Introduction
A well-built business analytics strategy allows managers to anticipate potential risks. Using the example of the New Zealand supermarket chain Pak’nSave, it is possible to consider the principle of developing an algorithm for applying modern approaches in organizing analytical activities. Four relevant assumptions will be made regarding a planning base.
Assumptions to Build Up Business Analytics in Pak’nSave
Since today, the field of retailing is potentially successful for many enterprises, modern companies invest much money in promoting their chains in the market and create stable customer demand. Various client engagement mechanisms are tried, and business analytics is the platform for planning and implementing relevant innovations. The New Zealand retailer Pak’nSave has a wide range of impact in the domestic market of the country, which gives the corporation an opportunity to vary the tools of assessing its success and recognition. Today, Pak’nSave uses the traditional practices of business analytics, preferring quantitative methods – supply chain, simulation, and optimization. Nevertheless, in order to improve business security and strengthen its position in the market, it is essential to develop a modern analytical strategy as the way of maximizing control over the company’s activities and success.
Contemporary business analytics is not limited to exclusively systematic IT work and is based on a broader volume of technology, knowledge, and experience. A market review by collecting and analyzing statistical data is one of the techniques that may contribute to creating a secure planning system at Pak’nSave. Close interaction with the target audience is the practice that has high prospects from the perspective of establishing a productive dialogue with customers and meeting their needs. Accordingly, applying such aspects as multivariation and econometrics can stimulate interest in the brand and its products.
Another valuable approach to creating a reliable and consistently productive system of business analytics is the review of market dynamics. According to Duan and Xiong (2015), this practice helps examine how intensive sales are based on the number of products sold in relation to a time range. The assessment takes into account seasonality, including its presence or features, as well as cyclicality. At the same time, the review of market activity provides information on such parameters as the degree of the target audience’s interest in particular products, opportunities for opening new industries, and other aspects.
For Pak’nSave, as a large-scale chain, it is crucial to assess the total sales market volumes in order to have an idea of the resources that need to be spent on creating demand. The understanding of market conditions, which allows to formulate specific tasks and develop organizational mechanisms, contributes to maintaining the security of assets and finance capitalization by eliminating risks. Corporate executives can promote both general forecasting ideas based on the evaluation of all factors and more specific strategies, for instance, budget allocation strategies.
Finally, the course on the technological development of the analytical field may be realized through system analysis, which provides for the maximum improvement of Pak’nSave’s work in the information sphere. Examining the company’s requirements in this direction allows developing those software systems that serve as supporting mechanisms for the enterprise. As a result, work optimization accelerates many aspects of activity and is the valuable principle of development in an innovative modern environment. Thus, the assumptions considered may justify the proposed steps to strengthen business analytics in Pak’nSave.
Support of Assumptions
In support of the aforementioned strategies and assumptions regarding the development of business analytics in Pak’nSave, it is possible to cite justifications from relevant sources. In particular, concerning the assumption to expand the scope of statistics, Evans (2015) argues that this practice gives an opportunity “to gain a richer understanding of data that goes beyond business intelligence” (p. 8). The interaction among certain variables may be traced if corresponding correlations are made, which, in turn, improves the quality of analytics.
Evaluating market dynamics is the practice that may also be substantiated by relevant findings. In particular, according to Kiron, Ferguson, and Prentice (2013), any company can benefit by analyzing the application of implementation strategies in a particular environment. Moreover, as the authors note, enterprises may overcome competition if their managers respond to changes in the preferences of the target market timely (Kiron et al., 2013). Therefore, such a strategy is practically significant and potentially successful.
The practice of resource competent allocation by assessing the total sales market volumes is an essential mechanism in business analytics. Kiron et al. (2013) state that those assets that a certain company has are a dynamic variable that depends on the quality of planning. Accordingly, to accumulate profits, it is crucial to take into account a number of conventions, for instance, the parameters of demand, the level of competition, and other significant aspects.
The importance of the sphere of technological innovations can also be proved by justifications from academic literature. As Evans (2015) argues, the capabilities of modern analytical mechanisms help to assess such a factor as clients’ perception of interaction with a particular enterprise. In relation to Pak’nSave, the retailer has a wide chain of supermarkets, and reliable data on client recognition are an essential component of the corporation’s organizational activity.
References
Duan, L., & Xiong, Y. (2015). Big data analytics and business analytics. Journal of Management Analytics, 2(1), 1-21.
Evans, J. R. (2015). Modern analytics and the future of quality and performance excellence. Quality Management Journal, 22(4), 6-17.
Kiron, D., Ferguson, R. B., & Prentice, P. K. (2013). From value to vision: Reimagining the possible with data analytics. MIT Sloan Management Review, 54(3), 1-19.