CN115510328A - Commodity brand marketing data analysis method based on big data - Google Patents
Commodity brand marketing data analysis method based on big data Download PDFInfo
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Abstract
The invention discloses a commodity brand marketing data analysis method based on big data, which belongs to the field of commodities and is used for solving the problem that marketing influence on a commodity brand cannot be known from multiple parties, and the method comprises the following steps: the method comprises the steps that a brand name of a commodity is input by a user terminal and sent to a brand identification module, and the brand identification module identifies the brand name of the commodity to obtain the brand name of a target commodity; the heat monitoring module monitors the heat condition of the target commodity in the previous week; the marketing strength of the target commodity is set by the strength setting module according to the first hot value; the marketing comparison module compares the heat conditions before and after the target commodity is marketed; the marketing adjustment module adjusts the marketing strength of the target commodity, adopts adaptive marketing strength for the brand commodities based on the marketing condition, and judges the marketing result of the brand commodities after the marketing strength is adopted.
Description
Technical Field
The invention belongs to the field of commodities, relates to a commodity brand marketing data analysis technology, and particularly relates to a commodity brand marketing data analysis method based on big data.
Background
The commodity is a labor result produced for sale, is a product of the development of the productivity of the human society to a certain historical stage, and is a labor product for exchange. Scientific summary of the commercial products: the goods "are private products in the first place. However, only when these personal products are not consumed by themselves, but by others, i.e., are produced for social consumption, they become commodities; they enter the society through the exchange for consumption ". The definition of the commodities in the accounting is that commodities are purchased outside or entrusted to be processed by a commodity circulation enterprise, and are accepted and put in storage for sale.
In order to increase the sales volume of the brand of the commodity, corresponding marketing measures are usually adopted to publicize and popularize the brand of the commodity, the marketing measures bring intuitive changes to the sales volume of the brand of the commodity, but the marketing effects of the marketing measures on the brand of the commodity cannot be known from other aspects, and therefore, a commodity brand marketing data analysis method based on big data is provided.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a commodity brand marketing data analysis method based on big data.
The technical problem to be solved by the invention is as follows:
how to adopt the adaptive marketing dynamics for the brand commodities based on the sales condition and judge the marketing results of the brand commodities after adopting the marketing dynamics.
The purpose of the invention can be realized by the following technical scheme:
a commodity brand marketing data analysis method based on big data comprises the following steps:
step S101, inputting a brand name of a commodity by a user terminal, sending the brand name to a brand identification module, identifying the brand name of the commodity by the brand identification module to obtain the brand name of a target commodity, and sending sales data of a previous week of the target commodity to a popularity monitoring module;
step S102, a heat monitoring module monitors the heat condition of the target commodity in the previous week, and a first heat value of the target commodity obtained through monitoring is sent to a marketing comparison module and a strength setting module;
step S103, a strength setting module sets the marketing strength of the target commodity according to the first heat value, sets the marketing grade of the target commodity and feeds the marketing grade back to a marketing platform, and the marketing platform sets corresponding marketing measures according to the marketing grade;
step S104, a data acquisition module acquires sales data of the target commodity after one week of marketing measures and sends the sales data to a marketing comparison module, the marketing comparison module is used for comparing the popularity of the target commodity before and after marketing, and a marketing ineffective signal, a marketing micro-effect signal, a marketing effective signal or a marketing strong-effect signal is generated and sent to a marketing adjustment module;
and S105, adjusting the marketing strength of the target commodity by the marketing adjusting module, and performing corresponding operation according to different signals.
Furthermore, the marketing platform is connected with a user terminal, a brand identification module, a heat monitoring module, a data acquisition module, a strength setting module, a marketing comparison module and a marketing adjustment module, the user terminal is used for inputting the brand name of the commodity and sending the brand name of the commodity to the marketing platform, and the marketing platform sends the brand name of the commodity to the brand identification module;
the brand identification module is connected with a storage module, the storage module is used for storing sales data of previous weeks of different commodity brands, the brand identification module is used for identifying the brand names of commodities to obtain the brand names of corresponding target commodities in the storage module, the sales data of the previous weeks of the target commodities are obtained according to the brand names and fed back to the marketing platform, and the marketing platform sends the sales data of the previous weeks of the target commodities to the heat monitoring module;
the system comprises a popularity monitoring module, a marketing comparison module, a strength setting module and a power setting module, wherein the popularity monitoring module is used for monitoring popularity of a target commodity in the previous week, monitoring to obtain a first popularity value of the target commodity and feeding the first popularity value back to the marketing platform; the strength setting module is used for setting the marketing strength of the target commodity according to the first heat value, setting the marketing grade of the target commodity and feeding the marketing grade back to the marketing platform, and the marketing platform sets corresponding marketing measures according to the marketing grade;
after the target commodity is subjected to corresponding marketing measures, the data acquisition module is used for acquiring sales data of the target commodity after one week of marketing measures and sending the sales data to the marketing platform, and the marketing platform sends the sales data of one week of the target commodity to the marketing comparison module; the module is compared in marketing and is used for comparing the hot condition around the target commodity marketing, compares and generates marketing invalid signal, the little effective signal of marketing, the effective signal of marketing or marketing high efficiency signal feedback to the marketing platform, the marketing platform is with marketing invalid signal, the little effective signal of marketing, the effective signal of marketing or marketing high efficiency signal transmission to marketing adjustment module, marketing adjustment module is used for adjusting the marketing dynamics of target commodity.
Further, the sales data are browsing times of a week before the target commodity, browsing duration, purchase quantity, platform home page display times of each browsing and display duration of each home page display.
Further, the identification process of the brand identification module specifically includes:
acquiring the brand name of an input commodity, and dividing the brand name into a plurality of comparison phrases;
then, brand names of a plurality of commodities in the storage module are obtained, and the brand names of the commodities in the storage module are divided into a plurality of words to be determined;
preliminarily screening a plurality of phrases to be determined according to the number of characters in each comparison phrase;
then acquiring strokes of each character in each comparison phrase, arranging and combining the strokes of each character to form a phrase code of each comparison phrase, and secondarily screening a plurality of undetermined phrases preliminarily screened according to the phrase codes;
if a plurality of phrases to be determined still exist after secondary screening, carrying out body comparison on the comparison phrases and the phrases to be determined so as to obtain a single and unique phrase to be determined corresponding to the comparison phrases;
and combining the phrases to be determined corresponding to each comparison phrase according to the sequence to obtain the brand name of the corresponding target commodity.
Further, the monitoring process of the heat monitoring module is specifically as follows:
acquiring the browsing times of the target commodity and the browsing time length of each browsing, and adding and summing the browsing time lengths of each browsing and dividing the sum by the browsing times to obtain the browsing average time length of the target commodity;
then, the purchase quantity of the target commodity, the platform home page display times and the display duration of each home page display are obtained, and the display duration of each home page display is added and summed up and divided by the home page display times to obtain the home page display average duration of the target commodity;
a first heat value of the target commodity is calculated.
Further, the setting process of the force setting module is specifically as follows:
comparing the first heat value with a first heat threshold value;
and judging the marketing level of the target commodity as a first marketing level, a second marketing level or a third marketing level.
Further, the first marketing level is ranked higher than the second marketing level, and the second marketing level is ranked higher than the third marketing level.
Further, the marketing comparison module specifically comprises the following comparison processes:
calculating a second heat value RD of the target commodity after the target commodity is subjected to marketing measures according to the monitoring process of the heat monitoring module;
comparing the first heat value with the second heat value, and if the first heat value is greater than or equal to the second heat value, generating a marketing invalid signal;
if the first heat value is smaller than the second heat value, subtracting the first heat value from the second heat value to obtain a heat difference value of the target commodity, generating a marketing micro-effect signal when the heat difference value is smaller than or equal to a first set threshold, and generating a marketing effective signal when the heat difference value is larger than the first set threshold and smaller than or equal to a second set threshold;
if the heat difference value is larger than a second set threshold value, generating a marketing strong effect signal; the first set threshold and the second set threshold are positive integers with fixed values, and the value of the first set threshold is smaller than that of the second set threshold.
Compared with the prior art, the invention has the beneficial effects that:
the brand name of a commodity is identified through a brand identification module to obtain the brand name of a target commodity, sales data of a previous week of the target commodity are sent to a heat monitoring module, the heat monitoring module is used for monitoring the heat condition of the previous week of the target commodity, the first heat value of the target commodity is monitored and sent to a marketing comparison module and a strength setting module, the strength setting module is used for setting marketing strength of the target commodity according to the first heat value and setting marketing grade of the target commodity, a marketing platform is used for setting corresponding marketing measures according to the marketing grade, then the marketing comparison module is used for comparing the heat conditions of the target commodity before and after marketing to generate a marketing invalid signal, a marketing micro-effect signal, a marketing effective signal or a marketing strong effect signal and sending the marketing invalid signal, the marketing micro-effect signal, the marketing effective signal or the marketing strong effect signal to a marketing adjustment module, and the marketing strength of the target commodity is adjusted through the marketing adjustment module.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a flow chart of the operation of the present invention;
fig. 2 is an overall system block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood 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.
In one embodiment, please refer to fig. 1, a method for analyzing marketing data of a brand of goods based on big data includes:
s101, inputting a brand name of a commodity by a user terminal, sending the brand name of the commodity to a marketing platform, sending the brand name of the commodity to a brand recognition module by the marketing platform, recognizing the brand name of the commodity by the brand recognition module to obtain the brand name of a corresponding target commodity in a storage module, obtaining sales data of a previous week of the target commodity according to the brand name, feeding the sales data back to the marketing platform, and sending the sales data of the previous week of the target commodity to a heat monitoring module by the marketing platform;
step S102, a heat monitoring module monitors the heat condition of a previous week of a target commodity, acquires the browsing times of the target commodity and the browsing time length of each browsing, the browsing time length of each browsing is added and summed and divided by the browsing times to obtain the browsing average time length LJT of the target commodity, then acquires the purchase quantity GML of the target commodity, and finally acquires the platform first page display times of the target commodity and the display time length of each first page display, the display time length of each first page display is added and summed and divided by the first page display times to obtain the first page display average time length SJT of the target commodity, a first heat value of the target commodity is calculated by a formula Rd = LJT × a1+ GML × a2+ SJT × a3, the heat monitoring module feeds the first heat value Rd of the target commodity back to a marketing platform, and the marketing comparison module and a strength setting module send the first heat value Rd of the target commodity to the marketing comparison module and the marketing degree setting module;
step S103, a strength setting module sets the marketing strength of the target commodity according to a first hot value, if Rd is less than X1, the marketing grade of the target commodity is a first marketing grade, if X1 is less than or equal to Rd and less than X2, the marketing grade of the target commodity is a second marketing grade, if X2 is less than or equal to Rd, the marketing grade of the target commodity is a third marketing grade, the strength setting module feeds the marketing grade of the target commodity back to a marketing platform, and the marketing platform sets corresponding marketing measures according to the marketing grade;
step S104, after the target commodity is subjected to corresponding marketing measures, a data acquisition module acquires sales data of the target commodity after one week of marketing measures and sends the sales data to a marketing platform, the marketing platform sends the sales data of the target commodity after one week of marketing measures to a marketing comparison module, the marketing comparison module compares the heat conditions before and after the target commodity is marketed, according to the monitoring process of a heat monitoring module, a second heat value RD of the target commodity after the marketing measures is obtained through calculation, the first heat value is compared with the second heat value, if the first heat value is larger than or equal to the second heat value, a marketing invalid signal is generated, if the first heat value is smaller than the second heat value, the heat value of the target commodity is obtained by subtracting the first heat value from the second heat value, if the heat value is smaller than or equal to a first set threshold value, a micro-effect signal is generated, if the heat value is larger than the first set threshold value and smaller than or equal to a second set threshold value, an effective marketing signal is generated; the marketing comparison module feeds back a marketing invalid signal, a marketing micro-effect signal, a marketing effective signal or a marketing strong-effect signal to the marketing platform, and the marketing platform sends the marketing invalid signal, the marketing micro-effect signal, the marketing effective signal or the marketing strong-effect signal to the marketing adjustment module;
step S105, a marketing adjusting module adjusts the marketing strength of the target commodity, if a marketing ineffective signal or a marketing micro-effect signal is received, the marketing grade of the target commodity is improved, if the target commodity is of a first marketing grade, the display duration of a home page, the full reduction of the preferential strength and the like are increased, and if the marketing effective signal or the marketing strong-effect signal is received, no operation is performed;
in this embodiment, please refer to fig. 2, the marketing platform is connected to a user terminal, a brand identification module, a heat monitoring module, a data acquisition module, a strength setting module, a marketing comparison module, and a marketing adjustment module;
in specific implementation, the user terminal is used for a worker to input personal information to register and log in a marketing platform and send the personal information to the marketing platform for storage; the personal information comprises the real name of the staff and the mobile phone number of real-name authentication;
the user terminal is used for inputting the brand name of the commodity and sending the brand name of the commodity to the marketing platform, and the marketing platform sends the brand name of the commodity to the brand identification module;
the brand identification module is connected with a storage module, the storage module is used for storing sales data of previous weeks of different commodity brands, the brand identification module is used for identifying the brand names of commodities to obtain the brand names of corresponding target commodities in the storage module, the sales data of the previous weeks of the target commodities are obtained according to the brand names and fed back to the marketing platform, and the marketing platform sends the sales data of the previous weeks of the target commodities to the heat monitoring module;
in this embodiment, the identification process of the brand identification module specifically includes:
acquiring the brand name of an input commodity, and dividing the brand name into a plurality of comparison phrases by adopting a Chinese word segmentation technology; only the brand name of Chinese is considered here, because the brand name of foreign goods usually has corresponding transliterated Chinese name;
then, brand names of a plurality of commodities in the storage module are obtained, and the brand names of the commodities in the storage module are divided into a plurality of phrases to be determined by adopting a Chinese word segmentation technology;
preliminarily screening a plurality of phrases to be determined according to the number of characters in each comparison phrase;
then acquiring strokes of each character in each comparison phrase, arranging and combining the strokes of each character to form a phrase code of each comparison phrase, and carrying out secondary screening on a plurality of undetermined phrases preliminarily screened according to the phrase codes;
if a plurality of to-be-determined phrases still exist after secondary screening, carrying out body comparison on the comparison phrases and the to-be-determined phrases so as to obtain independent and unique to-be-determined phrases corresponding to the comparison phrases;
combining the phrases to be determined corresponding to each comparison phrase in sequence to obtain the brand name of the corresponding target commodity;
for example: the brand name of a commodity input by a user terminal is named as 'Longmei Power Rong', the Longmei Power Rong is divided into two comparison phrases of 'Longmei' and 'Lianxing', firstly, the comparison phrase 'Longmei' has two characters, the number of characters is 2, a plurality of undetermined phrases are preliminarily screened according to the number of characters, strokes of two Chinese characters in the comparison phrase 'Longmei' are respectively '11' and '09', the phrase code of the comparison phrase 'Longmei' is 1109, and secondary screening is carried out on the plurality of undetermined phrases preliminarily screened according to the phrase code;
specifically explained, the sales data are browsing times of a week before the target commodity, browsing duration, purchase quantity, platform home page display times, display duration of a home page display and the like;
the heat monitoring module is used for monitoring the heat condition of the target commodity in the previous week, and the monitoring process is as follows:
acquiring the browsing times of the target commodity and the browsing time length during each browsing, and adding and summing the browsing time lengths during each browsing and dividing the sum by the browsing times to obtain the browsing average time length LJT of the target commodity;
acquiring the purchase quantity of a target commodity, and marking the purchase quantity as GML; acquiring the platform home page display times of the target commodity and the display time length of each home page display, and adding and summing the display time lengths of each home page display and dividing the sum by the home page display times to obtain the home page display average time length SJT of the target commodity;
calculating a first calorific value Rd of the target product through a formula Rd = LJT × a1+ GML × a2+ SJT × a 3; in the formula, a1, a2 and a3 are all weight coefficients with fixed numerical values, and the values of a1, a2 and a3 are all larger than zero;
the popularity monitoring module feeds back the first popularity value Rd of the target commodity to the marketing platform, and the marketing platform sends the first popularity value Rd of the target commodity to the marketing comparison module and the strength setting module;
in an embodiment of the present invention, the strength setting module is configured to set the marketing strength of the target product according to the first heat value, and the setting process specifically includes:
if Rd is less than X1, the marketing level of the target commodity is a first marketing level;
if the X1 is more than or equal to Rd and less than X2, the marketing grade of the target commodity is a second marketing grade;
if X2 is less than or equal to Rd, the marketing level of the target commodity is a third marketing level; wherein X1 and X2 are both first heat threshold values with fixed numerical values, and X1 is less than X2;
the strength setting module feeds the marketing grade of the target commodity back to the marketing platform, the marketing platform sets corresponding marketing measures according to the marketing grade, and the marketing measures specifically include:
if the marketing level is the first marketing level, the marketing measures are as follows: setting the time length of displaying a home page for not less than 18 hours, setting a preference policy of 300 minus 50, and sending one to buy the brand goods;
if the marketing level is the second marketing level, the marketing measures are as follows: setting a home page display time length of not less than 12 hours and setting a preference policy of 300 minus 30;
if the marketing level is the third marketing level, the marketing measures are as follows: setting a home page display time length of not less than 6 hours and setting a preference policy of fully 300 minus 10;
in the embodiment of the present invention, the level of the first marketing level is higher than that of the second marketing level, and the level of the second marketing level is higher than that of the third marketing level, wherein the marketing measures are merely preferred illustrative portions, and corresponding marketing measures may also be specifically set in actual situations, which are not further described herein;
after the target commodity is subjected to corresponding marketing measures, the data acquisition module is used for acquiring sales data of the target commodity after one week after the marketing measures and sending the sales data to the marketing platform, wherein the sales data of the target commodity after one week is the same as the sales data of the target commodity before one week, which is not described herein again, and the marketing platform sends the sales data of the target commodity for one week to the marketing comparison module;
the marketing comparison module is used for comparing the popularity conditions before and after the marketing of the target commodity, and the comparison process specifically comprises the following steps:
calculating to obtain a second heat value RD of the target commodity after the marketing measure according to the monitoring process of the heat monitoring module;
comparing the first heat value with the second heat value, and if the first heat value is greater than or equal to the second heat value, generating a marketing invalid signal;
if the first heat value is smaller than the second heat value, subtracting the first heat value from the second heat value to obtain a heat difference value of the target commodity;
if the heat difference is smaller than or equal to a first set threshold value, generating a marketing micro-effect signal;
if the heat difference value is larger than a first set threshold value and smaller than or equal to a second set threshold value, generating a marketing effective signal;
if the heat difference is larger than a second set threshold, generating a marketing strong effect signal; the first set threshold and the second set threshold are positive integers with fixed values, and the value of the first set threshold is smaller than that of the second set threshold;
marketing ineffective signal, marketing little effective signal, marketing effective signal or marketing strong effect signal feedback to marketing platform are compared to the module to the marketing, marketing platform will be marketing ineffective signal, marketing little effective signal, marketing effective signal or marketing strong effect signal transmission to marketing adjustment module, marketing adjustment module is used for adjusting the marketing dynamics of target commodity, and the adjustment process specifically as follows:
if the marketing ineffective signal or the marketing micro-effect signal is received, the marketing grade of the target commodity is improved, and if the target commodity is the first marketing grade, the display duration of the home page can be prolonged, the discount strength can be reduced, and the like;
if the marketing effective signal or the marketing strong signal is received, no operation is performed.
The above formulas are all calculated by taking the numerical value of the dimension, the formula is a formula of the latest real situation obtained by collecting a large amount of data and performing software simulation, the preset parameters in the formula are set by the technical personnel in the field according to the actual situation, the weight coefficient and the scale coefficient are specific numerical values obtained by quantizing each parameter, and the subsequent comparison is convenient.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (8)
1. A commodity brand marketing data analysis method based on big data is characterized by comprising the following steps:
step S101, a brand name of a commodity input by a user terminal is sent to a brand identification module, the brand identification module identifies the brand name of the commodity to obtain the brand name of a target commodity, and sales data of a week before the target commodity is sent to a heat monitoring module;
step S102, a heat monitoring module monitors the heat condition of the target commodity in the previous week, and a first heat value of the target commodity obtained through monitoring is sent to a marketing comparison module and a strength setting module;
step S103, a strength setting module sets the marketing strength of the target commodity according to the first heat value, sets the marketing grade of the target commodity and feeds the marketing grade back to a marketing platform, and the marketing platform sets corresponding marketing measures according to the marketing grade;
step S104, a data acquisition module acquires sales data of the target commodity after one week of marketing measures and sends the sales data to a marketing comparison module, the marketing comparison module is used for comparing the popularity of the target commodity before and after marketing, and a marketing ineffective signal, a marketing micro-effect signal, a marketing effective signal or a marketing strong-effect signal is generated and sent to a marketing adjustment module;
and S105, adjusting the marketing strength of the target commodity by the marketing adjusting module, and performing corresponding operation according to different signals.
2. The commodity brand marketing data analysis method based on big data according to claim 1, characterized in that the marketing platform is connected with a user terminal, a brand identification module, a heat monitoring module, a data acquisition module, a strength setting module, a marketing comparison module and a marketing adjustment module, wherein the user terminal is used for inputting a brand name of a commodity and sending the brand name of the commodity to the marketing platform, and the marketing platform sends the brand name of the commodity to the brand identification module;
the brand identification module is connected with a storage module, the storage module is used for storing sales data of previous weeks of different commodity brands, the brand identification module is used for identifying the brand names of the commodities to obtain the brand names of corresponding target commodities in the storage module, obtaining the sales data of the previous week of the target commodities according to the brand names and feeding the sales data back to the marketing platform, and the marketing platform sends the sales data of the previous week of the target commodities to the heat monitoring module;
the system comprises a popularity monitoring module, a marketing comparison module, a strength setting module and a power setting module, wherein the popularity monitoring module is used for monitoring popularity of a target commodity in the previous week, monitoring to obtain a first popularity value of the target commodity and feeding the first popularity value back to the marketing platform; the strength setting module is used for setting the marketing strength of the target commodity according to the first heat value, setting the marketing grade of the target commodity and feeding the marketing grade back to the marketing platform, and the marketing platform sets corresponding marketing measures according to the marketing grade;
after the target commodity is subjected to corresponding marketing measures, the data acquisition module is used for acquiring sales data of the target commodity after one week of marketing measures and sending the sales data to the marketing platform, and the marketing platform sends the sales data of one week of the target commodity to the marketing comparison module; the module is compared in marketing and is used for comparing the hot condition around the target commodity marketing, compares and generates marketing invalid signal, the little effective signal of marketing, the effective signal of marketing or marketing high efficiency signal feedback to the marketing platform, the marketing platform is with marketing invalid signal, the little effective signal of marketing, the effective signal of marketing or marketing high efficiency signal transmission to marketing adjustment module, marketing adjustment module is used for adjusting the marketing dynamics of target commodity.
3. The commodity brand marketing data analysis method based on big data as claimed in claim 2, wherein the sales data is browsing times of a week before the target commodity and browsing duration, purchase quantity, platform home page display times of each browsing and display duration of each home page display.
4. The big-data-based commodity brand marketing data analysis method according to claim 2, wherein the brand identification module specifically comprises the following identification processes:
acquiring the brand name of an input commodity, and dividing the brand name into a plurality of comparison phrases;
then, brand names of a plurality of commodities in the storage module are obtained, and the brand names of the commodities in the storage module are divided into a plurality of words to be determined;
preliminarily screening a plurality of phrases to be determined according to the number of characters in each comparison phrase;
then acquiring strokes of each character in each comparison phrase, arranging and combining the strokes of each character to form a phrase code of each comparison phrase, and carrying out secondary screening on a plurality of undetermined phrases preliminarily screened according to the phrase codes;
if a plurality of phrases to be determined still exist after secondary screening, carrying out body comparison on the comparison phrases and the phrases to be determined so as to obtain a single and unique phrase to be determined corresponding to the comparison phrases;
and combining the phrases to be determined corresponding to each comparison phrase according to the sequence to obtain the brand name of the corresponding target commodity.
5. The big-data-based commodity brand marketing data analysis method according to claim 2, wherein the monitoring process of the heat monitoring module is as follows:
acquiring the browsing times of the target commodity and the browsing time length of each browsing, and adding and summing the browsing time lengths of each browsing and dividing the sum by the browsing times to obtain the browsing average time length of the target commodity;
then, the purchase quantity of the target commodity, the platform home page display times and the display duration of each home page display are obtained, and the display duration of each home page display is added and summed up and divided by the home page display times to obtain the home page display average duration of the target commodity;
a first heat value of the target commodity is calculated.
6. The big-data-based commodity brand marketing data analysis method according to claim 2, wherein the setting process of the strength setting module is as follows:
comparing the first heat value with a first heat threshold value;
and judging the marketing level of the target commodity as a first marketing level, a second marketing level or a third marketing level.
7. The big-data based merchandising brand marketing data analysis method of claim 6, wherein the first marketing level is at a higher level than the second marketing level, and the second marketing level is at a higher level than the third marketing level.
8. The big-data-based commodity brand marketing data analysis method according to claim 2, wherein the comparison process of the marketing comparison module specifically comprises the following steps:
calculating a second heat value RD of the target commodity after the marketing measure according to the monitoring process of the heat monitoring module;
comparing the first heat value with the second heat value, and if the first heat value is greater than or equal to the second heat value, generating a marketing invalid signal;
if the first heat value is smaller than the second heat value, subtracting the first heat value from the second heat value to obtain a heat difference value of the target commodity, generating a marketing micro-effect signal when the heat difference value is smaller than or equal to a first set threshold, and generating a marketing effective signal when the heat difference value is larger than the first set threshold and smaller than or equal to a second set threshold;
if the heat difference is larger than a second set threshold, generating a marketing strong effect signal; the first set threshold and the second set threshold are positive integers with fixed values, and the value of the first set threshold is smaller than that of the second set threshold.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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