Market analysis can provide concrete data about the market size, market trends, growth rate, opportunities, profitability and industry cost structure. Market size can be defined through things like market potential and the turnover. Turnover depends on the number of customers and demand for a service or product. Market volume is quantitative and hence can provide meaningful insight into the industry dynamics (Chong, Han & Park, 2017) . Data can be drawn from trade association data, customer surveys, and government data. A simple way of forecasting the market growth rate is to inference historical data into the future. Even though the market growth rate provides a first-order estimate, this can be used to showcase important turning points (Bogue, Collins & Troy, 2017) . Critical inflection points in the growth rate can be forecasted by building a product diffusion curve. The nature of the curve can be calculated by examining the characteristics of the adoption rate of similar products.
For a CEO of a new venture, constructing a market analysis can appear to be an overwhelming task; the venture will gleam with delight into the future. Certainly, the will spend a huge amount of time working on the company’s market analysis. However, it is well worth it. One should not put all the extensive research that goes into the process to waste (Hanuska et al., 2016) . The analysis should be put into action. For internal purposes, the CEO should consider how the findings can be used to improve the business. The analysis can be used to make the business process to be efficient. When the analysis is conducted for external purposes, the CEO should be prepared to speak with capital providers about the research and conclusions. One should not merely box up the analysis and put it in the shelf for later. Revisiting the market analysis constantly to tweak it is critical.
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References
Bogue, J., Collins, O., & Troy, A. J. (2017). Market analysis and concept development of functional foods. In Developing new functional food and nutraceutical products (pp. 29-45). Academic Press.
Chong, E., Han, C., & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications , 83 , 187-205.
Hanuska, A., Chandramohan, B., Bellamy, L., Burke, P., Ramanathan, R., & Balakrishnan, V. (2016). Smart clothing market analysis . Technical Report.
