netrashetty
Netra Shetty
Discovery Communications, Inc. (DCI) (NASDAQ: DISCA, NASDAQ: DISCB, NASDAQ: DISCK) is an American global media and entertainment company. The company started as a single channel in 1985, The Discovery Channel. Today, DCI has global operations offering 28 network entertainment brands on more than 100 channels in more than 180 countries in 39 languages for over 1.5 billion subscribers around the globe.[1] Discovery Communications is based in Silver Spring, Maryland. The company's slogan is: "The number-one nonfiction media company."[2]
DCI both produces original programming and acquires content from producers worldwide. This non-fiction programming is offered through DCI's 28 network entertainment brands, including Discovery Channel, Military Channel, TLC, Animal Planet, Discovery Health Channel and a family of digital channels. DCI also distributes BBC America and BBC World News to cable and satellite operators in the United States.
ative or causal techniques involve the identification of variables that can be used to predict another variable of interest. For example, interest rates may be used to forecast the demand for home refinancing. Typically, this involves the use of linear regression, where the objective is to develop an equation that summarizes the effects of the predictor (independent) variables upon the forecasted (dependent) variable. If the predictor variable were plotted, the object would be to obtain an equation of a straight line that minimizes the sum of the squared deviations from the line (with deviation being the distance from each point to the line). The equation would appear as: y = a + bx, where y is the predicted (dependent) variable, x is the predictor (independent) variable, b is the slope of the line, and a is equal to the height of the line at the y-intercept. Once the equation is determined, the user can insert current values for the predictor (independent) variable to arrive at a forecast (dependent variable).
If there is more than one predictor variable or if the relationship between predictor and forecast is not linear, simple linear regression will be inadequate. For situations with multiple predictors, multiple regression should be employed, while non-linear relationships call for the use of curvilinear regression.
ECONOMETRIC FORECASTING
Econometric methods, such as autoregressive integrated moving-average model (ARIMA), use complex mathematical equations to show past relationships between demand and variables that influence the demand. An equation is derived and then tested and fine-tuned to ensure that it is as reliable a representation of the past relationship as possible. Once this is done, projected values of the influencing variables (income, prices, etc.) are inserted into the equation to make a forecast.
EVALUATING FORECASTS
Forecast accuracy can be determined by computing the bias, mean absolute deviation (MAD), mean square error (MSE), or mean absolute percent error (MAPE) for the forecast using different values for alpha. Bias is the sum of the forecast errors [∑(FE)]. For the exponential smoothing example above, the computed bias would be:
(60 − 41.5) + (72 − 54.45) + (58 − 66.74) + (40 − 60.62) = 6.69
If one assumes that a low bias indicates an overall low forecast error, one could compute the bias for a number of potential values of alpha and assume that the one with the lowest bias would be the most accurate. However, caution must be observed in that wildly inaccurate forecasts may yield a low bias if they tend to be both over forecast and under forecast (negative and positive). For example, over three periods a firm may use a particular value of alpha to over forecast by 75,000 units (−75,000), under forecast by 100,000 units (+100,000), and then over forecast by 25,000 units (−25,000), yielding a bias of zero (−75,000 + 100,000 − 25,000 = 0). By comparison, another alpha yielding over forecasts of 2,000 units, 1,000 units, and 3,000 units would result in a bias of 5,000 units. If normal demand was 100,000 units per period, the first alpha would yield forecasts that were off by as much as 100 percent while the second alpha would be off by a maximum of only 3 percent, even though the bias in the first forecast was zero.
A safer measure of forecast accuracy is the mean absolute deviation (MAD). To compute the MAD, the forecaster sums the absolute value of the forecast errors and then divides by the number of forecasts (∑ |FE| ÷ N). By taking the absolute value of the forecast errors, the offsetting of positive and negative values are avoided. This means that both an over forecast of 50 and an under forecast of 50 are off by 50. Using the data from the exponential smoothing example, MAD can be computed as follows:
(| 60 − 41.5 | + | 72 − 54.45 | + | 58 − 66.74 | + | 40 − 60.62 |) ÷ 4 = 16.35
Therefore, the forecaster is off an average of 16.35 units per forecast. When compared to the result of other alphas, the forecaster will know that the alpha with the lowest MAD is yielding the most accurate forecast.
Mean square error (MSE) can also be utilized in the same fashion. MSE is the sum of the forecast errors squared divided by N-1 [(∑(FE)) ÷ (N-1)]. Squaring the forecast errors eliminates the possibility of offsetting negative numbers, since none of the results can be negative. Utilizing the same data as above, the MSE would be:
[(18.5) + (17.55) + (−8.74) + (−20.62)] ÷ 3 = 383.94
As with MAD, the forecaster may compare the MSE of forecasts derived using various values of alpha and assume the alpha with the lowest MSE is yielding the most accurate forecast.
The mean absolute percent error (MAPE) is the average absolute percent error. To arrive at the MAPE one must take the sum of the ratios between forecast error and actual demand times 100 (to get the percentage) and divide by N [(∑ | Actual demand − forecast |÷ Actual demand) × 100 ÷ N]. Using the data from the exponential smoothing example, MAPE can be computed as follows:
[(18.5/60 + 17.55/72 + 8.74/58 + 20.62/48) × 100] ÷ 4 = 28.33%
As with MAD and MSE, the lower the relative error the more accurate the forecast.
It should be noted that in some cases the ability of the forecast to change quickly to respond to changes in data patterns is considered to be more important than accuracy. Therefore, one's choice of forecasting method should reflect the relative balance of importance between accuracy and responsiveness, as determined by the forecaster.
MAKING A FORECAST
William J. Stevenson lists the following as the basic steps in the forecasting process:
Determine the forecast's purpose. Factors such as how and when the forecast will be used, the degree of accuracy needed, and the level of detail desired determine the cost (time, money, employees) that can be dedicated to the forecast and the type of forecasting method to be utilized.
Establish a time horizon. This occurs after one has determined the purpose of the forecast. Longer-term forecasts require longer time horizons and vice versa. Accuracy is again a consideration.
Select a forecasting technique. The technique selected depends upon the purpose of the forecast, the time horizon desired, and the allowed cost.
Gather and analyze data. The amount and type of data needed is governed by the forecast's purpose, the forecasting technique selected, and any cost considerations.
Make the forecast.
rnal to the firm or can be part of the external environment. The internal audit comprises a detailed analysis by product/service of the market share and profitability of the various lines. In addition, strategies relating to marketing mix elements are reviewed and studied together with the use made of marketing research data (Lancaster & Reynolds 2001). At the same time, an examination is made of marketing budgets and how they were drawn up and related to previously set agreed objectives. The external audit examines the organization’s external environment. It commences with a review of the general economy and then makes an assessment of the prospects for the firm’s markets. The external audit attempts to estimate what should be the appropriate action taking into account economic and market indicators. The idea behind the marketing audit is to identify marketing strengths and resources that you can build on and use to advantage in the new product. The shrewd strategist always attacks from a position of strength. An essential step in the strategy-development process, therefore, is to understand what the strengths really are, and to identify and correct any weaknesses in the firm's marketing resources that could have a negative impact on the new product. A facet of the marketing audit is to look at the marketing performance over time at current products and, perhaps, at other recent launches. Consider market shares, margins, and marketing costs against the strategies employed (Proctor 2000). This paper is a proposal to create a study on the benefits of marketing audit to companies like retail banks in Botswana.
Aims and objectives
Understand the concept of marketing audit.
Determine the benefits of marketing audit.
Know the current trends in banking.
Analyze retail banking in Botswana.
Understand the benefits of marketing audit to retail banking in Botswana.
Literature review
The ranks of banking's competitors were further swelled when companies began selling deposits, insurance, and other services to households across the nation. At roughly the same time some industrial and service corporations launched credit card programs as a supplement to their main business lines, while insurance companies developed a full line of financial services for large corporations previously the mainstay of bank lending and leasing programs. Despite rapid ATM growth, full-service banking offices advanced from less than forty-six thousand to more than sixty thousand and may continue to grow for a time alongside computer networks and automated machines to benefit those customers who do not trust or understand the new information technologies and to serve as a necessary platform for the sale of an ever expanding range of bank services, from credit and savings to retirement planning and risk management (Ashdown 2002). Sustained prosperity often seems to breed contempt for prudent and careful lending practices. Moreover, a nation's overall prosperity masked serious economic problems in selected regions of a country where the collapse of real estate and energy markets resulted in a glut of nonperforming loans that overwhelmed the capital of both large and small banking organizations. Anxious to find new capital in order to rescue troubled banks and thrift institutions, many states passed liberal interstate banking laws in an effort to encourage outside banking companies to enter and bring their expertise to bear upon the problems at hand The high-net-worth private banking client is likely to remain an important factor in international banking for the foreseeable future, although the pattern of wealth ownership will probably shift steadily from in
DCI both produces original programming and acquires content from producers worldwide. This non-fiction programming is offered through DCI's 28 network entertainment brands, including Discovery Channel, Military Channel, TLC, Animal Planet, Discovery Health Channel and a family of digital channels. DCI also distributes BBC America and BBC World News to cable and satellite operators in the United States.
ative or causal techniques involve the identification of variables that can be used to predict another variable of interest. For example, interest rates may be used to forecast the demand for home refinancing. Typically, this involves the use of linear regression, where the objective is to develop an equation that summarizes the effects of the predictor (independent) variables upon the forecasted (dependent) variable. If the predictor variable were plotted, the object would be to obtain an equation of a straight line that minimizes the sum of the squared deviations from the line (with deviation being the distance from each point to the line). The equation would appear as: y = a + bx, where y is the predicted (dependent) variable, x is the predictor (independent) variable, b is the slope of the line, and a is equal to the height of the line at the y-intercept. Once the equation is determined, the user can insert current values for the predictor (independent) variable to arrive at a forecast (dependent variable).
If there is more than one predictor variable or if the relationship between predictor and forecast is not linear, simple linear regression will be inadequate. For situations with multiple predictors, multiple regression should be employed, while non-linear relationships call for the use of curvilinear regression.
ECONOMETRIC FORECASTING
Econometric methods, such as autoregressive integrated moving-average model (ARIMA), use complex mathematical equations to show past relationships between demand and variables that influence the demand. An equation is derived and then tested and fine-tuned to ensure that it is as reliable a representation of the past relationship as possible. Once this is done, projected values of the influencing variables (income, prices, etc.) are inserted into the equation to make a forecast.
EVALUATING FORECASTS
Forecast accuracy can be determined by computing the bias, mean absolute deviation (MAD), mean square error (MSE), or mean absolute percent error (MAPE) for the forecast using different values for alpha. Bias is the sum of the forecast errors [∑(FE)]. For the exponential smoothing example above, the computed bias would be:
(60 − 41.5) + (72 − 54.45) + (58 − 66.74) + (40 − 60.62) = 6.69
If one assumes that a low bias indicates an overall low forecast error, one could compute the bias for a number of potential values of alpha and assume that the one with the lowest bias would be the most accurate. However, caution must be observed in that wildly inaccurate forecasts may yield a low bias if they tend to be both over forecast and under forecast (negative and positive). For example, over three periods a firm may use a particular value of alpha to over forecast by 75,000 units (−75,000), under forecast by 100,000 units (+100,000), and then over forecast by 25,000 units (−25,000), yielding a bias of zero (−75,000 + 100,000 − 25,000 = 0). By comparison, another alpha yielding over forecasts of 2,000 units, 1,000 units, and 3,000 units would result in a bias of 5,000 units. If normal demand was 100,000 units per period, the first alpha would yield forecasts that were off by as much as 100 percent while the second alpha would be off by a maximum of only 3 percent, even though the bias in the first forecast was zero.
A safer measure of forecast accuracy is the mean absolute deviation (MAD). To compute the MAD, the forecaster sums the absolute value of the forecast errors and then divides by the number of forecasts (∑ |FE| ÷ N). By taking the absolute value of the forecast errors, the offsetting of positive and negative values are avoided. This means that both an over forecast of 50 and an under forecast of 50 are off by 50. Using the data from the exponential smoothing example, MAD can be computed as follows:
(| 60 − 41.5 | + | 72 − 54.45 | + | 58 − 66.74 | + | 40 − 60.62 |) ÷ 4 = 16.35
Therefore, the forecaster is off an average of 16.35 units per forecast. When compared to the result of other alphas, the forecaster will know that the alpha with the lowest MAD is yielding the most accurate forecast.
Mean square error (MSE) can also be utilized in the same fashion. MSE is the sum of the forecast errors squared divided by N-1 [(∑(FE)) ÷ (N-1)]. Squaring the forecast errors eliminates the possibility of offsetting negative numbers, since none of the results can be negative. Utilizing the same data as above, the MSE would be:
[(18.5) + (17.55) + (−8.74) + (−20.62)] ÷ 3 = 383.94
As with MAD, the forecaster may compare the MSE of forecasts derived using various values of alpha and assume the alpha with the lowest MSE is yielding the most accurate forecast.
The mean absolute percent error (MAPE) is the average absolute percent error. To arrive at the MAPE one must take the sum of the ratios between forecast error and actual demand times 100 (to get the percentage) and divide by N [(∑ | Actual demand − forecast |÷ Actual demand) × 100 ÷ N]. Using the data from the exponential smoothing example, MAPE can be computed as follows:
[(18.5/60 + 17.55/72 + 8.74/58 + 20.62/48) × 100] ÷ 4 = 28.33%
As with MAD and MSE, the lower the relative error the more accurate the forecast.
It should be noted that in some cases the ability of the forecast to change quickly to respond to changes in data patterns is considered to be more important than accuracy. Therefore, one's choice of forecasting method should reflect the relative balance of importance between accuracy and responsiveness, as determined by the forecaster.
MAKING A FORECAST
William J. Stevenson lists the following as the basic steps in the forecasting process:
Determine the forecast's purpose. Factors such as how and when the forecast will be used, the degree of accuracy needed, and the level of detail desired determine the cost (time, money, employees) that can be dedicated to the forecast and the type of forecasting method to be utilized.
Establish a time horizon. This occurs after one has determined the purpose of the forecast. Longer-term forecasts require longer time horizons and vice versa. Accuracy is again a consideration.
Select a forecasting technique. The technique selected depends upon the purpose of the forecast, the time horizon desired, and the allowed cost.
Gather and analyze data. The amount and type of data needed is governed by the forecast's purpose, the forecasting technique selected, and any cost considerations.
Make the forecast.
rnal to the firm or can be part of the external environment. The internal audit comprises a detailed analysis by product/service of the market share and profitability of the various lines. In addition, strategies relating to marketing mix elements are reviewed and studied together with the use made of marketing research data (Lancaster & Reynolds 2001). At the same time, an examination is made of marketing budgets and how they were drawn up and related to previously set agreed objectives. The external audit examines the organization’s external environment. It commences with a review of the general economy and then makes an assessment of the prospects for the firm’s markets. The external audit attempts to estimate what should be the appropriate action taking into account economic and market indicators. The idea behind the marketing audit is to identify marketing strengths and resources that you can build on and use to advantage in the new product. The shrewd strategist always attacks from a position of strength. An essential step in the strategy-development process, therefore, is to understand what the strengths really are, and to identify and correct any weaknesses in the firm's marketing resources that could have a negative impact on the new product. A facet of the marketing audit is to look at the marketing performance over time at current products and, perhaps, at other recent launches. Consider market shares, margins, and marketing costs against the strategies employed (Proctor 2000). This paper is a proposal to create a study on the benefits of marketing audit to companies like retail banks in Botswana.
Aims and objectives
Understand the concept of marketing audit.
Determine the benefits of marketing audit.
Know the current trends in banking.
Analyze retail banking in Botswana.
Understand the benefits of marketing audit to retail banking in Botswana.
Literature review
The ranks of banking's competitors were further swelled when companies began selling deposits, insurance, and other services to households across the nation. At roughly the same time some industrial and service corporations launched credit card programs as a supplement to their main business lines, while insurance companies developed a full line of financial services for large corporations previously the mainstay of bank lending and leasing programs. Despite rapid ATM growth, full-service banking offices advanced from less than forty-six thousand to more than sixty thousand and may continue to grow for a time alongside computer networks and automated machines to benefit those customers who do not trust or understand the new information technologies and to serve as a necessary platform for the sale of an ever expanding range of bank services, from credit and savings to retirement planning and risk management (Ashdown 2002). Sustained prosperity often seems to breed contempt for prudent and careful lending practices. Moreover, a nation's overall prosperity masked serious economic problems in selected regions of a country where the collapse of real estate and energy markets resulted in a glut of nonperforming loans that overwhelmed the capital of both large and small banking organizations. Anxious to find new capital in order to rescue troubled banks and thrift institutions, many states passed liberal interstate banking laws in an effort to encourage outside banking companies to enter and bring their expertise to bear upon the problems at hand The high-net-worth private banking client is likely to remain an important factor in international banking for the foreseeable future, although the pattern of wealth ownership will probably shift steadily from in
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