CN108550050A - A kind of user's portrait method based on call center data - Google Patents

A kind of user's portrait method based on call center data Download PDF

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CN108550050A
CN108550050A CN201810256594.0A CN201810256594A CN108550050A CN 108550050 A CN108550050 A CN 108550050A CN 201810256594 A CN201810256594 A CN 201810256594A CN 108550050 A CN108550050 A CN 108550050A
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user
data
portrait
information
neural network
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康彦昌
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Hefei And Jun Longitudinal Data Technology Co Ltd
Shizuishan And Jun Longitudinal Data Technology Co Ltd
Suzhou And Jun Longitudinal Data Technology Co Ltd
Kunshan And Jun Chang Da Data Technology Co Ltd
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Hefei And Jun Longitudinal Data Technology Co Ltd
Shizuishan And Jun Longitudinal Data Technology Co Ltd
Suzhou And Jun Longitudinal Data Technology Co Ltd
Kunshan And Jun Chang Da Data Technology Co Ltd
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Priority to CN201810256594.0A priority Critical patent/CN108550050A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities

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Abstract

In order to solve electric business, the real demand of client cannot be found out well by marketing or overcoming industry in most cases, the disadvantage of customer relationship can not well be handled, the present invention proposes a kind of user's portrait method based on call center data, pass through this method, this method leads to call center's network communication data by extraction, user is obtained to draw a portrait in the self information of special scenes, and accumulative a large amount of more scene communication datas, then the feature degree of user is described, transaction scene carries out retouching art, and itself fancy grade is described and analyzes, then it predicts the portrait of user by analysis result and generates data to take problem, form voice and questionnaire, it is final to realize precision marketing, improve user experience.

Description

A kind of user's portrait method based on call center data
Technical field
The invention belongs to big data digging technology field, more specifically, being related to a kind of based on call center data User's portrait method.
Background technology
Data mining, also known as Knowledge Discovery in Database (Knowledge Discovery from Datebase, letter Claim KDD), it is one and extracts the complex process for excavating the knowledge such as unknown, valuable pattern or rule from mass data.
In electric business, marketing or customer service industry, it usually needs customer relationship is handled by voice communication, but due to existing Electric business, marketing or client's industry it is more strange to the other end of personage of voice, so in most cases cannot be fine The real demand for findding out client, can not well handle customer relationship, and telemarketing now or network marketing then need to lead to Customer relationship can be handled well by crossing voice, solve customer issue, and cannot generate unnecessary controversial issue.
Invention content
In view of this, the present invention proposes a kind of user's portrait method based on call center data, in this way, the party Method leads to call center's network communication data by extraction, obtains user and draws a portrait in the self information of special scenes, and is accumulative a large amount of More scene communication datas, then the feature degree of user is described, scene of merchandising retouch art and itself fancy grade into Row description and analysis, then predict the portrait of user by analysis result and generate data to take problem, form voice and questionnaire, It is final to realize precision marketing, improve user experience.
A kind of user's portrait method based on call center data comprising following steps：
S1：By changing into word to recording, and semantic data is extracted by NLPIR Chinese word segmentation systems；
S2：The data acquired in step S1 are cleaned；
S3：Labeling processing is extracted to the step S2 data cleaned；
S4：Clustering is carried out to the user tag in step S3, forms user's portrait；
S5：Predict Test and the words art of corresponding user is determined according to the analysis result in step S4 to supplement incomplete use Family information material；
S6：The user information data in extraction supplement step S5 is excavated by communication data.
Further, step S1 is comprised the following specific steps that：
S11 first by the time with smaller spaced discrete, and records all calls in the call observation phase；
S12 is acquired the sound recordings and fileinfo of call center, and extracts user information and semantic information；
S13 arranges user information and semantic information, distinguishes user information, contextual data information and user's feelings Thread information.
Further, the voice of phone system acquisition includes：The recording of customer service class, core body class voice, electricity pin class voice, collection Class voice, questionnaire etc..
Further, the user information and semantic information extracted include：State, call mesh and demand point, call when Between, the duration of call, customer site, IO circuits, number field, number information, connect the frequency, operation system information, client identity Deng, satisfaction, name grammatical category information, keyword message：It such as swears at people, complain, quality inspection result, voice, intonation.Wherein, state, logical Words purpose and demand point belong to contextual data information, and operation system information, client identity, number field, number information etc. belong to In user information, and voice, intonation etc. then belong to user emotion information.
Further, step S2 is comprised the following specific steps that：
S21 carries out classification and scene analysis to collected data in S1 first；
S22 then clears up redundancy；
S23, the regularity then occurred to data are analyzed, and abnormal data is reasonably cleared data or change；
Further, step S3 includes the following steps：Keyword is extracted, and generates label, it is related to user for summarizing The data such as hobby, individual character, feature.
Further, using BP neural network is arrived in step S4, which is a kind of multilayer feedforward neural network, It may be implemented from the arbitrary nonlinear mapping for being input to output, have the characteristics that good self-organizing, adaptive.BP neural network It can learn and store a large amount of input-output mode map relationship, without disclosing the mathematics for describing this mapping relations in advance Equation.The learning rules used are to use steepest descent method, and the weights and threshold value of network are constantly adjusted by backpropagation, are made The error sum of squares of network is minimum, terminates study.
Wherein, using with the present invention during, most important formula is as follows in BP neural network, enables P={ P1, P2..., PmIndicate user's portrait set, wherein PiIndicate i-th of user's portrait.Ci={ Pi1, Pi2..., PinIndicate cluster after i-th A label, wherein Pij, indicate CiJ-th of element in classification.After cluster, the condition that respectively classifying meet is Uk I=1Ci =PCm≠Cr, thenCm∩Cr=φ, Min Δ p ∈ Cm, Δ pj ∈ Cr, Δ Cm, (sim (Pi, Pj)) ＞ Max Δs pi, pj ∈ Cm,Based on cluster result, it can be found that numerous The information implied between miscellaneous user's portrait extracts new user's portrait label.
Further, step 4 includes the following steps：
S41 carries out feature information extraction first, from state, call mesh and demand point, air time, the duration of call, visitor Family place, IO circuits, number field, number information, the connection frequency, operation system information, client identity etc., satisfaction, noun Category information, keyword message：It such as swears at people, complain, choosing 11 in quality inspection result, voice, intonation as feature tag, together When choose and carried out questionnaire survey, and marked the user of identity as training object, by 11 feature tags and user Input information of the object as BP neural network；
S42, by the input information of BP neural network, including 11 feature tags and user object as training data and 11 feature tags and user object are input to BP neural network, for training by inspection data when as training data Neural network takes learning rate η=0.3, error criterion ε=0.005 that can obtain trained neural network；
S43, by the input information of BP neural network, including 11 feature tags and user object as inspection data into Row input, judges whether the result that neural network prediction model obtains is accurate.
S44, by optimizing to the continuous study of BP neural network, the accurate output valve of final output, that is, it is corresponding The feature tag of identity, including existing feature tag, and generate unidentified feature tag.
Further, in the BP neural network, BP neural network uses the network topology of network structure 11 × 10 × 1 Structure, neuron function are Sigmoid characteristic functions.
Specific implementation mode
Case 1 is embodied：
A kind of user based on call center data draws a portrait method, using with collection industry, then its include the following steps：
S11 first by the time with smaller spaced discrete, and records all calls in the call observation phase；
S12 is acquired the sound recordings and fileinfo of call center, records and identifies to the collection in calling system Word is changed into, word semanteme, including part-of-speech tagging, name Entity recognition, user-oriented dictionary function are extracted by Chinese word segmentation system Deng, and extract user information and semantic information；
S13 arranges user information and semantic information, distinguishes user information, contextual data information and user's feelings Thread information.
S21 clears up redundancy, such as duplicate message, without semantic information；
S23, the regularity then occurred to data are analyzed, and abnormal data is reasonably cleared data or change；
S3：The data cleaned to step S23 extract keyword, and such as " out of funds also not ", " rich not also " " are put and relied not Also ", " no income ", " phone number usage time ", " arrearage situation ", " subjective attitude ", " user emotion " etc., and generate mark Label, for summarizing the data such as individual character related to user, feature.
S41 carries out feature information extraction first, from state, call mesh and demand point, air time, the duration of call, visitor Family place, IO circuits, number field, number information, the connection frequency, operation system information, client identity etc., satisfaction, noun Category information, keyword message：It such as swears at people, complain, choosing 11 in quality inspection result, voice, intonation as feature tag, together When choose and carried out questionnaire survey, and marked the user of identity as training object, by 11 feature tags and user Input information of the object as BP neural network；
S42, by the input information of BP neural network, including 11 feature tags and user object as training data and 11 feature tags and user object are input to BP neural network, for training by inspection data when as training data Neural network takes learning rate η=0.3, error criterion ε=0.005 that can obtain trained neural network；
S43, by the input information of BP neural network, including 11 feature tags and user object as inspection data into Row input, judges whether the result that neural network prediction model obtains is accurate.
S44, by optimizing to the continuous study of BP neural network, the accurate output valve of final output, that is, it is corresponding The feature tag of identity, including existing feature tag, and generate unidentified feature tag.
S5：Determine that the Predict Test of corresponding user and words art are incomplete to supplement according to the analysis result in step S44 User information data；
S6：The user information data in extraction supplement step S5 is excavated by communication data.
Several embodiments of the invention above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously Cannot the limitation to the scope of the claims of the present invention therefore be interpreted as.It should be pointed out that for those of ordinary skill in the art For, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to the guarantor of the present invention Protect range.Therefore, the protection domain of patent of the present invention should be determined by the appended claims.

Claims (7)

  1. A kind of method 1. user based on call center data draws a portrait comprising following steps：
    S1：By changing into word to recording, and semantic data is extracted by NLPIR Chinese word segmentation systems；
    S2：The data acquired in step S1 are cleaned；
    S3：Labeling processing is extracted to the step S2 data cleaned；
    S4：Clustering is carried out to the user tag in step S3, forms user's portrait；
    S5：Predict Test and the words art of corresponding user is determined according to the analysis result in step S4 to supplement incomplete user's letter Breath data；
    S6：The user information data in extraction supplement step S5 is excavated by communication data.
  2. 2. user's portrait method based on call center data as described in claim 1, it is characterised in that：The step S1 It comprises the following specific steps that：
    S11 first by the time with smaller spaced discrete, and records all calls in the call observation phase；
    S12 is acquired the sound recordings and fileinfo of call center, and extracts user information and semantic information；
    S13 arranges user information and semantic information, distinguishes user information, contextual data information and user emotion letter Breath.
  3. 3. user's portrait method based on call center data as described in claim 1, it is characterised in that：The step S2 It comprises the following specific steps that：
    S21 carries out classification and scene analysis to collected data in S1 first；
    S22 then clears up redundancy；
    S23, the regularity then occurred to data are analyzed, and abnormal data is reasonably cleared data or change.
  4. 4. user's portrait method based on call center data as described in claim 1, it is characterised in that：The step S3 It comprises the following specific steps that：Keyword is extracted, and generates label, it is several for summarizing hobby related to user, individual character, feature etc. According to.
  5. 5. user's portrait method based on call center data as described in claim 1, it is characterised in that：The step S4 It comprises the following specific steps that：
    S41 carries out feature information extraction first, from state, call mesh and demand point, air time, the duration of call, client Point, IO circuits, number field, number information, the connection frequency, operation system information, client identity etc., satisfaction, name part of speech letter Breath, keyword message：It such as swears at people, complain, choosing 11 in quality inspection result, voice, intonation as feature tag, selecting simultaneously It takes and had carried out questionnaire survey, and marked the user of identity as training object, by 11 feature tags and user object Input information as BP neural network；
    S42, by the input information of BP neural network, including 11 feature tags and user object are as training data and inspection 11 feature tags and user object are input to BP neural network by data when as training data, for training nerve Network takes learning rate η=0.3, error criterion ε=0.005 that can obtain trained neural network；
    S43, by the input information of BP neural network, including 11 feature tags and user object carried out as inspection data it is defeated Enter, judges whether the result that neural network prediction model obtains is accurate.
    S44, by the continuous study optimization to BP neural network, the accurate output valve of final output, that is, corresponding identity Feature tag, including existing feature tag, and the unidentified feature tag that generates.
  6. 6. user's portrait method based on call center data as claimed in claim 5, it is characterised in that：It is used in step S4 To BP neural network, the formula of clustering is as follows in the BP neural network, enables P={ P1, P2..., PmIndicate user's portrait collection It closes, wherein PiIndicate i-th of user's portrait.Ci={ Pi1, Pi2..., PinIndicate i-th of label after cluster, wherein Pij, table Show CiJ-th of element in classification.After cluster, the condition that respectively classifying meet is Uk I=1Ci=P
    Based on cluster result, it can be found that hidden between complicated user's portrait The information contained extracts new user's portrait label.
  7. 7. user's portrait method based on call center data as claimed in claim 5, it is characterised in that：The BP nerves In network, it is Sigmoid features that BP neural network, which uses the network topology structure of network structure 11 × 10 × 1, neuron function, Function.
CN201810256594.0A 2018-03-27 2018-03-27 A kind of user's portrait method based on call center data Pending CN108550050A (en)

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CN109711892A (en) * 2018-12-28 2019-05-03 浙江百应科技有限公司 The method for automatically generating client's label during Intelligent voice dialog
CN110717733A (en) * 2019-10-09 2020-01-21 浙江百应科技有限公司 Enterprise work mobile phone intelligent management technology solution
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CN111866292A (en) * 2020-07-20 2020-10-30 普强信息技术（北京）有限公司 Application method of customer label based on voice data

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Application publication date: 20180918