CN113538052A - Brand influence reconstruction method and system based on big data - Google Patents
Brand influence reconstruction method and system based on big data Download PDFInfo
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Abstract
The invention discloses a brand influence reconstruction method and a brand influence reconstruction system based on big data, wherein the method comprises the steps of setting a point of sale of a brand product as a data node, and collecting position information and sales information of the data node; traversing the data nodes according to the position information and the sales information to obtain an optimal node and an optimal neighbor node corresponding to the optimal node; dynamically adjusting the data nodes by analyzing the optimal nodes, the optimal neighbor nodes and the sales information of other data nodes between the optimal nodes and the optimal neighbor nodes; the system comprises a data acquisition module, a data processing and analyzing module, a dynamic adjustment module and a display module, wherein the data acquisition module, the data processing and analyzing module, the dynamic adjustment module and the display module are matched with each other to realize the simulation and the dynamic adjustment of data nodes; the invention realizes dynamic adjustment of the whole sales network through analysis of sales data, realizes reconstruction of brand influence and provides a new technical idea for regional brand construction.
Description
Technical Field
The invention relates to the field of data analysis, in particular to a brand influence reconstruction method and system based on big data.
Background
The primary task of forming brand value power to complete brand marketing is to enable targets and potential customers to frequently see your brand, and powerful channel support is needed to achieve the effect. The penetration and the layout of the regional market are increased, the whole brand is operated in the whole channel as far as possible, and the market share is enlarged; nowadays, the boundaries on the line and under the line become more and more fuzzy, the relationship between the two is more like the double wings of an airplane, a complete system is formed by balanced cooperation, and digitalization is the main technical driving force; in a wide area, if a brand wants to fly farther, the brand needs to perform energizing and deep ploughing of the existing channel, develop and operate a new channel at the same time, and integrate the whole channel to perform universal user operation. The brand needs to establish a global scene experience for consumers, integrate e-commerce platforms of different departments on the line, change the sense of difference brought by different consumption spaces, and establish a comprehensive and three-dimensional brand image in the center of the consumers. The brand establishes a brand data bank based on the universe user portrait to perform user data precipitation, which can become an effective support for brand differentiation competition in the future and establish a massive digital basis for more accurate brand marketing. A brand influence reconstruction method and a brand influence reconstruction system are urgently needed, and the brand influence reconstruction method and the brand influence reconstruction system are used for assisting a product manager in controlling brand operation and enhancing brand influence through analyzing massive digital information.
Disclosure of Invention
In order to solve the prior technical problem, the invention provides a brand influence reconstruction method based on big data, which comprises the following steps,
setting a point of sale of a brand product as a data node, and collecting position information and sales information of the data node;
according to the position information, obtaining a step length by calculating an average value of distances between the data nodes;
traversing the data nodes based on the sales information to obtain a first optimal node and first sales information of the first optimal node;
based on the first optimal node, obtaining a first optimal neighbor node and second sales information of the first optimal neighbor node according to the sales information and the step length;
acquiring third sales information of other data nodes based on the first optimal node and the first node distance from the first optimal node to the first optimal neighbor node;
and dynamically adjusting the data node according to the first sales information, the second sales information and the third sales information.
Preferably, in the process of obtaining the first optimal neighbor node, when the first optimal neighbor node is empty, traversing the data nodes except the first optimal node to obtain the second optimal node and the fourth sales information of the second optimal node; according to the step length, fifth sales information of the second optimal neighbor node and the second optimal neighbor node is obtained;
and dynamically adjusting the data node according to the fourth sales information, the fifth sales information and the third sales information.
Preferably, based on a second node distance from the first optimal node to the second optimal node, the data node closest to the first optimal node is collected, whether the data node is a second optimal neighbor node or a second optimal node is judged, and the data node is dynamically adjusted according to the judgment result.
Preferably, when the data node is a second optimal node, the data node is added based on the second node distance and the step length, and the brand product type of the data node is adjusted according to the first sales information and the fourth sales information.
Preferably, when the data node is a second optimal neighbor node, the data node is added based on a third node distance and a step length from the first optimal node to the second optimal neighbor node, and the brand product type of the data node is adjusted according to the first sales information and the fifth sales information.
Preferably, when the data node is not the second optimal neighbor node or the second optimal node, the data node is added based on the second node distance and the step length, and the brand product type of the data node is adjusted according to the first sales information, the fourth sales information and the fifth sales information.
Preferably, in the process of collecting sales information, at least monthly sales information, quarterly sales information and annual sales information are collected;
dynamically adjusting the data nodes according to the monthly sales information, the quarterly sales information and the annual sales information;
and adding or deleting data nodes according to the annual sales information.
Preferably, during the dynamic adjustment of the data node,
the dynamically adjusted content includes:
adding or deleting data nodes;
adjusting the brand product type of the data node;
and collecting customer satisfaction of the data nodes, and keeping brand influence of the data nodes according to the customer satisfaction, wherein the customer satisfaction comprises customer satisfaction on sales personnel, customer satisfaction on a storefront environment and customer satisfaction on a brand product type.
A big data based brand influence reconstruction system includes,
the data acquisition module is used for acquiring the position information and the sales information of the point of sale of the brand product;
the data processing and analyzing module is used for setting the sales node as a data node and obtaining an optimal node of the data node and an optimal neighbor node corresponding to the optimal node according to the sales information and the position information;
the dynamic adjustment module is used for dynamically adjusting other data nodes between the optimal node and the optimal neighbor node according to the first sales information of the optimal node and the second sales information of the optimal neighbor node to obtain dynamic adjustment information;
and the display module is used for displaying each data node and the position information, the sales information and the dynamic adjustment information corresponding to each data node.
Preferably, the dynamic adjustment module includes, in part,
the brand product type adjusting unit is used for adjusting the brand product type according to the monthly sales information, the quarterly sales information and the annual sales information of the sales information;
and the data node adjusting unit is used for adding or deleting the data nodes according to the annual sales information.
The invention discloses the following technical effects:
the invention realizes dynamic adjustment of the whole sales network through analysis of sales data, realizes reconstruction of brand influence and provides a new technical idea for regional brand construction.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the present invention provides a big data based brand influence reconstructing method, including the following steps,
a brand influence reconstruction method based on big data comprises the following steps,
setting a point of sale of a brand product as a data node, and collecting position information and sales information of the data node;
according to the position information, obtaining a step length by calculating an average value of distances between the data nodes;
traversing the data nodes based on the sales information to obtain a first optimal node and first sales information of the first optimal node;
based on the first optimal node, obtaining a first optimal neighbor node and second sales information of the first optimal neighbor node according to the sales information and the step length;
acquiring third sales information of other data nodes based on the first optimal node and the first node distance from the first optimal node to the first optimal neighbor node;
and dynamically adjusting the data node according to the first sales information, the second sales information and the third sales information.
Further, in the process of obtaining the first optimal neighbor node, when the first optimal neighbor node is empty, traversing the data nodes except the first optimal node to obtain a second optimal node and fourth sales information of the second optimal node; according to the step length, fifth sales information of the second optimal neighbor node and the second optimal neighbor node is obtained;
and dynamically adjusting the data node according to the fourth sales information, the fifth sales information and the third sales information.
Further, based on the second node distance from the first optimal node to the second optimal node, the data node closest to the first optimal node is collected, whether the data node is a second optimal neighbor node or a second optimal node is judged, and the data node is dynamically adjusted according to the judgment result.
Further, when the data node is a second optimal node, adding the data node based on the distance and the step length of the second node, and adjusting the brand product type of the data node according to the first sales information and the fourth sales information.
Further, when the data node is a second optimal neighbor node, adding the data node based on the third node distance and the step length from the first optimal node to the second optimal neighbor node, and adjusting the brand product type of the data node according to the first sales information and the fifth sales information.
Further, when the data node is not the second optimal neighbor node or the second optimal node, adding the data node based on the second node distance and the step length, and adjusting the brand product type of the data node according to the first sales information, the fourth sales information and the fifth sales information.
Further, in the process of collecting sales information, at least monthly sales information, quarterly sales information and annual sales information are collected;
dynamically adjusting the data nodes according to the monthly sales information, the quarterly sales information and the annual sales information;
and adding or deleting data nodes according to the annual sales information.
Further, in the process of dynamically adjusting the data nodes,
the dynamically adjusted content includes:
adding or deleting data nodes;
adjusting the brand product type of the data node;
and collecting customer satisfaction of the data nodes, and keeping brand influence of the data nodes according to the customer satisfaction, wherein the customer satisfaction comprises customer satisfaction on sales personnel, customer satisfaction on a storefront environment and customer satisfaction on a brand product type.
A big data based brand influence reconstruction system includes,
the data acquisition module is used for acquiring the position information and the sales information of the point of sale of the brand product;
the data processing and analyzing module is used for setting the sales node as a data node and obtaining an optimal node of the data node and an optimal neighbor node corresponding to the optimal node according to the sales information and the position information;
the dynamic adjustment module is used for dynamically adjusting other data nodes between the optimal node and the optimal neighbor node according to the first sales information of the optimal node and the second sales information of the optimal neighbor node to obtain dynamic adjustment information;
and the display module is used for displaying each data node and the position information, the sales information and the dynamic adjustment information corresponding to each data node.
Further, the dynamic adjustment module includes,
the brand product type adjusting unit is used for adjusting the brand product type according to the monthly sales information, the quarterly sales information and the annual sales information of the sales information;
and the data node adjusting unit is used for adding or deleting the data nodes according to the annual sales information.
The design idea of the invention is to take the area with the optimal node condition in the whole sale network as the selection basis of the focusing window and search on the whole network by utilizing the algorithm, thereby effectively improving the efficiency while ensuring the focusing precision. Meanwhile, a dynamic balance factor strategy can be introduced; updating the node state; resetting the initialization parameters; the termination condition is checked. Dynamic balance factors are introduced, the step length of the node is adjusted in real time, and the algorithm efficiency is improved; aiming at the problem of local optimal value of the algorithm, after each iteration, the initial parameters are reset according to the obtained optimal solution, so that the focusing real-time performance is effectively enhanced, and higher convergence speed and algorithm precision are ensured; the data used for data analysis in the invention are month data, quarter data and year data, the data characteristics of the month data, the quarter data and the year data have variability necessarily, in the process of data analysis, if a certain data node is always the optimal area, then, the data node is subjected to scale expansion, so that the regional brand competitiveness of the data node is improved, if a certain data node is always or mostly the worst region, the node concentration and whether a plurality of optimal regions exist in the step size range are considered, or in close proximity to, a large-scale data node, which may be deleted if any of the above conditions are met, if the condition is not met, the sales information of the neighbor node of the node is collected, and the sales information is comprehensively analyzed with information such as the brand type, the customer satisfaction degree, the regional brand awareness degree, the node intensity and the like of the node, so that an adjustment strategy of the node is given.
The invention discovers in the research of the sales network that one or more optimal nodes are inevitably present in the whole network, and the method of searching for the optimal nodes in the adjacent nodes in sequence by the optimal nodes can effectively serially connect a section of path, after the path is constructed, the optimal nodes in the network are searched again, the repeated optimal nodes are removed, the optimal nodes in the network are continuously searched under the current condition, and so on, a plurality of paths are constructed by the thought, the repeated nodes in the paths are removed, and the missing nodes are added, so that the reasonable sales network can be constructed, the problem of the regional intensity of the nodes is avoided, the brand coverage range and the cognitive degree of regional brands are effectively maintained, and in the technical scheme provided by the invention, based on the sales network and the sales regions formed by different nodes in the sales network, by collecting the sales information of different nodes, the brand popularity of the area and the brand product type popularity corresponding to the area can be judged, so that a brand manager can be helped to reasonably adjust the brand type of the area.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus once an item is defined in one figure, it need not be further defined and explained in subsequent figures, and moreover, the terms "first", "second", "third", etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the present invention in its spirit and scope. Are intended to be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A brand influence reconstruction method based on big data is characterized by comprising the following steps,
setting a point of sale of a brand product as a data node, and collecting position information and sales information of the data node;
according to the position information, obtaining a step length by calculating an average value of distances between the data nodes;
traversing the data nodes based on the sales information to obtain a first optimal node and first sales information of the first optimal node;
based on the first optimal node, obtaining a first optimal neighbor node and second sales information of the first optimal neighbor node according to the sales information and the step length;
acquiring third sales information of other data nodes based on the first optimal node and the first node distance from the first optimal node to the first optimal neighbor node;
and dynamically adjusting the data node according to the first sales information, the second sales information and the third sales information.
2. The big data based brand influence reconstruction method according to claim 1,
in the process of obtaining the first optimal neighbor node, when the first optimal neighbor node is empty, traversing the data nodes except the first optimal node to obtain a second optimal node and fourth sales information of the second optimal node; according to the step length, obtaining a second optimal neighbor node and fifth sales information of the second optimal neighbor node;
and dynamically adjusting the data node according to the fourth sales information, the fifth sales information and the third sales information.
3. The big data based brand influence reconstruction method according to claim 2,
and acquiring the data node closest to the first optimal node based on the second node distance from the first optimal node to the second optimal node, judging whether the data node is a second optimal neighbor node or the second optimal node, and dynamically adjusting the data node according to the judgment result.
4. A big-data based brand influence reconstruction method according to claim 3,
and when the data node is the second optimal node, adding the data node based on the second node distance and the step length, and adjusting the brand product type of the data node according to the first sales information and the fourth sales information.
5. The big-data-based brand influence reconstruction method according to claim 4,
when the data node is the second optimal neighbor node, adding the data node based on the third node distance from the first optimal node to the second optimal neighbor node and the step length, and adjusting the brand product type of the data node according to the first sales information and the fifth sales information.
6. The big-data-based brand influence reconstruction method according to claim 5,
when the data node is not the second optimal neighbor node or the second optimal node, adding the data node based on the second node distance and the step length, and adjusting the brand product type of the data node according to the first sales information, the fourth sales information and the fifth sales information.
7. The big-data-based brand influence reconstruction method according to claim 6,
in the process of collecting the sales information, at least monthly sales information, quarterly sales information and annual sales information are collected;
dynamically adjusting the data nodes according to the monthly sales information, the quarterly sales information and the annual sales information;
and adding or deleting the data nodes according to the annual sales information.
8. The big-data based brand influence reconstruction method according to claim 7,
in the process of dynamically adjusting the data nodes,
the dynamically adjusted content includes:
adding or deleting the data node;
adjusting the brand product type of the data node;
and collecting customer satisfaction degrees of the data nodes, and keeping brand influence of the data nodes according to the customer satisfaction degrees, wherein the customer satisfaction degrees comprise customer satisfaction degrees to sales personnel, customer satisfaction degrees to storefront environment and customer satisfaction degrees to brand product types.
9. A big data based brand influence reconstruction system is characterized by comprising,
the data acquisition module is used for acquiring the position information and the sales information of the point of sale of the brand product;
the data processing and analyzing module is used for setting the sales node as a data node and obtaining an optimal node of the data node and an optimal neighbor node corresponding to the optimal node according to the sales information and the position information;
the dynamic adjustment module is used for dynamically adjusting other data nodes between the optimal node and the optimal neighbor node according to the first sales information of the optimal node and the second sales information of the optimal neighbor node to obtain dynamic adjustment information;
and the display module is used for displaying each data node and the position information, the sales information and the dynamic adjustment information corresponding to each data node.
10. The big-data based brand influence reconstruction system of claim 9,
the dynamic adjustment module comprises a dynamic adjustment module and a dynamic adjustment module,
the brand product type adjusting unit is used for adjusting the brand product type according to the monthly sales information, the quarterly sales information and the annual sales information of the sales information;
and the data node adjusting unit is used for adding or deleting the data nodes according to the annual sales information.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060116927A1 (en) * | 2004-12-01 | 2006-06-01 | Miller Zell, Inc. | Method of creating and implementing a marketing plan for a retail store chain with measurable profit enhancement |
| CN102752761A (en) * | 2012-06-19 | 2012-10-24 | 江苏科技大学 | Particle swarm-based coverage optimization method of wireless sensor network mobile node |
| DE202016103271U1 (en) * | 2016-06-21 | 2016-07-12 | REWE - Zentral-Aktiengesellschaft | Device and computer system for carrying out a market simulation |
| CN107766984A (en) * | 2017-10-27 | 2018-03-06 | 郑州云海信息技术有限公司 | The optimal addressing system and method for businessman based on cloud computing |
| CN108459406A (en) * | 2018-03-15 | 2018-08-28 | 上海理工大学 | Microscope auto-focusing window selection method based on artificial fish-swarm algorithm |
| CN109636447A (en) * | 2018-11-19 | 2019-04-16 | 珠海市海苑科技有限公司 | Sales Volume of Commodity analysis system |
| CN111008849A (en) * | 2019-11-28 | 2020-04-14 | 山东爱城市网信息技术有限公司 | Block chain-based product sampling detection method and device and medium |
| CN111626891A (en) * | 2020-06-03 | 2020-09-04 | 四川大学 | Dynamic sale network community discovery method based on extended nodes |
| CN112907273A (en) * | 2021-01-11 | 2021-06-04 | 柳州市彭莫山农业科技有限公司 | Method and device for increasing sales of uncaria tea |
-
2021
- 2021-07-20 CN CN202110817805.5A patent/CN113538052B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060116927A1 (en) * | 2004-12-01 | 2006-06-01 | Miller Zell, Inc. | Method of creating and implementing a marketing plan for a retail store chain with measurable profit enhancement |
| CN102752761A (en) * | 2012-06-19 | 2012-10-24 | 江苏科技大学 | Particle swarm-based coverage optimization method of wireless sensor network mobile node |
| DE202016103271U1 (en) * | 2016-06-21 | 2016-07-12 | REWE - Zentral-Aktiengesellschaft | Device and computer system for carrying out a market simulation |
| CN107766984A (en) * | 2017-10-27 | 2018-03-06 | 郑州云海信息技术有限公司 | The optimal addressing system and method for businessman based on cloud computing |
| CN108459406A (en) * | 2018-03-15 | 2018-08-28 | 上海理工大学 | Microscope auto-focusing window selection method based on artificial fish-swarm algorithm |
| CN109636447A (en) * | 2018-11-19 | 2019-04-16 | 珠海市海苑科技有限公司 | Sales Volume of Commodity analysis system |
| CN111008849A (en) * | 2019-11-28 | 2020-04-14 | 山东爱城市网信息技术有限公司 | Block chain-based product sampling detection method and device and medium |
| CN111626891A (en) * | 2020-06-03 | 2020-09-04 | 四川大学 | Dynamic sale network community discovery method based on extended nodes |
| CN112907273A (en) * | 2021-01-11 | 2021-06-04 | 柳州市彭莫山农业科技有限公司 | Method and device for increasing sales of uncaria tea |
Non-Patent Citations (1)
| Title |
|---|
| 彭雷清: ""如何安排销售网点"", 《市场营销》, 31 December 2001 (2001-12-31), pages 52 - 53 * |
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