CN116645121A - Marketing model migration iteration method and processing method based on multidimensional data fusion - Google Patents
Marketing model migration iteration method and processing method based on multidimensional data fusion Download PDFInfo
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Abstract
The invention provides a marketing model migration iteration method and a processing method based on multidimensional data fusion, comprising the following steps: comparing each first marketing data dimension with a second marketing data dimension to be migrated to determine a corresponding marketing model to be migrated, wherein the first marketing model comprises a marketing display matrix; processing a first marketing display matrix included in the marketing model to be migrated according to the same marketing data dimension and the different marketing data dimension to obtain a second marketing display matrix; establishing a corresponding multidimensional space according to the second marketing display matrix, and dividing the multidimensional space corresponding to the second marketing display matrix based on the original space state of the first marketing display matrix to obtain a second marketing display matrix with the first space state; and obtaining a second marketing model after migration and iteration by combining the second space state and the marketing result configuration information, and generating a corresponding marketing data input template according to the second marketing data dimension of the second marketing model.
Description
Technical Field
The invention relates to a data processing technology, in particular to a marketing model migration generation selection method and a processing method based on multidimensional data fusion.
Background
The enterprise marketing model has a plurality of types, 28 marketing models exist on the market, wherein the commonly used models comprise a Boston matrix model and a GE matrix model, for example, the Boston matrix model divides the existing products of an enterprise into four different types by researching multidimensional data of the enterprise, plans the products and adopts different decisions, so that resources of the enterprise can be reasonably and effectively distributed.
In practical application, due to the conditions of updating the system and the like, migration iteration needs to be performed on the marketing model, and in the prior art, when the migration iteration of the marketing model is performed, the migration iteration is often realized by manually reconstructing a coordinate axis and reconfiguring corresponding data. However, the migration iterative approach of the prior art is inefficient.
Therefore, how to efficiently assist users in automatically migrating and selecting marketing models is an urgent problem to be solved.
Disclosure of Invention
The embodiment of the invention provides a marketing model migration generation selection method and a processing method based on multidimensional data fusion, which can efficiently assist a user to automatically migrate generation selection of a marketing model.
In a first aspect of the embodiment of the present invention, a marketing model migration iteration method based on multidimensional data fusion is provided, including:
the method comprises the steps that a server obtains first marketing data dimensions corresponding to initial first marketing models, each first marketing data dimension is compared with second marketing data dimensions to be migrated to determine a corresponding marketing model to be migrated, and the first marketing models comprise marketing display matrixes;
the server acquires the same marketing data dimension and the same distinguishing marketing data dimension in the first marketing data dimension and the second marketing data dimension, and processes a first marketing display matrix included in a marketing model to be migrated according to the same marketing data dimension and the same distinguishing marketing data dimension to obtain a second marketing display matrix;
the server establishes a corresponding multidimensional space according to the second marketing display matrix, and performs segmentation processing on the multidimensional space corresponding to the second marketing display matrix based on the original space state of the first marketing display matrix to obtain a second marketing display matrix with the first space state;
and the server determines all coordinate axis values and coordinate axis separation points in the second marketing display matrix to obtain a second space state corresponding to the second marketing display matrix, obtains a second marketing model after migration iteration by combining the second space state and marketing result configuration information, and generates a corresponding marketing data input template according to the second marketing data dimension of the second marketing model.
Optionally, the server acquires first marketing data dimensions corresponding to the initial first marketing model, compares each first marketing data dimension with second marketing data dimensions to be migrated to determine a corresponding marketing model to be migrated, where the first marketing model includes a marketing display matrix, and includes:
acquiring the number of the same sub-dimensions in the first marketing data dimension and the second marketing data dimension, and determining a first marketing data dimension with the largest number of the same sub-dimensions as the second marketing data dimension, wherein the first marketing data dimension comprises a first sub-dimension, and the second marketing data dimension comprises a second sub-dimension;
and taking the first marketing model corresponding to the first marketing data dimension with the largest number of the determined same sub-dimensions as the marketing model to be migrated.
Optionally, the server obtains the same marketing data dimension and the same distinguishing marketing data dimension in the first marketing data dimension and the second marketing data dimension, processes the first marketing display matrix included in the marketing model to be migrated according to the same marketing data dimension and the same distinguishing marketing data dimension, and obtains a second marketing display matrix, including:
determining the same marketing data dimension and a different marketing data dimension in a first marketing data dimension and a second marketing data dimension, the same marketing data dimension comprising the same sub-dimension, the different marketing data dimension comprising a different sub-dimension, the second marketing data dimension comprising entirely the first marketing data dimension;
Determining a first axis of the same sub-dimension in coordinate axes included in a first marketing display matrix, wherein the first axis at least comprises one of an X axis, a Y axis and a Z axis;
determining a corresponding number of second axes according to the number of the distinguished sub-dimensions, wherein the second axes and the first axes are coordinate axes in different directions;
and establishing a corresponding second shaft in the first marketing display matrix to obtain a second marketing display matrix.
Optionally, the server establishes a corresponding multidimensional space according to the second marketing display matrix, performs segmentation processing on the multidimensional space corresponding to the second marketing display matrix based on the original space state of the first marketing display matrix, and obtains a second marketing display matrix with the first space state, including:
establishing a corresponding multidimensional space according to the second marketing display matrix, wherein the multidimensional space comprises a space formed by a plurality of coordinate axes with different dimensions;
acquiring an original space state of a first marketing display matrix, wherein the original space state comprises a plurality of space corresponding areas, and each space corresponding area is provided with a first coordinate value interval of a corresponding first axis;
generating a second initial coordinate value interval of a second axis included in the second marketing display matrix, and processing the multidimensional space corresponding to the second marketing display matrix based on the second initial coordinate value interval of the second axis and the first coordinate value intervals of all the first axes to obtain a plurality of corresponding first space states of the second marketing display matrix.
Optionally, the server determines all coordinate axis values and coordinate axis separation points in the second marketing display matrix to obtain a second space state corresponding to the second marketing display matrix, and obtains a second marketing model after migration and iteration by combining the second space state and marketing result configuration information, and generates a corresponding marketing data input template according to a second marketing data dimension of the second marketing model, including:
if the user selects the second axis, and inputs the maximum coordinate axis value corresponding to the second axis and the separation point value corresponding to the coordinate axis separation point, updating the second coordinate value interval according to the separation point value to obtain a third coordinate value interval;
combining a first subinterval included in the first coordinate value intervals of different first axes and a third subinterval corresponding to the third coordinate value interval to obtain a second space state corresponding to a second marketing display matrix, wherein the second space state comprises a plurality of subspaces;
and analyzing the marketing result configuration information to obtain a plurality of result configuration sub-information, storing the result configuration sub-information corresponding to the corresponding subspace to obtain a second marketing model, and generating a corresponding marketing data input template according to the second marketing data dimension of the second marketing model.
Optionally, the method further comprises:
if the user selects the first axis, inputting the maximum coordinate axis value corresponding to the first axis and the separation point value corresponding to the coordinate axis separation point; and deleting the largest coordinate axis value and the coordinate axis separation point of the corresponding first axis, and updating the first coordinate value interval according to the new largest coordinate axis value and the new coordinate axis separation point, wherein the first coordinate value interval comprises a first subinterval.
Optionally, the combining the first subinterval included in the first coordinate value interval of the different first axes and the third subinterval corresponding to the third coordinate value interval to obtain a second space state corresponding to the second marketing display matrix, where the second space state includes a plurality of subspaces, and includes:
sequentially combining the first subinterval of each first shaft with each second subinterval of the first shaft and each third subinterval of the second shaft to obtain a plurality of interval sets;
and determining a corresponding subspace in the second marketing display matrix according to each interval set, and forming a second space state corresponding to the second marketing display matrix based on all the subspaces.
Optionally, the generating the corresponding marketing data input template according to the second marketing data dimension of the second marketing model includes:
acquiring the dimension number of the second marketing data dimension, and generating a corresponding number of columns at a marketing data input template according to the dimension number;
and acquiring the dimension name of each second marketing data dimension, and setting each dimension name to correspond to the corresponding column.
In a second aspect of the embodiment of the present invention, a processing method is provided, including any of the second marketing models described in the first aspect, where the second marketing model performs data processing by:
receiving indexes of corresponding dimensions based on a marketing data input template, wherein the indexes at least comprise market share, sales growth rate, profit margin, yield, competitive capacity coefficient and industry attraction coefficient;
after judging that the marketing data input template receives the indexes of all dimensions, sequentially determining each axis in a second marketing display matrix corresponding to each index, and determining a subinterval corresponding to the corresponding axis as a target subinterval according to the value of each index;
and sequentially determining a section set, subspaces and result configuration sub-information corresponding to all target subsections, and outputting the corresponding result configuration sub-information to a corresponding display end as output of the second marketing model.
A third aspect of an embodiment of the present invention provides a processing system, including the second marketing model of any of the first aspect, the second marketing model performing data processing based on the following modules, including:
the input module is used for receiving indexes of corresponding dimensions based on the marketing data input template, wherein the indexes at least comprise market share, sales growth rate, profit margin, yield, competitive capacity coefficient and industry attraction coefficient;
the judging module is used for sequentially determining each axis in the second marketing display matrix corresponding to each index after the marketing data input template is judged to receive the indexes of all dimensions, and determining the subinterval corresponding to the corresponding axis as a target subinterval according to the value of each index;
the determining module is used for sequentially determining a section set, subspaces and result configuration sub-information corresponding to all target sub-sections, and outputting the corresponding result configuration sub-information to the corresponding display end as the output of the second marketing model.
The beneficial effects are that:
1. when the method and the system are used for carrying out migration iteration on the marketing model, the close marketing model can be determined by combining the requirements of users, the original two-dimensional display marketing model can be updated in a three-dimensional mode, and multi-dimensional data are fused while migration is carried out. When the similar marketing model is determined, the same marketing data dimension and the different marketing data dimension are obtained, a new second marketing display matrix is obtained according to the same marketing data dimension and the different marketing data dimension, and meanwhile, the first space state and the second space state of the second marketing display matrix are updated to obtain the three-dimensional second marketing model after migration and iteration. In conclusion, the scheme can efficiently assist the user to automatically migrate and select the marketing model according to the requirements of the user.
2. According to the scheme, when the similar marketing model is determined, the first marketing data dimension with the same sub-dimension and the largest quantity can be found and used as the first marketing model, so that the relatively similar marketing model can be found through the mode, the change amount of data can be reduced when the marketing model is built subsequently, and the construction efficiency is improved. When the marketing model is built, the scheme can determine the first axis with the same sub-dimension, and determine the second axis with different sub-dimensions, so as to obtain a second marketing display matrix with a three-dimensional display state. The first subinterval included in the first coordinate value interval of different first axes and the third subinterval corresponding to the third coordinate value interval can be combined subsequently, and a second space state corresponding to the second marketing display matrix is obtained.
3. The method also can combine the dimension number of the newly constructed second marketing data dimension to generate a corresponding number of columns at the marketing data input template, and simultaneously combine the dimension name of each second marketing data dimension, and correspondingly set each dimension name and the corresponding column to obtain the marketing data input template. When the user is applied, the index of all dimensions can be received by combining the marketing data input template, so that the corresponding result configuration sub-information is obtained, and then the result configuration sub-information is output to the corresponding display end for display.
Drawings
FIG. 1 is a schematic flow chart of a marketing model migration iteration method based on multi-dimensional data fusion provided by an embodiment of the invention;
FIG. 2 is a schematic flow chart of a processing method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a processing system according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein.
It should be understood that, in various embodiments of the present invention, the sequence number of each process does not mean that the execution sequence of each process should be determined by its functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
It should be understood that in the present invention, "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements that are expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present invention, "plurality" means two or more. "and/or" is merely an association relationship describing an association object, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. "comprising A, B and C", "comprising A, B, C" means that all three of A, B, C comprise, "comprising A, B or C" means that one of the three comprises A, B, C, and "comprising A, B and/or C" means that any 1 or any 2 or 3 of the three comprises A, B, C.
It should be understood that in the present invention, "B corresponding to a", "a corresponding to B", or "B corresponding to a" means that B is associated with a, from which B can be determined. Determining B from a does not mean determining B from a alone, but may also determine B from a and/or other information. The matching of A and B is that the similarity of A and B is larger than or equal to a preset threshold value.
As used herein, "if" may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to detection" depending on the context.
The technical scheme of the invention is described in detail below by specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
Referring to fig. 1, a flow chart of a marketing model migration iteration method based on multi-dimensional data fusion provided by an embodiment of the present invention includes S1-S4:
s1, a server acquires first marketing data dimensions corresponding to initial first marketing models, each first marketing data dimension is compared with second marketing data dimensions to be migrated to determine a corresponding marketing model to be migrated, and the first marketing models comprise marketing display matrixes.
It should be noted that the first marketing model and the marketing model to be migrated in this solution may be a boston matrix model or a GE matrix model, where the boston model in the prior art generally has two marketing data dimensions, one may be, for example, a market share dimension, and the other may be, for example, a sales growth rate dimension, which is a two-dimensional coordinate space, and may set corresponding division points on corresponding coordinate axes, so as to divide the two-dimensional coordinate space into multiple subspaces, for example, into 4 subspaces, which may be subspaces corresponding to a problem product, an explicit product, a thin dog product, and a bulls product, respectively. The above-described contents are related art and will not be repeated.
When the marketing model is migrated, the method can be used for comparing each first marketing data dimension with the second marketing data dimension to be migrated in the mode, and finding out an adjacent existing model to determine the corresponding marketing model to be migrated.
In some embodiments, S1 (the server obtains first marketing data dimensions corresponding to the initial first marketing model, compares each first marketing data dimension with second marketing data dimensions to be migrated to determine a corresponding marketing model to be migrated, the first marketing model comprising a marketing presentation matrix) comprises S11-S12:
S11, the number of the same sub-dimensions in the first marketing data dimension and the second marketing data dimension is obtained, a first marketing data dimension with the largest number of the same sub-dimensions as the second marketing data dimension is determined, the first marketing data dimension comprises a first sub-dimension, and the second marketing data dimension comprises a second sub-dimension.
Wherein the first marketing data dimension comprises a first sub-dimension and the second marketing data dimension comprises a second sub-dimension. The first sub-dimension and the second sub-dimension may each be plural.
The method can obtain the number of the same sub-dimensions in the first marketing data dimension and the second marketing data dimension, then determine the first marketing data dimension with the largest number of the same sub-dimensions as the second marketing data dimension, and it can be understood that the larger the number of the same sub-dimensions as the second marketing data dimension is, the higher the similarity between the first marketing model and the second marketing data dimension required to be migrated by the user is, and the smaller the number of the dimensions required to be changed is when the migration is performed subsequently, the higher the migration efficiency is.
And S12, taking the first marketing model corresponding to the first marketing data dimension with the largest number of the determined same sub-dimensions as a marketing model to be migrated.
The first marketing model corresponding to the first marketing data dimension with the largest number of the determined same sub-dimensions, namely, the first marketing model closest to the user demand is determined as the marketing model to be migrated.
S2, the server acquires the same marketing data dimension and the same distinguishing marketing data dimension in the first marketing data dimension and the second marketing data dimension, and processes the first marketing display matrix included in the marketing model to be migrated according to the same marketing data dimension and the same distinguishing marketing data dimension to obtain a second marketing display matrix.
The same marketing data dimension refers to the same data dimension in the first marketing data dimension and the second marketing data dimension, and the different marketing data dimension refers to different data dimension in the first marketing data dimension and the second marketing data dimension.
The first marketing display matrix included in the marketing model to be migrated is processed by combining the same marketing data dimension and the different marketing data dimension, and a second marketing display matrix is obtained.
It will be appreciated that the second marketing presentation matrix is an initial marketing model that determines the data dimension and is subsequently further processed in conjunction with the user's input information.
In some embodiments, S2 (the server obtains the same marketing data dimension and the same distinguishing marketing data dimension in the first marketing data dimension and the second marketing data dimension, and processes the first marketing display matrix included in the marketing model to be migrated according to the same marketing data dimension and the distinguishing marketing data dimension to obtain the second marketing display matrix) includes S21-S24:
s21, determining identical marketing data dimensions and distinguishing marketing data dimensions in a first marketing data dimension and a second marketing data dimension, wherein the identical marketing data dimensions comprise identical sub-dimensions, the distinguishing marketing data dimension comprises distinguishing sub-dimensions, and the second marketing data dimension completely comprises the first marketing data dimension.
First, the scheme obtains the same marketing data dimension and the different marketing data dimension in the first marketing data dimension and the second marketing data dimension. It is understood that the same marketing data dimension includes the same sub-dimension, and that the different marketing data dimensions include the different sub-dimensions.
Illustratively, the first marketing data dimension includes a sub-dimension A and a sub-dimension B, and the second marketing data dimension includes a sub-dimension A, a sub-dimension B, and a sub-dimension C. Then the same marketing data dimension is a child dimension a and a child dimension B, and child dimension C is a distinguishing marketing data dimension.
It should be noted that the second marketing data dimension in the present solution need to entirely include the first marketing data dimension, such as the form in the example above.
S22, determining a first axis of the coordinate axes included in the first marketing display matrix in the same sub-dimension, wherein the first axis at least comprises one of an X axis, a Y axis and a Z axis.
The method can determine a first axis of the coordinate axes included in the first marketing display matrix for the same sub-dimension, wherein the first axis at least comprises one of an X axis, a Y axis and a Z axis.
For example, the sub-dimension a corresponds to the X axis, and the sub-dimension B corresponds to the Y axis, and the X axis and the Y axis of the first marketing display matrix are the first axes. In some embodiments, it may also be determined to one axis.
S23, determining a corresponding number of second shafts according to the number of the distinguished sub-dimensions, wherein the second shafts and the first shafts are coordinate axes in different directions.
The method and the device can determine a corresponding number of second axes according to the number of distinguished sub-dimensions, wherein the second axes and the first axes are coordinate axes in different directions. For example, the second axis may be the Z axis, and the X and Y axes form a marketing model with a three-dimensional presentation space.
S24, establishing a corresponding second shaft in the first marketing display matrix to obtain a second marketing display matrix.
After the second axis is determined, the scheme establishes a corresponding second axis in the first marketing display matrix to obtain a second marketing display matrix.
It should be noted that, the second marketing data dimension input by the user in the scheme needs to have 3 data dimensions, which correspond to X, Y and Z axes respectively, so that the migrated marketing model is a three-dimensional model, and in the process of implementing model migration iteration, multidimensional data are fused to obtain a brand-new three-dimensional marketing model.
And S3, the server establishes a corresponding multidimensional space according to the second marketing display matrix, and performs segmentation processing on the multidimensional space corresponding to the second marketing display matrix based on the original space state of the first marketing display matrix to obtain a second marketing display matrix with the first space state.
After the second marketing display matrix is obtained, the scheme can be combined with the second marketing display matrix to establish a corresponding multidimensional space, and the multidimensional space can be the multidimensional display space corresponding to X, Y and the Z axis.
The scheme can divide the multidimensional space corresponding to the second marketing display matrix based on the original space state of the first marketing display matrix to obtain the second marketing display matrix with the first space state.
The original space state is the space state displayed in two dimensions in the prior art. The scheme can combine the original space state to divide the multidimensional space corresponding to the second marketing display matrix, so as to obtain the second marketing display matrix with the first space state.
Illustratively, the original space state has 4 presentation spaces on a two-dimensional plane, which are a problem product, a star product, a thin dog product, and a gold cow product, respectively. The second marketing display matrix of the scheme also has a Z-axis space, so that the multi-dimensional space corresponding to the second marketing display matrix can be divided by combining the original space state of the first marketing display matrix, and the second marketing display matrix with the first space state is obtained.
In some embodiments, S3 (the server establishes a corresponding multidimensional space according to the second marketing display matrix, performs segmentation processing on the multidimensional space corresponding to the second marketing display matrix based on the original spatial state of the first marketing display matrix, and obtains a second marketing display matrix with the first spatial state) includes S31-S33:
s31, establishing a corresponding multidimensional space according to the second marketing display matrix, wherein the multidimensional space comprises a space formed by a plurality of coordinate axes with different dimensions.
Firstly, the scheme establishes a corresponding multidimensional space according to the second marketing display matrix, wherein the multidimensional space comprises a space formed by a plurality of coordinate axes with different dimensions. For example, a multi-dimensional presentation space defined by X, Y and Z-axis.
S32, acquiring an original space state of a first marketing display matrix, wherein the original space state comprises a plurality of space corresponding areas, and each space corresponding area is provided with a first coordinate value interval of a corresponding first axis.
The original space state comprises a plurality of space corresponding areas which can be four areas corresponding to a problem product, a star product, a thin dog product and a golden cow product. Each space corresponding region is provided with a first coordinate value interval of a corresponding first axis. For example, for a thin dog product, the corresponding X-axis may be market share, the first coordinate value interval may be 0-50, the corresponding Y-axis may be sales growth rate, the first coordinate value interval may be 0-50, and other spatial principles are similar and will not be repeated.
And S33, generating a second initial coordinate value interval of a second axis included in the second marketing display matrix, and processing the multidimensional space corresponding to the second marketing display matrix based on the second initial coordinate value interval of the second axis and the first coordinate value intervals of all the first axes to obtain a plurality of corresponding first space states of the second marketing display matrix.
The second initial second coordinate value interval included in the second marketing display matrix is generated, for example, the second initial second coordinate value interval of the Z axis is generated, for example, the Z axis represents profit margin, and the corresponding second coordinate value interval is, for example, 0-1000.
According to the scheme, the multidimensional space corresponding to the second marketing display matrix is processed based on the second coordinate value interval of the second axis initiation and the first coordinate value intervals of all the first axes, so that a plurality of corresponding first space states of the second marketing display matrix are obtained.
Wherein the first spatial state is a three-dimensional state. It can be understood that the model state at this time is that four areas corresponding to the problem product, the star product, the thin dog product and the golden cow product are elongated on the Z axis, and a three-dimensional space corresponding to the 4 areas is formed.
And S4, determining all coordinate axis values and coordinate axis separation points in the second marketing display matrix by the server to obtain a second space state corresponding to the second marketing display matrix, obtaining a second marketing model after migration and iteration by combining the second space state and marketing result configuration information, and generating a corresponding marketing data input template according to the second marketing data dimension of the second marketing model.
It should be noted that, since the Z-axis is also spatially divided, for example, 0-500 is a division, and 500-1000 is a division.
Therefore, the server in the scheme can determine all coordinate axis values and coordinate axis separation points in the second marketing display matrix to obtain a second space state corresponding to the second marketing display matrix.
For example, there is a division point on the Z axis, and a value corresponding to 500 is set, and in this case, the stereoscopic space corresponding to the 4 regions may be divided into 8 stereoscopic spaces, that is, the second spatial state.
Finally, the scheme can combine the second space state and the marketing result configuration information to obtain a second marketing model after migration and iteration, and a corresponding marketing data input template is generated according to the second marketing data dimension of the second marketing model.
In some embodiments, S4 (the server determines all coordinate axis values and coordinate axis separation points in the second marketing display matrix to obtain a second spatial state corresponding to the second marketing display matrix, and obtains a second marketing model after migration and iteration by combining the second spatial state and marketing result configuration information of the second spatial state, and generates a corresponding marketing data input template according to a second marketing data dimension of the second marketing model) includes S41-S43:
And S41, if the user selects the second axis, and inputs the maximum coordinate axis value corresponding to the second axis and the separation point value corresponding to the coordinate axis separation point, updating the second coordinate value interval according to the separation point value to obtain a third coordinate value interval.
If the user selects the second axis and inputs the maximum coordinate axis value corresponding to the second axis and the separation point value corresponding to the coordinate axis separation point, updating the second coordinate value interval according to the separation point value to obtain a third coordinate value interval.
Illustratively, the second axis is the Z axis corresponding to the profit margin, and the maximum coordinate axis is 1000, and then the corresponding interval is 0-1000. The number of the separation points corresponding to the coordinate axis separation points is, for example, 500, and the obtained third coordinate value interval is 0-500 and 500-1000.
S42, combining the first subintervals included in the first coordinate value intervals of different first axes and the third subintervals corresponding to the third coordinate value intervals to obtain a second space state corresponding to the second marketing display matrix, wherein the second space state comprises a plurality of subspaces.
The first sub-interval included in the first coordinate value interval of different first axes and the third sub-interval corresponding to the third coordinate value interval are combined to obtain a second space state corresponding to the second marketing display matrix, wherein the second space state comprises a plurality of subspaces, for example, 8 subspaces.
In some embodiments, S42 (combining the first subinterval included in the first coordinate value interval of the different first axes and the third subinterval corresponding to the third coordinate value interval to obtain the second spatial state corresponding to the second marketing display matrix, where the second spatial state includes a plurality of subspaces) includes S421-S422:
s421, the first subintervals of each first axis are combined with each second subinterval of each first axis and each third subinterval of each second axis in sequence to obtain a plurality of interval sets.
The first subinterval of each first axis is sequentially combined with each second subinterval of the first axis and each third subinterval of the second axis to obtain a plurality of interval sets.
For example, one first axis is the X axis, the corresponding first subinterval is 0-50, the other first axes are the Y axis, the corresponding second subinterval is 0-50, the second axis is the Z axis, the corresponding third subinterval is 0-500, and the corresponding interval set is a summary of the three subintervals.
S422, determining a corresponding subspace in the second marketing display matrix according to each interval set, and forming a second space state corresponding to the second marketing display matrix based on all the subspaces.
According to the scheme, corresponding subspaces are determined in the second marketing display matrix according to each interval set, and then all subspaces are combined to form a second space state corresponding to the second marketing display matrix.
S43, analyzing the marketing result configuration information to obtain a plurality of result configuration sub-information, storing the result configuration sub-information corresponding to the corresponding subspace to obtain a second marketing model, and generating a corresponding marketing data input template according to the second marketing data dimension of the second marketing model.
It will be appreciated that the present solution parses the marketing results configuration information to obtain a plurality of results configuration sub-information, such as high profit margin problem products, low profit margin problem products, high profit margin products, low profit margin products, high profit margin thin dog products, low profit margin thin dog products, high profit margin golden cow products, low profit margin golden cow products, and the like.
The scheme can correspondingly store the result configuration sub-information and the corresponding subspace to obtain a second marketing model, and then generate a corresponding marketing data input template by combining the second marketing data dimension of the second marketing model.
Wherein the generating a corresponding marketing data input template according to the second marketing data dimension of the second marketing model includes S431-S432:
S431, acquiring the dimension number of the second marketing data dimension, and generating a corresponding number of columns at the marketing data input template according to the dimension number.
For example, the second marketing data dimension has a dimension number of 3, then a corresponding number of column levels of 3 are generated at the marketing data input template.
S432, obtaining dimension names of each second marketing data dimension, and setting each dimension name to correspond to a corresponding column.
Meanwhile, the dimension names of each second marketing data dimension, such as market share, sales growth rate and profit margin, are acquired, and then each dimension name is set corresponding to a corresponding column.
On the basis of the above embodiment, S45-S46 are further included:
s45, if the user selects the first axis, inputting the maximum coordinate axis value corresponding to the first axis and the separation point value corresponding to the coordinate axis separation point.
It can be understood that the user of the scheme can also adjust the data of the first axis, if the user selects the first axis, the maximum coordinate axis value corresponding to the first axis and the separation point value corresponding to the coordinate axis separation point are input.
S46, deleting the largest coordinate axis value and the coordinate axis separation point of the corresponding first axis, and updating the first coordinate value interval according to the new largest coordinate axis value and the new coordinate axis separation point, wherein the first coordinate value interval comprises a first subinterval.
By the method, the original coordinate axis data can be updated during migration, so that the model is iteratively updated according to the current requirements of the user.
Referring to fig. 2, a flowchart of a processing method according to an embodiment of the present invention is provided, where the processing method includes the second marketing model of the above embodiment, and the second marketing model performs data processing through the following steps, including A1-A3:
a1, receiving indexes of corresponding dimensions based on a marketing data input template, wherein the indexes at least comprise market share, sales growth rate, profit margin, yield, competitive capacity coefficient and industry attraction coefficient.
After the second marketing model is obtained by the scheme, the scheme can process the input data by combining the second marketing model to obtain a processing result.
When a user inputs data, indexes of corresponding dimensions can be received in combination with the marketing data input template, wherein the indexes at least comprise market share, sales growth rate, profit margin, yield, competitiveness coefficient and industry attraction coefficient.
A2, after judging that the marketing data input templates receive the indexes of all dimensions, sequentially determining each axis in a second marketing display matrix corresponding to each index, and determining a subinterval corresponding to the corresponding axis as a target subinterval according to the value of each index.
After receiving the indexes of all dimensions, determining each axis in the second marketing display matrix corresponding to each index in turn, and determining the subinterval corresponding to the corresponding axis as a target subinterval according to the value of each index.
A3, sequentially determining a section set, subspaces and result configuration sub-information corresponding to all target sub-sections, and outputting the corresponding result configuration sub-information to a corresponding display end as output of the second marketing model.
After the corresponding results are matched, the interval set, subspaces and result configuration sub-information corresponding to all target subintervals are sequentially determined, and then the corresponding result configuration sub-information is used as output of the second marketing model to the corresponding display end.
Referring to fig. 3, a schematic structural diagram of a processing system according to an embodiment of the present invention is provided, where the processing system includes the second marketing model of the above embodiment, and the second marketing model performs data processing based on the following modules, including:
The input module is used for receiving indexes of corresponding dimensions based on the marketing data input template, wherein the indexes at least comprise market share, sales growth rate, profit margin, yield, competitive capacity coefficient and industry attraction coefficient;
the judging module is used for sequentially determining each axis in the second marketing display matrix corresponding to each index after the marketing data input template is judged to receive the indexes of all dimensions, and determining the subinterval corresponding to the corresponding axis as a target subinterval according to the value of each index;
the determining module is used for sequentially determining a section set, subspaces and result configuration sub-information corresponding to all target sub-sections, and outputting the corresponding result configuration sub-information to the corresponding display end as the output of the second marketing model.
The present invention also provides a storage medium having stored therein a computer program for implementing the methods provided by the various embodiments described above when executed by a processor.
The storage medium may be a computer storage medium or a communication medium. Communication media includes any medium that facilitates transfer of a computer program from one place to another. Computer storage media can be any available media that can be accessed by a general purpose or special purpose computer. For example, a storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuits, ASIC for short). In addition, the ASIC may reside in a user device. The processor and the storage medium may reside as discrete components in a communication device. The storage medium may be read-only memory (ROM), random-access memory (RAM), CD-ROMs, magnetic tape, floppy disk, optical data storage device, etc.
The present invention also provides a program product comprising execution instructions stored in a storage medium. The at least one processor of the device may read the execution instructions from the storage medium, the execution instructions being executed by the at least one processor to cause the device to implement the methods provided by the various embodiments described above.
In the above embodiments of the terminal or the server, it should be understood that the processor may be a central processing unit (english: central Processing Unit, abbreviated as CPU), or may be other general purpose processors, digital signal processors (english: digital Signal Processor, abbreviated as DSP), application specific integrated circuits (english: application Specific Integrated Circuit, abbreviated as ASIC), or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in a processor for execution.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (10)
1. The marketing model migration iteration method based on multidimensional data fusion is characterized by comprising the following steps of:
the method comprises the steps that a server obtains first marketing data dimensions corresponding to initial first marketing models, each first marketing data dimension is compared with second marketing data dimensions to be migrated to determine a corresponding marketing model to be migrated, and the first marketing models comprise marketing display matrixes;
the server acquires the same marketing data dimension and the same distinguishing marketing data dimension in the first marketing data dimension and the second marketing data dimension, and processes a first marketing display matrix included in a marketing model to be migrated according to the same marketing data dimension and the same distinguishing marketing data dimension to obtain a second marketing display matrix;
the server establishes a corresponding multidimensional space according to the second marketing display matrix, and performs segmentation processing on the multidimensional space corresponding to the second marketing display matrix based on the original space state of the first marketing display matrix to obtain a second marketing display matrix with the first space state;
and the server determines all coordinate axis values and coordinate axis separation points in the second marketing display matrix to obtain a second space state corresponding to the second marketing display matrix, obtains a second marketing model after migration iteration by combining the second space state and marketing result configuration information, and generates a corresponding marketing data input template according to the second marketing data dimension of the second marketing model.
2. The method for iterating migration of a marketing model based on multi-dimensional data fusion of claim 1, wherein the method comprises the steps of,
the server obtains first marketing data dimensions corresponding to the initial first marketing model, compares each first marketing data dimension with second marketing data dimensions to be migrated to determine a corresponding marketing model to be migrated, wherein the first marketing model comprises a marketing display matrix and comprises:
acquiring the number of the same sub-dimensions in the first marketing data dimension and the second marketing data dimension, and determining a first marketing data dimension with the largest number of the same sub-dimensions as the second marketing data dimension, wherein the first marketing data dimension comprises a first sub-dimension, and the second marketing data dimension comprises a second sub-dimension;
and taking the first marketing model corresponding to the first marketing data dimension with the largest number of the determined same sub-dimensions as the marketing model to be migrated.
3. The method of claim 2, wherein the method further comprises the steps of,
the server obtains the same marketing data dimension and the same distinguishing marketing data dimension in the first marketing data dimension and the second marketing data dimension, processes a first marketing display matrix included in a marketing model to be migrated according to the same marketing data dimension and the same distinguishing marketing data dimension, and obtains a second marketing display matrix, and the method comprises the following steps:
Determining the same marketing data dimension and a different marketing data dimension in a first marketing data dimension and a second marketing data dimension, the same marketing data dimension comprising the same sub-dimension, the different marketing data dimension comprising a different sub-dimension, the second marketing data dimension comprising entirely the first marketing data dimension;
determining a first axis of the same sub-dimension in coordinate axes included in a first marketing display matrix, wherein the first axis at least comprises one of an X axis, a Y axis and a Z axis;
determining a corresponding number of second axes according to the number of the distinguished sub-dimensions, wherein the second axes and the first axes are coordinate axes in different directions;
and establishing a corresponding second shaft in the first marketing display matrix to obtain a second marketing display matrix.
4. The method for iterating migration of a marketing model based on multi-dimensional data fusion of claim 3,
the server establishes a corresponding multidimensional space according to the second marketing display matrix, performs segmentation processing on the multidimensional space corresponding to the second marketing display matrix based on the original space state of the first marketing display matrix, and obtains a second marketing display matrix with the first space state, and the method comprises the following steps:
Establishing a corresponding multidimensional space according to the second marketing display matrix, wherein the multidimensional space comprises a space formed by a plurality of coordinate axes with different dimensions;
acquiring an original space state of a first marketing display matrix, wherein the original space state comprises a plurality of space corresponding areas, and each space corresponding area is provided with a first coordinate value interval of a corresponding first axis;
generating a second initial coordinate value interval of a second axis included in the second marketing display matrix, and processing the multidimensional space corresponding to the second marketing display matrix based on the second initial coordinate value interval of the second axis and the first coordinate value intervals of all the first axes to obtain a plurality of corresponding first space states of the second marketing display matrix.
5. The method for iterating migration of a marketing model based on multi-dimensional data fusion of claim 4, wherein the method comprises the steps of,
the server determines all coordinate axis values and coordinate axis separation points in the second marketing display matrix to obtain a second space state corresponding to the second marketing display matrix, obtains a second marketing model after migration and iteration by combining the second space state and marketing result configuration information, and generates a corresponding marketing data input template according to a second marketing data dimension of the second marketing model, wherein the method comprises the following steps:
If the user selects the second axis, and inputs the maximum coordinate axis value corresponding to the second axis and the separation point value corresponding to the coordinate axis separation point, updating the second coordinate value interval according to the separation point value to obtain a third coordinate value interval;
combining a first subinterval included in the first coordinate value intervals of different first axes and a third subinterval corresponding to the third coordinate value interval to obtain a second space state corresponding to a second marketing display matrix, wherein the second space state comprises a plurality of subspaces;
and analyzing the marketing result configuration information to obtain a plurality of result configuration sub-information, storing the result configuration sub-information corresponding to the corresponding subspace to obtain a second marketing model, and generating a corresponding marketing data input template according to the second marketing data dimension of the second marketing model.
6. The iterative method for migration of a marketing model based on multidimensional data fusion of claim 5, further comprising:
if the user selects the first axis, inputting the maximum coordinate axis value corresponding to the first axis and the separation point value corresponding to the coordinate axis separation point;
and deleting the largest coordinate axis value and the coordinate axis separation point of the corresponding first axis, and updating the first coordinate value interval according to the new largest coordinate axis value and the new coordinate axis separation point, wherein the first coordinate value interval comprises a first subinterval.
7. The method of claim 5, wherein the method comprises the steps of,
the first sub-intervals included in the first coordinate value intervals of different first axes and the third sub-intervals corresponding to the third coordinate value intervals are combined to obtain a second space state corresponding to the second marketing display matrix, and the second space state comprises a plurality of subspaces and comprises:
sequentially combining the first subinterval of each first shaft with each second subinterval of the first shaft and each third subinterval of the second shaft to obtain a plurality of interval sets;
and determining a corresponding subspace in the second marketing display matrix according to each interval set, and forming a second space state corresponding to the second marketing display matrix based on all the subspaces.
8. The method of claim 5, wherein the method comprises the steps of,
the generating a corresponding marketing data input template according to the second marketing data dimension of the second marketing model includes:
acquiring the dimension number of the second marketing data dimension, and generating a corresponding number of columns at a marketing data input template according to the dimension number;
And acquiring the dimension name of each second marketing data dimension, and setting each dimension name to correspond to the corresponding column.
9. A processing method, comprising the second marketing model according to any one of the multi-dimensional data fusion-based marketing model migration iteration method of claim 1 to claim 8, characterized in that the second marketing model performs data processing by:
receiving indexes of corresponding dimensions based on a marketing data input template, wherein the indexes at least comprise market share, sales growth rate, profit margin, yield, competitive capacity coefficient and industry attraction coefficient;
after judging that the marketing data input template receives the indexes of all dimensions, sequentially determining each axis in a second marketing display matrix corresponding to each index, and determining a subinterval corresponding to the corresponding axis as a target subinterval according to the value of each index;
and sequentially determining a section set, subspaces and result configuration sub-information corresponding to all target subsections, and outputting the corresponding result configuration sub-information to a corresponding display end as output of the second marketing model.
10. A processing system comprising the second marketing model of the multi-dimensional data fusion-based marketing model migration iteration method of any one of claims 1 to 8, wherein the second marketing model performs data processing based on:
The input module is used for receiving indexes of corresponding dimensions based on the marketing data input template, wherein the indexes at least comprise market share, sales growth rate, profit margin, yield, competitive capacity coefficient and industry attraction coefficient;
the judging module is used for sequentially determining each axis in the second marketing display matrix corresponding to each index after the marketing data input template is judged to receive the indexes of all dimensions, and determining the subinterval corresponding to the corresponding axis as a target subinterval according to the value of each index;
the determining module is used for sequentially determining a section set, subspaces and result configuration sub-information corresponding to all target sub-sections, and outputting the corresponding result configuration sub-information to the corresponding display end as the output of the second marketing model.
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