Chapter 6: Prospective Analysis: Forecasting Key Concepts in Chapter 6 • Strategy‚ accounting and financial performance analyses provide valuable information that help to shape forecast assumptions. • Forecasts of future performance should be comprehensive‚ including all condensed financial statements. • The starting point for forecasts should be the time series behaviour of key measures such as sales growth‚ earnings and ROE (and its components). Overall Structure of the Forecast • Typically
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of value in 2012 Main drivers of growth : Acceptance of higher-priced products with new claims by urban consumers New consumers in second and third-tier cities continued to show greater interest in self-grooming. The leading companies introduced products in smaller pack-sizes and innovative pack formats to leverage consumer base in second-tier cities and stimulate their interest and desire to try out such products. 1.2. Beauty and personal care becomes more gender-specific Main drivers
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marketing mix. • Pricing is an important strategic issue because it is related to product positioning. • Pricing affects other marketing mix elements such as product features‚ channel decisions‚ and promotion. • Pricing is an approach in which marketers put a value tag on a particular product. Pricing Pricing Strategy • Pricing can be done during the various stages of the product lifecycle. These are called Pricing Strategies
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ANALYSIS :- In this era of high volatility in markets‚ investors look at several parameters before investing in stocks for example EPS‚ earning growth rate‚ debt/equity ‚ p/e ratios. These parameters will no doubt help us to select the best companies but beta is one such parameter which can tell us about the returns with the volatility of a particular stock with respect to market. The concept of beta is actually very simple - it ’s a measure of individual stock risk relative to the overall
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POS systems with the latest forecasting trends. Some tactics they have implemented include an extensive Point-of-sale system (POS)‚ which captures transaction data on nearly every person who walks through a cafe’s door. The sale of each entrée represents one customer. They forecast monthly guest counts‚ retail sales‚ banquet sales‚ and concert sales (if applicable) at each café. In order to evaluate management‚ a 3-year weighted moving average is applied to cafe sales. As the text described‚ the
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Quantitative Methods ADMS 3330 3 0 3330.3.0 Forecasting QMB Chapter 6 © M.Rochon 2013 Quantitative Approaches to Forecasting Are based on analysis of historical data concerning one or more time series. Time series - a set of observations measured at successive points in time‚ or over successive periods of time. If the historical data: • are restricted to past values of the series we are trying to forecast‚ it is a time series method. 1 Components of a Time Series 1)
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Factors affecting impulse buying behavior in FMCG Sector Thesis submitted for the partial fulfillment of Masters of Business Administration By Akanksha Kothari A-04 Bhumika Prajapati A-16 Deepthi Edward A -19 Parth Sarthi A-38 Prashant Dwivedi A-41
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launched in the Indian market place from 1996 to 2003. The different promotions include free gift offers‚ price offs‚ extra product offers‚ exchange offers‚ buy-more-and-save offers‚ contests and sweepstakes. The most frequently launched promotion is the free gift offer followed by the sweepstake offer and extra product offer. Some differences in trends are found across FMCG‚ Consumer Durable and Service sectors. A detailed description of each type of promotion is provided in order to highlight the
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The Policy Process Part II Lenue Richardson HCS/455 March 14‚ 2013 University of Phoenix The Policy Process Part II Introduction The development of policy is not something that can be done in an efficient manner. However; there are times when policies are very burdensome and can be a very big challenge‚ one that is loaded with all sorts of committees and everything else‚ it is truly an experience. Although the creating of a policy is a very different experience it is necessary
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1) Raw data‚ not seasonalized 2) Seasonal Adjustment used: Census II X-12 multiplicative (MASA): Used because of the presence of seasonal variations that are increasing with the level of my series. Increasing degree of variability overtime… TX non seasonalized and seasonalized 3) Combined seasonally adjusted with non-seasonally adjusted De-seasonalizing the data helped with the removal of seasonal component that creates higher volatility in model. Now‚ variations
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