CN108520469A - A kind of user based on electric business platform purchases behavior analysis method again - Google Patents
A kind of user based on electric business platform purchases behavior analysis method again Download PDFInfo
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- CN108520469A CN108520469A CN201810629584.7A CN201810629584A CN108520469A CN 108520469 A CN108520469 A CN 108520469A CN 201810629584 A CN201810629584 A CN 201810629584A CN 108520469 A CN108520469 A CN 108520469A
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- repeat buying
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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
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
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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
Abstract
The invention discloses a kind of users based on electric business platform to purchase behavior analysis method again, which is characterized in that including：Choose effective purchaser record of user in statistical time range；Carry out data cleansing；To every effective purchaser record stamp whether be repeat buying label, whether be platform repeat buying label or whether be insurance kind repeat buying label；Count purchase total number of users, repeat buying number of users, the purchase total number of users of each platform, each platform repeat buying total number of users, the repeat buying total number of users of the purchase total number of users of each insurance kind and each insurance kind；Calculate repurchase rate, platform repurchase rate and the insurance kind repurchase rate in statistical time range.The advantageous effect that the present invention is reached is that have intuitive measurement to act on the validity of marketing program, there is intuitive distinguishing hierarchy to the loyalty of client simultaneously, can precisely be released to product insurance kind for enterprise attracts user's purchase active belt to help, the buying rate of purposive raising platform and insurance kind.
Description
Technical field
The present invention relates to electric business the analysis of public opinion fields, and behavior point is purchased again more particularly to a kind of user based on electric business platform
Analysis method.
Background technology
With the development of hyundai electronics business platform, insurance company starts and Internet enterprises and third party insurance platform
Cooperation it is increasingly close, welcome new opportunities and challenges.Internet Insurance Consumption broken traditional insurance sale solicit patrons family,
Pattern is promoted repeatedly, by links such as insurance service insertion purchase, payment, services, agrees with user's demand for insurance, more and more users
Gradually recognize to insure the importance and necessity in life, so that the buying rate of internet insurance products constantly carries
It is high.Correspondingly, being based on electric business platform, the purchase of user produces mass data, passes through the processing in business intelligence field, these numbers
According to will constantly bring new effective information to network sale enterprise, reliable decision support is provided during operation for enterprise,
For example price fixing of numerous insurances in network sale is based on many-sided big data such as customers' credit, operation data, historical behavior.
At the same time, the integral on electric business platform, service, product, which many marketing activities such as completely subtract also, becomes the profit of insurance company's promotion
How device removes the successful of the marketing program of assessment company, how to weigh related letter in the case where current social information is complicated
It is particularly important whether in time, accurately breath reaches target customer.In fact, after user has purchased product, when product ensures the phase
Being limited to the user having when the phase can select to buy again, and the loyalty of this kind of user obviously higher than only buys primary user, therefore,
The method for needing many user behavior analysis such as the platform, the insurance kind that are selected when a kind of repeat buying to user, will be to corporate business
Development brings prodigious help.
Invention content
The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a kind of use based on electric business platform
Behavior analysis method is purchased again in family.
In order to solve the above technical problems, the present invention provides a kind of user based on electric business platform purchases behavior analysis method again,
It is characterized in that , Bao include Yi Xia Bu Sudden：
Step 1. chooses effective purchaser record of user in statistical time range；User is extracted on each platform to each insurance kind
Purchaser record；
Step 2. carries out data cleansing, and the user that filtering order moves back entirely filters duplicate customer；
Step 3. places an order in the case where not differentiating between platform and not differentiating between insurance kind, according to same user same year is in one's duty
Situation, to every effective purchaser record stamp whether be repeat buying label；
Step 4. distinguishing platform but in the case of do not differentiate between insurance kind, according in the same user same time in identical platform
On lower one-state, to every effective purchaser record stamp whether be platform repeat buying label；
Step 5. is in the case where distinguishing insurance kind but not differentiating between platform, according to same user to same danger within the same time
Kind lower one-state, to every effective purchaser record stamp whether be insurance kind repeat buying label；
Step 6. is based on above step, in statistical time range, counts purchase total number of users, repeat buying number of users, each
The repetition of the purchase total number of users of platform, each platform repeat buying total number of users, the purchase total number of users of each insurance kind and each insurance kind
Total number of users is bought, in order to assess the validity of marketing program, in the period specifically marketed, repeat buying number of users is used
Exponential smoothing gives the data of different time different weights, distance battalion based on the history observation before the marketing time
Sell time close repeat buying number to larger weight, distance marketing time remote repeat buying number gives smaller weight, to
Weighting to predict the repeat buying number of users (predicting repeat buying number of users) within the marketing time based on observation；
Step 7. calculates repurchase rate, platform repurchase rate and insurance kind repurchase rate in statistical time range；It is right
It is analyzed in the purchase user on the low purchase platform of purchase rate again, analyzes the cross-platform purchase situation of this kind of user；If with
Cross-platform behavior is not present in family, then samples to pay a return visit or send to this kind of client and recall short message；By the high insurance kind of repurchase rate
Or the product insurance kind or product low with buying rate is made comparisons, and the compensation of content and product is specially ensured from product price, product
It is compared in rate, to position the low reason of buying rate.
In the step 1, the user that moves back entirely of filtering order only retains the user of effective purchase order, in the step 2,
Filtering duplicate customer refers to insurer of the filtering using same certificate number either same cell-phone number or same mailbox, the step
In rapid 6, purchase total number of users also needs the user that filtering duplicate customer and order move back entirely.
In the step 7, definition repurchase rate is as follows, buys and uses shared by the number of users of repeat buying in statistical time range
The ratio of family sum；It is as follows to define platform repurchase rate, it should shared by the number of users of repeat buying on statistical time range inner platform
The ratio of purchase total number of users on platform；It is as follows to define insurance kind repurchase rate, the insurance kind repeat buying in statistical time range
The ratio of the purchase total number of users of the insurance kind shared by number of users.
In the step 3, the behavior for defining repeat buying is as follows, in same year, after user places an order for the first time, in not same date
It places an order again as repeat buying, and the user that duplicate customer and order move back entirely need to be filtered, generate the user of repeated purchase behavior
As repeat buying user.
In the step 4, define platform repeat buying behavior it is as follows, in same year, user some platform for the first time under
Dan Hou, in the repeat buying that not same date places an order in identical platform on the platform again, and need to filter duplicate customer and
The user that order moves back entirely；The user for generating platform repeated purchase behavior is the repeat buying user on the platform.
In the step 5, define insurance kind repeat buying behavior it is as follows, in same year, user to some insurance kind for the first time under
Dan Hou is placed an order again as the repeat buying of the insurance kind to the insurance kind in not same date, and need to filter duplicate customer and order is complete
The user moved back；The user for generating insurance kind repeated purchase behavior is the repeat buying user of the insurance kind.
The advantageous effect that the present invention is reached:
1. the repeat buying situation of user can be fully assessed by the repeat buying index and statistical method that newly define,
There is intuitive measurement to act on the validity of marketing program in enterprise's period, while having intuitive layer to the loyalty of client
The more user of number is purchased in secondary division again, then loyalty is higher；
2. being analyzed for the repeat buying situation of different insurance kinds, different platform upslides warranty family, for purchase rate again
High platform and insurance kind analyzes reason, can precisely be released to product insurance kind for enterprise and user's purchase active belt is attracted to help,
The buying rate of purposive raising platform and insurance kind.
Description of the drawings
Fig. 1 is user's repeat buying analysis process figure of exemplary embodiment of the present invention.
Specific implementation mode
The present invention is further illustrated with exemplary embodiment below in conjunction with the accompanying drawings：
As shown in Figure 1, present invention implementation user's repeat buying analysis flow chart of data processing is as follows：
Step 11. chooses the purchaser record of user in statistical time range, to the data prediction of user's purchase, removal surrender note
Record and invalid record.
Step 12. filters duplicate customer, the i.e. throwing to using same certificate number either same cell-phone number or same mailbox
Guarantor is identified as same user
When different user uses same passport NO. (including identity card, officer's identity card, shield in the front and back product of purchase twice
According to, driver's license, Hong Kong and Taiwan's pass or other) either using same cell-phone number or use same mailbox, can Direct Recognition be same
User 1 in one insurer, such as table 1, user 2 and user 3 should be same purchase user, the added word after user's purchaser record
Section " User ID ", the identification new as user mark.
1 user of table buys information
| User | Buy the date | Mobile phone | Mailbox | Certificate number | Platform | User ID | …… |
| User 1 | 20160103 | 18911122233 | 12345@qq.com | Card1 | web | 1 | |
| User 2 | 20160701 | 13444422277 | test@126.com | Card1 | app | 1 | |
| User 3 | 20161231 | 18911122233 | test@126.com | xxx | web | 1 |
Step 13. is not in the case where differentiating between platform and not differentiating between insurance kind, " User ID " generated using step 12, according to
Same user in the in one's duty lower one-state of same year, to every effective purchaser record stamp whether be repeat buying label, can
New field " rebuy_flag ", 0 indicates non-duplicate purchase, and 1 indicates the user of User ID=1 in repeat buying, such as table 2
Situation is purchased again.
2 user of table purchases information again
Step 14. is in the case where distinguishing platform but not differentiating between insurance kind, " User ID " generated using step 12, according to same
Whether lower one-state of the one user same time in identical platform, it is platform repeat buying to be stamped to every effective purchaser record
Label, can on the basis of step 13 new field " plat_flag " again, 0 indicates no platform repeat buying, and 1 indicates platform
The user platform of User ID=1 in repeat buying, such as table 3 purchases situation again.
3 user platform of table purchases information again
| User ID | Order number | Buy the date | Insurance kind | Platform | rebuy_flag | plat_flag |
| 1 | 11111 | 20150801 | Health insurance | web | 0 | 0 |
| 1 | 11112 | 20160101 | Health insurance | app | 0 | 0 |
| 1 | 11113 | 20160801 | Health insurance | web | 1 | 0 |
| 1 | 11114 | 20160831 | Accident insurance | web | 1 | 1 |
Step 15. is in the case where distinguishing insurance kind but not differentiating between platform, " User ID " generated using step 12, according to same
The one user same time, whether to the lower one-state of same insurance kind it was insurance kind repeat buying to be stamped to every effective purchaser record
Label, can on the basis of step 13, step 14 new field " class_flag " again, 0 indicates non-insurance kind repeat buying, 1 table
Show that the user platform of User ID=1 in insurance kind repeat buying, such as table 4 purchases situation again.
4 user's insurance kind of table purchases information again
| User ID | Order number | Buy the date | Insurance kind | Platform | rebuy_flag | plat_flag | class_flag |
| 1 | 11111 | 20150801 | Health insurance | web | 0 | 0 | 0 |
| 1 | 11112 | 20160101 | Health insurance | app | 0 | 0 | 0 |
| 1 | 11113 | 20160801 | Health insurance | web | 1 | 0 | 1 |
| 1 | 11114 | 20160831 | Accident insurance | web | 1 | 1 | 0 |
Step 16. is based on above step, in statistical time range, counts purchase total number of users, repeat buying number of users, each
The repetition of the purchase total number of users of platform, each platform repeat buying total number of users, the purchase total number of users of each insurance kind and each insurance kind
Buy total number of users.In order to assess the validity of marketing program, in the period specifically marketed, repeat buying number of users is used
Exponential smoothing gives the data of different time different weights, distance battalion based on the history observation before the marketing time
Sell time close repeat buying number to larger weight, distance marketing time remote repeat buying number gives smaller weight, to
Weighting to predict the repeat buying number of users (predicting repeat buying number of users) within the marketing time, such as based on observation
The final repeat buying number of users of fruit is more than prediction repeat buying number of users, then the marketing program in obvious time period is effective
, otherwise in vain
Step 17. calculates repurchase rate, platform repurchase rate and insurance kind repurchase rate in statistical time range, point
Not Deng Yu number of users/purchase total number of users of repeat buying, the purchase on platform on number of users/correspondence platform of repeat buying use
The purchase total number of users of the number of users of family sum and insurance kind repeat buying/correspondence insurance kind.Follow-up business personnel can be for purchase rate again
Purchase customer analysis on low purchase platform, sees the cross-platform purchase situation of this kind of user, if there is no cross-platform by user
Behavior can be directed to this kind of client sampling return visit or send and recall short message.For the high insurance kind of repurchase rate even product
Low with buying rate makes comparisons, and can ensure from product price, product and be compared in content and the loss ratio of product, further be positioned
The low reason of buying rate plays aid decision to follow-up marketing program.
Present invention is mainly used for offer, a kind of user based on electric business platform purchases behavior analysis method again, can reach as follows
Advantageous effect：
1. the repeat buying situation of user can be fully assessed by the repeat buying index and statistical method that newly define,
There is intuitive measurement effect for the validity of marketing program in enterprise's period, while having intuitively to the loyalty of client
Distinguishing hierarchy purchases the more user of number again, then loyalty is higher；
2. being analyzed for the repeat buying situation of different insurance kinds, different platform upslides warranty family, for purchase rate again
High platform and insurance kind analyzes reason, can precisely be released to product insurance kind for enterprise and user's purchase active belt is attracted to help,
The buying rate of purposive raising platform and insurance kind.
Above example does not limit the present invention in any way, every to be made in a manner of equivalent transformation to above example
Other improvement and application, belong to protection scope of the present invention.
Claims (6)
1. a kind of user based on electric business platform purchases behavior analysis method again, it is characterised in that , Bao include Yi Xia Bu Sudden：
Step 1. chooses effective purchaser record of user in statistical time range；That is purchase of the extraction user on each platform to each insurance kind
Record；
Step 2. carries out data cleansing, and the user that filtering order moves back entirely filters duplicate customer；
Step 3. is not in the case where differentiating between platform and not differentiating between insurance kind, according to same user in the in one's duty lower single feelings of same year
Condition, to every effective purchaser record stamp whether be repeat buying label；
Step 4. distinguishing platform but in the case of do not differentiate between insurance kind, according in the same user same time in identical platform
Lower one-state, to every effective purchaser record stamp whether be platform repeat buying label；
Step 5. is in the case where distinguishing insurance kind but not differentiating between platform, according to same user to same insurance kind within the same time
Lower one-state, to every effective purchaser record stamp whether be insurance kind repeat buying label；
Step 6. is based on above step, in statistical time range, counts purchase total number of users, repeat buying number of users, each platform
Purchase total number of users, each platform repeat buying total number of users, the purchase total number of users of each insurance kind and each insurance kind repeat buying
Total number of users, in the period specifically marketed, index is used to repeat buying number of users to assess the validity of marketing program
Exponential smoothing gives the data of different time different weights, when distance is marketed based on the history observation before the marketing time
Between close repeat buying number give larger weight, distance marketing time remote repeat buying number gives smaller weight, to be based on
Observation weights to predict the repeat buying number of users (predicting repeat buying number of users) within the marketing time；
Step 7. calculates repurchase rate, platform repurchase rate and insurance kind repurchase rate in statistical time range；For weight
Purchase user on the low purchase platform of purchase rate analyzes, and analyzes the cross-platform purchase situation of this kind of user；If user is not
There are cross-platform behaviors, then sample to pay a return visit or send to this kind of client and recall short message；By the high insurance kind of repurchase rate or production
The product insurance kind low with buying rate or product are made comparisons, and are specially ensured in content and the loss ratio of product from product price, product
Comparison, to position the low reason of buying rate.
2. a kind of user based on electric business platform as described in claim 1 purchases behavior analysis method again, it is characterised in that：It is described
In step 1, the user that moves back entirely of filtering order only retains the user of effective purchase order, in the step 2, filters duplicate customer
Refer to insurer of the filtering using same certificate number either same cell-phone number or same mailbox, in the step 6, purchase is used
Family sum also needs the user that filtering duplicate customer and order move back entirely.
3. a kind of user based on electric business platform as claimed in claim 2 purchases behavior analysis method again, it is characterised in that：It is described
In step 7, definition repurchase rate is as follows, buys the ratio of total number of users shared by the number of users of repeat buying in statistical time range
Rate；It is as follows to define platform repurchase rate, the purchase on statistical time range inner platform on the platform shared by the number of users of repeat buying
Buy the ratio of total number of users；It is as follows to define insurance kind repurchase rate, in statistical time range shared by the number of users of insurance kind repeat buying
The ratio of the purchase total number of users of the insurance kind.
4. a kind of user based on electric business platform as claimed in claim 3 purchases behavior analysis method again, it is characterised in that：It is described
In step 3, the behavior for defining repeat buying is as follows, in same year, after user places an order for the first time, not same date place an order again as
Repeat buying, and the user that duplicate customer and order move back entirely need to be filtered, the user for generating repeated purchase behavior is repeat buying
User.
5. a kind of user based on electric business platform as claimed in claim 4 purchases behavior analysis method again, it is characterised in that：It is described
In step 4, the behavior for defining platform repeat buying is as follows, and in same year, user is after some platform places an order for the first time, not on the same day
The repeat buying that phase places an order in identical platform on the platform again, and the use that duplicate customer and order move back entirely need to be filtered
Family；The user for generating platform repeated purchase behavior is the repeat buying user on the platform.
6. a kind of user based on electric business platform as claimed in claim 5 purchases behavior analysis method again, it is characterised in that：It is described
In step 5, the behavior for defining insurance kind repeat buying is as follows, in same year, after user places an order for the first time to some insurance kind, not on the same day
Phase places an order again as the repeat buying of the insurance kind to the insurance kind, and need to filter the user that duplicate customer and order move back entirely；It generates
The user of insurance kind repeated purchase behavior is the repeat buying user of the insurance kind.
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Cited By (5)
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| CN109559058A (en) * | 2018-12-12 | 2019-04-02 | 广州蓝深科技有限公司 | A kind of e-commerce user behavioral data analytical technology based on cloud computing |
| CN110399085A (en) * | 2019-07-23 | 2019-11-01 | 广州创力信息科技有限公司 | A kind of mobile phone cloud control analog manual operation's system |
| CN110570233A (en) * | 2019-08-16 | 2019-12-13 | 苏宁云计算有限公司 | User buyback time prediction method and device for e-commerce platform |
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| CN113469730A (en) * | 2021-06-08 | 2021-10-01 | 北京化工大学 | Customer repurchase prediction method and device based on RF-LightGBM fusion model under non-contract scene |
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