Using Business Analytics to Understand Advertising

Business analytics can be defined as the process by which analysts collect data with the intent of measuring the performance of a business and providing valuable conclusions that can aid in better decision making. This is done through various statistical techniques and methods. Business analytics helps identify the best strategies to enhance productivity, prepare the company for any upcoming changes, and determine the most appropriate ways to advertise its products and services.

Data Classification

The data collected from a random sample of a wide demographic variety of respondents would give us more insights into how the population, in general, feel about the stereotype versus empowerment advertising. To better understand the average feelings of the advertising, the variables reinforcing and transform are converted to numeric equivalents from the ordinal scale.
Gender No of Samples Ad Frequency (avg) Stereotype (avg) Stereotype % Reinforcing (avg) Transform (avg)
Female 86 46.90697674 45.24418605 96.46% 3.348837209 1.88372093
Male 19 38.89473684 34.94736842 89.85% 2.684210526 1.368421053
Grand Total 105 45.45714286 43.38095238 95.43% 3.228571429 1.79047619

The column charts above show the split of how the Males and Females responded about the degree of reinforcement and the transformation the stereotype or empowering ads do respectively.

Let us now see how respondents with different education levels see these ads

Education No of Samples Ad Frequency (avg) Stereotype (avg) Stereotype % Reinforcing (avg) Transform (avg)
Associate Degree 6 205 189.5 0.9243902 3 2.5
Bachelor Degree 44 21.13636364 19.886363 0.9408602 3.06818181 1.545454545
Doctorate Degree 10 29.5 28.9 0.9796610 3.2 1.3
High school diploma 3 9 8.6666666 0.9629629 3.3333333 1.66666666
J.D. 2 27.5 22.5 0.8181818 3 0.5
Master Degree 16 51.75 50.5 0.9758454 3.25 1.625
Some undergraduate courses 24 58.6666666 57.291666 0.9765625 3.5833333 2.5
Grand Total 105 45.4571428 43.380952 0.9543264 3.2285714 1.79047619

From the above summary of the data, we can say that among the respondents of different educational level, generally, all respondents felt that the proportion of the ads showing stereotype ads are same as the other respondents. Similarly, all respondents also felt that these ads are equally reinforcing the stereotypes. But, respondents with associate degree and JD felt that the empowering ads are more transformational than the rest.

The column chart above shows the split of how the respondents with different educational levels responded about the degree of reinforcement the stereotype ads do

The column chart above shows the split of how the respondents with different educational levels responded about the degree of transformation the empowering ads do.

Let us now see how respondents with different income levels see these ads

Income No of Samples Ad Frequency (avg) Stereotype (avg) Stereotype % Reinforcing (avg) Transform (avg)
$0 to <  $10,000 24 42.25 40.666666 96.25% 3.1666666 1.9166666
$10,000 to <  $20,000 21 17.76190476 17.333333 97.59% 3.5238095 1.8571428
$110,000 to <  $130,000 3 88.3333333 85 96.23% 2.3333333 1.3333333
$150,000 or more 1 20 20 100.00% 4 3
$20,000 to <  $30,000 15 52.6666666 47.666666 90.51% 3.2 1.7333333
$30,000 to <  $40,000 14 96.07142857 92.42857 96.21% 3.0714285 2.0714285
$40,000 to <  $50,000 3 16.66666667 14 84.00% 3.3333333 2
$50,000 to <  $60,000 9 14.33333333 13.222222 92.25% 3.3333333 1.777777
$60,000 to <  $70,000 5 8 7 87.50% 3.4 1
$70,000 to <  $80,000 4 158.5 156 98.42% 3.25 1.75
$80,000 to <  $90,000 1 3 2 66.67% 3 1
$90,000 to <  $110,000 5 22 21.8 99.09% 2.8 1.2
Grand Total 105 45.4571428 43.380952 95.43% 3.2285714 1.7904761

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Descriptive Statistics

From the above summary of the data, we can say that among the respondents of different income level, generally, all respondents felt that the proportion of the ads showing stereotype ads are same as the other respondents. There are some income levels where the proportions vary, but there is no trend in the variation and could be attributed to randomness. Similarly, all respondents also felt that these stereotypes ads are equally reinforcing and that the empowering ads are equally transformational without any observable trend with the change in income levels.

Another, important aspect that we would like to analyze is if the amount of spending on beauty products that the respondents do make them see these ads differently. Lets us see the data from the respondents to understand this.

Spending No of Samples Ad Frequency (avg) Stereotype (avg) Stereotype % Reinforcing (avg) Transform (avg)
0-99 10 19.50 18.90 96.92% 3.00 1.60
100-199 11 132.64 127.18 95.89% 3.18 1.64
200-299 17 26.12 24.53 93.92% 3.29 1.29
300-399 12 26.42 25.58 96.85% 3.58 2.08
400-499 8 69.13 65.88 95.30% 2.75 1.75
500-599 14 20.93 17.14 81.91% 3.07 1.64
600-699 7 34.29 33.43 97.50% 3.29 2.29
700-799 2 53.00 53.00 100.00% 3.50 2.00
800-899 2 10.00 10.00 100.00% 3.50 3.00
1000-1099 6 26.33 23.50 89.24% 3.33 2.33
1200-1299 1 10.00 10.00 100.00% 3.00 2.00
1500-1599 7 29.43 29.43 100.00% 3.29 1.29
2000-2099 2 30.00 28.50 95.00% 3.50 2.50
2500-2599 3 210.00 206.67 98.41% 3.33 2.67
3000-3099 1 2.00 2.00 100.00% 3.00 1.00
4000-4099 1 75.00 75.00 100.00% 3.00 2.00
4900-5000 1 5.00 5.00 100.00% 4.00 3.00
Grand Total 105 45.46 43.38 95.43% 3.23 1.79
We might expect that people who spend more might not see that these stereotype ads do not reinforce much. But from the above summary of the data, we can say that among the respondents of different spending level, generally, all respondents felt that the proportion of the ads showing stereotype ads are same as the other respondents. There is no observable trend in how these respondents see the reinforcing and transformational strength of the stereotype and empowering ads respectively. If you would like us to assist you with any paper derived from this topic, avail our descriptive statistics assignment help right away.