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Under the boom of market economy in China, marketing mix is being updated constantly to facilitate the allround development of various industries. To accurate evaluate the effect of corporate marketing mix, this paper designs an effect analysis model for corporate marketing mix based on artificial neural network (ANN). Firstly, a complete evaluation index system (EIS) was created for corporate marketing mix, and the weight of each index was assigned through analytic hierarchy process (AHP). Then, a marketing mix of big marketing + service marketing was proposed, and subject to fuzzy comprehensive evaluation (FCE). Finally, an FCE model was constructed for the effect of corporate marketing mix, based on threelayer backpropagation neural network (BPNN). The effectiveness of the model was verified through experiments. The research findings provide a reference for the application of ANN in other fields of effect analysis.
artificial neural network (ANN), marketing, marketing mix, effect analysis
In recent years, China has made great progress in the development of market economy. With the dawn of the new economic era, the global market continues to merge, and the marketing demand expands in virtually every industry. To facilitate allround industrial development, marketing mix is being constantly updated [14]. Corporate marketing is mainly affected by four factors, namely, product, price, channel, and promotion. The effectiveness of corporate marketing mix relies on the strategic choice and integrated use of the four factors, both of which greatly promote the brand building, personalization, and contextualization of corporate marketing [59].
The existing studies on corporate marketing mix mainly propose innovative theories and models [1014]. Kouchaki and Jami [15] sorted out the four major strategies of marketing mix, pointing out that marketing mix is mainly affected by the selection of target market, corporate marketing environment, corporate strategy of marketing development, and situation of corporate resources. Ma [16] developed an innovative marketing mix model for smart terminal services, proposed a 6P+7P marketing mix strategy based on the theory of big marketing, and constructed an operation framework for smart terminal services. Intyas and Primyastanto [17] innovated the marketing strategy of household appliances, and proposed a 4P+4C marketing mix theory, including product + consumer, price + cost, place + convenience, and promotion + communication; this strategy is driven by consumer demand, acceptance cost, convenience, communication, and interaction. Wahyuningrum et al. [18] designed a 4V marketing mix based on differentiation, maximal added value, functional flexibility, and resonance, and put forward marketing mix strategies for the marketing of main melody films in China, e.g. commercial packaging, opening precision marketing channels, and crossindustry cooperation.
Many scholars have explored corporate marketing mix in the context of social network or the Internet [1921]. Zhang and Yang [22] suggested that corporate marketing in the new economic era needs to rely on the Internet, and identified the main features of marketing, namely, globalization, mobility, refinement, fragmentation, and contextualization, calling for innovative models of Internet marketing. Bukova et al. [23] discussed the trend of corporate marketing mix in the digital age, and proposed to make improvement in terms of technical operation, crossindustry operation, and onlineoffline integration. Alizadeh and Serdaroglu [24] summarized the influencing factors and features of marketing in mobile social network, discovered problems like massive data and backward management, and conducted an empirical analysis. Moradi et al. [25] systematically explored multiple issues in online marketing (e.g. data collection, information dissemination, influence analysis, and user feedback), and designed data acquisition systems for platforms suitable for online marketing (e.g. WeChat, Weibo, and Taobao), based on search engine algorithms and online data features.
Artificial neural network (ANN) is a powerful tool for selflearning, selfadaptation, and data processing. In the effect analysis of corporate marketing mix, the ANN can reduce the constraints of artificial nonlinear factors, making the analysis more accurate. Therefore, the ANN has obvious advantages over traditional methods for marketing effect analysis. For this reason, this paper designs an ANNbased effect analysis model for corporate marketing mix. Firstly, a complete evaluation index system (EIS) was constructed for the effect of corporate marketing mix, and the weight of the index was determined through analytic hierarchy process (AHP). Then, the authors proposed the marketing mix of big marketing + service marketing, and conducted fuzzy comprehensive evaluation (FCE) of the marketing mix. After that, an FCE model was constructed based on threelayer backpropagation neural network (BPNN). The effectiveness of the model was verified through experiments.
For better effect analysis on corporate marketing mix, a fourlayer hierarchical EIS was established, consisting of 4 primary indices (financial benefits, consumer benefits, internal operations, learning & growth), 13 secondary indices, and 33 tertiary indices.
Layer 1 (goal):
E={effect of corporate marketing mix}
Layer 2 (primary indices)
E={E_{1}, E_{2}, E_{3}, E_{4}}={financial benefits, consumer benefits, internal operations, learning & growth}
Layer 3 (secondary indices)
E_{1}={E_{11}, E_{12}}={income, cost}
E_{2}={E_{21}, E_{22}, E_{23}, E_{24}, E_{25}}={attention, spread, search popularity, purchase, user perception}
E_{3}={E_{31}, E_{32}, E_{33}}={platform design, operation management, publicity}
E_{4}={E_{41}, E_{42}, E_{43}}={learning ability, innovation ability, growth & progress}
Layer 4 (tertiary indices)
E_{11}={E_{111}, E_{112}, E_{113}}={online sales, sales growth rate, market share of online products}
E_{12}={E_{211}, E_{212}}={sales expense ratio, platform investment as a proportion of marketing expense}
E_{21}={E_{21}}={product fans}
E_{22}={E_{221}, E_{222}, E_{223}}={reads, likes, forwards}
E_{23}={E_{231}, E_{232}, E_{233}}={activity visits, visitor growth rate, consultations}
E_{24}={E_{241}, E_{242}, E_{243}}={online turnover, conversion rate, repurchase rate}
E_{25}={E_{251}, E_{252}}={user satisfaction, user loyalty}
E_{31}={E_{311}, E_{312}, E_{33}}={content universality, functional comprehensiveness}
E_{32}={E_{321}, E_{322}, E_{323}, E_{324}}={push frequency, total number of partners/service providers, product update frequency, troubleshooting speed}
E_{33}={E_{331}, E_{332}}={online + offline activities, number of backlinks}
E_{41}={E_{411}, E_{412}, E_{412}}={training expense of marketing staff, employee skill improvement, employee participation}
E_{42}={E_{421}, E_{422}}={improvement of platform functions, security and mobility of online transactions}
E_{43}={E_{431}, E_{432}}={increment of corporate recognition, influence on public platforms}
The weight of each index in the EIS was assigned through AHP.
Step 1. Construct the judgment matrix.
The weight coefficient of each index relative to the goal layer was determined based on the actual survey results and the value of each index. After pairwise comparison, a judgement matrix was constructed against a 9point scale.
Step 2. Sort the indices on each layer and calculate consistency index.
The latent roots and eigenvectors of the judgment matrix JM were calculated and sorted:
$JM\cdot W={{\lambda }_{max}}\cdot W$ (1)
where, λ_{max} is the largest latent root of judgement matrix JM; W is the normalized eigenvector. The component ω_{t} of W is the weight of the corresponding index in singlelayer ranking. Finally, weight ω_{t} was determined by computing λ_{max} and W by sumproduct method.
Next, the judgement matrix JM constructed by the decisionmaker of corporate marketing mix was subject to consistency test. The consistency index can be calculated by:
$CI=\frac{{{\lambda }_{\max }}n}{n1}$ (2)
where, n is the dimension. The consistency ratio CR can be calculated by:
$CR=\frac{CI}{RI}$ (3)
where, RI is the average random consistency index, which can be obtained by looking up the table.
The effect of a corporate marketing mix strategy largely depends on the reallife influence of the strategy over the consumer attention to product, spread of advertisement, search popularity, purchase, and user perception of product. Based on online marketing big data, this paper analyzes the consumer influence of corporate marketing mix strategy. Figure 1 explains the principle of online marketing big data acquisition system. Figure 2 illustrates the principle of a single data acquisition module. The influence of corporate marketing mix strategy can be calculated by:
$INF=N+\frac{\sum\limits_{i=0}^{P}{{{S}_{i}}}}{P}$ (4)
where, N is the number of consumers interested in the product; P is the total number of positive reviews of the product; S_{i} is the spread of the ith positive review:
${{S}_{i}}={{\mu }_{L}}{{P}_{ai}}+{{\mu }_{C}}{{P}_{Ci}}+{{\mu }_{F}}{{P}_{Fi}}+{{\mu }_{CF}}{{P}_{CFi}}$ (5)
where, P_{L}_{i}, P_{C}_{i}, P_{F}_{i}, and P_{CFi }are the number of likes, comments, forwards, and forward + comments of the ith positive review, respectively; μ_{L}, μ_{C}, μ_{F}, and μ_{CF} are the proportional coefficients of P_{L}_{i}, P_{C}_{i}, P_{F}_{i}, and P_{CFi}, respectively. The proportional coefficients satisfy:
${{\mu }_{L}}+{{\mu }_{C}}+{{\mu }_{F}}+{{\mu }_{CF}}=1$ (6)
$\text{0}<{{\mu }_{L}}<{{\mu }_{C}}<{{\mu }_{F}}<{{\mu }_{CF}}<1$ (7)
The excess difference in product involvement might hinder the quantification of influence. To solve the problem, the natural logarithm e was introduced:
$INF={{N}^{\text{1/}e}}+\frac{\sum\limits_{i=0}^{P}{{{S}_{i}}}}{P}$ (8)
Figure 1. The principle of online marketing big data acquisition system
Figure 2. The principle of a single data acquisition module
Simple accumulation cannot reflect the exact trend of influence, and the magnitude difference of massive forwards needs to be narrowed down to a suitable range. As a result, the square root calculation was introduced:
$INF={{N}_{Day}}^{\text{1/}e}+\sqrt{\sum\limits_{i=0}^{{{P}_{Day}}}{{{S}_{Dayi}}}}$ (9)
where, S_{Dayi} is the spread of the ith positive review on a day; P_{Day} is the total number of positive reviews on that day. After summing and averaging the daily influence, the influence ranking among consumers can be obtained through horizontal comparison:
$IN{{F}_{total}}=\frac{\sum\limits_{Day}{INF}}{P}$ (10)
User perception of a product can be measured in seven dimensions: love, dislike, like, anger, sadness, fear, and indifference. Here, the sentiment value of each product review is calculated by weighted summation. The sentiment value SV_{i} of the ith review can be computed by:
$S{{V}_{i}}=\sum\limits_{Q}{{{\sigma }_{j}}*AD{{J}_{j}}}*{{\left( NEG \right)}^{{{q}_{j}}}}*{{\lambda }_{j}}*{{\upsilon }_{j}}$ (11)
where, Q is the number of emotional words in the ith review; σ_{j} is the strength of adversative (σ_{j}=1 if there is no such word); ADJ_{j} is the adjective before the ith emotional word in the review (ADJ_{j}=1 if there is no such word); NEG=1 is the negative word before the ith emotional word in the review; q_{j} is the number of negative words (q_{j}=0 if there is no such word); λ_{j} is the strength of emotional word; υ_{j} is the polarity of word. Figure 3 shows the analysis process of product reviews.
Figure 3. The analysis flow of product reviews
Figure 4. The proposed marketing mix strategy
Figure 5. The operation framework of the proposed strategy
Considering the features and interactions of marketing strategies for different environments, industries, and products, this paper puts forward a marketing mix strategy of big marketing and service marketing (Figure 4). The proposed strategy aims to achieve the following goals: high product quality, low price differentiation, comprehensive channels, and enhanced consumer participation. In addition, modern information technology (IT) was integrated to manage the operation process of the marketing mix fully and effectively. The operation framework of the proposed strategy is clarified in Figure 5.
The proposed marketing mix strategy was subject to FCE through the following steps:
Step 1. Construct the EIS of the proposed strategy:
$E=\left\{ E_{\text{1}}^{\text{(1)}},E_{\text{2}}^{\text{(1)}},...,E_{N}^{\text{(1)}} \right\}$ (12)
$E_{i}^{\text{(1)}}=\left\{ E_{i1}^{\text{(2)}},E_{i2}^{\text{(2)}},...,E_{i{{K}_{i}}}^{\text{(2)}} \right\} $ (13)
$E_{ij}^{\text{(2)}}=\left\{ E_{ij1}^{\text{(3)}},E_{ij2}^{\text{(3)}},...,E_{ij{{L}_{ij}}}^{\text{(3)}} \right\} $ (14)
where, K_{i}^{(1)} is the number of secondary indices under the ith primary index; L_{ij}^{(3)} is the number of tertiary indices under the jth secondary index of the ith primary index.
Step 2. Determine the set of evaluation levels:
$R=\left\{ {{r}_{1}},{{r}_{2}},...,{{r}_{M}} \right\}$ (15)
where, M is the number of evaluation levels r_{k} for the evaluation of a specific index.
Step 3. Construct the fuzzy relationship matrix F based on singlefactor evaluation
Take primary indices for example to illustrate the matrix construction process. First, the ith index E_{i} of the EIS was adopted for evaluation. Let δ_{ij} be the membership of index E_{i} to the jth evaluation level. Then, the result of index E_{i} can be expressed as a fuzzy set F_{i}, a fuzzy subset (i.e. the single factor evaluation set) of the set of evaluation levels R:
${{F}_{i}}=\left( {{\delta }_{il}},{{\delta }_{i2}},...,{{\delta }_{iM}} \right) $ (16)
Taking the memberships of each single factor evaluation sets as the elements in a row, a matrix can be established as:
${{\Phi }_{i}}=\left\{ \begin{matrix} {{r}_{il}} \\ {{r}_{i2}} \\ \vdots \\ {{r}_{iM}} \\\end{matrix} \right.\left[ \begin{matrix} \delta _{11}^{i} & \delta _{12}^{i} & \cdots & \delta _{1M}^{i} \\ \delta _{21}^{i} & \delta _{22}^{i} & \cdots & \delta _{2M}^{i} \\ \cdots & \cdots & \cdots & \cdots \\ \delta _{k1}^{i} & \delta _{k2}^{i} & \cdots & \delta _{kM}^{i} \\\end{matrix} \right]\text{ }$ (17)
The above analysis shows that the construction of the fuzzy relationship matrix is to solve the single factor evaluation vectors, i.e. obtain the single factor decisions. Since the indices of our strategy are all quantitative, the membership δ_{ij} of index E_{i} to level r_{j} can be determined through linear analysis.
Step 4. Determine the weight set W for the indices.
Primary indices were still adopted to explain the construction of the weight set. Considering the importance difference between indices, each index E_{i} should be assigned a reasonable weight ω_{i}. The weight set W of the indices can be expressed as:
$W=\left( {{\omega }_{1}},{{\omega }_{2}},...,{{\omega }_{N}} \right)$ (18)
The weight set W can be regarded as a fuzzy subset of E.
Step 5. Perform the primary FCE.
Using the fuzzy operator, the fuzzy relationship matrix F=(F_{1}, F_{2}, …, F_{n})^{T} can be established as:
$\Phi_{i}=\left(\omega_{i 1}^{(2)}, \omega_{i 2}^{(2)}, \ldots, \omega_{i K_{i}}^{(2)}\right) \cdot\left[\begin{array}{cccc}r_{11}^{(2)} & r_{12}^{(2)} & \cdots & r_{1 M}^{(2)} \\ r_{21}^{(2)} & r_{22}^{(2)} & \cdots & r_{2 M}^{(2)} \\ \vdots & \vdots & \vdots & \vdots \\ r_{K_{i} 1}^{(2)} & r_{K_{i} 2}^{(2)} & \cdots & r_{K_{i} M}^{(2)}\end{array}\right]=\left(\phi_{1}, \phi_{2 \cdots}, \phi_{K_{i}}\right)$ (19)
where, (ω_{i}_{1}^{(2)}, ω_{i}_{1}^{(2)}, …, ω_{iKi}^{(2)}) is the ranking weight vectors of the secondary indices under the ith primary index.
Step 6. Perform the secondary FCE.
The fuzzy relationship matrix of secondary FCE can be defined as:
$\Phi_{i j}=\left(\omega_{i j 1}^{(3)}, \omega_{i j 2}^{(3)}, \ldots, \omega_{i j L_{i j}}^{(3)}\right) \cdot\left[\begin{array}{cccc}r_{11}^{(3)} & r_{12}^{(3)} & \cdots & r_{1 K_{i}}^{(3)} \\ r_{21}^{(3)} & r_{22}^{(3)} & \cdots & r_{2 K_{i}}^{(3)} \\ \vdots & \vdots & \vdots & \vdots \\ r_{L_{i j} 1}^{(3)} & r_{L_{i j}^{2}}^{(3)} & \cdots & r_{L_{i j} K_{i}}^{(3)}\end{array}\right]$ (20)
where, (ω_{ij}_{1}^{(3)}, ω_{ij}_{1}^{(3)}, …, ω_{ijLij}^{(3)})is the ranking weight vectors of the tertiary indices under the jth secondary index of the ith primary index. The results of primary and secondary FCEs were combined into the final evaluation of the proposed strategy.
Step 7. Analyze the FCE results and finalized the evaluation level.
Figure 6. The workflow of the FCE
Due to the sheer number of evaluation indices, the FCE results were processed by the weighted average method to determine the levels and sort the weights of multidimensional fuzzy vectors. Figure 6 explains the workflow of our FCE strategy. Taking φ_{j} as the weight, the weighted average of the evaluation levels r_{j} can be conducted by:
${{r}_{j}}=\sum\limits_{j=1}^{M}{{{{{r}'}}_{j}}{{\phi }_{j}}/\sum\limits_{j=1}^{M}{{{\phi }_{j}}}}$ (21)
where, r'_{j} is the quantified value of level r_{j}. The normalized result of the evaluation can be expressed as:
$FCE=\Phi \cdot {{R}^{T}}$ (22)
This paper designs an FCE model based on threelayer BPNN, which takes the features of corporate marketing mix strategy as the input vectors, and the FCE values as the outputs. The overall structure of the threelayer BPNN is described in Figure 7. Below is a detailed introduction of the network.
Figure 7. The structure of threelayer BPNN
In the input layer, the nodes were determined according to the data representation method and the problem to be solved in the evaluation of corporate marketing mix strategy. Let N be the number of input layer nodes. The input and output of the layer can be respectively expressed as:
$I_{s}^{1}={{a}_{s}}$ (23)
$O_{st}^{1}={{a}_{s}}$ (24)
The intermediate layer solves the membership of each input index to each evaluation level. Suppose there are M evaluation indices in the network. Then, the intermediate layer needs to have M*N nodes. The membership was described by the following trigonometric function:
If t=1, B_{t} corresponds to indices b_{1} and b_{2}, and the membership function can be expressed as:
$\mu_{1}(a)=\left\{\begin{array}{ll}1 & a \leq b_{1} \\ \frac{b_{2}a}{b_{2}b_{1}} & b_{1}<a<b_{2} \\ 0 & a>b_{2}\end{array}\right.$ (25)
If 1<t<N, B_{t} corresponds to indices b_{t}_{1}, b_{t}, and b_{t}_{+1}, and the membership function can be expressed as:
$\mu_{t}(a)=\left\{\begin{array}{ll}0 & a \leq b_{t1}<o r a \geq b_{t+1} \\ \frac{ab_{t1}}{b_{t}b_{t1}} & b_{t1}<a<b_{t} \\ \frac{b_{t+1}a}{b_{t+1}b_{t}} & b_{t} \leq a \leq b_{t+1}\end{array}\right.$ (26)
If t=N, B_{t} corresponds to indices b_{N}_{1 }and b_{N}, and the membership function can be expressed as:
$\mu_{N}(a)=\left\{\begin{array}{lc}0 & x \leq b_{N1} \\ \frac{xb_{N1}}{b_{N}b_{N1}}& b_{N1} <x \leq b_{N} \\ 1 & x>b_{N}\end{array}\right.$ (27)
The input and output of the intermediate layer can be respectively expressed by:
$I_{st}^{2}=O_{st}^{1}$ (28)
$O_{st}^{2}={{B}_{st}}\left( {{a}_{t}} \right)$ (29)
where, B_{st}(a_{t}) is the membership function of the tth evaluation level. The output O^{2}_{st} is the membership of the tth evaluation level, that is, the degree of fuzzy relationship between the sth index and the tth level.
The output layer makes the FCE of the input vectors of the corporate marketing mix strategy, and derives the evaluation vectors corresponding to the evaluation level domain from the evaluation level domain. The input and output of the output layer can be respectively expressed by:
$I_{st}^{2}=O_{st}^{\text{2}}$ (30)
$O_{st}^{\text{3}}=\sum\limits_{t=1}^{M}{{{\omega }_{t}}I_{st}^{\text{3}}}$ (31)
In this paper, the secondary FCE results (Table 1) are obtained by fuzzy fusion of the tertiary indices, which are designed to evaluate the marketing mix strategy of big marketing + service marketing, and the primary FCE results are obtained by fuzzy fusion of the secondary FCE results. Considering the relevant weights, the final FCE result of the proposed strategy was determined. Next, 600 data samples of the final result were imported to the BPNN for training, so that the result can approximate the complex mapping between fuzzy evaluation results and the ideal values of the indices. After 3,400 iterations, the BPNN gradually converged. Figure 8 records the decrease of the approximation error with the growing number of iterations.
Table 1. The secondary FCE results
Secondary indices 
Levels 

Strongly good 
Moderately good 
Slightly good 
Neutral 
Slightly bad 
Moderately bad 
Strongly bad 

E_{11} 
0.04 
0.20 
0.33 
0.25 
0.12 
0.04 
0.02 
E_{12} 
0.02 
0.38 
0.22 
0.20 
0.14 
0.03 
0.01 
E_{21} 
0.09 
0.21 
0.24 
0.19 
0.22 
0.03 
0.02 
E_{22} 
0.08 
0.30 
0.33 
0.10 
0.12 
0.04 
0.03 
E_{23} 
0.02 
0.34 
0.30 
0.20 
0.10 
0.02 
0.02 
E_{24} 
0.06 
0.30 
0.20 
0.19 
0.16 
0.05 
0.05 
E_{25} 
0.02 
0.22 
0.30 
0.21 
0.11 
0.05 
0.09 
E_{31} 
0.09 
0.31 
0.28 
0.10 
0.18 
0.03 
0.01 
E_{32} 
0.13 
0.37 
0.21 
0.12 
0.11 
0.05 
0.01 
E_{33} 
0.09 
0.37 
0.18 
0.10 
0.18 
0.06 
0.02 
E_{41} 
0.08 
0.29 
0.30 
0.10 
0.19 
0.03 
0.01 
E_{42} 
0.03 
0.31 
0.21 
0.18 
0.20 
0.05 
0.02 
E_{43} 
0.07 
0.39 
0.11 
0.11 
0.26 
0.05 
0.01 
Figure 8. The decrease of approximation error with the growing number of iterations
Figure 9. The timevariation of the number of forwards
To verify the effect of our model on the influence of corporate marketing mix strategy, the proposed model was applied to analyze the big data of online marketing of sports brands. The proposed information acquisition system directly collects the online big data, eliminating the human impact on the marketing process. The collected data were analyzed by visualization technology, providing an intuitive picture to the effect of the marketing mix.
Figure 9 illustrates how the number of forwards of positive reviews on the products of different brands varies with time. It can be seen that, under our marketing mix strategy, the positive reviews on the products of brands 1, 2, and 5 were forwarded much more frequently than those of other brands. The forwarding of positive reviews is positively correlated with the brand influence. This result is confirmed by the spread of positive reviews on the products of different brands (Figure 10). The widely spread reviews have a nonnegligible impact on product optimization.
Figure 10. The spread depth of positive reviews
Figure 11. The trend of consumer influence of corporate marketing mix strategy
Figure 12. The trend of reviews on products of different brands
More than 10 million data samples on positive reviews, forwards, and likes were summarized, averaged, and normalized. Based on the proportional coefficients μ_{L}, μ_{C}, μ_{F}, and μ_{CF}, the trend of consumer influence of corporate marketing mix strategy was plotted (Figure 11). Combining Figures 8, 9, and 11, it can be seen that consumer influence was not closely correlated with the number of reviews or followers in important periods; neither was consumer influence deeply associated with the frequency of reviews. Hence, the semantic information in reviews needs to be further mined.
Figure 12 presents the trend of reviews on products of three brands. Obviously, the product reviews on the three brands tended to be positive. Despite the presence of negative words, the reviews have positive impact on product optimization. Figure 13 displays the influence of our strategy on the sentiments of reviews and product optimization.
Figure 13. The influence of our strategy on review sentiments and product optimization
This paper presents an ANNbased effect analysis model for corporate marketing mix. First, the authors designed a complete EIS for the effect of corporate marketing mix, and determined the weight of each index through AHP. Based on the secondary FCE results, the final FCE result of corporate marketing mix was calculated, and adopted as the inputs of the ANN. On this basis, a corporate marketing mix strategy was established by combining big marketing with service marketing, and subject to the FCE. Finally, an FCE model was created for the effect analysis of corporate marketing mix, based on threelayer BPNN. Finally, the proposed model was proved effective through experiments, and the consumer influence of our strategy was analyzed based on the model outputs.
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