads are sorted according to the target lead score to obtain a lead sorting order. The business operation leads are classified according to the lead sorting order to evaluate the quality of the leads and obtain qualified leads and unqualified leads. Following up on qualified leads according to the lead sorting order can promptly follow up on business operation leads with higher priorities, reduce the time cost of sorting out each business operation lead, and improve the efficiency of lead following up. The lead follow-up intention of unqualified leads is obtained, and unqualified leads are followed up according to the lead follow-up intention, without having to sort out the leads one by one and follow up on any leads, thereby improving the efficiency of lead following up. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a flowchart of the clue following method provided in an embodiment of the present application;

[0050] Figure 2 This is a flowchart of the training process of the clue scoring model provided in the embodiment of the present application;

[0051] Figure 3 yes Figure 2 Flowchart of step S220 in FIG.

[0052] Figure 4 yes Figure 2 Flowchart of step S230 in FIG.

[0053] Figure 5 yes Figure 2 Flowchart of step S240 in FIG.

[0054] Figure 6 yes Figure 5 Flowchart of step S510 in FIG.

[0055] Figure 7 yes Figure 5 Flowchart of step S530 in FIG.

[0056] Figure 8 is a structural diagram of a clue following device provided in an embodiment of the present application;

[0057] Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0059] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and so on, in the specification, claims, and drawings, are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0061] Customer leads refer to leads of users or potential customers who are interested in purchasing a product. Related technologies manually review and sort individual customer leads, then follow up on each one individually. This process is time-consuming. Faced with a massive volume of customer leads, it's difficult to follow up on them in a timely manner, resulting in low efficiency.

[0062] Based on this, embodiments of the present application provide a lead following method, a lead following device, an electronic device, and a computer-readable storage medium, aiming to improve the efficiency of lead following.

[0063] The lead following method, lead following device, electronic device and computer-readable storage medium provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, the lead following method in the embodiments of the present application is described.

[0064] The clue following method provided in the embodiment of the present application relates to the field of artificial intelligence technology. The clue following method provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the clue following method, etc., but is not limited to the above forms.

[0065] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0066] It should be noted that in each specific embodiment of the present application, when it comes to the need to perform relevant processing based on data related to the user's identity or characteristics, such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of such data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0067] Figure 1 This is an optional flowchart of the lead following method provided in an embodiment of the present application. The lead following method may include but is not limited to steps S110 to S160.

[0068] Step S110, obtaining business operation leads and lead scoring models;

[0069] Step S120, scoring the business operation leads using the lead scoring model to obtain a target lead score;

[0070] Step S130, sorting the business operation leads according to the target lead scores to obtain a lead sorting order;

[0071] Step S140: Classify the business operation leads according to the lead sorting order to obtain qualified leads and unqualified leads;

[0072] Step S150, following up on qualified leads according to the lead ranking order;

[0073] Step S160: Obtain the lead follow-up intention of the unqualified lead, and follow up the unqualified lead according to the lead follow-up intention.

[0074] In steps S110 to S160 shown in the embodiment of the present application, business operation leads are classified according to the lead sorting order to evaluate the quality of the leads and obtain qualified leads and unqualified leads. Following up on qualified leads according to the lead sorting order can promptly follow up on business operation leads with higher priorities, reducing the time cost of sorting out each business operation lead and improving the efficiency of lead follow-up. Obtaining the lead follow-up intention of unqualified leads and following up on unqualified leads according to the lead follow-up intention, without having to sort out the leads one by one and follow up on any leads, improves the efficiency of lead follow-up.

[0075] In step S110 of some embodiments, customer profiles are identified and screened based on business management tasks such as trusts and funds to generate customer management leads. Customer management leads refer to lead information of users or potential users who are interested in purchasing the products for sale. Customer management leads include basic customer information, purchase history, and customer behavior data. Customer behavior data includes application usage data and product usage data. Application usage data can be page views, page view time, etc., and product usage data can be product usage frequency, product usage duration, etc. In the insurance field, customer management leads can be information about customers who are interested in insurance products such as auto insurance and life insurance. The lead scoring model is used to score business management leads.

[0076] In step S120 of some embodiments, the business operation lead is input into the lead scoring model for scoring to obtain a target lead score. The target lead score is used to indicate the lead conversion probability that the business operation lead can be converted into a business order. The higher the target lead score, the higher the lead conversion probability of the business operation lead being converted into a business order, and the higher the lead quality of the business operation lead. If the lead conversion probability is 1, it means that the business operation lead can be converted into a business order; if the lead conversion probability is 0, it means that the business operation lead cannot be converted into a business order. The target lead score output by the lead scoring model directly affects the follow-up decision results of the business operation lead, so it is necessary to ensure the accuracy of the lead scoring model's prediction of the lead score.

[0077] See also Figure 2 In some embodiments, the training process of the clue scoring model may include but is not limited to steps S210 to S240:

[0078] Step S210: Obtain sample business leads and sample lead scores;

[0079] Step S220: updating the model parameters of the first preset scoring model based on the sample business leads and the sample lead scores to obtain a first scoring sub-model;

[0080] Step S230: updating the model parameters of the second preset scoring model based on the sample business leads and the first scoring sub-model to obtain a second scoring sub-model;

[0081] Step S240: Determine a clue scoring model based on the first scoring sub-model and the second scoring sub-model.

[0082] In step S210 of some embodiments, sample business leads and sample lead scores may be obtained from industry websites and lead databases. The sample business leads are business leads used for model training, and the sample lead scores are real lead labels for the sample business leads, which are used to indicate the lead conversion probability of converting the sample business leads into business orders.

[0083] In step S220 of some embodiments, sample business leads and sample lead scores are used as training samples, the first preset scoring model is trained on the training samples, and the model parameters of the first preset scoring model are updated to obtain a first scoring sub-model. Due to the digital transformation of business activities and multi-channel data collection methods, the number of business leads obtained every day is massive. In order to improve the processing efficiency of business leads, the first preset scoring model adopts a decision tree. In addition, the structure of the decision tree is intuitive, and the decision tree can be visualized as a tree diagram, which can accurately capture the features that have a significant impact on the probability of lead conversion, thereby improving the interpretability of the lead scoring model. At the same time, the decision tree predicts the lead score through a series of decision rules, which can accurately capture the nonlinear relationship between the sample business leads and the lead score, thereby improving the accuracy of the lead score.

[0084] In step S230 of some embodiments, a second preset scoring model is created, and the second preset scoring model may be a decision tree. Specifically, a preset number of features are randomly selected from the sample business leads to obtain a sample attribute set. The information gain of each feature in the sample attribute set is calculated, and the feature with the largest information gain is selected as the splitting point of the current node. A decision tree is constructed based on the splitting point to obtain the second preset scoring model. The first preset scoring model and the second preset scoring model are both weak learners, and may be models of the same network architecture or models of different network architectures. The network architecture is a tree structure of a decision tree, such as the branches and depth of the decision tree. The second preset scoring model is trained based on the sample business leads and the first scoring sub-model, and the model parameters of the second preset scoring model are updated to utilize the second preset scoring model to capture the complex nonlinear relationship of the sample business leads and the feature interaction information between the models, so as to correct the lead scoring error of the first scoring sub-model, thereby gradually improving the prediction accuracy of the lead scoring, until the training is completed and the second scoring sub-model is obtained.

[0085] In step S240 of some embodiments, the first scoring sub-model and the second scoring sub-model are aggregated to obtain a lead scoring model. Model aggregation can combine the advantages of multiple models, enhance model diversity, and achieve better scoring prediction results than a single model. This enables the lead scoring model to have good scoring prediction performance even when dealing with unknown samples, thereby improving the generalization of the lead scoring model. Furthermore, through joint training of multiple models, complex patterns and feature interactions in leads can be captured to fully learn sample business leads, thereby improving the scoring accuracy of the lead scoring model. Specifically, the model parameters of the first scoring sub-model and the model parameters of the second scoring sub-model can be added or weighted to obtain the lead scoring model.

[0086] Through the above steps S210 to S240, a lead scoring model can be obtained to evaluate the lead quality of business operation leads based on the lead scoring model, so that business operation leads can be selectively followed up according to the lead quality, rather than following up on massive business operation leads one by one, thereby improving the efficiency of lead follow-up.

[0087] See also Figure 3 In some embodiments, step S220 may include but is not limited to steps S310 to S330:

[0088] Step S310: scoring the sample business leads using the first preset scoring model to obtain a fourth predicted lead score;

[0089] Step S320, performing loss calculation based on the fourth predicted clue score and the sample clue score to obtain fourth loss data;

[0090] Step S330: Update the model parameters of the first preset scoring model according to the fourth loss data to obtain a first scoring sub-model.

[0091] In step S310 of some embodiments, the sample business lead is input into the first preset scoring model for lead scoring to obtain a fourth predicted lead score. The fourth predicted lead score is the lead score for the sample business lead predicted by the first preset scoring model.

[0092] In step S320 of some embodiments, the fourth predicted clue score is used as the predicted value, and the sample clue score is used as the true value. The results are input into a loss function to calculate the loss, thereby measuring the difference between the true value and the predicted value, thereby obtaining fourth loss data. The loss function may be a mean square error function, a cross entropy loss function, or the like.

[0093] In some embodiments, in step S330, to improve the accuracy of lead score prediction, the loss data is used to optimize the first preset scoring model. The fourth loss data is minimized, the model parameters of the first preset scoring model are updated, and the first preset scoring model with the minimum fourth loss data is used as the first scoring sub-model.

[0094] Through the above steps S310 to S330, a first scoring sub-model with better clue scoring performance can be obtained, thereby improving the scoring prediction accuracy of the first scoring sub-model.

[0095] See also Figure 4 In some embodiments, step S230 may include but is not limited to steps S410 to S440:

[0096] Step S410, obtaining the model gradient of the first scoring sub-model to obtain model residual data;

[0097] Step S420: Scoring the sample business leads using a second preset scoring model to obtain a first predicted lead score;

[0098] Step S430, performing loss calculation based on the model residual data and the first prediction clue score to obtain first loss data;

[0099] Step S440: Update the model parameters of the second preset scoring model according to the first loss data to obtain a second scoring sub-model.

[0100] In step S410 of some embodiments, for sample business leads that lack key features such as purchase history, the first scoring sub-model may not accurately predict the score for the sample business leads. In order to determine the scoring error of the first scoring sub-model, the derivative of the loss function with respect to the model parameters of the first scoring sub-model is calculated to obtain the model gradient, and the model gradient is used as the model residual data.

[0101] In step S420 of some embodiments, during the training process of the second preset scoring model, the sample business lead is input into the second preset scoring model for lead scoring to obtain a first predicted lead score. The first predicted lead score is the lead score predicted by the second preset scoring model for the sample business lead.

[0102] In step S430 of some embodiments, to enable the second preset scoring model to focus on samples poorly predicted by the first scoring sub-model and more effectively learn complex patterns in the samples, the model residual data and the first prediction clue score are input into a loss function for loss calculation to obtain first loss data. The loss function can be a mean squared error function, a cross entropy function, or the like. This loss function is the same as the loss function used during the training of the first scoring sub-model.

[0103] In some embodiments, in step S440, the second preset scoring model is optimized based on the first loss data to reduce the scoring error of the first scoring sub-model and gradually improve the accuracy of the scoring prediction. The first loss data is minimized, and the model parameters of the second preset scoring model are updated. The second preset scoring model that minimizes the first loss data is used as the second scoring sub-model.

[0104] In the above steps S410 to S440, the second scoring sub-model is trained based on the first scoring sub-model. The features learned by the previous model can be applied to the next model, and the correlation relationship between the features extracted by different models can be established to enhance the connection between the features, so that the clue scoring model can adapt well to clues with complex data structures and improve the accuracy of clue scoring.

[0105] See also Figure 5In some embodiments, step S240 may include but is not limited to steps S510 to S530:

[0106] Step S510, obtaining model weight data of the second scoring sub-model;

[0107] Step S520: performing model weighting on the second scoring sub-model according to the model weight data to obtain a reference scoring sub-model;

[0108] Step S530 : performing model aggregation on the first scoring sub-model and the reference scoring sub-model to obtain a clue scoring model.

[0109] In step S510 of some embodiments, the model weight data is used to indicate the contribution of the second scoring sub-model to the clue scoring model. The model weight data can be preset or calculated in real time.

[0110] In step S520 of some embodiments, the model weight data is multiplied by the model parameters of the second scoring sub-model to obtain a reference scoring sub-model.

[0111] In step S530 of some embodiments, the model parameters of the first scoring sub-model and the model parameters of the reference scoring sub-model are added to obtain a clue scoring model. By combining multiple weak learners, a strong learner can be constructed, thereby improving the accuracy of clue scoring.

[0112] Through the above steps S510 to S530, the model parameters of different scoring sub-models can be integrated to enhance the generalization of the lead scoring model and improve the scoring accuracy of business operation leads.

[0113] See also Figure 6 In some embodiments, step S510 may include but is not limited to steps S610 to S650:

[0114] Step S610: scoring the sample business leads using the first scoring sub-model to obtain a second predicted lead score;

[0115] Step S620, performing loss calculation based on the second predicted clue score and the sample clue score to obtain second loss data;

[0116] Step S630: scoring the sample business leads using the second scoring sub-model to obtain a third predicted lead score;

[0117] Step S640, determining third loss data based on the second loss data, the third prediction clue score and the preset model weight;

[0118] Step S650: Update the preset model weight according to the third loss data to obtain model weight data.

[0119] In step S610 of some embodiments, the sample business lead is input into the first scoring sub-model for lead scoring to obtain a second predicted lead score.

[0120] In step S620 of some embodiments, the second predicted clue score and the sample clue score are input into a loss function for loss calculation to obtain second loss data. The loss function can be a mean squared error function, a cross entropy function, etc. It should be noted that this loss function is the same as the loss function used to train the first scoring sub-model and the loss function used to train the second scoring sub-model.

[0121] In step S630 of some embodiments, the sample business lead is input into the second scoring sub-model for lead scoring to obtain a third predicted lead score.

[0122] In step S640 of some embodiments, the preset model weight and the third predicted clue score are multiplied to obtain an intermediate clue score, and the intermediate clue score and the second loss data are added to obtain the third loss data.

[0123] In step S650 of some embodiments, the preset model weight is updated by minimizing the third loss data, and the preset model weight when the third loss data takes the minimum value is used as the model weight data.

[0124] Through the above steps S610 to S650, the optimal model weight data can be obtained to accurately evaluate the contribution of the second scoring sub-model to the clue scoring model, so as to obtain a clue scoring model with better scoring performance and improve the accuracy of clue scoring.

[0125] See also Figure 7 In some embodiments, step S530 may include but is not limited to steps S710 to S730:

[0126] Step S710: The first scoring sub-model and the reference scoring sub-model are aggregated to obtain a candidate scoring model.

[0127] Step S720, obtaining the model scoring accuracy of the candidate scoring model;

[0128] Step S730 , screening the candidate scoring models according to the model scoring accuracy to obtain a clue scoring model.

[0129] In step S710 of some embodiments, the model parameters of the first scoring sub-model and the model parameters of the reference scoring sub-model are added to obtain a candidate scoring model.

[0130] In step S720 of some embodiments, a business operation lead for testing the model performance of a candidate scoring model is obtained to obtain a test business lead. The actual lead score of the test business lead is obtained to obtain a test lead score. The test business lead is input into the candidate scoring model for lead scoring to obtain a candidate prediction lead score. The difference between the test lead score and the candidate prediction lead score is calculated to obtain a difference score. The difference score is the absolute value of the difference between the test lead score and the candidate prediction lead score. If the difference score is less than a preset scoring threshold, the candidate prediction lead score is considered to be a correct score. The number of leads of the correctly scored test business leads is obtained to obtain a first number. The number of leads of the test business leads is obtained to obtain a second number. The ratio of the first number to the second number is used as the model scoring accuracy.

[0131] In step S730 of some embodiments, if the model scoring accuracy is greater than or equal to a preset accuracy threshold, the candidate scoring model is used as the clue scoring model. If the model scoring accuracy is less than the preset accuracy threshold, the candidate scoring model is retrained.

[0132] In the above steps S710 to S730, candidate scoring models are screened according to the model scoring accuracy, so as to obtain a lead scoring model with high scoring accuracy, so as to accurately evaluate the quality of business operation leads.

[0133] In step S130 of some embodiments, the business leads are sorted from highest to lowest according to the target lead score to obtain a lead ranking order. The lead ranking order indicates the order in which the business leads are ranked in the lead sequence. The lower the lead ranking order, the higher the quality of the business lead.

[0134] In step S140 of some embodiments, if the lead ranking order is less than or equal to a preset order threshold, indicating that the lead quality of the business operation lead is high, the business operation lead is considered a qualified lead. If the lead ranking order is greater than the preset order threshold, indicating that the lead quality of the business operation lead is low, the business operation lead is considered an unqualified lead.

[0135] In step S150 of some embodiments, qualified leads are followed up sequentially according to the lead ranking order, rather than having to follow up on all leads, which greatly improves the efficiency of lead follow-up. For example, if the lead ranking order is 2, the qualified lead will be the second lead to be followed up.

[0136] In step S160 of some embodiments, the lead follow-up intention of the unqualified lead is obtained. The lead follow-up intention is used to indicate whether to follow up on the unqualified lead or not to follow up on the unqualified lead. If the lead follow-up intention is to follow up on the unqualified lead, the unqualified lead is followed up in the order in which the unqualified leads are ranked. If the lead follow-up intention is to not follow up on the unqualified lead, the unqualified lead is placed in the lead pool for cultivation.

[0137] After following up on a lead, the voice recording of the lead follow-up is captured and converted into text, resulting in the follow-up text. Keyword detection is performed on the follow-up text to obtain keywords and their frequencies. The follow-up text is scored based on the keywords and frequencies to obtain a follow-up score. A follow-up strategy is generated based on the follow-up score, keywords, and frequency to improve the business capabilities of the lead follow-up recipients.

[0138] The lead tracking method of an embodiment of the present application includes: obtaining sample business leads and sample lead scores; inputting the sample business leads into a first preset scoring model for lead scoring to obtain an initial predicted lead score; calculating losses based on the initial predicted lead score and the sample lead score to obtain initial loss data; minimizing the initial loss data and adjusting the model parameters of the first preset scoring model to obtain a first scoring sub-model; calculating the model gradient of the first scoring sub-model to obtain model residual data; inputting the sample business leads into a second preset scoring model for lead scoring to obtain a first predicted lead score; calculating losses based on the model residual data and the first predicted lead score to obtain first loss data; minimizing the first loss data and adjusting the model parameters of the second preset scoring model to obtain a second scoring sub-model; scoring the sample business leads using the first scoring sub-model to obtain a second predicted lead score; calculating losses based on the second predicted lead score and the sample lead score to obtain second loss data; and scoring the sample business leads using the second scoring sub-model to obtain a third predicted lead score. Multiplying the third predicted lead score by the preset model weights, and adding the multiplication result to the second loss data to determine the third loss data. Minimize the third loss data to update the preset model weights to obtain model weight data for the second scoring sub-model. Multiply the model weight data by the model parameters of the second scoring sub-model to obtain a reference scoring sub-model. Add the model parameters of the first scoring sub-model and the reference scoring sub-model to obtain a lead scoring model.

[0139] Obtain business operation leads and a lead scoring model, score the business operation leads using the lead scoring model to obtain a target lead score, sort the business operation leads according to the target lead score to obtain a lead sorting order, classify the business operation leads according to the lead sorting order to obtain qualified leads and unqualified leads, follow up on qualified leads according to the lead sorting order, obtain the lead follow-up intention of unqualified leads, and follow up on unqualified leads according to the lead follow-up intention.

[0140] See also Figure 8 The embodiment of the present application further provides a clue following device, which can implement the above-mentioned clue following method, and the clue following device includes:

[0141] Acquisition module 810, for acquiring business leads and lead scoring models;

[0142] Scoring module 820, used to score business operation leads using a lead scoring model to obtain a target lead score;

[0143] A sorting module 830 is used to sort the business operation leads according to the target lead scores to obtain a lead sorting order;

[0144] A classification module 840 is used to classify business operation leads according to the lead sorting order to obtain qualified leads and unqualified leads;

[0145] A first follow-up module 850 is used to follow up on qualified leads according to the lead sorting order;

[0146] The second follow-up module 860 is configured to obtain the lead follow-up intention of the unqualified lead and follow up the unqualified lead according to the lead follow-up intention.

[0147] The specific implementation of the clue following device is basically the same as the specific embodiment of the clue following method described above, and will not be repeated here.

[0148] The present application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned lead tracking method when executing the computer program. The electronic device can be any smart terminal including a tablet computer, an in-vehicle computer, or the like.

[0149] See also Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0150] The processor 910 may be implemented using a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0151] The memory 920 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 920 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 920 and is called by the processor 910 to execute the clue tracking method of the embodiments of this application;

[0152] Input / output interface 930, used to implement information input and output;

[0153] Communication interface 940, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, Wi-Fi, Bluetooth, etc.);

[0154] bus 950 , which transmits information between various components of the device (e.g., processor 910 , memory 920 , input / output interface 930 , and communication interface 940 );

[0155] The processor 910 , the memory 920 , the input / output interface 930 , and the communication interface 940 are connected to each other in communication within the device via a bus 950 .

[0156] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned clue following method is implemented.

[0157] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0158] The lead following method, lead following device, electronic device and computer storage medium provided in the embodiments of the present application classify business operation leads according to the lead sorting order to evaluate the quality of the leads and obtain qualified leads and unqualified leads. Following up on qualified leads according to the lead sorting order can promptly follow up on business operation leads with higher priorities, reducing the time cost of sorting out each business operation lead and improving the efficiency of lead following up. Obtaining the lead following up intention of unqualified leads and following up on unqualified leads according to the lead following up intention, without having to sort out the leads one by one and follow up on any leads, thereby improving the efficiency of lead following up.

[0159] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0160] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0161] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0162] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0163] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0164] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0165] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0166] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0167] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0168] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store programs.

[0169] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A clue following method, characterized in that: The method comprises: Obtain business leads and lead scoring models; Scoring the business operation leads using the lead scoring model to obtain a target lead score; Sorting the business operation leads according to the target lead scores to obtain a lead ranking order; Classifying the business operation leads according to the lead sorting order to obtain qualified leads and unqualified leads; Following up on the qualified leads according to the lead ranking order; Obtaining the lead follow-up intention of the unqualified lead, and following up on the unqualified lead according to the lead follow-up intention; The clue scoring model is trained according to the following steps: Obtaining sample business leads and sample lead scores; updating model parameters of a first preset scoring model based on the sample business leads and the sample lead scores to obtain a first scoring sub-model; updating model parameters of a second preset scoring model based on the sample business leads and the first scoring sub-model to obtain a second scoring sub-model; both the first scoring sub-model and the second scoring sub-model are decision trees; determining the lead scoring model based on the first scoring sub-model and the second scoring sub-model; Determining the clue scoring model according to the first scoring sub-model and the second scoring sub-model includes: Obtaining model weight data of the second scoring sub-model; performing model weighting on the second scoring sub-model according to the model weight data to obtain a reference scoring sub-model; performing model aggregation on the first scoring sub-model and the reference scoring sub-model to obtain the clue scoring model; The obtaining of model weight data of the second scoring sub-model includes: The sample business leads are scored using the first scoring sub-model to obtain a second predicted lead score; the loss is calculated based on the second predicted lead score and the sample lead score to obtain second loss data; the sample business leads are scored using the second scoring sub-model to obtain a third predicted lead score; the third loss data is determined based on the second loss data, the third predicted lead score and the preset model weight; the preset model weight is updated based on the third loss data to obtain the model weight data.

2. The method according to claim 1, characterized in that The updating of the model parameters of the second preset scoring model according to the sample business leads and the first scoring sub-model to obtain the second scoring sub-model includes: Obtaining the model gradient of the first scoring sub-model to obtain model residual data; Scoring the sample business leads using the second preset scoring model to obtain a first predicted lead score; Performing loss calculation based on the model residual data and the first prediction clue score to obtain first loss data; The model parameters of the second preset scoring model are updated according to the first loss data to obtain the second scoring sub-model.

3. The method according to claim 1, characterized in that The performing model aggregation on the first scoring sub-model and the reference scoring sub-model to obtain the clue scoring model includes: Performing model aggregation on the first scoring sub-model and the reference scoring sub-model to obtain a candidate scoring model; Obtaining the model scoring accuracy of the candidate scoring model; The candidate scoring models are screened according to the model scoring accuracy to obtain the clue scoring model.

4. The method according to claim 1, wherein The updating of the model parameters of the first preset scoring model according to the sample business leads and the sample lead scores to obtain a first scoring sub-model includes: Scoring the sample business leads using the first preset scoring model to obtain a fourth predicted lead score; performing loss calculation based on the fourth predicted clue score and the sample clue score to obtain fourth loss data; The model parameters of the first preset scoring model are updated according to the fourth loss data to obtain the first scoring sub-model.

5. A clue following device, characterized in that: The device comprises: Acquisition module, used to obtain business leads and lead scoring models; A scoring module, configured to score the business operation leads using the lead scoring model to obtain a target lead score; A sorting module, configured to sort the business operation leads according to the target lead scores to obtain a lead sorting order; A classification module, configured to classify the business operation leads according to the lead sorting order to obtain qualified leads and unqualified leads; A first follow-up module, configured to follow up the qualified leads according to the lead sorting order; a second follow-up module, configured to obtain the lead follow-up intention of the unqualified lead and follow up the unqualified lead according to the lead follow-up intention; The device is also used for: Obtaining sample business leads and sample lead scores; updating model parameters of a first preset scoring model based on the sample business leads and the sample lead scores to obtain a first scoring sub-model; updating model parameters of a second preset scoring model based on the sample business leads and the first scoring sub-model to obtain a second scoring sub-model; both the first scoring sub-model and the second scoring sub-model are decision trees; determining the lead scoring model based on the first scoring sub-model and the second scoring sub-model; Obtaining model weight data of the second scoring sub-model; performing model weighting on the second scoring sub-model according to the model weight data to obtain a reference scoring sub-model; performing model aggregation on the first scoring sub-model and the reference scoring sub-model to obtain the clue scoring model; The sample business leads are scored using the first scoring sub-model to obtain a second predicted lead score; the loss is calculated based on the second predicted lead score and the sample lead score to obtain second loss data; the sample business leads are scored using the second scoring sub-model to obtain a third predicted lead score; the third loss data is determined based on the second loss data, the third predicted lead score and the preset model weight; the preset model weight is updated based on the third loss data to obtain the model weight data.

6. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 4 when executing the computer program.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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