Sales clue issuing method and device, electronic equipment and storage medium

Abstract

The invention provides a sales clue issuing method and device, electronic equipment and a storage medium, and the method comprises the steps: recognizing sales clues in a plurality of data sources, and obtaining a plurality of pieces of sales clue data; determining a clue label of each piece of sales clue data, and constructing a feature set of each piece of sales clue data based on the clue label; determining a feature tag of each salesperson, and constructing a feature set of each salesperson based on the feature tags; matching the feature set of each piece of sales clue data with the feature set of each salesman to determine a matching result; and based on the matching result, distributing the sales clue data to corresponding sales personnel. Based on identification of sales clues in a plurality of data sources, determination of labels, construction of a feature set and analysis of sales personnel features, efficient clue matching and distribution can be realized, and the working efficiency of sales is improved.

Description

Technical Field

[0001] The present invention relates to the field of computer information processing technology, and in particular to a sales lead sending method, device, electronic device and storage medium. Background Art

[0002] In the contemporary digital marketing field, there are various channels for promotion and customer acquisition. After obtaining a large number of promotion leads through various forms and channels, the characteristics of the leads vary greatly among the massive leads. It is crucial to achieve accurate matching with sales and quickly issue a large number of leads.

[0003] The existing matching process of lead distribution in the marketing field is generally based on round-robin distribution, round-robin distribution based on sales performance, distribution based on geographical areas, etc., which rely on manual intervention and human subjective consciousness. The existing distribution method has the problem of taking a long time when distributing sales leads in batches, and cannot achieve a fast distribution process. Summary of the invention

[0004] The present invention provides a sales lead sending method, device, electronic device and storage medium, which are used to solve the defect of long time consumption when sending sales leads in batches in the prior art, and realize the rapid sending of sales leads.

[0005] The present invention provides a sales lead sending method, comprising the following steps: Identify sales leads from multiple data sources to obtain multiple sales lead data; Determine a clue tag for each sales clue data, and construct a feature set for each sales clue data based on the clue tag, wherein the clue tag is used to characterize key content of the sales clue data; Determine a feature tag of each salesperson, and construct a feature set of each salesperson based on the feature tag, wherein the feature tag is used to characterize the sales feature of the salesperson; Matching the feature set of each sales lead data with the feature set of each salesperson to determine a matching result; Based on the matching results, the sales lead data are distributed to corresponding sales personnel.

[0006] According to a sales lead distribution method provided by the present invention, the step of determining a lead label for each sales lead data includes: Determine the word frequency information in each sales lead data based on the word frequency-inverse text index TF-IDF algorithm; Based on the word frequency information, a lead label of each sales lead data is determined.

[0007] According to a sales lead delivery method provided by the present invention, matching the feature set of each sales lead data with the feature set of each salesperson to determine a matching result includes: Based on a support vector machine algorithm, the feature set of each sales lead data is matched with the feature set of each salesperson to determine a matching result.

[0008] According to a sales lead distribution method provided by the present invention, the sales lead data is distributed to corresponding sales personnel based on the matching result, including: Based on a predefined sharding strategy, each sales lead data is sharded to obtain sharded sales lead data; Based on the matching results, the segmented sales lead data is distributed to corresponding sales personnel.

[0009] According to a sales lead delivery method provided by the present invention, the sales leads in multiple data sources are identified to obtain multiple sales lead data, including: Determine the keyword information corresponding to the sales leads; Based on the keyword information, data in multiple data sources are identified to determine multiple sales lead data.

[0010] According to a sales lead delivery method provided by the present invention, after identifying sales leads from multiple data sources and obtaining multiple sales lead data, the method further includes: Based on the similarity between each sales lead data, duplicate data is determined from the multiple sales lead data, and the duplicate data is deleted.

[0011] The present invention also provides a sales lead sending device, comprising the following modules: A sales lead acquisition module is used to identify sales leads from multiple data sources and obtain multiple sales lead data; A clue feature set determination module, used to determine a clue tag of each sales clue data, and construct a feature set of each sales clue data based on the clue tag, wherein the clue tag is used to characterize the key content of the sales clue data; A sales feature set determination module, used to determine a feature tag of each salesperson, and to construct a feature set of each salesperson based on the feature tag, wherein the feature tag is used to characterize the sales feature of the salesperson; A matching module, used to match the feature set of each sales lead data with the feature set of each salesperson to determine a matching result; A distribution module is used to distribute the sales lead data to corresponding sales personnel based on the matching results.

[0012] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the sales lead delivery method described in any one of the above methods is implemented.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the sales lead delivery method as described in any one of the above is implemented.

[0014] The present invention also provides a computer program product, including a computer program, wherein when the computer program is executed by a processor, the sales lead sending method as described in any one of the above is implemented.

[0015] The sales lead distribution method, device, electronic device and storage medium provided by the present invention realize the automatic distribution process of sales leads by matching the feature set of the determined sales lead data with the feature set of the salesperson, determining the matching result, and distributing each sales lead data to the corresponding salesperson based on the matching result. Based on the identification of sales leads in multiple data sources, the determination of labels, the construction of feature sets and the analysis of salesperson characteristics, efficient lead matching and distribution can be achieved, thereby improving sales work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are 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.

[0017] Figure 1 It is a flow chart of the sales lead sending method provided by the present invention.

[0018] Figure 2 It is a schematic diagram of the distribution process provided by the present invention.

[0019] Figure 3 It is a schematic diagram of the slicing process provided by the present invention.

[0020] Figure 4 It is a structural schematic diagram of the sales lead sending device provided by the present invention.

[0021] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Figure 1 It is a flow chart of the sales lead sending method provided by the present invention, such as Figure 1 As shown, the method includes the following: Step 110, identifying sales leads from multiple data sources to obtain multiple sales lead data; Step 120, determining a clue tag of each sales clue data, and constructing a feature set of each sales clue data based on the clue tag, wherein the clue tag is used to characterize key content of the sales clue data; Step 130, determining a feature tag of each salesperson, and constructing a feature set of each salesperson based on the feature tag, wherein the feature tag is used to characterize the sales feature of the salesperson; Step 140, matching the feature set of each sales lead data with the feature set of each salesperson to determine a matching result; Step 150: Distribute the sales lead data to corresponding sales personnel based on the matching results.

[0024] The execution subject of the sales lead delivery method provided by the present invention may be an electronic device, a component in an electronic device, an integrated circuit, or a chip. The electronic device may be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device may be a mobile phone, a tablet computer, a laptop computer, a PDA, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), etc., and the non-mobile electronic device may be a server, a network attached storage (NAS), or a personal computer (PC), etc., which is not specifically limited by the present invention.

[0025] The following takes the method for sending sales leads provided by the present invention executed by a computer as an example to describe the technical solution of the present invention in detail.

[0026] In step 110, sales leads in multiple data sources are identified to obtain multiple sales lead data.

[0027] Sales lead data can come from multiple different data sources, such as customer relationship management (CRM) systems, social media, website registrations, email marketing, customer feedback, etc., and aggregated leads can be obtained through different channels.

[0028] Use data crawling tools and API interfaces to collect and integrate sales lead information from different channels to ensure data integrity and accuracy.

[0029] After obtaining the sales lead information, the sales lead information is cleaned and processed. In this process, the sales lead information needs to be processed and integrated with data sources from other sources, including but not limited to deduplication of sales lead information, identifying the validity of sales lead information, and ensuring that the sales lead information enters the lead pool relatively completely.

[0030] In step 120, a clue tag of each sales clue data is determined, and a feature set of each sales clue data is constructed based on the clue tag, wherein the clue tag is used to characterize the key content of the sales clue data.

[0031] Optionally, keywords of each sales lead data can be automatically extracted through natural language processing technology to generate lead tags. These tags should be able to concisely characterize the key content of the lead, such as industry, product type, customer needs, etc.

[0032] A sales lead data may contain multiple lead tags. Feature extraction is performed on each lead tag, and a feature set is constructed using the multiple features obtained to obtain the feature set of the sales lead data.

[0033] In step 130, a feature tag of each salesperson is determined, and a feature set of each salesperson is constructed based on the feature tag, wherein the feature tag is used to characterize the sales feature of the salesperson.

[0034] The salesperson characteristic tags can include: Sales ability characteristics: such as performance, closing rate, customer satisfaction, etc. Experience and expertise characteristics: such as industry experience, sales field (product type, market type). Communication and service characteristics: such as customer feedback, communication skills, service quality, etc.

[0035] Based on the salesperson's feature labels, a feature set of the salesperson is constructed and properly coded for subsequent analysis.

[0036] Among them, the feature set of sales personnel is determined based on their skill characteristics in order to accurately describe the capabilities and characteristics of each salesperson so as to make more accurate decisions in sales lead allocation, task scheduling and performance analysis.

[0037] Building a feature set based on salesperson skill characteristics is a key step to improve sales management efficiency and intelligence. By properly selecting and encoding skill characteristics, we can accurately describe the capabilities of salespeople, help sales management systems optimize functions such as lead allocation, task scheduling, and performance evaluation, and thus improve the overall performance of the sales team and customer satisfaction.

[0038] In step 140, the feature set of each sales lead data is matched with the feature set of each salesperson to determine a matching result.

[0039] You can calculate the similarity between the sales lead and the salesperson's feature set using methods such as cosine similarity, Euclidean distance, etc. Use supervised learning models (such as classification algorithms) to match leads to people. For example, train a model to predict which salespersons are suitable for which sales leads.

[0040] Calculate the matching score for each sales lead and the salesperson's feature set, and select the salesperson with the highest score as the corresponding lead distribution object.

[0041] In step 150, based on the matching results, the sales lead data are distributed to corresponding sales personnel.

[0042] Based on the matching results determined in step 140 , the sales leads are automatically distributed to corresponding sales personnel.

[0043] Optionally, during the distribution process, priority can be adjusted based on the salesperson's workload, historical performance, etc. to ensure effective distribution of leads.

[0044] Optionally, the specific distribution process can be as follows Figure 2 The distribution process provided by the present invention is shown in the schematic diagram.

[0045] Obtaining aggregated leads through different channels is an important data source, providing a basis for subsequent lead matching and distribution.

[0046] Process and integrate leads with data sources from other sources, including but not limited to deduplication of leads and identification of lead validity, ensuring that leads enter the lead pool relatively completely and obtaining multiple sales lead data.

[0047] Extraction of clue feature portraits. In this process, the cleaned and processed clues are divided into different levels through the rule engine according to the pre-made clue level rules. At the same time, the clues are labeled according to their own identity information and order information, as well as call records, uplink text messages, enterprise WeChat chats and other information to form user features.

[0048] Sales feature extraction and filling. In this process, sales basic information such as sales knowledge level, region, age, dialect proficiency and sales level are combined to form sales features.

[0049] Model training matching results. This model uses the sklearn learning library to integrate user and sales information through large-scale data aggregation operations and support vector machines, and inputs user characteristics and sales characteristics as variables into the trained machine matching model. According to various label information in user characteristics, such as purchased insurance product information, identity information, source channel information, level information and other information, targeted matching and scoring are performed with sales characteristics, and the matching scores of leads and sales are output. The matching degree of leads and sales is obtained based on the specific scores, providing data support for the subsequent lead distribution and sales.

[0050] The sales lead distribution method provided by the present invention matches the feature set of the determined sales lead data with the feature set of the salesperson, determines the matching result, and distributes each sales lead data to the corresponding salesperson based on the matching result, thereby realizing the automatic distribution process of the sales lead. Based on the identification of sales leads in multiple data sources, the determination of labels, the construction of feature sets, and the analysis of salesperson characteristics, efficient lead matching and distribution can be achieved, thereby improving the work efficiency of sales.

[0051] In one embodiment, determining a clue label for each sales clue data includes: determining word frequency information in the sales clue data based on a word frequency-inverse text index TF-IDF algorithm; and determining a clue label for each sales clue data based on the word frequency information.

[0052] TF-IDF is a statistical method used for text analysis to evaluate the importance of a word in a document. It takes into account two factors: term frequency (TF) and inverse document frequency (IDF).

[0053] After cleaning and word segmentation, the frequency of each word in each sales lead data is calculated to form a TF matrix.

[0054] Traverse all sales leads, count the occurrences of each word in the entire document set, and calculate the IDF value.

[0055] Based on the calculated TF and IDF values, a TF-IDF matrix is ​​constructed. Each row of the matrix represents a sales lead, each column represents a word, and the value in the matrix is ​​the TF-IDF value of the corresponding word.

[0056] For each sales lead, select the words with the highest TF-IDF value as the tag of the lead. These words with high TF-IDF value can usually better characterize the key content and theme of the lead.

[0057] Integrate the extracted keywords into clue tags, ensuring that the tags are concise and representative. Consider using text summarization techniques or rules to optimize the generated tags.

[0058] In one embodiment, matching the feature set of each sales lead data with the feature set of each salesperson to determine a matching result includes: matching the feature set of each sales lead data with the feature set of each salesperson based on a support vector machine algorithm to determine a matching result.

[0059] Support Vector Machine (SVM) is a supervised learning algorithm for classification and regression. It achieves the best classification effect by finding an optimal hyperplane to separate different categories in the data set and maximize the interval of the classification boundary (that is, the minimum distance from the support vector to the hyperplane).

[0060] When the features of the data are not linearly separable, SVM can map the data to a higher-dimensional space through a kernel function to achieve linear separability.

[0061] Use the historical data set to train the SVM model. During the training process, the SVM will try to find an optimal hyperplane to separate the matching combinations (i.e., the feature sets of sales leads and salespeople) from the mismatching combinations. Select a suitable kernel function to improve the matching effect of the model. If the relationship between the sales lead and the salesperson's features is more complex, you can choose a nonlinear kernel function, such as the RBF kernel.

[0062] Use the trained SVM model to make predictions. For each pair of sales lead and salesperson feature set, the model will give a match score or match category. Based on the predicted results, the sales lead is assigned to the most suitable salesperson. If the output of the model is a score, a threshold can be set to determine whether it is a match.

[0063] In one embodiment, based on the matching results, the sales lead data are distributed to corresponding sales personnel, including: based on a predefined sharding strategy, each sales lead data is sharded to obtain sharded sales lead data; based on the matching results, the sharded sales lead data is distributed to corresponding sales personnel.

[0064] Sharding is a data distribution strategy that splits a large-scale dataset into multiple smaller parts and then distributes these shards to different processing units.

[0065] Sharding strategy refers to how to split and process sales lead data. Predefined sharding strategies can be designed based on the following criteria: Time sharding: Shard the data by time period based on the time when the lead was generated. Geographic sharding: Shard the lead data to sales personnel in different regions based on the customer's geographical location. Demand sharding: Assign relevant leads to sales personnel with relevant product knowledge based on the customer's demand type.

[0066] The specific sharding process can be as follows Figure 3 The slicing process diagram provided by the present invention is shown as follows: After slicing, based on the allocation result, the slicing sales lead data is distributed to the corresponding sales personnel.

[0067] It is understandable that the standard format of the dispatch clues are sharded according to different allocation strategies, and the size of the shards is comprehensively evaluated in combination with the CPU and memory of the dispatch program, giving full play to the advantages of multi-threading, splitting a large batch of tasks into multiple small tasks and executing them separately, ensuring that a large number of clues can be dispatched in a shorter time, improving the efficiency of clue dispatch and reducing the time spent on dispatch.

[0068] In one embodiment, sales leads in multiple data sources are identified to obtain multiple sales lead data, including: determining keyword information corresponding to the sales leads; and based on the keyword information, identifying data in multiple data sources to determine multiple sales lead data.

[0069] Keywords are simplified expressions of customer needs, behavioral characteristics or interests, and are usually guided by customer behavior or interests.

[0070] To determine the keywords of sales leads, you can extract common keywords from past customer data, sales records, and success stories, and identify high-value keywords based on frequency or conversion rate.

[0071] Once the keywords of sales leads are determined, the next step is to match the keywords in various data sources to identify potential sales lead data.

[0072] Based on semantic analysis technology (such as natural language processing), it can help understand the context of keywords. For example, if a customer mentions "looking for solutions to improve production efficiency" in an article, it can still be a potential lead even if your product or service is not explicitly mentioned. By analyzing the context, you can filter out noise data that is not related to sales leads, thereby improving the accuracy of matching. Identify data from multiple data sources and determine multiple sales lead data.

[0073] In one embodiment, after identifying sales leads from multiple data sources to obtain multiple sales lead data, the method further includes: determining duplicate data from the multiple sales lead data based on similarities between the sales lead data, and deleting the duplicate data.

[0074] Similarity analysis is to evaluate the similarities between different leads in the sales lead data to identify duplicate or similar sales leads. By calculating the similarity score of each lead data, it is possible to determine which leads actually point to the same customer or demand, thereby reducing unnecessary repeated follow-ups.

[0075] Alternatively, the similarity can be measured by calculating the proportion of common words between two strings, which is suitable for comparing longer text descriptions. For each sales lead, the similarity score of each field is calculated using the aforementioned similarity algorithm. After identifying duplicate data, the duplicate data is deleted.

[0076] The sales lead sending device provided by the present invention is described below. The sales lead sending device described below and the sales lead sending method described above can correspond to each other.

[0077] like Figure 4 As shown, the device comprises: The sales lead acquisition module 410 is used to identify sales leads from multiple data sources and obtain multiple sales lead data; A clue feature set determination module 420 is used to determine a clue tag of each sales clue data, and to construct a feature set of each sales clue data based on the clue tag, wherein the clue tag is used to characterize the key content of the sales clue data; The sales feature set determination module 430 is used to determine the feature label of each salesperson and construct a feature set of each salesperson based on the feature label, wherein the feature label is used to characterize the sales feature of the salesperson; A matching module 440, used to match the feature set of each sales lead data with the feature set of each salesperson to determine a matching result; The distribution module 450 is used to distribute the sales lead data to corresponding sales personnel based on the matching results.

[0078] The sales lead distribution device provided by the present invention matches the feature set of the determined sales lead data with the feature set of the salesperson, determines the matching result, and distributes each sales lead data to the corresponding salesperson based on the matching result, thereby realizing the automatic distribution process of the sales lead. Based on the identification of sales leads in multiple data sources, the determination of labels, the construction of feature sets, and the analysis of salesperson characteristics, efficient lead matching and distribution can be achieved, thereby improving the work efficiency of sales.

[0079] In one embodiment, the clue feature set determination module 420 is specifically used to: Determining the lead label of each sales lead data includes: Determine the word frequency information in each sales lead data based on the word frequency-inverse text index TF-IDF algorithm; Based on the word frequency information, a lead label of each sales lead data is determined.

[0080] In one embodiment, the matching module 440 is specifically configured to: The matching of the feature set of each sales lead data with the feature set of each salesperson to determine a matching result includes: Based on a support vector machine algorithm, the feature set of each sales lead data is matched with the feature set of each salesperson to determine a matching result.

[0081] In one embodiment, the distribution module 450 is specifically used to: The distributing each sales lead data to a corresponding salesperson based on the matching result includes: Based on a predefined sharding strategy, each sales lead data is sharded to obtain sharded sales lead data; Based on the matching results, the segmented sales lead data is distributed to corresponding sales personnel.

[0082] In one embodiment, the sales lead acquisition module 410 is specifically used to: The identifying of sales leads from multiple data sources to obtain multiple sales lead data includes: Determine the keyword information corresponding to the sales leads; Based on the keyword information, data in multiple data sources are identified to determine multiple sales lead data.

[0083] In one embodiment, the sales lead acquisition module 410 is further specifically used for: After identifying the sales leads from the multiple data sources to obtain multiple sales lead data, the method further includes: Based on the similarity between each sales lead data, duplicate data is determined from the multiple sales lead data, and the duplicate data is deleted.

[0084] Figure 5 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 5As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530 and a communication bus 540, wherein the processor 510, the communication interface 520 and the memory 530 communicate with each other through the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute the sales lead delivery method, which includes: identifying sales leads in multiple data sources to obtain multiple sales lead data; Determine a clue tag for each sales clue data, and construct a feature set for each sales clue data based on the clue tag, wherein the clue tag is used to characterize key content of the sales clue data; Determine a feature tag of each salesperson, and construct a feature set of each salesperson based on the feature tag, wherein the feature tag is used to characterize the sales feature of the salesperson; Matching the feature set of each sales lead data with the feature set of each salesperson to determine a matching result; Based on the matching results, the sales lead data are distributed to corresponding sales personnel.

[0085] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the 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 a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0086] On the other hand, the present invention further provides a computer program product, the computer program product includes a computer program, the computer program can be stored in a non-transitory computer-readable storage medium, when the computer program is executed by a processor, the computer can execute the sales lead delivery method provided by the above methods, the method includes: identifying sales leads in multiple data sources to obtain multiple sales lead data; Determine a clue tag for each sales clue data, and construct a feature set for each sales clue data based on the clue tag, wherein the clue tag is used to characterize key content of the sales clue data; Determine a feature tag of each salesperson, and construct a feature set of each salesperson based on the feature tag, wherein the feature tag is used to characterize the sales feature of the salesperson; Matching the feature set of each sales lead data with the feature set of each salesperson to determine a matching result; Based on the matching results, the sales lead data are distributed to corresponding sales personnel.

[0087] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the sales lead delivery method provided by the above methods is implemented, and the method includes: identifying sales leads in multiple data sources to obtain multiple sales lead data; Determine a clue tag for each sales clue data, and construct a feature set for each sales clue data based on the clue tag, wherein the clue tag is used to characterize key content of the sales clue data; Determine a feature tag of each salesperson, and construct a feature set of each salesperson based on the feature tag, wherein the feature tag is used to characterize the sales feature of the salesperson; Matching the feature set of each sales lead data with the feature set of each salesperson to determine a matching result; Based on the matching results, the sales lead data are distributed to corresponding sales personnel.

[0088] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0089] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A sales lead delivery method, characterized in that: The method comprises: Identify sales leads from multiple data sources to obtain multiple sales lead data; Determine a clue tag for each sales clue data, and construct a feature set for each sales clue data based on the clue tag, wherein the clue tag is used to characterize key content of the sales clue data; Determine a feature tag of each salesperson, and construct a feature set of each salesperson based on the feature tag, wherein the feature tag is used to characterize the sales feature of the salesperson; Matching the feature set of each sales lead data with the feature set of each salesperson to determine a matching result; Based on the matching results, the sales lead data are distributed to corresponding sales personnel.

2. The sales lead distribution method according to claim 1, characterized in that: Determining the lead label of each sales lead data includes: Determine the word frequency information in each sales lead data based on the word frequency-inverse text index TF-IDF algorithm; Based on the word frequency information, a lead label of each sales lead data is determined.

3. The sales lead distribution method according to claim 1, characterized in that: The matching of the feature set of each sales lead data with the feature set of each salesperson to determine a matching result includes: Based on a support vector machine algorithm, the feature set of each sales lead data is matched with the feature set of each salesperson to determine a matching result.

4. The sales lead distribution method according to claim 1, characterized in that: The distributing each sales lead data to a corresponding salesperson based on the matching result includes: Based on a predefined sharding strategy, each sales lead data is sharded to obtain sharded sales lead data; Based on the matching results, the segmented sales lead data is distributed to corresponding sales personnel.

5. The sales lead delivery method according to claim 1, characterized in that: The identifying of sales leads from multiple data sources to obtain multiple sales lead data includes: Determine the keyword information corresponding to the sales leads; Based on the keyword information, data in multiple data sources are identified to determine multiple sales lead data.

6. The sales lead delivery method according to claim 1, characterized in that: After identifying the sales leads from the multiple data sources to obtain multiple sales lead data, the method further includes: Based on the similarity between each sales lead data, duplicate data is determined from the multiple sales lead data, and the duplicate data is deleted.

7. A sales lead distribution device, characterized in that: include: A sales lead acquisition module is used to identify sales leads from multiple data sources and obtain multiple sales lead data; A clue feature set determination module, used to determine a clue tag of each sales clue data, and construct a feature set of each sales clue data based on the clue tag, wherein the clue tag is used to characterize the key content of the sales clue data; A sales feature set determination module, used to determine a feature tag of each salesperson, and to construct a feature set of each salesperson based on the feature tag, wherein the feature tag is used to characterize the sales feature of the salesperson; A matching module, used to match the feature set of each sales lead data with the feature set of each salesperson to determine a matching result; A distribution module is used to distribute the sales lead data to corresponding sales personnel based on the matching results.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the sales lead sending method as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the sales lead sending method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the sales lead sending method according to any one of claims 1 to 6 is implemented.

Images