Machine Learning In Retail Is Transforming Customer Experience Essay.

How Machine Learning in Retail is Transforming Customer Experience

Based on the article, the following are discussed:

Industry: The industry where analytics is applied is the retail industry where business analytics is intended to boost customer experience.

Potential and meaningful business problem to be solved: The main business problem that is discussed here is the changing demands of consumers regarding the company products. This directly affects sales and the overall revenue realized by firms. Therefore, the companies' main problem is to address ever-changing consumer specifications and demand concerning their products which should be addressed to ensure smooth sales.

Types of analytics used: The article takes into use the four types of analytics: descriptive, diagnostic, predictive, and prescriptive analytics. Using historical data, the firms analyze the information to come up with trends and seasonality in the sales of the past product. This is important in providing descriptive insights regarding the company sales. For the diagnostic analytics, the companies are said to check into social media sites to establish customer experiences, a factor that enables them to know the reason behind sales’ increase or decline. Predictively, the article states that the companies develop models such as regression analysis with significant sales predictors; an equation that can be used to forecast future sales. Finally, based on the established results on the factors affecting company sales, companies can come up with data-driven conclusions that are prescriptive to the future decisions by the company.

The main challenge of the analytics: The main challenge is that the companies must employ highly skilled personnel to handle the data as unskilled individuals will lead to wrong analysis and interpretation thus misleading business decisions and eventually business failure. Equally, there is a larger set of data from sales regarding consumer behavior, a factor that demands a high-tech and complex technology such as ERP systems; such systems are expensive to acquire and demand a high cost of maintenance thus another challenge.

Recommendation: The stakeholders can come up with a business analytic tool that automatically records, analyzes, and compare daily sales records regarding consumer demands to come up with patterns. This will help avoid data logging and omission of daily trends hence providing a more insightful conclusion (Bongsug & David, 2013).

How the use of data and AI is transforming the Automotive Industry?

Based on the article, the following are discussed:

Industry: The industry where the analytics is applied is the automobile industry where business analytics is intended to come up with vehicles automated with artificial intelligence (AI).

Potential and meaningful business problem to be solved: The main business problem that is discussed here is how to ensure that automotive companies and consumers benefit from artificial intelligence. For instance, how vehicles can be automated based on the available data to ensure the companies produce vehicles that are loved by consumers in which case, the producers benefit from sales and consumers benefit from getting vehicles within their specifications.

Types of analytics used: The main analysis method used in this article is predictive analytics using artificial intelligence. This is evident by the statement that by using artificial intelligence (AI), it is possible to develop models that make predictions and respond to changing situations; a step that exceeds the abilities of the current vehicle automation with applications for driving experience, maintenance, and manufacturing. This analytic, as stated, is key to coming up with vehicles that are user friendly and those that are less pollutant to the environment depending on the previous data on customer specifications and company policy.

Potential and meaningful business problem to be solved

The main challenge of analytics: As stated in the article, this will require upskilling of workers to operate the artificial intelligence. This will not only take time but will also be expensive to implement. Equally, most automotive companies rely on big software companies to develop predictive analytic software. This involves a huge sum of money in acquisition and maintenance which is a challenge.

Recommendation: While embracing technology for artificial predictive analytics, it is important for automotive manufacturers to specifically employ already skilled software engineers that will offer training to existing and new employees at affordable costs. As well, the companies must practice inventions and innovations which will ensure the predicted factors are realized hence the purpose of predictive analytics met (Patrizia, et al., 2020).

The business analytics chosen for this case are diagnostic and predictive analytics. By definition, diagnostic analytics is an analytic kind where the historical data is used to evaluate the current events to establish possible a cause-and-effect relationship (Valadares de Oliveira, et al., 2022). On the other hand, predictive analytics is the kind where historical data are used in making a forecast concerning a business problem; this is usually done through the development of predictive models such as linear regression (Buric, et al., 2022). While these are different analytics, they have one aim, that is, to make a data-driven decision for the overall success of a business. In the real world, the two analytic approaches are used in establishing the sales revenue in the fashion industry. I select the industry considering that it is the most dynamically changing based on consumer preference. For instance, a newly designed women’s cap won by a celebrity will likely attract millions of people to purchase the product until another celebrity comes up with a new cap design that is considered more current (Patrizia, et al., 2020). Thus, there is a greater variation in product sales within this industry that may push some manufacturers out of the market if a proper market analysis is done. This calls for the widespread use of diagnostic and predictive analytics. Diagnostically, a fashion company analyst, like that for the Italian fashion company, given the firm’s historical data, may develop sales trends. This is a key driving force in studying why the fluctuations persist and what are the possible causes. To establish this, several possible influencers of fashion, such as the number of celebrities embracing, the number of alternative products, product price, color, and advertisement levels can be modeled to check if they are significant influencers of fashion product revenue for the Italian fashion firm. Based on the significant number of predictors, a conclusion will be made on what factors to be included in addressing the revenue problem for the firm. In conjunction with this, the resulting model for relationship is used in making a forecast of the future company revenue thus incorporating predictive analytics. This will be done to come up with the future effects of the current conditions on fashion product sales. With such an analytic, it becomes possible to control for the negative influencers of Fashion revenue hence ensuring higher profit gains (Rao & Provodnikova, 2021). Besides, it helps ensure that no future losses or unfair competition, factors that have proven unhealthy for the fashion business, be controlled for the smooth and effective running of the business. It is evident from this case that though the two model analytics are different, they work hand in hand and are both vital in upholding a better performance in the business world.

1. How do we best ingrain analytics into the organization’s decision-making processes?

Types of analytics used

Making an organizational decision by the conglomerate to scale back its existing operation on low revenue products based on the low revenue is not appropriate. This is considering the fact the processes of insightful data-driven decisions are not taken into consideration, for instance, the diagnostic causes of the low business revenue and the decisions regarding how influential factors can be controlled (Sharma, et al., 2014). Therefore, for an effective decision-making process, the firm should take into consideration the data-driven decision process that includes setting specific goals, selecting the sources of data, setting metrics, and monitoring how the metrics progress regularly. Since the firm’s main intention is to make a profit, a reason why it is transforming from low revenue products to next-generation products (Rao & Provodnikova, 2021). Through the firm’s historical data regarding consumer behavior, revenue by product, sales by region, and the worker productivity since its inception or since the matters associated with low revenue began, it is possible for the firm to initiate the four processes of business analytics (descriptive, diagnostics, predictive and prescriptive) to provide a business insight that can direct to the efficient decision-making process on whether to proceed with the old business or embrace the emerging technology. Through diagnostics analytics, the business will be able to relate the possible significant influencers of revenue in the low sales region (Vasarhelyi, 2018). As such, insightful problem conclusions will be made on what factors to be addressed to realize high sales. As well, prescription analytics can be applied to come up with data-driven driven decisions that help solve the underlying problem of low sales (Shabbir & Gardezi, 2020). Subsequently, the made decisions can then be implemented for a given period, say a year to be observed then a new descriptive and inferential comparison be done to check if there is a significant improvement in the sales and how close is the revenue to the projected values. If the problem persists, then the idea of investing in the new generation technology can be embraced under strict adherence to market and product study to avoid future losses or low revenue.

2. How do we organize and coordinate analytics capabilities across the organization?

Analytics can be organized through the use of a single ERP system that collects data from different departments. Assume that the conglomerate has a single software that incorporates daily data from all the departments and analyzes the information both per department and the company in general (Bongsug & David, 2013). It is from such automatically analyzing software that analysis and company performance can be shared with every department at the same time. As well, the coordination between company subcategories is done hence meaningful business decisions are made and shared (Rao & Provodnikova, 2021).

3. How should we source, train and deploy analytics talent?

For an effective data-driven decision, there is a requirement for a strong analytic team that ranges from a data scientist that performs data analytics through programming and mathematics; to data engineers who deal with building, designing, and maintaining datasets that can be used in projects; and data analysts that perform direct analysis (Rao & Provodnikova, 2021). From these three professions, a blending can be established to achieve that ensures a strong analytic team. The conglomerate can therefore employ a data analytic team from the blend to train a team of experts that will efficiently analyze data (Vasarhelyi, 2018). As well, the company can start a creative and innovative team under the guidance of the data analytic professionals to come up with a strong analytic team that helps achieve data-driven decisions.

References

Bongsug, C. & David, O., 2013. Business analytics for supply chain: a dynamic-capabilities framework. International Journal of Information technology & Decision Making, 12(1), pp. 9-26.

Buric, M. N., Raicevic, M., Kascelan, L. & Kascelan, V., 2022. Socio-Demographic Impacts on the Personal Savings Portfolio Choice: A Decision Tree Approach. International Journal of Business Analytics (IJBAN), 9(1), pp. 1-23.

Patrizia, G., Enrica, P., Roberta, P. & Daniele, G., 2020. Trends in the Fashion Industry. The Perception of Sustainability and Circular Economy: A Gender/Generation Quantitative Approach. Sustainability, Volume 12.

Rao, D. D. & Provodnikova, A., 2021. Analyzing the role of business analytics adoption on effective entrepreneurship. Global Journal of Business and Integral Security.

Shabbir, M. & Gardezi, S., 2020. Application of big data analytics and organizational performance: the mediating role of knowledge management practices. J Big Data, 7(47), p. J Big Data.

Sharma, R., Mithas, S. & Kankanhalli, A., 2014. Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organizations. Eur J Inf Syst, Volume 23, pp. 433-441.

Valadares de Oliveira, M. P., McCormack, K. P., Bronzo, M. & Trkman, P., 2022. The Effect of Individual Analytical Orientation and Capabilities on Decision Quality and Regret. International Journal of Business Analytics (IJBAN), 9(1), pp. 1-19.

Vasarhelyi, M., 2018. Understanding usage and value of audit analytics for internal auditors: An organizational approach. Int. J. Account. Inf. Syst, Volume 28, pp. 59-76.