CN110348876A - A kind of customer personalized data method for building up of e-commerce website - Google Patents
A kind of customer personalized data method for building up of e-commerce website Download PDFInfo
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- CN110348876A CN110348876A CN201910449614.0A CN201910449614A CN110348876A CN 110348876 A CN110348876 A CN 110348876A CN 201910449614 A CN201910449614 A CN 201910449614A CN 110348876 A CN110348876 A CN 110348876A
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Abstract
The invention discloses a kind of customer personalized data method for building up of e-commerce website, its step includes: to track client by definition event and cookie first with browser client, collects the attribute contact point data, behavior contact point data and participation contact point data of client；Then the personal preference database that analysis forms client is calculated to the data aggregate of collection.The present invention concentrates the attribute of each client, behavior, participation tidal data recovering to a unified personal data, the value of each element in data set, which is individually assessed, is constituted personal level label to determine the summation of these values, form everyone core to segment market with business, client is positioned as one using customer data to segment market, and coordinate to interact, so as to improve advertisement variety of effects index, it reduces client connection cost and improves customer lifelong value, the hobby and demand of client is obtained, in time convenient for the development and successfully of e-commerce.
Description
Technical field
The invention belongs to big data and technical field of Internet information, more particularly to a kind of e-commerce website client
Property data method for building up.
Background technique
Data are personalized power, how using individual client's data be successful personalization and waste an opportunity between difference
It is different.Each attribute of individual client's data is usually stored in different positions in e-commerce industry at present, such as it
It can reside in crm system, order fulfillment system, data warehouse, marketing automation system, in personalized platform.Keep storage
The atrophy in isolated island of these attribute datas is generally meant that in the attribute of different location, they cannot be used for personalization, Bu Nengji
When grasp the hobby and demand of each client, the development for being unfavorable for e-commerce is implemented.
Therefore, the emphasis how to solve the above problems as those skilled in the art's research.
Summary of the invention
It is an object of the invention to provide a kind of customer personalized data method for building up of e-commerce website, can be fully solved
In place of above-mentioned the deficiencies in the prior art.
The purpose of the present invention is realized by following technical proposals:
1. a kind of customer personalized data method for building up of e-commerce website, which comprises the following steps:
1) to the data collection of client:
Client is tracked by definition event and cookie using browser client, collects the attribute contact points of client
According to, behavior contact point data and participate in contact point data；
2) the personal preference database that analysis forms client is calculated to the data aggregate collected in step 1).
2. the customer personalized data method for building up of a kind of e-commerce website according to claim 1, feature exist
In: attribute contact point data collection in step 1) method particularly includes:
11) point data is contacted by the attribute that browser client obtains client, attribute contact point data include client
Manage position, customer conducts industry, source and customer type；
12) to it is above-mentioned 11) in data clean, reject invalid data；
13) processing that labels is carried out to above-mentioned 12) the middle data obtained；
14) the attribute contact point data set for being integrally formed client is carried out to above-mentioned 13) the middle data obtained.
3. the customer personalized data method for building up of a kind of e-commerce website according to claim 1, feature exist
In: behavior contact point data collection in step 1) method particularly includes:
101) point data is contacted by the behavior that browser client obtains client, behavior contact point data include that client searches
Rope content, client, which click, checks that the commodity of shopping cart are added in content, client and client clicks Email；
102) to it is above-mentioned 101) in data clean, reject invalid data；
103) processing that labels is carried out to above-mentioned 102) the middle data obtained；
104) the behavior contact point data set for being integrally formed client is carried out to above-mentioned 103) the middle data obtained.
4. the customer personalized data method for building up of a kind of e-commerce website according to claim 1, feature exist
In: contact point data collection is participated in step 1) method particularly includes:
1001) point data is contacted by the participation that browser client obtains client, participating in contact point data includes client
Number of clicks, client browse the time of certain commodity, client to comment area check and language and characters between client and businessman
Consulting exchange；
1002) to it is above-mentioned 1001) in data clean, reject invalid data；
1003) processing that labels is carried out to above-mentioned 1002) the middle data obtained；
1004) the participation contact point data set for being integrally formed client is carried out to above-mentioned 1003) the middle data obtained.
5. the customer personalized data method for building up of a kind of e-commerce website according to claim 1, feature exist
In: the collection of attribute contact point data is for understanding the personal portrait of client in step 1)；The collection that behavior contacts point data is used
In identification the benefit of client demand；The collection of contact point data is participated in for grasping the real point of interest of client.
6. the customer personalized data method for building up of a kind of e-commerce website according to claim 1, feature exist
In: the specific method of data aggregate calculating analysis includes: in step 2)
21) the had point of contact segment type collected in unified step 1)；
22) classify to the contact point data collected in step 1)；
23) extraction step 1) in collect contact point data relevant information；
24) relevant information of the addition in addition to the contact point data being collected into step 1)；
25) third party Aggregations API is called to carry out polymerization calculating, statistical analysis forms database.
7. the customer personalized data method for building up of a kind of e-commerce website according to claim 6, feature exist
In: contact point data is classified in step 22), and class categories include: product class data, interaction class data and voice class number
According to.
Compared with prior art, the beneficial effects of the present invention are:
The present invention concentrates the attribute of each client, behavior, participation tidal data recovering to a unified personal data, data
The value for each element concentrated, which is individually assessed, is constituted personal level label to determine the summation of these values, forms everyone
Client is positioned as one using customer data and segmented market, and coordinates to interact by the core to segment market with business, so as to improve
Advertisement variety of effects index reduces client connection cost and simultaneously improves customer lifelong value, obtain in time client hobby and
Demand, convenient for the development and successfully of e-commerce.
Detailed description of the invention
Fig. 1 is the collection process schematic of attribute contact point data；
Fig. 2 is the collection process schematic of behavior contact point data；
Fig. 3 is the collection process schematic for participating in contact point data；
Fig. 4 is data aggregate process of calculation analysis schematic diagram.
Specific embodiment
The present invention is further illustrated with attached drawing combined with specific embodiments below.
As shown in Figures 1 to 4, the customer personalized data method for building up of a kind of e-commerce website, comprising the following steps:
1) client is tracked by definition event and cookie using browser client, collects the attribute contact points of client
According to, behavior contact point data and participate in contact point data；
Wherein the collection of attribute contact point data is usually obtained by system database, network interface, which includes client
Geographical location, customer conducts industry, source and customer type are logged in, wherein source can be the data collected from different web sites
Information, such as client log in the log-on message that Taobao, Jingdone district or other e-commerce websites leave, and customer type includes client's property
Not, the age；Behavior contact point data and participation contact point data are usually obtained by system log；Behavior contact point data include visitor
Family search content, client click the Email for checking that the commodity of shopping cart are added in content, client and client clicks, wherein one
A little electric business platforms can be movable to pushes customer product introduction and recent some businessmans by way of Email, what client clicked
Email can therefrom know whether client is interested in these push；Participate in contact point data include client's number of clicks,
Client browse the time of certain commodity, client to comment area check and between client and businessman language and characters consulting exchange,
Wherein client's number of clicks refers to that client to the number of checking of commodity, reflects client to the parent of the product to a certain extent
It looks at, while some hesitate again；
For attribute contact point data collection method particularly includes:
11) point data is contacted by the attribute that browser client obtains client；
12) to it is above-mentioned 11) in data clean, reject invalid data, data cleansing is existed in initial data
Mistake, missing, singular value the problems such as found and corrected, make they meet the quality of data requirement, the main work of data cleansing
Work be data incompleteness value processing, generally for missing data use mean value Shift Method, by the attribute of variable be divided into numeric type and
Nonumeric type is respectively processed, if missing values are numeric types, just according to the variable in other all object values
Average value fills the variate-value of the missing, if missing values are non-numeric types, according to the mode principle in statistics, with this
Variable carrys out the variate-value of the polishing missing in the most value of the value number of this user；
13) processing that labels is carried out to above-mentioned 12) the middle data obtained, it can according to the label that attribute contact point data provides
To be similar to teenager's male/female, middle aged male/female, old male/female etc.；
14) the attribute contact point data set for being integrally formed client is carried out to above-mentioned 13) the middle data obtained.
Similarly, for behavior contact point data collection method particularly includes:
101) point data is contacted by the behavior that browser client obtains client；
102) to it is above-mentioned 101) in data clean, reject invalid data；
103) processing that labels is carried out to above-mentioned 102) the middle data obtained, the label that point data provides is contacted according to behavior
System, Japan and Korea S style can be similar to, U.S.A is style, sports style, Casual Style；
104) the behavior contact point data set for being integrally formed client is carried out to above-mentioned 103) the middle data obtained.
Similarly, for participating in contact point data collection method particularly includes:
1001) point data is contacted by the participation that browser client obtains client；
1002) to it is above-mentioned 1001) in data clean, reject invalid data,
1003) processing that labels is carried out to above-mentioned 1002) the middle data obtained, the mark provided according to participation contact point data
Label can be strong, general, indifferent to or irresolute for purchase intention；
1004) the participation contact point data set for being integrally formed client is carried out to above-mentioned 1003) the middle data obtained.
2) the personal preference database that analysis forms client is calculated to the data aggregate collected in step 1)；
Realize what data aggregate calculated method particularly includes:
21) the had point of contact segment type collected in unified step 1), the purpose of this step is to convert data to
Computable value type, using to main method have: 1) nominal data standardize.For nominal datas such as place, genders,
Value type is converted into be standardized.2) identifier data is handled.User ID, practical judgment ID in analysis of the invention simultaneously
It is not involved in calculating, only using them as identifier, therefore can be without processing.3) classification type data normalization.Some data
Itself has certain data rule, belongs to classification type, then needs to encode using category feature, be converted into and can be used for calculating
Numerical value.4) numerical information standardizes.Numerical information standardization is mainly that the data of logarithm Value Types are standardized.5) also
The method for having some data to need to use feature binaryzation is pre-processed.The process of feature binarization threshold is by numeric type number
0/1 is converted data to by the way that a threshold value is arranged according to the two-value data for being converted into Boolean type；
22) classify to the contact point data collected in step 1), the different information reflected according to data, by data
It is divided into；Product class data, interaction class data and voice class data, product class data mainly store customer information and client's correspondence is looked into
The product seen, interaction class data mainly store the text information that client exchanges with businessman, voice class data mainly store client and
The speech exchange information of businessman；
23) extraction step 1) in collect contact point data relevant information, due to use big data acquisition technique, these
Include the relevant information of client interests in data, also include the incoherent information of client interests, for example client repairs data
Change the data that front and back stores, the data stored before modifying are the incoherent information of client interests, and the purpose of this step is filtering
Incoherent system data is analyzed with user interest, reduces subsequent data analysis expense, the method for use is mainly data screening
Common vertical filtering method and horizontal screening method；
24) relevant information of the addition in addition to the contact point data being collected into step 1), this step mainly pass through manually
The mode of input will be in the relevant information input database other than the contact point data that be collected into；
25) third party Aggregations API is called to carry out polymerization calculating, statistical analysis forms database.
By the above method, by the attribute of each client, behavior participates in tidal data recovering to a unified personal data collection
In, the value of each element in data set, which is individually assessed, is constituted personal level label to determine the summation of these values, is formed
Client is positioned as one using customer data and segmented market, and coordinates to interact by everyone core to segment market with business,
It so as to improve advertisement variety of effects index, reduces client connection cost and improves customer lifelong value, convenient for grasping customer demand
And hobby, conducive to the development of e-commerce.For example, if the geographical location of client is auspicious in the data acquisition system of a people
Scholar, the product of search are anoraks, click 10 search results, have arranged sequence by price, the residence time is greater than 3 minutes, sees also
Comment, that then thinks that his label is that desire to purchase anorak but wavering to price.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within mind and principle.
Claims (7)
1. a kind of customer personalized data method for building up of e-commerce website, which comprises the following steps:
1) to the data collection of client:
Client is tracked by definition event and cookie using browser client, collects attribute the contact point data, row of client
For contact point data and participate in contact point data；
2) polymerization is carried out to the data collected in step 1) and calculates the personal preference database that analysis forms client.
2. the customer personalized data method for building up of a kind of e-commerce website according to claim 1, it is characterised in that: step
It is rapid 1) in attribute contact point data collection method particularly includes:
11) point data is contacted by the attribute that browser client obtains client, attribute contact point data include client geographic position
It sets, customer conducts industry, source and customer type；
12) to it is above-mentioned 11) in data clean, reject invalid data；
13) processing that labels is carried out to above-mentioned 12) the middle data obtained；
14) the attribute contact point data set for being integrally formed client is carried out to above-mentioned 13) the middle data obtained.
3. the customer personalized data method for building up of a kind of e-commerce website according to claim 1, it is characterised in that: step
It is rapid 1) in behavior contact point data collection method particularly includes:
101) point data is contacted by the behavior that browser client obtains client, behavior contact point data include in custom search
Hold, client clicks the Email for checking that the commodity of shopping cart are added in content, client and client clicks；
102) to it is above-mentioned 101) in data clean, reject invalid data；
103) processing that labels is carried out to above-mentioned 102) the middle data obtained；
104) the behavior contact point data set for being integrally formed client is carried out to above-mentioned 103) the middle data obtained.
4. the customer personalized data method for building up of a kind of e-commerce website according to claim 1, it is characterised in that: step
Rapid 1) middle participation contact point data collection method particularly includes:
1001) point data is contacted by the participation that browser client obtains client, participating in contact point data includes client's click
Number, client browse the time of certain commodity, client to comment area check and language and characters between client and businessman are seeked advice from
Exchange；
1002) to it is above-mentioned 1001) in data clean, reject invalid data；
1003) processing that labels is carried out to above-mentioned 1002) the middle data obtained；
1004) the participation contact point data set for being integrally formed client is carried out to above-mentioned 1003) the middle data obtained.
5. the customer personalized data method for building up of a kind of e-commerce website according to claim 1, it is characterised in that: step
It is rapid 1) in attribute contact point data collection for understanding the personal portrait of client；Behavior contacts the collection of point data for identification
The benefit of client demand；The collection of contact point data is participated in for grasping the real point of interest of client.
6. the customer personalized data method for building up of a kind of e-commerce website according to claim 1, it is characterised in that: step
It is rapid 2) in data aggregate calculate analysis specific method include:
21) the had point of contact segment type collected in unified step 1)；
22) classify to the contact point data collected in step 1)；
23) extraction step 1) in collect contact point data relevant information；
24) relevant information of the addition in addition to the contact point data being collected into step 1)；
25) third party Aggregations API is called to carry out polymerization calculating, statistical analysis forms database.
7. the customer personalized data method for building up of a kind of e-commerce website according to claim 6, it is characterised in that: step
Rapid 22) middle contact point data is classified, and class categories include: product class data, interaction class data and voice class data.
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