CN106384253A - Consumption behavior analysis method in bankcard transaction and consumption behavior analysis device thereof - Google Patents
Consumption behavior analysis method in bankcard transaction and consumption behavior analysis device thereof Download PDFInfo
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- CN106384253A CN106384253A CN201610872621.8A CN201610872621A CN106384253A CN 106384253 A CN106384253 A CN 106384253A CN 201610872621 A CN201610872621 A CN 201610872621A CN 106384253 A CN106384253 A CN 106384253A
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- 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
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Abstract
The invention relates to a consumption behavior analysis method in bankcard transaction and a consumption behavior analysis device thereof. The analysis method comprises the steps that statistics of transaction data of a period of transaction time is performed according to the classification basis of the class codes of specially engaged commercial units for card swiping consumption of bankcards within the territory of China so that transaction summarization is acquired, wherein the transaction summarization includes times of transaction of each card in the transaction time period and the number of cards corresponding to card swiping consumption in the transaction time period; the transaction summarization is calculated, and the support degree of each class code in the corresponding range of the transaction times of each card is acquired by utilizing the number of cards corresponding to card swiping consumption in the transaction time period; the confidence of each association rule is determined by utilizing the support degree of each class code according to the association rules; and a strong association rule set is determined according to the confidence of each association rule.
Description
Technical field
The present invention relates to information service field, particularly to a kind of customer-action analysis method in bank card business dealing and
Device.
Background technology
Continuous development with bank card market and the continuous expansion accepting market, the depth to bank card business dealing data analysis
Degree and the inadequate weakness of range become increasingly conspicuous.Preferably the trend of trace analysis bank card business development and the development in market become
Change, in the analysis decision to whole Bank Card Industry, become more and more crucial.
Information centre of Unionpay stores substantial amounts of holder's consumer transaction data, and traditional analysis method is to each in transaction
Feature one by one Macro or mass analysis it is impossible to find the incidence relation between multiple features.And, traditional data analysis framework is
Do not adapt to the requirement under big data environment it is impossible to mass data is carried out with real-time processing and depth excavation.
Content of the invention
The main purpose of the embodiment of the present invention is to propose customer-action analysis method and device in a kind of bank card business dealing,
Substantial amounts of holder's consumer transaction data of information centre of Unionpay storage is excavated, reaches and precisely push consumption to consumer
The purpose of information.
For achieving the above object, the invention provides a kind of customer-action analysis method in bank card business dealing, including：
Class code according to the franchised business in bankcard consumption within Chinese territory for the bank card is classification foundation, one section of friendship of statistics
The transaction data of easy time, obtains transaction and collects；Wherein, described transaction collect including：Every card transaction in time bracket
Stroke count, in time bracket the card number corresponding to bankcard consumption；
Described transaction is collected and calculates, obtain every using the card number corresponding to bankcard consumption in time bracket
The support of each class code in the stroke count respective range of card transaction；
According to correlation rule, determine the confidence level of every correlation rule using the support of each class code described；
Strong association rule collection is determined according to the confidence level of described every correlation rule.
Optionally, in an embodiment of the present invention, the method for described calculating is frequent item set algorithm.
Optionally, in an embodiment of the present invention, the stroke count respective range of described every card transaction includes 80-100 pen
Card transaction, the card transaction more than 500.
Optionally, in an embodiment of the present invention, described Strong association rule integrates and is more than or equal to given threshold as confidence level
The set of correlation rule.
For achieving the above object, present invention also offers customer-action analysis device in a kind of bank card business dealing, including：
Data pre-processing unit, the class code for the franchised business according to bank card bankcard consumption within Chinese territory is
Classification foundation, the transaction data of one section of exchange hour of statistics, obtain transaction and collect；Wherein, described transaction collect including：In transaction
In time period every card transaction stroke count, in time bracket the card number corresponding to bankcard consumption；
Support acquiring unit, calculates for collecting to described transaction, using bankcard consumption in time bracket
Corresponding card number obtains the support of each class code in the stroke count respective range of every card transaction；
Confidence level acquiring unit, for according to correlation rule, the support using each class code described determines every
The confidence level of correlation rule；
Analytic unit, for determining Strong association rule collection according to the confidence level of described every correlation rule.
Optionally, in an embodiment of the present invention, described support acquiring unit collects to described transaction and adopts frequent episode
Set algorithm is calculated.
Optionally, in an embodiment of the present invention, the stroke count phase of every card transaction that described support acquiring unit adopts
Scope is answered to include the card transaction of 80-100 pen, the card transaction more than 500.
Optionally, in an embodiment of the present invention, the Strong association rule that described analytic unit obtains integrates and is more than as confidence level
Set equal to the correlation rule of given threshold.
Technique scheme has the advantages that：
The technical program introduces MPP technology and distributed structure/architecture, meets under transaction big data environment
Require, full dose process and association rule mining can be carried out to mass data.
Brief description
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
Have technology description in required use accompanying drawing be briefly described it should be apparent that, drawings in the following description be only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, acceptable
Other accompanying drawings are obtained according to these accompanying drawings.
Customer-action analysis method flow diagram in a kind of bank card business dealing that Fig. 1 proposes for the embodiment of the present invention；
Customer-action analysis device block diagram in a kind of bank card business dealing that Fig. 2 proposes for the embodiment of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation description is it is clear that described embodiment is only a part of embodiment of the present invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of not making creative work
Embodiment, broadly falls into the scope of protection of the invention.
The operation principle of the technical program is：The present invention utilizes big data technology, by the bank card business dealing number to magnanimity
According to, carry out data prediction, data mining and business and explain and assess, therefrom steady between multinomial feature in excavation bank card business dealing
Determine correlation rule, obtain the hobby of holder's consumer behavior, predict the consumer behavior in holder's future, and propose targetedly
Marketing activity.
Based on above-mentioned operation principle, the embodiment of the present invention proposes a kind of customer-action analysis method in bank card business dealing, such as
Shown in Fig. 1.Including：
Step 101)：Class code according to the franchised business in bankcard consumption within Chinese territory for the bank card is classification foundation,
The transaction data of one section of exchange hour of statistics, obtains transaction and collects；Wherein, described transaction collect including：In time bracket
Every card transaction stroke count, in time bracket the card number corresponding to bankcard consumption；
Step 102)：Described transaction is collected and calculates, using the card corresponding to bankcard consumption in time bracket
Number obtains the support of each class code in the stroke count respective range of every card transaction；
Step 103)：According to correlation rule, the support using each class code described determines every correlation rule
Confidence level；
Step 104)：Strong association rule collection is determined according to the confidence level of described every correlation rule.
In the present embodiment, for step 101, the transaction data to a period of time in advance, according to bank card in
In border, the class code (MCC) of the franchised business of bankcard consumption is classification foundation, and statistics obtains transaction and collects.Transaction collects bag
Include：Every be stuck in the stroke count of transaction in time bracket and in time bracket bankcard consumption card number.For example：80-100
The card of pen and two groups of transaction data of the card more than 500.Transaction data is：In the business of different MCC in this section of exchange hour
Family carries out the data message of bankcard consumption.
In a step 102, using frequent item set algorithm, transaction is collected and calculate, obtain the stroke count phase of every card transaction
The support of each MCC in the range of answering.In the present embodiment, support is：In identical time bracket, a certain MCC's
The transactional cards number that trade company carries out bankcard consumption carries out total transactional cards number of bankcard consumption divided by the trade company in all of MCC.
Collected according to transaction it may be necessary to adjust the threshold value of minimum support in frequent item set analysis, such as：To transaction pen
The card more than 500 for the number, takes the MCC that support is more than 5%, to transaction stroke count in the card of 80-100 pen, takes support to be more than 30%
MCC.
In step 103, according to support, calculate confidence level, see table 1：
Table 1
| Preceding paragraph | Consequent | Support % | Confidence level % |
| 58xx | 54xx | 36.7 | 95.3 |
| 53xx and 58xx | 54xx | 21.9 | 98.0 |
The first row, MCC for the implication that 58xx support is 36.7 is：In certain time bracket, in 58xx class business
The card number of indoor bankcard consumption accounts for the 36.7% of total transactional cards number.In 54xx class business while bankcard consumption in 58xx class trade company
The card number of indoor bankcard consumption accounts for 95.3% of the card number of bankcard consumption in 58xx class trade company.
Second row, accounts for total transactional cards number in the card number of 53xx class trade company and 58xx class trade company bankcard consumption simultaneously
21.9%, on the basis of 53xx class trade company and 58xx class trade company simultaneously bankcard consumption, and bankcard consumption in 54xx class trade company
Card number account for 53xx class trade company and 58xx class trade company simultaneously the card number of bankcard consumption 98%.
At step 104, Strong association rule collection is determined according to the confidence level of described every correlation rule.According to practical experience
Value, confidence level is more than the correlation rule of a certain threshold value as Strong association rule.
In the present embodiment, using confidence level be more than 90% correlation rule as Strong association rule.In Table 1,58xx →
54xx and 53xx+58xx → 54xx is Strong association rule.
That is, after 58xx class trade company bankcard consumption, up to 95.3% probability enters 54xx class trade company and swipes the card
Consumption, after completing 58xx class trade company bankcard consumption, the server of Service Market department to consumer precisely send with regard to
The consumption information of 54xx class trade company, guide and stimulate consumer bankcard consumption.
From above-described embodiment, the technical program obtains the hobby of holder's consumer behavior, predicts holder in the future
Consumer behavior, and targetedly marketing activity is proposed, thus reducing operating cost and improving the double of enterprise competitiveness
Weight purpose.
As shown in Fig. 2 customer-action analysis device block diagram in a kind of bank card business dealing proposing for the embodiment of the present invention.Bag
Include：
Data pre-processing unit 201, for the classification generation of the franchised business in bankcard consumption within Chinese territory according to bank card
Code is classification foundation, the transaction data of one section of exchange hour of statistics, obtains transaction and collects；Wherein, described transaction collect including：?
In time bracket every card transaction stroke count, in time bracket the card number corresponding to bankcard consumption；
Support acquiring unit 202, calculates for collecting to described transaction, is disappeared using swiping the card in time bracket
The corresponding card number of expense obtains the support of each class code in the stroke count respective range of every card transaction；
Confidence level acquiring unit 203, for according to correlation rule, being determined every using the support of each class code described
The confidence level of bar correlation rule；
Analytic unit 204, for determining Strong association rule collection according to the confidence level of described every correlation rule.
One of ordinary skill in the art will appreciate that realizing all or part of flow process in above-described embodiment method, Ke Yitong
Cross computer program to complete come the hardware to instruct correlation, described program can be stored in general computer read/write memory medium
In, this program is upon execution, it may include as the flow process of the embodiment of above-mentioned each method.Wherein, described storage medium can be magnetic
Dish, CD, read-only memory (Read-Only Memory, ROM) or random access memory (Random Access
Memory, RAM) etc..
Those skilled in the art are it will also be appreciated that various functions that the embodiment of the present invention is listed are by hardware or soft
Part is realizing the design requirement depending on specific application and whole system.Those skilled in the art can be for every kind of specific
Application, it is possible to use various methods realize described function, but this realization is understood not to protect beyond the embodiment of the present invention
The scope of shield.
Apply specific embodiment in the present invention principle of the present invention and embodiment are set forth, above example
Explanation be only intended to help and understand the method for the present invention and its core concept；Simultaneously for one of ordinary skill in the art,
According to the thought of the present invention, all will change in specific embodiments and applications, in sum, in this specification
Hold and should not be construed as limitation of the present invention.
Above specific embodiment, has been carried out further specifically to the purpose of the present invention, technical scheme and beneficial effect
Bright, be should be understood that the specific embodiment that these are only the present invention, the protection model being not intended to limit the present invention
Enclose, all any modification, equivalent substitution and improvement within the spirit and principles in the present invention, done etc., should be included in the present invention
Protection domain within.
Claims (8)
1. in a kind of bank card business dealing customer-action analysis method it is characterised in that include：
Class code according to the franchised business in bankcard consumption within Chinese territory for the bank card is classification foundation, during one section of transaction of statistics
Between transaction data, obtain transaction collect；Wherein, described transaction collect including：The pen of every card transaction in time bracket
Number, in time bracket the card number corresponding to bankcard consumption；
Described transaction is collected and calculates, obtain every card using the card number corresponding to bankcard consumption in time bracket and hand over
The support of each class code in easy stroke count respective range；
According to correlation rule, determine the confidence level of every correlation rule using the support of each class code described；
Strong association rule collection is determined according to the confidence level of described every correlation rule.
2. the method for claim 1 is it is characterised in that the method for described calculating is frequent item set algorithm.
3. the method for claim 1 is it is characterised in that the stroke count respective range of described every card transaction includes 80-100
The card transaction of pen, the card transaction more than 500.
4. the method for claim 1 sets threshold it is characterised in that described Strong association rule integrates to be more than or equal to as confidence level
The set of the correlation rule of value.
5. in a kind of bank card business dealing customer-action analysis device it is characterised in that include：
Data pre-processing unit, the class code for the franchised business in bankcard consumption within Chinese territory according to bank card is classification
Foundation, the transaction data of one section of exchange hour of statistics, obtain transaction and collect；Wherein, described transaction collect including：In exchange hour
Section in every card transaction stroke count, in time bracket the card number corresponding to bankcard consumption；
Support acquiring unit, calculates for collecting to described transaction, and using bankcard consumption in time bracket, institute is right
The card number answered obtains the support of each class code in the stroke count respective range of every card transaction；
Confidence level acquiring unit, for according to correlation rule, determining every association using the support of each class code described
The confidence level of rule；
Analytic unit, for determining Strong association rule collection according to the confidence level of described every correlation rule.
6. device as claimed in claim 5 is it is characterised in that described support acquiring unit collects to described transaction using frequency
Numerous set algorithm is calculated.
7. device as claimed in claim 5 is it is characterised in that every of the employing of described support acquiring unit is blocked the pen concluded the business
Number respective range includes the card transaction of 80-100 pen, the card transaction more than 500.
8. device as claimed in claim 5 is it is characterised in that the Strong association rule that described analytic unit obtains integrates as confidence level
Set more than or equal to the correlation rule of given threshold.
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| CN111221885A (en) * | 2020-01-06 | 2020-06-02 | 中国银联股份有限公司 | Method and system for calculating data ranking |
| CN111951035A (en) * | 2019-05-17 | 2020-11-17 | 上海树融数据科技有限公司 | Consumption analysis method, system, device and consumption analysis platform |
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