Business Analytics: Coffee House Industry Paper Example

Introduction

Many factors influence the performance of firms in the current competitive business environment, but the most important factors include product and service quality, customer satisfaction and low costs. Consequently, competitive organizations are those that have a strategic and long-term plan for these factors. Strategic plans are associated with long-term planning while tactical plans are related to midterm decisions. Strategic decisions play a very important role for firms who want to retain their competitiveness for a long time. On the other hand, tactical decisions play an influential role on the strategic decisions (Amiri, 2006; Arntzen, et al., 1995; Melo, et al., 2006). The customers switching to the competitors' products and services show the dynamism of the consumer behavior that should be considered in decision making at proper time horizons. The parameters will not remain constant during the planning period and, therefore, a framework covering multiple periods should be considered to take into consideration the dynamism of the parameters (Arntzen, et al., 1995; Fumero & Vercellis, 1997). Nowadays, business analytics play a central role for organizations wishing to identify trends in consumer behavior to aid them in both strategic and tactical decisions. This is even more important for the coffee industry actors who are not only experiencing competition from new entrants into the market but also the increasing use of substitutes such as tea and chocolate. This paper explores the effects of analytic techniques on the operations of Starbucks, which is a major player in the coffee sector.

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The Effects of Analytic Techniques on the Operations of Starbucks

Starbucks is one of the largest companies worldwide. The company had over 27,000 stores spread all over the world that generated revenue of $22 billion in 2017. This success is mostly attributed to the innovative use of big data analytics. According to the company, data is very important part of the global strategy where the executive leadership team uses big data analytics as well as ethnographic data to support the pricing strategy, marketing strategy, planning, product development, and optimization of promotion.

Data analytics encompasses the identification of patterns in large data (classification) and using these patterns to predict future trends. The predictive analytics involving the qualitative and quantitative analysis of extensive data with computer algorithms have become very essential to companies. The payoff from these techniques arises from using the modeling outcomes to optimization of decisions to create value. Any business organization establishes a model through which value is created for its customers and a strategy to exploit optimal returns from the value created. The literature suggests that a firm's business model identifies the value propositions for customers and stakeholders, the processes and resources necessary for delivering the value propositions, and the method for profitability. The term customer value has various interpretations. There are various interpretations of what is meant by customer value. According to Zeithaml (1988), it may mean delivering to customers what they desire, low price, delivering quality for what is paid, or mutually beneficial exchange. According to Woodruff (1997), customer value refers to the perceived consumer preferences for product attributes, performance, and achievement of customer goals. Woodruff's definition suggests that customer value has the desired (customer's desire in a product/service) and perceived value (customer's perceived benefit of a product/service) perspectives.

An organization must first identify the kind of value their potential clients want in order to develop a strategy for value creation. O'Cass and Ngo (2011) suggest that after identifying the consumer needs, the next step is to develop a pre-emptive strategy that can offer the required benefits to the consumers. The approaches to customer value creation focus on different dimensions (product quality, delivery performance, price, service support, personal interaction, process costs etc.) of how customers perceive value (Ulaga, 2003). Although these dimensions of customer value creation have their antecedents in the manufacturer-supplier relationships, they can also apply to the coffee house sector context. In an industry where billions of people consume coffee daily, it can be difficult to determine their behavior unless statistical tools are used to predict trends. Starbucks uses this big data to identify consumer needs and use the results of modeling to optimize their decision making.

The Use of Business Analytics to Create Value for Customers and Stakeholders

In all the discussion of the use of business analytics, it is easy to lose sight of a basic truism. Customer value cannot be delivered by simply having big data. However, big data becomes more useful or creates customer value if it results in new insights about building deeper relationships with the consumers. Big data creates value only when it leads to an increased understanding of the existing or potential value of consumers. Therefore, business analytics must be the basis for the better decision-making based on the new insights it produces. For the coffee industry, business analytics is one of the strategies used to create customer value that enables them to generate revenue. Stiff industry competition is driving companies to find new competitive strategies. It is well understood that the factors affecting the operation of a firm within its industry can be very many. In this regard, it is of great importance for the company to select only those that create value for its customers and stakeholders.

The Application of Business Analytics to Real Estate Decisions

Starbucks was having a difficult time by the time Howard Schultz returned as the Chief Executive Officer in 2008 (Rachel, 2018). His first step was to shut down hundreds of Starbucks store locations. Due to the company's floundering at the time, the CEO insisted on the firm taking a much more analytical approach in deciding on new store locations. The store locations that were shut down were not profitable to the company after all. Since 2008, the company has been using the combination of art and science to select store selections that can increase the likelihood of success.

The company has formed a partnership with Esri, which is a location analytics firm whose technology platform helps the company to analyze the suitability of retail locations. In analyzing the store location, Esri uses demographic data such as population density, traffic patterns, and average incomes to help Starbucks to identify new store locations (Thau, 2014). According to Quartz (2015), Starbucks uses its own real estate analytics team who using both qualitative and quantitative data decide where to place their next store. The in-house business analytics experts analyze the geographic maps and information systems data to determine the most appropriate locations to put up new stores. Starbucks can estimate from all of this data the potential profitability of a store, and decide on the economic viability of the new coffee shop.

This activity is more focused on the creation of stakeholder value than consumer value. The development of new stores is part of the company's expansionist strategy where the company aims at increasing revenue. The new revenue, in turn, increases the profitability of the company and returns on investment to the stakeholders. This is very important because rival companies are also expanding into new locations where they increase their own competitiveness. Therefore, the company would risk losing their market position if they failed to expand while their rivals increased their number of stores in lucrative locations. In addition to the existing competition, there is the threat of new entrants with so many coffee house brands. Collectively, the existing brands and new entrants compel Starbucks to be in constant search of new competitive advantages. As such, the company needs to focus on customer satisfaction to retain its loyal customers while pursuing the acquisition of new ones (O'Kelly, 2008).

Menu Design and Optimization

The company's focus on customer satisfaction depends very much on the consumer experiences and preferences inside the existing store. The coffee house giant employs data to help align its product offerings with the preferences of the clients. An example of Starbucks' application of data in the design of the menu is the introduction of the k-cups and bottled drinks. The decision regarding the kind of products to create resulted from the market research using data from its stores. Sarah Whitten (2016a) reporting for the CNBC noted that Starbucks used consumer data from its stores to design its new line of products in line with the consumer behavior. By examining how customers ordered beverages while in Starbucks locations and supplementing this data with industry reports on at-home consumption, the company created K-Cups and bottled beverages to sell in grocery stores. She further reported that Starbucks used Mintel's report where it argued that 43% of tea lovers skip the sugar and 25% do not add milk to the iced coffee at home to create unsweetened ice tea K-cups as well as variants of iced coffee without milk.

This report shows that the company is enhancing its ability to apply business analytics to create new products that meet consumer preferences. Data is playing an integral role in the introduction of the digital menu boards in some of Starbucks's locations that will allow the company to optimize the decision on the products they strategically feature to drive and increase sales. For instance, the digital menus can feature offers based on the location, weather conditions, and time of the day just to mention but a few.

Personalized Attention

The competitive advantage of this coffee house results from its exclusive data that may not be available to many other brands. As of 2017, Starbucks had more than 13 million customers who signed up for the company's rewards loyalty program. According to Whitten (2017b), the Starbucks Rewards program constituted 36% of company sales in the United States and 29% of the transactions were carried out via mobile payments. This data provides the company with a general view of the consumer behavior and are able to adjust the products provided accordingly. In other words, Starbucks uses data analytics to find out the type of products their customers are ordering and how they are ordering and then provide personalized product and marketing attention to help increase sales.

The Future Use of Data Analytics in Starbucks

Starbucks is already using data analytics to create products that suit consumer preferences as shown by the case of K-cups and bottled beverages. This implies that the company is in constant pursuit of driving sales not only in its stores but also the at-home consumers through the grocery stores. Data analytics is going to play an important role in identifying future changes in consumer behavior. With a very large network of store locations, it can be argued that Starbucks is likely to use data analytics for regional profiling of consumers and tailor-make products depending on location-specific consumer characteristics. This would be a departure from the current one-size-fits-all approach to the products for all the location stores.

Conclusion

From Starbucks' use of business analytics, it is clear that big data is playing an important role in customer and stakeholder value creation in the coffee house industry. The industry players are using existing data to identify new consumer trends as well as deciding on new store locations. The anal...