Signature Assignment: Business Decision-Making Project – Part 3

Among various types of numerical types of descriptive statistics, Measures of Central Tendency and Measures of Dispersion are important when it comes to analyzing data that will have been collected for the case study. The Using of Measures of Central Tendencies such as mean, mode, and median, the researcher, will, for instance, seek to establish the average price of a smartphone in the market from the collected data. Calculating the modal price, on the other hand, will help the researcher to understand which price is the most recurring in the pricing strategy of competitors of Apple Inc. In doing so, the researcher will have not only found out pricing strategy that most fits with demand; it will have also been possible to get the comprise price at which consumers are willing to buy a smartphone with generic features. From here henceforth, it will be a matter of adopting a pricing strategy that will justify very specific features or specs of iPhone not found in competitors’ offerings.

Similarly, numerical calculation from survey data may as well involve finding Measures of Dispersion such as variance and standard deviation. These will provide the spread of data across different competitors in a manner that will allow for establishing significant pricing trends and trends in smartphone features (Kothari, 2004). Variance as a numerical measure is particularly important in understanding how pricing, for instance, is adopted by various competitors and eventually how it becomes a strategic point of competition.

Types of Graphical Measures

Measures of Frequency and Measures of Position are important types of descriptive statistics of graphical model. Measures of frequency provide counts, percentages, and rates will be tabulated to give a more clear understanding of unorganized sets of numerical values from say, a survey (Kothari, 2004). Before using raw sets of numerical data from the field to make inference about the phenomenon under investigation (in this case, stiff competition in the smartphone market), organizing the data using measures of frequency ensures that data become possible to extract pertinent information. Using frequency distribution tables as an example, the data collected can be organized into classes and illustrated in a tabular form for easy interpretation for inference. Using cumulative frequency tables and drawing cumulative frequency distribution polygons from such will also help to provide descriptive data in graphical format.

On the other hand, Measures of Position such as percentile ranks and quartile ranks can be helpful in mapping out a company’s competitive position. One possible way to map out the competitive position of Apple is to track the relationship between price and iPhone’s key features or benefits over time. However, this can be a daunting task because of two reasons. First, according to a survey carried out in 2004 by Strativity, a global research and consulting firm, most customers cannot state which features of a product or service make them willing to pay a given price (D’Aveni, 2007). Second, this same survey states that 50% of salespeople are unable to tell the features of their products or services that justify their pricing (D’aveni, 2007). Since customers justify why they are willing to pay a given price and managers, on the other hand, cannot justify their pricing, even Apple is likely to find it difficult to map out its competitive position. A simple statistical analysis, called price-benefit positioning mapping introduced by Richard D’Aveni, (2007), provides valuable insights into what constitutes the relationship between price and benefits. A company is also able to track how its competitive positions over time.

Applying price-benefit positioning mapping to Apple Inc., executives of Apple can make use of the tool to benchmark itself against competitors, their strategies, and also forecast the future of its iPhone market. One type of graphical descriptive statistics that is equivocal here is a measure of position, one of which is regression analysis (Kothari, 2004). Regression analysis, which examines the relationship between a dependent variable (which is stiff competition in this case) and several other independent variables (one of which was identified as “availability of substitutes”), is useful in creating a mathematical model of whatever kind of relationship that exists between the variables. Using SAS Analytics or Excel or even SPSS, executives of Apple Inc. can come up with r-square statistics for each variable, which can, in turn, be plotted as positions to draw expected price line, for example.

Role of Probability in Solving the Business Problem

Solving the business problem using a probability approach is one that cannot be underestimated. The regression analysis above-mentioned is certainly one way to do this. Through a regression equation, Apple’s executives can, for example, identify a major driver of price for a smartphone at any time in the market. For instance, the executives can create a price-benefit positioning map for a smartphone (iPhone, in this case) for predict intents of rivals and subsequently preempt them (D’Aveni, 2007). The company can do this simply by drawing positioning maps for projections of market trends. A good example here would be where Apple maps its competitors’ future trajectories and realizes that they are likely to gain more market share by offering more for less. Apple’s executives would then focus on expanding on their R&D initiatives now to seize the opportunity in future.

References D’Aveni, R. A. (2007). Mapping Your Competitive Position. Havard Busines Review. Retrieved 13 Aug 2017, from https://hbr.org/2007/11/mapping-your-competitive-position Kothari, C. R. (2004). Research methodology: Methods and techniques. New Age International.