Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model 5C Marketing Analysis
Posted by Sabrina Warren on Jan-10-2018
1 What is 5C marketing framework?
5C marketing framework is a tool to analyze the situational forces that form the business environment. The analysis emphasizes micro and macro environmental factors that exert a strong influence on the organizations' business operations. Marketing managers can conduct the 5C analysis to timely identify the strengths and weaknesses in the internal environment, and possible risks and opportunities present in the external environment.
2 Application of the 5C model on Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model
2.1 Company
Some examples of the company related factors are given below:
2.1.1 Research and development
Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model spends heavily on the research and development activities to preserve its leadership position in various product segments. Heavy investment in building the IT network, marketing, product design and process optimization supports the distribution and promotion strategies.
2.1.2 Culture
Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model has a strong culture of process and product innovation. Top management supports the innovative and creative ideas, and employees are encouraged to participate in the problem solving process. The organizational culture supports the vision, mission and values.
2.1.3 Scale of production
Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Modelhas a large scale of production, which enables the company to achieve the benefits of economies of scale. Large scale production enhances the competitive strength of the company and enables the company to produce better quality products at reduced costs.
2.1.4 SWOT-Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model
The SWOT analysis is an effective tool to analyze the company related factors. Application of this tool in Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model’ context involves identification of key strengths, weaknesses, opportunities and threats.
The following table presents the SWOT in the concisely summarized form:
Strengths:
|
Weaknesses:
|
Opportunities:
|
Threats:
|
2.2 Customers
Customer analysis mainly covers the following points:
2.2.1 Market segments
- Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model targets both high end and low-end market segments.
- The organization's decision to choose broader and multiple segments have expanded the scope of opportunities.
- The targeted segments are expected to have a steady market growth rate in future.
- The primary customer segment of the Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model is the family with children, which requires Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model to do social, emotional and functional jobs to keep this market segment happy and satisfied. The functional job includes performing core operations, the social job includes providing augmented services to promote family and social gatherings, and emotional job includes showing concern and commitment to take care of customers.
2.2.2 Frequency and quantity of purchases
- The quantity and frequency of purchase in the targeted market are high, and both are favorable growth indicators for the organization.
- Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model can adapt its marketing strategies according to changes in frequency and quantity by offering more discounts and family deals.
2.2.3 Brand loyalty
- Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model operates in the low-involvement product category.
- Usually, developing brand loyalty in low-involvement markets is challenging compared to high-involvement markets as a lot of alternative options are available and psychological switching costs are also low.
- Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model ’ customers are price sensitive. Their price sensitivity, changing tastes and preferences and high health consciousness requires Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model to invest in customer research activities and closely monitor their attitude and consumption behavior.
2.2.4 Customer needs
- It is important to identify the critical customer desired features and incorporate them into marketing and advertising strategies.
- Customers changing attitudes towards healthy alternatives and prioritizing the quality over price also have important consequences for the organization.
2.3 Competitors
Porter five forces is a useful tool to conduct competitor analysis:
2.3.1 Bargaining power of buyers
- Strong bargaining power of buyers puts downward pressure on pricing and induces Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model to offer the high quality product at discounted pricing.
- Strong bargaining power makes it easier for Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model ’ customers to switch to other alternatives.
- There are three major reasons for strong buyer bargaining power:
- High substitute availability.
- A wide number of alternatives.
- Low economic and psychological switching costs.
2.3.2 Bargaining power of suppliers
- Weak bargaining power of supplier makes it comparatively less important strategic issue for Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model as suppliers cannot dictate the prices and have to accept the Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model ’ terms and conditions.
- Three factors result in moderate to weak supplier power:
- A large number of suppliers
- High overall supply
- Suppliers’ weak control over their distribution network
2.3.3 Competitive rivalry
- Currently, the rivalry among competitors is high, which makes it difficult for Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model to achieve its market growth objectives.
- The product differentiation is low and setting the differentiation basis has become increasingly challenging.
- Intense competitive rivalry is a major reason for Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model ’ declining profitability.
2.3.4 Threat of substitutes
- The technological advancement has raised the threat of substitutes for Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model .
- Changing trends towards healthy products also raises the consequences of this threat for Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model .
- Overall, the threat of substitutes is strong for the following reasons:
- High performance/cost ratio of substitute products.
- High availability of substitute products.
- Low switching cost.
2.3.5 Threat of new entrants
- Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model faces moderate new entrant threat, which means new entrants do not have a significant influence on Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model ’ market share.
- High level marketing know-how with huge expenditure on marketing activities is required to enter the industry.
- Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model faces a moderate threat of new entrants for the following reasons:
- High brand development cost weakens the threat.
- Low switching cost increases the threat.
- High capital cost weakens the threat.
2.4 Collaborators
- An in-depth collaborator analysis requires Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model to conduct a detailed value chain analysis and carefully consider the bargaining power of suppliers to explore the collaboration opportunities.
- Collaborators include the downstream and upstream value chain partners, business allies, community leaders, government and others. To choose the appropriate collaborator partners, Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model needs to evaluate different value chain factors, like- value chain flexibility, efficiency, agility, revenue sharing among value chain partners and strengths and weaknesses of possible collaborators. The detailed collaborator analysis can allow Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model to enhance and its supply chain efficiency and increase control over it through vertical integration.
- When operating at the international stage, multinational organizations like Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model must understand the local preferences of their customers and make all decisions (ranging from production to marketing) accordingly.
- An agile and flexible supply chain can make collaboration easier for Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model .
- Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model has partnered with various collaborators that allowed the company to develop new product lines and enhance the product development and distribution process.
- It is important for Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model to understand the behaviors, relationships, choices, purpose and
context of collaborators to make the right decision. Some important points that must be integrated into
the collaborator analysis are:
- What is the business environment in which potential collaborators operate and what strategies they are using to play in the market?
- What are their key strategic priorities and choices?
- What are their internal and external communication mechanisms?
- What are their key strengths and weaknesses, and what opportunities and threats external environment imposed on them?
2.5 Context
Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model must understand the external environmental context in which it is operating to make the right business decisions and forecast the future. One important tool to understand business context is the PEST analysis.
2.5.1 Political Context
Understanding the political context requires Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model to identify possible political issues such as labor or tax laws, changing trade regulations or legislative problems.
- The present governance system requires Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model to study the changing government policies closely
- Presence in multiple markets increases the risk of political instability.
- The geo political risks have increased for Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model due to recent developments in the global political scenario.
2.5.2 Social Context
Understanding the social context requires Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model to analyze the major trends in culture, education and demographic patterns.
- A general rise in the health consciousness of customers imposes a risk to the Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model .
- Population growth and rising low-end market segments offer opportunities to Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model
- The attitude towards migration in markets where Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model is present requires the company to consider its impact on changing demographics carefully.
2.5.3 Economic Context
Understanding the economic context requires Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model to identify major economic issues like growth in important economic indicators, changes in the labor costs and business cycle stages.
- Inflation exerts a strong impact on the pricing structure of Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model .
- Presence in multiple markets requires marketing managers of Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model to adapt their strategies according to consumer behavior, which is different during recession and boom.
- The downward market pressure and changes in customers’ purchasing power should also be considered to make effective marketing strategies.
2.5.4 Technological Context
Understanding the technological context requires Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model to understand recent technological developments and their impact on the organization’s cost structure and other business operations.
- The entrance of new market players and their investment in research and development requires Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model to protect their intellectual property rights.
- The technological advancement has shortened the product life cycles, requiring Forecasting Korean Stock Price Index Kospi Using Back Propagation Neural Network Model Bayesian Chiao s Model and Sarima Model to enhance its value chain efficiency.
- Technological development has lowered production cost and increased the need to restructure the supply chain.
Overall, the purpose of understanding the context is to determine if any opportunities or risks are imposed by major external environmental forces.
3 Conclusion
Collecting the information about all 5 C elements (company, customers, competitors, collaborators and context) is a first step towards developing effective and informed marketing strategies. 5C analysis sets the foundation for developing a wise and well-defined marketing plan.
4 References
Armstrong, G. M., Kotler, P., Harker, M. J., & Brennan, R. (2018). Marketing: an introduction. London: Pearson UK.
Bradt, G., (2017). Consider 5Cs--Customers, Collaborators, Capabilities, Competitors, Conditions--In Onboarding Prep. Forbes. Retrieved from: https://www.forbes.com/sites/georgebradt/2017/11/22/consider-5cs-customers-collaborators-capabilities-competitors-conditions-in-onboarding-prep/#34f1dd4f321c
Dobbs, M. (2014). Guidelines for applying Porter’s five forces framework: A set of industry analysis templates. Competitiveness Review, 24(1), 32-45.
Grundy, T. (2006). Rethinking and reinventing Michael Porter’s five forces model. Strategic Change, 15(5), 213-229.
Jones, S. C. (2002). Summary of Rossiter’s article on ‘Forms of Marketing Knowledge’. Marketing Theory, 2(4), 333-337.
Mathur, D. (2018). Policing: Reinvention Strategies in a Marketing Framework. South Asian Journal of Management, 25(2), 214-216.
Schmidt, C. R. (2017). Technology's impact on the marketing function. Strategic Management, 22(3), 19-28.
Vachon, S., & Klassen, R. D. (2008). Environmental management and manufacturing performance: The role of collaboration in the supply chain. International journal of production economics, 111(2), 299-315.
Weinstein, A. (2016). Superior customer value: Strategies for winning and retaining customers. Boca Raton: CRC Press.
Weinstein, A. (2018). Superior Customer Value: Finding and Keeping Customers in the New Economy. Abingdon: Routledge.
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