Wine Market Analysis and Product Strategy Assessment Answer

Abstract

Wine has been argued to foster lifespan and cardiovascular health when consumed in moderation. Nevertheless, in recent years, wine consumption has decreased. The wine industry must comprehend these best indicators of wine quality. The relationship between wine tasting and specific consumer preferences can be understood by using regression analysis to achieve this. As a result, the business could be able to produce wines that are suited to the tastes of various groups. Additionally, this might enable people to decide with knowing how much wine they should drink in relation to their health. The objective of the current study was to analyze various wine quality indicators, assess their predictability, and identify relevant wine quality predictors. Alcohol, sulfates, volatile acidity, free sulfur dioxide, total sulfur dioxide, and chlorides were statistically significant predictors of wine quality at a 95% confidence level according to the multiple linear regression. The coefficient of variation of the regression model was 0.36. The results of this study may aid in improving wine production and quality by allowing winemakers and industry professionals to better understand the elements that affect wine quality. They can utilize this knowledge to make more informed decisions about their production procedures and the materials they employ. Additionally, this research may contribute to the creation of new tools and methods that will raise the standard of wine production. Other statistical models could however be used and compared with the OLS one to gauge their performances. Such models include neural networks, generalized additive models, and generalized linear models.

Introduction

Overview

The market for products can be improved with the use of product strategy and analytics. Product analytics entails looking at how consumers interact with a product or service to measure, evaluate, and analyze user interaction and behavior data to optimize and improve a product or service (Hajli et al., 2020). On the other hand, product strategy directs a team's work and links to every completed task, from major themes to minor details, to achieve objectives and enhance the product. Product analytics is a potent tool that can assist in developing and improving goods that satisfy consumer wants and preferences (Do, 2018). It is possible to gain a thorough knowledge of how the product is being used and how it may be improved through gathering and evaluating data on user behavior and feedback.

One of the important markets where product strategy and analytics can be used is the wine industry. To comprehend the state of the market and offer a distinctive trend forecast, the wine market size, share, analysis, and industry trends may be examined. To create a successful product strategy for wine quality, it is necessary to take a meticulous approach to identify the critical elements that influence wine quality overall, understand the target market and its preferences, and create a strategy to cater to those needs. To do this, one must first have a thorough understanding of the various quality aspects that influence the finished product. Some wine drinkers can be drawn to wines made organically, while others might be looking for distinctive flavor profiles or old vintages.

A product plan can then be developed to address the target market's particular needs once the target market has been identified. This may entail obtaining premium grapes from particular vineyards, using specialized processes for the wine's fermentation, or maturing it in particular kinds of barrels. Additionally, product analytics can be used to examine market trends, consumer preferences, and sales data, all of which can be used to gradually improve the product strategy (Aho, 2015). Wines of the highest caliber can be produced by employing a sophisticated and multidimensional approach to product planning and product analytics that caters to the wide range of demanding consumer preferences. This can further be boosted by the free availability of the datasets on the product.

This study, therefore, used open-source red wine quality data and correlation studies to analyze the significant determinants of wine quality. This would therefore help to raise wine quality and the wine market. The SPSS Software package was utilized to conduct the statistical analysis.

Study Objectives

1. To identify the quantity and the direction among an array of wine quality predictor
2. To determine the predictability of the predictors

• To find out the significant predictors of wine quality.

Data and Methods

The data dealing with wine components were retrieved from the Kaggle repository ( https://www.kaggle.com/datasets/uciml/red-wine-quality-cortez-et-al-2009/discussion ). The presented information exhibits an amalgam of chemical properties that characterize numerous varieties of wines, along with quality ratings ranging from 3 to 8. These details include fixed acidity, which indicates the presence of non-volatile acids in the wine, and volatile acidity, which measures the amount of acetic acid. The term "citric acid" refers to an element that occurs naturally and gives the wine its acidic flavor. As opposed to chlorides, which measure the salt content, residual sugar measures the quantity of sugar that remains in the wine after fermentation. Additionally, free sulphur dioxide and total sulphur dioxide levels distinguish the wine's sulphur content, while density shows the wine's weight per unit volume. The wine's pH measures how acidic it is, while sulphates, an addition, prevent microbial development by acting as a barrier. The amount of ethanol present in the wine is indicated by its alcohol content. The grades for wine quality depict the lower number indicating lower quality and the higher number indicating the highest quality. This dataset provides a thorough examination of the chemical composition of wine varieties and their related quality ratings.

Methods

The dataset was imported into the SPSS platform. The missing values analysis was conducted. When working with datasets from the real world, missing data can be a frequent problem. The cause of the missing data should be considered because it may have an impact on the analysis's statistical findings. There are various categories of missing data, including entirely random, randomly occurring, and not randomly occurring (Abaoud & Taylor, 2022). Before performing statistical analysis, it is crucial to handle missing data effectively. Using statistical models that can manage missing data, imputing missing data, or deleting missing data are some techniques for addressing the issue (Emmanuel et al., 2021).

Furthermore, the distribution of wine quality based on the dataset was checked with the utilization of a bar graph. Moreover, a box and whisker plot was developed to check the presence of outliers in the alcohol content of the wine in the dataset. A box and whisker plot, also known as a box plot, is a graphical representation of the distribution of a dataset through its quartiles (Walker et al., 2018). It is a useful tool for quickly visualizing the spread and skewness of the dataset.

Moreover, the pairwise Pearson correlation was conducted to find the relationship between the variables. The correlation index of 0.7 was used as a threshold for determining the highly correlated variables. This was necessary to eliminate them from the regression mode, therefore, eliminating the multicollinearity effect in the model. Since multicollinearity reduces the regression model's accuracy, it's crucial to get rid of the highly linked variables. The correlation index can be used as a threshold for this. This enhances the regression model's effectiveness and permits more precise predictions (Shrestha, 2020). Additionally, it aids in a better understanding of how each independent variable affects the dependent variable. A multiple regression model was then constructed using all the predictors and the wine quality as the target variable. The model was interpreted at a 95% confidence interval.

Results

The univariate analysis on individual columns depicted that the data had no missing values in any of its columns. The results are shown in the figure below.

The analysis of the wine quality distribution is depicted in the figure below.

The figure shows that the wine was a majorly scored quality of 5 followed by 6. The best and the worst qualities had the lowest counts among all wine qualities. The outlier detection analysis showed the outcome shown in the diagram below.

The figure above shows that the alcohol variable in the wine datasets has outliers. There could be many reasons why outliers occur in the alcohol content in wine data. Some possible reasons could include measurement errors, data entry errors, sampling errors, or natural variations in the production of the wine. Further, the relationship between the variables is shown in the figure below.

The wine dataset's correlation table shows the correlation coefficient between different pairs of variables. A statistical indicator of the strength and direction of the linear link between two variables is the correlation coefficient. The range of the correlation coefficient is -1 to 1, with a coefficient of 1 denoting a perfect positive correlation (as one variable rises, the other rises), a coefficient of -1 denoting a perfect negative correlation (as one variable rises, the other decreases), and a coefficient of 0 denoting no correlation (there is no linear relationship between the two variables). For instance, a mild negative association between fixed acidity and volatile acidity is indicated by the correlation coefficient of -0.256. Accordingly, volatile acidity tends to decrease as fixed acidity rises, albeit this association is not very strong.

Similar to how alcohol and citric acid have a 0.313 correlation coefficient, which shows a somewhat positive association between the two variables. Accordingly, citric acid tends to grow along with an increase in alcohol level, though this correlation is weak. Furthermore, the table depicted that there were no variable pairs with a strong correlation based on a 0.7 threshold. They were then all incorporated into the predictive regression model. The dependent variable's variance is 36.1% according to the model, which has an R-squared value of 0.361. The number of predictors in the model is considered in the adjusted R-squared value, which is 0.356. The estimate's standard error is 0.648.

The ANOVA table demonstrates that the regression model is statistically significant (F(11, 1587) = 81.348, p 0.001), indicating that there is a statistically significant link between at least one of the predictor variables and the dependent variable. The estimated coefficients (B) and standard errors for each predictor variable, as well as the standardized coefficients (Beta) and t-values, are displayed in the coefficients table. The standardized coefficients, which are standardized to have a mean of 0 and a standard deviation of 1, enable comparison of the relative relevance of the predictor variables. The t-values and corresponding p-values show the significance of each predictor variable's relationship to the dependent variable.

According to the coefficients table, there are statistically significant correlations between quality and alcohol, sulfates, volatile acidity, free sulfur dioxide, total sulfur dioxide, and chlorides (p 0.05). However, there are no statistically significant correlations between quality and citric acid, residual sugar, fixed acidity, density, or pH (p > 0.05).

Discussion

Wine is one of the world's most consumed beverages (Lloyd, 2017). Determining the main factors that affect its quality is therefore crucial. These consist of the winemaking processes, terroir, viticultural practices, and grape varieties. Next to the winemaker's expertise, the quality of the grapes plays the most important role. The wine's quality can be improved with the appropriate maturing procedure, a suitable blend of oak, and other components (Koufos et al., 2022). In addition to storage conditions, how wine is presented can have an impact on its quality.

The main goals of this study were to assess the quantity and direction of a variety of wine quality indicators, as well as their predictability and significance as predictors of wine quality. Several statistical methods and tools were used to analyze the data to accomplish these goals. The data had no missing values and therefore could be used without any prior data-cleaning procedures.

The alcohol variable in the wine dataset showed outliers, according to the box plot visualization. These anomalies may have been caused by several things, such as measurement errors, data entry problems, sample errors, natural fluctuation in wine production, harsh weather, or other environmental factors impacting grape growth and development (Gutiérrez-Escobar et al., 2021).

Based on a 0.7 criterion, the correlation analysis revealed that there were no variable pairs with a substantial association. This, therefore, enhanced the incorporation of all variables into the predictive regression model. The variance of the dependent variable in the model was 36.1%. This, therefore, indicates that there were other variables responsible for the remaining variance in the model.

Furthermore, the coefficients table revealed that quality and alcohol, sulfates, volatile acidity, free sulfur dioxide, total sulfur dioxide, and chlorides had statistically significant associations Conversely, the relationships between quality and citric acid, residual sugar, fixed acidity, density, or pH, however, were not statistically significant (p > 0.05). Wine quality has been demonstrated to be significantly influenced by alcohol concentration, while sulfates have been shown to enhance quality by preventing dangerous microorganisms (Spence, 2020). The taste and aroma of wine can be impacted by volatile acidity and free sulfur dioxide levels, which can also have an impact on quality (Khalafyan et al., 2023). Climate, viticulture, and winemaking are a few of the many aspects that have an impact on wine quality (Buckley Fine Wines, 2018) and could be as well be incorporated into the model.

Overall, this study offered insightful information about the connections between several wine quality indicators and wine quality. The results can help the wine industry make better decisions and raise the standard of wine production.

Limitations and recommendations

This study investigated the connection between the chemical composition of wine and its quality ratings. The research revealed that several elements, including sulfates, volatile acidity, and alcohol content, had a significant impact on wine quality. The study did, however, have certain shortcomings. First off, just one variety of wine was included in the dataset, which may not be entirely typical of all wine varietals. Also, other non-linear correlations might have gone unnoticed because the study only looked at linear relationships between the predictors and the target variable. Additionally, the study did not consider outside variables like grape variety or winemaking techniques that can affect wine quality.

It is advised that a larger and more varied dataset be used, which contains a wider range of wine varietals and quality ratings, to enhance future study in this field. Future research should also consider non-linear correlations between the predictors and the target variable, employing more advanced methods like machine learning algorithms. Finally, it is advised that additional elements including grape variety, winemaking techniques, and storage conditions be included in future studies as they may have an impact on wine quality. Future studies can help generate higher-quality wines by addressing these drawbacks and suggestions to provide a more thorough understanding of the elements that affect wine quality.

Conclusion

The study was carried out to examine the significant predictors of wine quality. All predictor variables were included in the regression model because the correlation analysis revealed that there were no variable pairings with a high association. The coefficients table showed no statistically significant correlations between quality and citric acid, residual sugar, fixed acidity, density, or ph. However, there were statistically significant correlations between quality and sulfates, alcohol, volatile acidity, free sulfur dioxide, total sulfur dioxide, and chlorides. Researchers and winemakers can benefit from these discoveries by bettering wine quality and comprehending the aspects that affect it.

References

Abaoud, A., & Taylor, J. M. (2022). Missing Data. Journal of Nursing Education . https://doi.org/10.3928/01484834-20220112-02

Aho, A. M. (2015). Product data analytics service model for a manufacturing company. Lecture Notes in Business Information Processing . https://doi.org/10.1007/978-3-319-21009-4_22

Buckley Fine Wines. (2018). The 4 Factors and 4 Indicators of Wine Quality . https://www.jjbuckley.com/wine-knowledge/blog/the-4-factors-and-4-indicators-of-wine-quality/1009

Do, N. (2018). Identifying experts for engineering changes using product data analytics. Computers in Industry . https://doi.org/10.1016/j.compind.2017.12.004

Emmanuel, T., Maupong, T., Mpoeleng, D., Semong, T., Mphago, B., & Tabona, O. (2021). A survey on missing data in machine learning. Journal of Big Data . https://doi.org/10.1186/s40537-021-00516-9

Gutiérrez-Escobar, R., Aliaño-González, M. J., & Cantos-Villar, E. (2021). Wine polyphenol content and its influence on wine quality and properties: A review. In Molecules . https://doi.org/10.3390/molecules26030718

Hajli, N., Tajvidi, M., Gbadamosi, A., & Nadeem, W. (2020). Understanding market agility for new product success with big data analytics. Industrial Marketing Management . https://doi.org/10.1016/j.indmarman.2019.09.010

Khalafyan, A., Temerdashev, Z., Abakumov, A., Yakuba, Y., Sheludko, O., & Kaunova, A. (2023). Multidimensional analysis of the interaction of volatile compounds and amino acids in the formation of sensory properties of natural wine. Heliyon . https://doi.org/10.1016/j.heliyon.2023.e12814

Koufos, G. C., Mavromatis, T., Koundouras, S., Fyllas, N. M., Theocharis, S., & Jones, G. V. (2022). Greek Wine Quality Assessment and Relationships with Climate: Trends, Future Projections, and Uncertainties. Water (Switzerland) . https://doi.org/10.3390/w14040573

Lloyd, B. (2017). Wine is one of the most consumed beverages worldwide . https://www.vintage99.com/post/wine-is-one-of-the-most-consumed-beverages-worldwide

Shrestha, N. (2020). Detecting Multicollinearity in Regression Analysis. American Journal of Applied Mathematics and Statistics . https://doi.org/10.12691/ajams-8-2-1

Spence, C. (2020). Wine psychology: basic & applied. In Cognitive Research: Principles and Implications . https://doi.org/10.1186/s41235-020-00225-6

Walker, M. L., Dovoedo, Y. H., Chakraborti, S., & Hilton, C. W. (2018). An Improved Boxplot for Univariate Data. American Statistician . https://doi.org/10.1080/00031305.2018.1448891

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