Free Essay: What is the problem you would like to solve?

A small supermarket with less than 50 workers has had a relatively modest annual turnover, which puts it at risk of closing down. The implementation of data analytics aims to solve the problem of low profitability by expanding business opportunities by analyzing the business market and stakeholder needs (Wamba, 2017). Data analytics tools such as the one integrated into the IBM Cognos Express platform, with low cost and the possibility of deploying more functions as needs grow, can evaluate robustly, with full guarantees in the reduction of risks and the consideration of success value proposals made from the extraction of information through the analysis of the business data and its environment (Howson et a., 2018). Some of these proposals could range from the creation of an online store to the realization of specific offers and promotions through the provision of services such as home delivery deliveries, telephone attention to orders, and other endless opportunities. Thanks to data analytics, not only would the business identify these opportunities, but also be entirely considerable from the point of view of profitability and the expansion of marketing opportunities.

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What data do you have access to, or can you gather, that can inform your solution to this problem?

The business collects data from different sources, including loyalty cards, credit card payments, use of promotional coupons, prices, and services offered by the competition, among others. The business collects data that informs the wishes of the buyers through reports of sessions and usability, the number of clicks, most visited pages, schedules, region, loading time, consumption habits, and psychological profiles, among others. Data analytics tools make it possible to separate, sort, contextualize, and translate the data so that it has real application in the company (Wamba, 2017).

What are some questions you would like to answer using descriptive analytics, and why is this valuable in addressing your problem?

Doing good marketing is a work of numbers. Discovering the trends of the business niche, knowing what the consumers are looking for, and determining what they expect from the business are crucial in identifying what steps should be taken to increase profitability.

Descriptive analytics can be used to answer the following questions whose answers are hidden in the businesses metrics, automatic reports, and databases:

• What are the current trends in the market?
• What are the main customer preferences?
• What are the customers' expectations?

Today, more than experience is needed to create and execute marketing strategies that ensure businesses achieve full profitability. The market is in constant change, with customers having equally changing needs, preferences, and expectations. With digital transformation in almost every aspect of everyday life, buyers are increasingly demanding; they know that there is a wide variety of options in the market, and they consume a large amount of information that can easily take them away from the brand and business offers - because of this, making decisions based on speculation only means loss of return on investment (ROI) and stagnation in the retention rate. For a business to achieve sustainability in this dynamic market, it must identify these customer demands so that it can adjust its products and services to meet these needs.

How can this problem be addressed using predictive analytics?

The digital market transformation has brought with it two resources that can be used to position the business products and services in the minds of customers: Big Data and Data mining. These resources offer the ability to collect any amount of information about behaviors and trends through figures and large blocks of data that, in principle, seem unconnected, but that makes sense with the business organization and with correct interpretation (Choi, Chan, & Yue, 2016). Integrating data with marketing and product development actions will allow the business to execute objective and predictive tactics that can translate into thousands of dollars in profits and an increase in sales equitable with the marketing investment. According to Homburg, Jozic, and Kuehnl (2017), knowing the consumer is the main challenge in today's market. In this task, data-driving marketing is the best option for the businesses' commercial and strategic communications department. In this way, predictive analytics can make segmentations, psychological models, and reports of consumer feelings.

Can this problem be addressed by running an experiment? If so, briefly sketch how.

Low business profitability can be solved by running a survey of market trends and consumer preferences. Recent Adobe surveys indicate that companies that use CRM data, real-time data Analytics and cross-data achieve a better understanding of who they are, what they do, and what the consumer wants (Homburg et al., 2017; Sun, Strang, & Firmin, 2017). They are also more likely to offer real added value to their services and increase profitability by better outlining their strategies. Forbes figures reveal that companies that use data analysis increase their profitability six times more compared to companies that execute tactics based on qualitative assumptions. Data analytics allows the business to accurately measure its performance and make timely changes by obtaining vital data about the consumers and campaign results in a matter of seconds. Unlike surveys, the strategies executed based on data analysis allow valuable reports to be made for the company's economy and its future in a matter of hours. Likewise, obtaining real-time results gives the business the advantage of making rapid and precise changes that will prevent it from significant financial losses.

A market analysis can be done by using the following data interpretation tools (Choi et al., 2016; Sun et al., 2017):

Discriminant linear analysis: this is a statistical method to classify products, people, and any tangible object of value for the objectives of the analysis into different categories. This type of analysis can help better profile consumers, perform micro-segmentations, detect errors, and direct advertising investment to the most effective channels.

Cluster analysis: it consists of separating objects belonging to a homogeneous group because they represent exclusive variables. Market analysts use it to extract groups of consumers or activities that, although they differ from the rest, have similar attributes to the original group. In this way, the business can obtain niche trends and identify consumer habits.

Factor analysis: the factor analysis focuses on identifying only the most representative constants in a given block of data. This method is especially useful when drawing up marketing and customer service strategies that meet the expectations of the buyer.

Multidimensional scale: the big data benchmarking. It consists of making perceptual maps in order to launch and correctly position new products in the market. In this case, the data extracted includes opinions, perspectives, and comments of the consumers.

References

Choi, T. M., Chan, H. K., & Yue, X. (2016). Recent development in big data analytics for business operations and risk management. IEEE transactions on cybernetics, 47(1), 81-92. Available on DOI 10.1109/TCYB.2015.2507599

Homburg, C., Jozic, D., & Kuehnl, C. (2017). Customer experience management: toward implementing an evolving marketing concept. Journal of the Academy of Marketing Science, 45(3), 377-401.

Howson, C., Sallam, R. L., Richardson, J. L., Tapadinhas, J., Idoine, C. J., & Woodward, A. (2018). Magic quadrant for analytics and business intelligence platforms. Gartner, Inc., Tech. Rep, 2. Retrieved from https://www.gartner.com/doc/reprints?id=1-65P04FG&ct=190125&st=sb

Sun, Z., Strang, K., & Firmin, S. (2017). Business analytics-based enterprise information systems. Journal of Computer Information Systems, 57(2), 169-178. Available on DOI: 10.1080/08874417.2016.1183977.

Wamba, S. F. (2017). Big data analytics and business process innovation. Business Process Management Journal. Retrieved from https://doi.org/10.1108/BPMJ-02-2017-0046