CN108182596A - One kind is based on enterprise marketing management method under big data environment - Google Patents
One kind is based on enterprise marketing management method under big data environment Download PDFInfo
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- CN108182596A CN108182596A CN201711406434.1A CN201711406434A CN108182596A CN 108182596 A CN108182596 A CN 108182596A CN 201711406434 A CN201711406434 A CN 201711406434A CN 108182596 A CN108182596 A CN 108182596A
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Abstract
The invention discloses one kind based on enterprise marketing management method under big data environment, include the following steps：The preference profiles data of user's buying behavior are extracted, and by preference profiles data transmission to subscriber information management module；Subscriber information management module carries out preference profiles data the processing extraction interested feature attribute of commodity of user, and registers customers as information and the interested feature attribute of commodity of user is stored to user characteristics library；When user's search commercial articles in website and webpage, decimation blocks transfer the commodity for meeting search result from commodity information database, and the commodity for meeting search result are compared, and comparison result is ranked up by accordance with the interested feature attribute of commodity of user stored in user characteristics library；Filtering module is filtered the result exported in decimation blocks；By treated, ranking results are presented in website and webpage personalized recommendation module.The present invention has the advantages that accuracy is good, working efficiency is high, has a wide range of application.
Description
Technical field
The invention belongs to big data processing technology fields, and in particular to one kind is based on enterprise marketing management under big data environment
Method.
Background technology
As big data application deepens continuously to global all trades and professions, and root, traditional data managing method is
Through no longer meeting the data management demand of enterprise, in future, data managing method is in the acquisition of data, storage, tissue and analysis
Method and utilizing etc. for data will all be reformed according to the demand of enterprise's big data, be overturned.People have not known
It has been stepped into unconsciously the big data epoch, big data has become a kind of natural resources with very big potential value, is third time
The cadenza of tide.
Research to big data, is exactly the research of data mining technology to some degree, and data mining technology is
Excavate imply, novel, effective, potentially useful data information in big data, and these information also make sense profit
With.Data mining：The complex process of the interested knowledge of people is extracted or excavated from mass data, these knowledge are submerged
Among magnanimity and noise data.
With the propulsion and development of IT application in enterprises, sales data is suddenly poly- to be increased, since sales data is in business decision
Important function, excavate sales data in useful information be that urgently company solves the problems, such as.Working out can be in big data ring
The data processing method of sales data effective information is excavated under border, correctly using the effective information excavated in mass data
It is also the active demand of enterprise.The sales data forward prediction future sales data included in magnanimity sales data are utilized herein
Trend perceives market situation, grasps market trends, and effective sales data trend is provided with reference to letter to enterprise marketing policymaker
Breath, to produce, marketing and judging that market situation provides decision-making foundation.
Invention content
It is an object of the invention to overcome above-mentioned the deficiencies in the prior art, provide a kind of based on enterprise's pin under big data environment
Sell management method.
One kind is based on enterprise marketing management method under big data environment, which is characterized in that includes the following steps：
1）The new sales data of each area monthly is stored in independent file in historic sales data, and new sales data is pressed
Classification is divided into several data blocks, and data block, which is imported into MAP chart block, is resolvable to key assignments, and key assignments is arranged according to the difference of value
Sequence, the key assignments data after being sorted；
2）Key assignments data are cleaned, the cleaning process for check and remove repetition record, smooth noise, null value filling,
Unified inconsistency record, the inquiry data verification quality of data, generation data cleansing report, according to the cleaning report of generation,
Successive ignition is carried out for data cleansing place not in place and obtains repeated washing, until precision is met the requirements；
3）Key assignments data after cleaning are divided into several data blocks, and the data block carries out k-means clusters again, and it is former to obtain prediction
Beginning data；
4）Stationarity inspection is carried out to prediction initial data, calculus of differences is done for jiggly initial data, after calculus of differences
To the stationarity of checking sequence again, if unstable continue to do calculus of differences to time series, until data qualifier；
5）Qualified prediction initial data substitutes into ARIMA models and calculates, and calculates sales volume of the commodity in a certain region, supply shortage
Rate, coverage rate, same period rate of growth data；
6）Sales volume, supply shortage rate, coverage rate, same period rate of growth data are substituted into grey topological model, are existed so as to predict commodity
Future sales trend in a certain region；
7）Sales management personnel work out production, logistics and advertising campaign plan according to the future sales trend in a certain region.
Preferably, the step 1）In new sales data be the date, sales volume, place, retail price, whether run out of goods.
Preferably, the step 4）In calculus of differences is done to time series after, if data also occur it is unstable, reorganize number
It is deleted according to as white noise data.
Compared with prior art, beneficial effects of the present invention：
The present invention has the advantages that accuracy is good, Neng Gouyou by buying personalized recommendation commodity to user under big data background
Effect improves the sales volume of electric business enterprise；User's buying behavior is extracted and analyzed by way of being handled based on big data,
So as to excavate the interested commodity of user, accurately commercial product recommending is realized, have that working efficiency is high, it is excellent to have a wide range of application
Point.
Description of the drawings
Fig. 1 is the flow diagram of enterprise marketing management method under a kind of environment based on big data of the present invention.
Specific embodiment
Referring to Fig. 1, one kind is based on enterprise marketing management method under big data environment, which is characterized in that includes the following steps：
1）The new sales data of each area monthly is stored in independent file in historic sales data, and new sales data is pressed
Classification is divided into several data blocks, and data block, which is imported into MAP chart block, is resolvable to key assignments, and key assignments is arranged according to the difference of value
Sequence, the key assignments data after being sorted；
2）Key assignments data are cleaned, the cleaning process for check and remove repetition record, smooth noise, null value filling,
Unified inconsistency record, the inquiry data verification quality of data, generation data cleansing report, according to the cleaning report of generation,
Successive ignition is carried out for data cleansing place not in place and obtains repeated washing, until precision is met the requirements；
3）Key assignments data after cleaning are divided into several data blocks, and the data block carries out k-means clusters again, and it is former to obtain prediction
Beginning data；
4）Stationarity inspection is carried out to prediction initial data, calculus of differences is done for jiggly initial data, after calculus of differences
To the stationarity of checking sequence again, if unstable continue to do calculus of differences to time series, until data qualifier；
5）Qualified prediction initial data substitutes into ARIMA models and calculates, and calculates sales volume of the commodity in a certain region, supply shortage
Rate, coverage rate, same period rate of growth data；
6）Sales volume, supply shortage rate, coverage rate, same period rate of growth data are substituted into grey topological model, are existed so as to predict commodity
Future sales trend in a certain region；
7）Sales management personnel work out production, logistics and advertising campaign plan according to the future sales trend in a certain region.
The step 1）In new sales data be the date, sales volume, place, retail price, whether run out of goods.
The step 4）In calculus of differences is done to time series after, if data also occur it is unstable, reorganize data conduct
White noise data are deleted.
Technical solution of the present invention is exemplarily described invention above in conjunction with attached drawing, it is clear that present invention specific implementation
It is not subject to the restrictions described above, as long as the various unsubstantialities for employing inventive concept and technical scheme of the present invention progress change
Into or it is not improved the design of invention and technical solution are directly applied into other occasions, in protection scope of the present invention
Within.
Claims (3)
1. one kind is based on enterprise marketing management method under big data environment, which is characterized in that includes the following steps：
1）The new sales data of each area monthly is stored in independent file in historic sales data, and new sales data is pressed
Classification is divided into several data blocks, and data block, which is imported into MAP chart block, is resolvable to key assignments, and key assignments is arranged according to the difference of value
Sequence, the key assignments data after being sorted；
2）Key assignments data are cleaned, the cleaning process for check and remove repetition record, smooth noise, null value filling,
Unified inconsistency record, the inquiry data verification quality of data, generation data cleansing report, according to the cleaning report of generation,
Successive ignition is carried out for data cleansing place not in place and obtains repeated washing, until precision is met the requirements；
3）Key assignments data after cleaning are divided into several data blocks, and the data block carries out k-means clusters again, and it is former to obtain prediction
Beginning data；
4）Stationarity inspection is carried out to prediction initial data, calculus of differences is done for jiggly initial data, after calculus of differences
To the stationarity of checking sequence again, if unstable continue to do calculus of differences to time series, until data qualifier；
5）Qualified prediction initial data substitutes into ARIMA models and calculates, and calculates sales volume of the commodity in a certain region, supply shortage
Rate, coverage rate, same period rate of growth data；
6）Sales volume, supply shortage rate, coverage rate, same period rate of growth data are substituted into grey topological model, are existed so as to predict commodity
Future sales trend in a certain region；
7）Sales management personnel work out production, logistics and advertising campaign plan according to the future sales trend in a certain region.
It is 2. as described in claim 1 a kind of based on enterprise marketing management method under big data environment, it is characterised in that：The step
Rapid 1）In new sales data be the date, sales volume, place, retail price, whether run out of goods.
It is 3. as described in claim 1 a kind of based on enterprise marketing management method under big data environment, it is characterised in that：The step
Rapid 4）In calculus of differences is done to time series after, if data also occur it is unstable, reorganization data as white noise data deletion.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201711406434.1A CN108182596A (en) | 2017-12-22 | 2017-12-22 | One kind is based on enterprise marketing management method under big data environment |
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| CN201711406434.1A CN108182596A (en) | 2017-12-22 | 2017-12-22 | One kind is based on enterprise marketing management method under big data environment |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110414638A (en) * | 2019-07-17 | 2019-11-05 | 武汉工程大学 | A kind of stereo warehouse management system and management method based on RFID |
| CN113094678A (en) * | 2019-12-23 | 2021-07-09 | 合肥天源迪科信息技术有限公司 | Enterprise information security management system |
Citations (1)
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| CN104298771A (en) * | 2014-10-30 | 2015-01-21 | 南京信息工程大学 | Massive web log data query and analysis method |
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2017
- 2017-12-22 CN CN201711406434.1A patent/CN108182596A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104298771A (en) * | 2014-10-30 | 2015-01-21 | 南京信息工程大学 | Massive web log data query and analysis method |
Non-Patent Citations (1)
| Title |
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| 秦亚辉: "大数据环境下企业销售数据处理方法与市场感知研究", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110414638A (en) * | 2019-07-17 | 2019-11-05 | 武汉工程大学 | A kind of stereo warehouse management system and management method based on RFID |
| CN113094678A (en) * | 2019-12-23 | 2021-07-09 | 合肥天源迪科信息技术有限公司 | Enterprise information security management system |
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