Competitive analysis is exploring the competing organizations in a specific industry, market niche, or sector that could affect the prospects of marketing the products or services of a particular company. The competitive analysis could be an in-depth study of the major competitors alone or it could consider large number of competitors but the depth of the study would be impacted. Even though it is assumed that competitive analysis is meant only for business enterprises with profit motives, even non-profit organizations could equally benefit from this analysis. If such non-profit organizations had been working towards a same or common goal, then this analysis is termed as comparative analysis.
For any business to grow and expand its operations, one of the first and foremost tasks should be the mapping of the competitive landscape. Only when the competition is understood clearly, the business venture and its management would be able to spot the opportunities open to them or exploit the opportunities that develop during the business operations. Further, the right marketing strategies or even the design of the product or service could be decided only through a thorough competitive analysis and understanding the competition in the market. In certain aspects, competitive analysis is similar to SWOT analysis.
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Key ratio analysis is also known as financial ratio analysis or accounting ratio analysis. This method is basically the determination of the ratio of the relative magnitude of any two selective numerical values in the financial statements of a business enterprise. In general accounting procedures, several standard key ratios are analyzed to evaluate the financial strength or weakness of an organization from various angles or perspectives. This key ratio analysis method is used by the management and other managers within the business venture, by the current shareholders and potential buyers of shares in the market, the creditors of the firm, security analysts, investment and fund managers, stock market brokers and consultants, and even government agencies.
This key ratio analysis helps all these groups to determine the soundness of the organization and compare its performance with that of its competitors, as well as its place in the economy. Most of the ratios are presented as percentages, while a few are expressed as decimal values. At times, the reciprocal values of the ratios are also used to compare certain financial factors. Normally, the values for calculating the key ratios are obtained from the profit and loss or income statements, cash flow statements, balance sheets, inventory details, investment and capital expenditure statements, funds flow statements such as borrowings, etc.
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PEST analysis is the method that scans the external macro-environmental factors, namely, political, economic, social, and technological, which affect the operations of any business venture. PEST is also known as STEP. When other factors are added to this analysis, such as environmental, educational, legal, and demographic, this method is termed as PESTEL, PESTLE, STEEPLE, and STEEPLED. PEST analysis provides the management with the framework to analyze the macro-environmental factors that directly impact the operations of the business. PEST analysis is able to assist the management in identifying the markets, the activities of the competitors, and the factors that influence customer preferences and purchase decisions.
The main reason for the effectiveness of PEST analysis is that it considers all the macro-environmental aspects that have a bearing on the growth and prosperity of any business venture. The PEST factors assist the management in understanding the actual impact of each factor on the company operations. PEST analysis also provides the different perspectives of the external environment like the existing position, business potential, market trends, and future direction. The management is able to build visions for future direction and develop strategies to capitalize on market trends and business potentials, by analyzing the various factors and their implications provided by PEST analysis.
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SWOT analysis is one of the most commonly used and highly efficient management tools for strategic planning. In this method, the Strengths, Weaknesses, Opportunities, and Threats that are involved in a business venture or a project are evaluated. To be effective, this method must clearly specify the objectives of the business venture or the project and identify the internal, as well as the external, favorable and unfavorable factors to achieve those objectives. It is believed hat Albert Humphrey, the leader of a convention at the Stanford University between 1960s and 1970s used the data of Fortune 500 companies to arrive at this method.
Normally, strengths are defined as the characteristics of the business that provide it an edge over other competitors in the industry. Weaknesses are the characteristics that are disadvantageous to the company relative to the competitors. Both these factors are invariably internal to the organization. Opportunities are generally termed as external chances that help the company in improving the sales or profits in existing and future business environment. Similarly, threats are the external elements in the business environment that could lead to problems or troubles for the business. However, these two factors could have internal components also like possibility of improving present business processes or strike threats by employees.
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Variance is a concept that had emanated from statistics and probability theory. Variance is generally used as a descriptor of a distribution. Variance explains the distance between actual values and the mean value. Variance was introduced by Ronald Fisher in his paper, ‘The Correlation Between Relatives on the Supposition of Mendelian Inheritance’ in 1918. He stated that the deviations of measurement from its mean follow the normal law of errors quite closely. Hence, he concluded that the variability might be measured uniformly by the standard deviation that corresponds to the square root of mean square error. Variance analysis is also known as analysis of variance (ANOVA).
However, in management accounting and budgeting of business enterprises, the variance is considered as the difference between a standard, planned, or budgeted amount and the actual amount incurred or received. Variances are computed for both revenues and costs. To put it in simple terms, variance is the difference between what was expected and what was actually received. If you had expected to sell a product for $1.25 and you were able to receive only $1.00, then the variance is $0.25 less than expected. This means that you actually received $0.25 less than your estimation, an unfavorable variance. This quantum of unfavorable or favorable variance obtained through variance analysis is used to arrive at informed decisions on revenue generation and cost control. Variance analysis had led to earned value management principle (EVM).
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The ‘what if…’ simulation is also known as sensitive analysis (SA) or uncertainty analysis (UA). The ‘what if…’ simulation is a technique of changing the parameters in a model or a situation to determine the exact effects of these changes. In other words, sensitive analysis is the study of variations or uncertainties in the outputs of any mathematical model and how these variations or uncertainties could be apportioned either quantitatively or qualitatively to each source of variation in the inputs of the model. The ‘what if…’ simulation investigates the correctness of a study that includes some type of mathematical modeling.
In social sciences, natural sciences, and economics, mathematical models are used to solve issues or problems, when such problems do not lend themselves normally to a straightforward or clear understanding of the relationship between the input factors that go into the model and the output variables. This ‘what if…’ simulation is necessary to understand how the mathematical models behave in response to input changes, so that the models are correctively used. It is obvious that inputs are subject to various sources of uncertainties such as measurement errors, lack of proper information, partial or poor understanding of the driving mechanisms and forces. These uncertainties impose a limit to our confidence about the output of the model. The sensitive analysis solves these issues.
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