Web Analytics at Quality Alloys Case Solution And Analysis, HBR Case Study Solution & Analysis of Harvard Case Studies

Web Analytics at Quality Alloys Case Study Solution

Analysis of distribution of the pounds sold data with that of the daily visit data.

The distribution of pounds sold data and daily visit data are compared. The distribution of pounds sold data follows empirical rule better because 99% of its actual values fall within 3 standard deviation of the mean. However only 98% of daily visit data falls within 3 standard deviation of mean. Histogram results indicate that visits are not Normal. Z scores indicate that more data clustered around center than Norman and more data at far right than at far left. The data is skewed positive with positive kurtosis.

Main Conclusions

Figure 10 A of traffic sources indicates that referring sites have more weight age as compared to others. If search engine source is analyzed then Google is on the top. Windows is top operating system among all. If all top ten referring sites are taken into account then the visits would sum to 38,754. South America is top on geographical sources which are followed by North America (Figure 10e).

Recommendations

QA Inc. wants to come out from the traditional style of marketing and has developed their own website to gather larger orders from old and newer customers. The data reveals that the company has seen a high variability in number of website visits from May, 2008 to August 2009. The overall average of 150 daily visits doesn’t gives the clear picture as the data is non-symmetrical and website saw daily visits of 75-100 visits per day in majority of days throughout the year.

It is recommended that the company must have data of more than a year to have a much clear picture. Apart from this, the company must have the data of daily visits against the date. Like this, it would be possible for the firm that which particular day saw low daily visits and on which date there were higher number of visitors. By doing this, they would know what they should do to boost the number of visits. Furthermore, the company should use different strategies to attract greater number of visitors per day. They could also enhance the company’s digital marketing department so that more customers could flock to the website.

Appendix

Figure 1a

Figure 1b

Figure 1c

Figure 1d

Figure 1e

Figure 2a

		Initial Period
	Visits	Unique Visits	Revenue	Profit	Lbs. Sold
mean	1,055	976	$608,250	$200,233	18,737
median	899	846	$586,170	$208,913	17,270
std. dev.	355.03	319.60	$155,930	$60,692	5,427.39
min	626	594	$274,568	$62,580	8,633
max	1,632	1,509	$890,077	$275,218	28,053


		Pre-Promotion
	Visits	Unique Visits	Revenue	Profit	Lbs. Sold
mean	563	517	$534,314	$159,932	18,441
median	558	510	$534,542	$152,476	17,215
std. dev.	80.87	70.94	$150,503	$42,683	5,965.63
min	383	366	$315,647	$100,388	8,992
max	795	734	$951,216	$273,175	31,969


		Promotion
	Visits	Unique Visits	Revenue	Profit	Lbs. Sold
mean	1,814	1,739	$456,399	$131,930	17,113
median	1,663	1,585	$413,937	$114,328	17,299
std. dev.	758.10	743.03	$161,741	$47,777	6,519.07
min	1,000	930	$268,160	$81,841	7,814
max	3,726	3,617	$897,164	$266,477	31,496


		Post-Promotion
	Visits	Unique Visits	Revenue	Profit	Lbs. Sold
mean	857	801	$371,728	$111,046	14,578
median	848	800	$348,397	$104,530	13,647
std. dev.	70.89	72.36	$145,728	$49,065	5,941.56
min	772	709	$133,967	$32,825	3,826
max	963	912	$615,950	$206,441	23,762

Figure 3a

Means
Period	Visits	Unique Visits	Revenue	Profit	Lbs. Sold
Initial	1,055	976	608,250	200,233	18,737
Pre-Promotion	563	517	534,314	159,932	18,441
Promotion	1,814	1,739	456,399	131,930	17,113
Post-Promotion	857	801	371,728	111,046	14,578

Figure 5a

correlation (rev, lbs. sold)

0.8689

Figure 6a

correlation (revenue, visits)		-0.0594
(effectively zero)

Figure 8a


*Lbs. Sold*

Mean	18,681.56
Standard Error	401.69
Median	17,673.00
Mode	28,865.00
Standard Deviation	6,840.51
Sample Variance	46,792,548.87
Kurtosis	0.5637
Skewness	0.6323
Range	40,914
Minimum	3,826
Maximum	44,740
Sum	5,417,651
Count	290