Discuss about the Basic Business Statistics for Population Data.
The world is a rapidly changing environment that brings new ideas, and population data with each day that passes. The things that people could only dream of in the past decades are now a living reality in today’s world (Hair Jr. and Lukas, 2014). Advancements in companies, their daily business activities and functions have grown hundreds of times and are now more appealing to the consumers than they were a few years back. The same thing has happened with the human population in general. People are now more aware about the market and the consumer products, and their general behaviour is now quite comprehensive and highly demanding. This has forced businesses all over the world to institute necessary changes in order to meet market demands and satisfy consumer behaviours.
Berenson et al. (2012) reports that businesses are taking much of their time in decision making since it is in their obligations to satisfy the demands of the consumers. Understanding consumer satisfaction is one of the most obvious challenges faced by business organisations. Others include launching of new products, pricing, competition, effective market research, predicting consumer behavior, et cetera.The need to handle such challenges is the reason behind organizations investing in market research (Levine, 1999; Burns and Burns, 2008; Zikmund et al., 2008). This paper gives a detailed analysis report of a market research process involving a taste test for a snack shop with two different snack flavours. The data set is described and analysed through statistical graphs and numerical summaries. Finally, a real life example of similar tests is given and the relevant market research questions.
The essence of analysing and interpreting data is typically to give meaning to what would otherwise be a mere representation of values and numbers. Nonetheless, the importance of data statistics in business research is dependent on the clarity of the defined research survey question or the problem being surveyed (Kotz et al, 2012; Gentle et al., 2012; Bryman and Bell, 2015). This suggests that, the researcher needs to properly edit the data collected before analysing it so as to detect any errors in the early stages of statistical interpretation. This can be approached in a number of ways including consistency checks and other checks; and what the given data can or cannot achieve in the intended research. Therefore, it would be important to follow these guidelines in interpreting and analysing the given dataset of the snack food described in sample of 1000 survey data. Each hypothetical sample has 100 observations. In essence, there are five variables in this dataset (Murakami and Livingstone, 2016).
In the first variable, each person was given 2 versions of the product and was asked which version they preferred – this variable was categorical. The second variable is on gender and is also categorical. The third variable wanted to know the test of each person with regard to the snack food and this was categorical as well. However, the fourth variable was numerical as it sought to know what each person would pay for the snack food. Similarly, the fifth variable is also numerical indicating the age of the participants as below 40 or above 40 years.
Two other questions would include:
The following table and chart give a summary description of the variable “how much they would pay”:
|
Minimum: |
O |
|
Maximum: |
4.1 |
|
Count: |
100 |
|
Mean: |
2.6O2 |
|
Median: |
3.1 |
|
Mode: |
3 |
|
Standard Deviation: |
1.1O8 |
|
Variance: |
1.271 |
|
Mid-Range: |
1.9 |
|
Quartiles: |
Quartiles: |
Comment: on average, people are willing to pay up to 2.602 an amount for the snack. With the standard deviation of 1.108 the snack can be sold up to a maximum of 4.1 but the best price is at 3.1 which would be fair to most of the participants.
The filter includes the people that like the product and the appropriate numerical summaries for the follow variable
|
Row Labels |
Count of Which version is the best? |
|
Neither |
5 |
|
version 1 |
2O |
|
version 2 |
25 |
|
Grand Total |
5O |
Therefore the proportion of neither in equal to 5/5O=O.1;
And the proportion that likes version 2 is equal to 25/5O=O.5
Comment: the snack is generally likeable and people would buy it. However, version too is higher preferred as compared to version one. Therefore, the business should have to produce more of version two and less of version one.
|
Minimum: |
2.9 |
|
Maximum: |
4.1 |
|
Count |
50 |
|
Mean: |
3.1O42 |
|
Median: |
3.1 |
|
Mode: |
3 |
|
Standard Deviation: |
O.157 |
|
Quartiles: |
Quartiles: |
Comment: the best price for the snack according to taste would land at 3.1, this is the value depicted as the mean value and median value most preferred by the participants for purchase.
Graphical and numerical summaries relating the variable 2, 3 and 4
This section gives graphical and numerical summaries that describe the variables:
Gender and do they like the product?
|
Proportion of people that like the product |
Column Labels |
||
|
Row Labels |
like |
hate |
Grand Total |
|
male |
68.00% |
32.00% |
100.00% |
|
female |
72.00% |
28.00% |
100.00% |
|
Grand Total |
70.00% |
30.00% |
100.00% |
|
count of people that like product |
Column Labels |
||
|
Row Labels |
like |
hate |
Grand Total |
|
male |
34 |
16 |
50 |
|
female |
36 |
14 |
50 |
|
Grand Total |
70 |
30 |
100 |
Apparently, more females than males like the snack. However, the percentage representing those who like the product is way higher than those who do not like the snack. Therefore, it would be advisable to deliver the product to the market with the positive expectation that it will be bought at a profit.
For the MALES the summary of the amount they would pay is:
|
Minimum: |
O |
|
Maximum: |
4.1 |
|
Range: |
4.1 |
|
Count: |
6O |
|
Mean: |
2.797 |
|
Median: |
3.1 |
|
Mode: |
3.2 |
|
Standard Deviation: |
O.9772 |
|
Quartiles: |
Quartiles: |
For the FEMALES the summary of the amount they would pay is
|
Minimum value: |
O |
|
Maximum: |
3.5 |
|
Range: |
3.5 |
|
Count: |
4O |
|
Mean: |
2.411 |
|
Median: |
3.1 |
|
Mode: |
3.3 |
|
Standard Deviation: |
1.3O34 |
|
Quartiles: |
Quartiles: |
On average, the male gender is willing to pay more for the snack than the female gender. However, the median score is the same suggesting that the best price for selling the product at a profit would be the shared median amount of 3.1.
Confidence intervals
Excluding people that do not like the product, the 90% confidence interval for the proportion of people that prefer version 1 would be represented by:
Mean +-1.96(SD/ sq. root of n)
20 +- 1.96 (0.157/7.07)= 20.044 or 19.956
Excluding people that do not like the product, the 90% confidence interval for the average amount they would pay for the males is:
Mean +-1.96(SD/ sq. root of n)
For females:
4.411 +- 1.96 (1.3034/ 6.325) = 4.815 or 4.007
Using a 5 % level of significance the test of independence for the variables: Gender and do they like the product; indicates that indeed gender has an influence on the outcome test of the participants. From the results, it is true that the female gender likes the snack more than the male gender. Therefore, it is expected that the females would be the most frequent buyers of the snack as compared to their male counterparts.
Using the 5 % level of significance, the claim, there is difference between the mean amount males would pay and the amount females would pay is true. Evidently, the male gender is willing to pay more for the snack than the female gender by a difference of 0.4. Therefore, it would be more profitable selling to the male consumers than their female counterparts.
The problems of getting survey data in the real world
In the real world, marketing surveys are faced by various challenges (Werle, Wansink and Payne, 2015). These include the financial challenge and the ethical issues regarding human survey with humans as the object of test. Other challenges include approval to carry the test and the willingness of individuals to take part in the survey. Additionally, it would be more difficult separating reliable data from non-genuine survey results.
Example of an actual product would be the McDonalds Snackwarp and some of the survey questions would include:
Source: Wilson et al. (2012)
Conclusion
Marketing research has no means of arriving to a marketing decision by itself; neither does it guarantee success in product marketing. Nonetheless, by conducting a systematic, analytical and objective marketing research, businesses can minimize the level of uncertainty in making decisions and increase the magnitude and probability of success.
References
Berenson, M., Levine, D., Szabat, K.A. and Krehbiel, T.C., 2012. Basic business statistics: Concepts and applications. Pearson Higher Education AU.
Bryman, A. and Bell, E., 2015. Business research methods. Oxford University Press, USA.
Burns, R.P. and Burns, R., 2008. Business research methods and statistics using SPSS.
Duffey, K.J., Pereira, R.A. and Popkin, B.M., 2013. Prevalence and energy intake from snacking in Brazil: analysis of the first nationwide individual survey. European journal of clinical nutrition, 67(8), pp.868-874.
Gentle, J.E., Härdle, W.K. and Mori, Y. eds., 2012. Handbook of computational statistics: concepts and methods. Springer Science & Business Media.
Hair Jr, J.F. and Lukas, B., 2014. Marketing research. McGraw-Hill Education Australia.
Kotz, S. and Johnson, N.L. eds., 2012. Breakthroughs in Statistics: Foundations and basic theory. Springer Science & Business Media.
Levine, D.M., Berenson, M.L. and Stephan, D., 1999. Statistics for managers using Microsoft Excel (Vol. 660). Upper Saddle River, NJ: Prentice Hall.
Murakami, K. and Livingstone, M.B.E., 2016. Associations between meal and snack frequency and diet quality and adiposity measures in British adults: findings from the National Diet and Nutrition Survey. Public health nutrition, 19(09), pp.1624-1634.
Werle, C.O., Wansink, B. and Payne, C.R., 2015. Is it fun or exercise? The framing of physical activity biases subsequent snacking. Marketing letters, 26(4), pp.691-702.
Wilson, A., Zeithaml, V.A., Bitner, M.J. and Gremler, D.D., 2012. Services marketing: Integrating customer focus across the firm. McGraw Hill.
Zikmund, W.G., Babin, B.J., Carr, J.C. and Griffin, M., 2013. Business research methods. Cengage Learning.
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