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What is competitive intelligence?

Intelligence gathering is a widely used tool to collect information that will ultimately, improve

the products or services being offered by a company. Competitive intelligence, on the other hand,

is the collection of information specifically from competitors. This information may or may not

be against them. Firms use competitive intelligence to gain early warnings of competitor moves

and strategies, new-product launches, new or changing markets, and potential competitive

strengths and weaknesses.” (Kerin, Hartley, Berowitz & Rudelis, 2009, p. 100)

What is the importance of competitive intelligence and analysis in modern-day marketing?

Competitive intelligence is of extreme importance in today’s business environment. Every

company wants to differentiate itself from others by offering better products and this is possible

only with quality competitive intelligence. Today’s intense competition has forced every

company to gather information about its customers as well as competitors so that products and

services can be designed and modified according to needs and wants to customers. This will

certainly result in a company’s success.

How can a company’s marketing organization ensure that it is able to identify newly emerging

competitors in time to plan and execute an effective marketing strategy in response to these

competitors?

The only way a company can keep its eyes on newly emerging competitors is through

competitive intelligence. It must utilize its relevant people and keep a track of different

companies and market trends, only then will it be able to properly plan and execute an effective

marketing plan.

Reference

Kerin, R.A., Hartley, S.W., Berkowitz, E.N., & Rudelis, W. (2009). Markeng (9th ed.). New York: McGraw

Hill.

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1. Find the y-intercept of the equation of the regression line for the following data: Please round to the nearest thousandth. Consumer Price Index 30.2 48.3 112.3 162.2 191.9 197.8 Cost of Pizza 0.15 0.35 1.00 1.25 1.75 2.00 2. Find the slope of the equation of the regression line for the following data:Please give your answers rounded to the nearest hundredth.. Consumer Price Index 30.2 48.3 112.3 162.2 191.9 197.8 Cost of Pizza 0.15 0.35 1.00 1.25 1.75 2.00 3.For a certain set of data, the Pearson correlation coefficient was 0.87. It was calculated based on 50 data points. Answer the following questions, based off of this information. (a)Let alpha be 0.05. Is the correlation coefficient significant? [ Select ] ["Yes", "No"] (b)Using your answer from (a), is it reasonable to fit the regression line to the data? [ Select ] ["Yes", "No"] (c)If you were to the predict a value of the dependent variable, would you use the regression line or the mean of all the values of the dependent variable? [ Select ] ["Regression line, or "Mean of all the values of the dependent variable"] 4. 4. For a certain set of data, the Pearson correlation coefficient was -0.467. It was calculated based on 8 data points. Answer the following questions, based off of this information. (a) Let alpha be 0.05. Is the correlation coefficient significant? [ Select ] ["Yes", or "No", "", ""] (b) Using your answer from (a), is it reasonable to fit the regression line to the data? [ Select ] ["Yes", or "No"] (a)If you were to the predict a value of the dependent variable, would you use the regression line or the mean of all the values of the dependent variable? [ Select ] ["Regression Line", or "Mean of all of the values of the dependent variable"] 5. In a study conducted by University of Arizona researchers, the total weight (in pounds) of garbage discarded in one week and the household size were recorded for 62 households. The linear correlation coefficient is r = 0.759 and the regression equation is = 0.445 + 0.119x, where x represents the total weight of discarded garbage. The mean of the 62 garbage weights is 27.4 pounds and the 62 households have a mean size of 3.71 people. What is the best predicted number of people in a household that discards 50 pounds of garbage? Round to the nearest hundredth6. A sample of 40 women is obtained, and their heights (in inches) and pulse rates (in beats per minutes) are measured. The linear correlation coefficient is 0.202 and the equation of the regression line is = 18.2 + 0.920x, where x represents height (based on data from the National Health Examination Survey). The mean of the 40 heights is 63.2 in. and the mean of the 40 pulse rates is 76.3 beats per minute. Find the best predicted pulse rate of a woman who is 70 inches tall. Please round to the nearest tenth.

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