Brand value estimation method based on multi-party online data

Abstract

The invention belongs to the technical field of asset assessment and brand value assessment, and particularly relates to a multi-party online data-based brand value assessment method, which comprises the following steps of S1, determining a value assessment object range; s2, obtaining, collecting, cleaning and converting online data; s3, generating an estimated value characteristic value; s4, constructing an index system; s5, determining a weight coefficient; s6, calculating an initial value of brand value; s7, carrying out market verification on the initial value of the brand value; s8, determining an original value of the brand value; and S9, brand value display value and visualization. The brand value estimation method based on the multi-party online data has the following remarkable advantages that the data source is wide, the timeliness is high, the method is objective, and the estimation result is reliable.

Description

Technical Field

[0001] The present invention belongs to the technical field of asset evaluation and brand valuation, and specifically is a valuation method for brand value based on multi-party online data. Background Art

[0002] With the rise of the new consumption wave, especially the emergence of new domestic brands, brands have become an indispensable and important asset in corporate competition. Brand value not only reflects the competitive position of a company, but also affects the company's market value, capital operation and market strategy formulation. Therefore, accurate valuation of brand value can not only provide strong data support for corporate strategy formulation, but also provide decision makers with more decision-making basis in financial activities such as mergers and acquisitions and investments.

[0003] However, the current brand value valuation methods have many shortcomings. First, the data sources are relatively limited and mainly offline data, which is not suitable for the characteristics of new domestic brands that are mainly online marketing, resulting in the valuation results being biased by one or several data sources. Secondly, because traditional data collection methods are more cumbersome, the timeliness of the valuation results is not high, and it is difficult to reflect the current value status and market dynamics of the brand. Furthermore, many valuation methods are too subjective and lack unified evaluation standards, which leads to doubts about the reliability of the valuation results. Finally, for brands that have been established for a short time or have not been listed, the current valuation methods do not have good calculation results. Summary of the invention

[0004] The present invention proposes a brand value valuation method based on multiple online data, comprising the following steps:

[0005] S1. Determination of the scope of valuation object;

[0006] S2, acquisition, collection, cleaning and conversion of online data;

[0007] S3, generation of valuation feature values;

[0008] S4, construction of the indicator system;

[0009] S5. Determination of weight coefficient;

[0010] S6. Calculate the initial brand value;

[0011] S7. Market verification of initial brand value;

[0012] S8. Determination of the original value of brand value;

[0013] S9. Brand value display and visualization.

[0014] Furthermore, in step S1, the scope of the valuation object is determined based on the categories involved in the brand object to be valued, and competitive brands are selected as valuation objects with reference to public industry data; under the same category, the above-selected brands are used as the valuation brand pool.

[0015] Furthermore, in step S2, it is necessary to obtain relevant data of the categories to which different brands belong respectively, and organize and collect the data according to the time period. The relevant data include: market size, social voice of the brand on social platforms, positive and negative public opinions, popularity index of information platforms, search index on search engines, exposure, interaction, purchase and repurchase data of e-commerce sales platforms; then the organized and collected relevant data are cleaned and normalized.

[0016] Furthermore, in step S3, valuation feature values ​​of different dimensions are generated based on the converted data, and the dimensions include: market share, current market size, market growth rate, brand exposure, user interaction, product purchase, user loyalty, public opinion volume, user praise, public opinion differentiation, search index, information index, search heat, popularity, brand added value, first place of large single products, and sales discount rate.

[0017] Furthermore, in step S4, the above dimensions are classified according to five indicators, namely, market performance, consumer preference, public opinion trend, brand popularity, and product competitiveness, to construct an indicator system.

[0018] Furthermore, in step S5, the weight coefficients corresponding to the above-mentioned indicators are determined according to industry public data or by using the AHP hierarchical analysis method.

[0019] Furthermore, in step S6, the initial value of the indicator is obtained by multiplying the valuation characteristic value of the indicator and the corresponding weight coefficient, and the sum of the initial values ​​of the five indicators is the initial value of the brand.

[0020] Furthermore, in step S7, the weight coefficient is adjusted using principal component analysis or entropy weight method in combination with the stock price fluctuation in the secondary market.

[0021] Furthermore, in step S8, the brand value initial value is obtained by recalculating according to the adjusted weight coefficient according to the method of step S6.

[0022] Furthermore, in step S9, the initial brand value is adjusted to a brand value display value, and is visualized according to actual needs.

[0023] The beneficial effects of the present invention are:

[0024] The present invention proposes a brand value valuation method based on multiple online data, which has the following significant advantages:

[0025] Wide range of data sources: Utilize a variety of data collection and collation technologies to collect online data from multiple aspects and channels to provide more comprehensive data support for valuation;

[0026] High timeliness: Real-time or near-real-time acquisition, processing and analysis of online data to ensure that the valuation results can reflect the current status of the brand in a timely manner. The shortest time to reflect the changes in brand value can be daily;

[0027] Objective method: Reduce human intervention and bias, and provide more objective valuation results through data-driven methods;

[0028] Valuation results are reliable: Multi-faceted data fusion analysis improves the accuracy and credibility of valuation. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The present invention is a flowchart of a method for estimating brand value based on multi-party online data. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] The following is combined with Figure 1 The present invention is further described as follows: a method for estimating brand value based on multiple online data comprises the following steps:

[0032] S1. Determination of the scope of valuation objects. In this embodiment, the scope of valuation objects is determined by selecting competing brands as valuation objects based on the categories involved in the brand object to be valued and referring to public industry data; under the same category, the above selected brands are used as the valuation brand pool;

[0033] S2. Acquisition, collection, cleaning and conversion of online data. In this embodiment, it is necessary to obtain relevant data of the categories of different brands respectively, and organize and collect them according to time periods. The relevant data include: market size, social voice of the brand on social platforms, positive and negative public opinions, popularity index of information platforms, search index on search engines, exposure, interaction, purchase and repurchase data of e-commerce sales platforms; then clean the collected and organized relevant data and perform normalization conversion. In this embodiment, data cleaning includes removing outliers and supplementing important missing values. Normalization conversion usually uses Z-score or Sigmoid function for conversion;

[0034] S3. Generate valuation feature values ​​of different dimensions based on the converted data. In this embodiment, the dimensions include: market share, market size status, market growth rate, brand exposure, user interaction, product purchase, user loyalty, public opinion volume, user praise, public opinion differentiation, search index, information index, search heat, popularity, brand added value, first place of major single products, and sales discount rate;

[0035] S4. Construction of the index system. The above dimensions are classified according to the five indicators of market performance, consumer preference, public opinion trend, brand popularity, and product competitiveness to construct the index system. In this embodiment, the market performance indicators include three dimensions: market share, market size status, and market growth rate; the consumer preference indicators include four dimensions: brand exposure, user interaction, product purchase, and user loyalty; the public opinion trend indicators include three dimensions: public opinion volume, user praise, and public opinion differentiation; the brand popularity indicators include four dimensions: search index, information index, search popularity, and popularity; the product competitiveness indicators include three dimensions: brand added value, major single product first place, and sales discount rate;

[0036] S5. Determination of weight coefficients: Determine the weight coefficients corresponding to the above indicators according to industry public data or by using AHP hierarchical analysis method. In this embodiment, the weight coefficient of each indicator is equal to the sum of the weight coefficients of all dimensions included in the indicator, and the sum of the weight coefficients of the five indicators is 100%;

[0037] S6. To calculate the initial value of the brand value, the initial value of the indicator should be obtained by multiplying the valuation characteristic value of the indicator and the corresponding weight coefficient. The sum of the initial values ​​of the five indicators is the initial value of the brand;

[0038] S7. Market verification of the initial brand value: In combination with the stock price fluctuations in the secondary market, the weight coefficient is adjusted using the principal component analysis method or the entropy weight method. In this embodiment, the purpose of adjusting the weight coefficient is to make the calculated fluctuation trend of the brand value conform to the objective situation, that is, the market value change trend of similar companies listed in the secondary market;

[0039] S8. The original value of the brand value is determined. According to the adjusted weight coefficient, the initial value of the brand value is recalculated according to the method of step S6 to obtain the initial value of the brand value. In this embodiment, the larger the initial value of the brand value is, the higher the brand value is;

[0040] S9, brand value display value and visualization, that is, adjusting the brand value initial value to the brand value display value, and performing visualization according to actual needs. In this embodiment, the preferred solution is to multiply the brand value initial value with the largest value by the corresponding coefficient to obtain the brand value display value 100, and multiply the other brand value initial values ​​by the coefficient to obtain the corresponding brand value display value, and then arrange the brand value display values ​​according to actual needs for visualization, such as: stacked bar chart, bubble chart, rose chart, etc.

[0041] In summary, the present invention is not limited to the above specific embodiments. Those skilled in the art may make several changes and modifications without departing from the spirit and scope of the present invention. The scope of protection of the present invention shall be subject to the claims of the present invention.

Claims

1. A brand value valuation method based on multiple online data, characterized in that: The following steps are involved: S1. Determination of the scope of valuation object; S2, acquisition, collection, cleaning and conversion of online data; S3, generation of valuation feature values; S4, construction of the indicator system; S5. Determination of weight coefficient; S6. Calculate the initial brand value; S7. Market verification of initial brand value; S8. Determination of the original value of brand value; S9. Brand value display and visualization.

2. The brand value valuation method based on multi-party online data according to claim 1 is characterized in that: In step S1, the scope of the valuation object is determined based on the categories involved in the brand object to be valued, and competitive brands are selected as valuation objects with reference to public industry data; under the same category, the above-selected brands are used as the valuation brand pool.

3. The brand value valuation method based on multiple online data according to claim 2 is characterized in that: In step S2, it is necessary to obtain relevant data of the categories of different brands respectively, and organize and collect them according to the time period. The relevant data include: market size, social voice of the brand on the social platform, positive and negative public opinion, popularity index of the information platform, search index on the search engine, exposure, interaction, purchase and repurchase data of the e-commerce sales platform; then the organized and collected relevant data are cleaned and normalized.

4. The method for valuing brand value based on multi-party online data according to claim 3 is characterized in that: In step S3, valuation feature values ​​of different dimensions are generated based on the converted data, and the dimensions include: market share, current market size, market growth rate, brand exposure, user interaction, product purchase, user loyalty, public opinion volume, user praise, public opinion differentiation, search index, information index, search heat, popularity, brand added value, first place of large single products, and sales discount rate.

5. The method for valuing brand value based on multi-party online data according to claim 4, characterized in that: In step S4, the above dimensions are classified according to five indicators, namely, market performance, consumer preference, public opinion trend, brand popularity, and product competitiveness, to construct an indicator system.

6. The method for valuing brand value based on multi-party online data according to claim 5, characterized in that: In step S5, the weight coefficients corresponding to the above-mentioned indicators are determined according to industry public data or by using the AHP hierarchical analysis method.

7. The method for valuing brand value based on multi-party online data according to claim 6, characterized in that: In step S6, the initial value of the indicator is obtained by multiplying the valuation characteristic value of the indicator and the corresponding weight coefficient, and the sum of the initial values ​​of the five indicators is the initial value of the brand.

8. The method for valuing brand value based on multi-party online data according to claim 7, characterized in that: In step S7, the weight coefficient is adjusted by using the principal component analysis method or the entropy weight method in combination with the stock price fluctuation in the secondary market.

9. The method for valuing brand value based on multi-party online data according to claim 8, characterized in that: In step S8, the brand value initial value is obtained by recalculating according to the adjusted weight coefficient and the method of step S6.

10. The method for valuing brand value based on multi-party online data according to claim 9, characterized in that: In the step S9, the initial brand value is adjusted to the brand value display value, and visual display is performed according to actual needs.

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