Applying Analysis to a Business Issue or Opportunity

Answer

Resolving Business Problem Using Analytics

Background and Problem

As a multinational, Starbucks must keep expanding into new markets (Pillania, 2009). The main problem that Starbucks Multinational Company faces is which region to open new stores as a way of expanding their brand without suffering setup costs. This decision can only be arrived by analyzing the rate of regional business growth. Although it would be simple to decide that the region where the business is most common is the preferred choice, it is essential to consider that one of the major aims is also to expand the brand across the world. Therefore, the region with significant growth would be the preferred choice; it will depend on the store count. If a region has far too many, then choosing it as a way of expanding the brand on a global basis beats the business rationale. A comparison of the regions to find a rationally right region can be clearly shown through an analytics process.

Data Source 

To understand the distribution of Starbucks stores across the world, store count data is useful. This data is from the Starbucks Company. The data is stores count of Starbucks across the world grouped by the world’s regional sections. The regions include the Americas, which includes totals for South and North American continents. The region, which covers Europe, Africa, and Middle East countries. Then the Far East and Russia are covered in Cap region. The store count is given in quarterly form by taking the store count for every quarter of the year for the said regions since the year 2016 to 2019.

How Data was Collected 

Understanding how data was collected is crucial since it adds to the credibility or otherwise for the data. Although the company does not provide information on how the data was counted, it is clear that it would not require complicated steps other than checking records for the required metrics. From reliable sources, whenever a new Starbucks is opened, the records are entered well up to the headquarters level. Through this means, the exact number of stores at any moment can be traced to any region. This thus makes the data more reliable, especially given that it is from the data owners and meant for investors.

In order to solve the distribution issue at hand, the data has the attributes, which translate to fields for working out. There are four fields. It has the region field. Notably, the field indicates the name of the segment whose record is being entered. It is a character field and takes the values ‘Americas, or cap.’ It also contains the year field. It records the year for which a particular record represents. The year field ranges from 2016-2019. The quarter field is a character field and records the quarter part of the year for which the entry belongs. It ranges from Q1-Q4. The count field is an essential field, which records the stores count. It is a numerical field. In summary, a single record would have a stores count for a specific region within a specified quarter of a particular year. However, to demonstrate where to penetrate more with less effect, only the annual trend would be sufficient for the said regions.

Analysis

Note that the number of stores count implies the business level in the region. The higher the number, the higher the business and vice versa (Liabotis, 2019; Ayal & Zif, 2009). An area with the increasing rate of business would be ideal for penetration of newer markets within the region as a useful expansion because it would imply that the popularization efforts might not be more resourceful than if less popular. As implied earlier, the region with already many Starbucks stores is not an ideal choice for effective expansion targeting new markets across the world even if it has shown a high increase of stores count in the recent past. An area with a slow business would imply more challenges facing the business in the region; this would be less considered for expansion as compared to other areas. Bearing this in mind, the SAS results for our data is as displayed in a bar chart in figure 1 below.

Figure 1. Analysis results from SAS

Figure 2. Starbucks stores count Analysis results

Source: https://static.seekingalpha.com/uploads/2014/4/2/7873931-1396442649070495-DurableCompetitveAdvantage_origin.png. Copyright 2014 by G.L. seeking alpha.

The analysis results chart in figure1 is similar to the external analysis growth results by means of interpretation of figure 2 above. However, the years covered differ, but the interpretation of the results is the same as far as growth overtime is concerned.

From the analysis results, the Americas region has shown a consistently high rate of increasing business through the increasing stores count since the year 2016 to 2019. It has a very high number of stores and currently stands at over 17000. This is a very high number. The Cap region has shown an increasing business activity but not as much the Americas region. The rate of increase has been steady over time and currently stands at approximately 9500 stores. The EMEA region on the hand has shown the slowest increase in business.

Recommendations

Based on the earlier rationale describing how an expansion consideration could be made, a few recommendations can be made:

Consider the Americas region when an expansion desired is for the general expansion of the business to maximize profit

Consider the cap area when an expansion is required to penetrate the globe further rather than just for instant profit as would otherwise be recommended for the case of Americas region

Consider the EMEA region when an expansion is for popularization, but it needs to be understood that it may be of no business value given that the other two regions exist. This region most likely faces challenges that would not favor a quick and effective expansion.

Conclusion

Conclusively speaking, the business issue at hand was to choose a global region where opening new stores would be an effective business decision in expanding Starbucks Company across the globe within business rationality. The analysis results in points to cap as the eventually suitable region for this kind of business decision issue.