Sales revenue of Amazon.com is showing

an increasing trend and it also has a seasonal component i.e. Q4 of every year

is exceptionally high as compared to rest of the three quarters. Triple Exponential

Smoothening also known as the Holt-Winters method is used for forecasting the

sales of Amazon from Q4 2017 till Q3 2018 as it has a trend and seasonal impact.

Quarterly data from Q1 2007 till Q3 2017

is used for calculating the forecast. Sales data from Q1 2007 till Q4 2018 i.e.

8 quarters is taken as the base for forecast. Average sales of this period are

taken as the starting point for calculating the starting level and seasonal

factor for the same period. (Hyndman and Athanasopoulos, 2013)

To keep a check on the forecast error

various indicators are calculated namely BIAS, MAD, MSE & MAPE. ?, ? &

? are then solved using the solver tool in excel to find out the optimum level

where MSE is the lowest. ?, ? & ? is kept in the range of 0.05 to 0.95. The

optimum solution provided by the solver tool and two other scenarios are shown

below.

Solver

Scenario 1

Scenario 2

?

0.25

0.70

0.50

?

0.58

0.70

0.50

?

0.95

0.70

0.50

BIAS

0.36

0.08

0.20

MAD

0.73

1.65

1.19

MSE

1.23

5.77

3.02

MAPE

3.73%

7.73%

5.60%

It can be observed from the above table

that solver’s solution is the optimum one with the lowest MSE. The optimum

solution has a low alpha factor which means that forecast have lower impact of

historical data and higher weightage of the average of the base quarters and

trend. High beta & gamma factors mean that trend and seasonality have

higher impacts on the forecast. Graph showing actual and forecasted values are

shown below.

Source:

Amazon.com Investors Relations, 2017

Multiple Regression Analysis

Multiple regression analysis is used to

find if there is a statistically significant relationship between several predictor

variables and dependent variable and the strength of the relationship. It helps

in analysing and finding trends if any in different sets of data. (Statsoft,

2013)

Amazon is an e-commerce company and is

accessed through internet by its customers. Year on year worldwide internet

user data from 1996 to 2016 is tested as predictor variable against net revenue

of Amazon.com i.e. dependent variable. The data is plotted on a scatter graph

as shown below.

Source: Internetworldstats, 2017 &

Amazon.com Investors Relations, 2017

A regression analysis was run on the

data. The coefficient of correlation was 98.84% which indicates a very high

correlation between the variables. The coefficient of determination came out to

be 97.69% which indicates that ~ 98% variance can be explained through the

internet user statistics during the analysis period. The statistical p-test

shows that the probability of null hypothesis being true is less than 5% hence

null hypothesis was rejected. Linear equation generated through the multiple

regression analysis is as follows:

y = 50.629 * x – 62.565

The internet users line fit plot,

trendline and equation are shown in the below graph.

Regression analysis can be used by the

company to predict the growth or change in the dependent variable based on the

predictor variable. As in this case the company can predict its revenue based

on the increase in the number of internet users. This kind of analysis will

also help the company in taking strategic decisions like how they can

contribute or take steps to increase the number of internet users.

Correlation

Correlation analysis is used to test the

relationship between two or more variables. It is important to understand the

correlation between different variables so that accurate predictions could be

made about the future. A positive correlation means that the variables move in

the same direction and negative correlation means that the variables move in

the opposite direction. It differs from regression in the terms that

correlation quantifies the degree to which two variables are related but it

does not fit a line through which the value of another variable can be

calculated whereas regression provides a line of best fit through which the

dependent value can be calculated. (Statisticshowto, 2017)

A correlation analysis has been run on

number of active users and net revenue. The correlation between the two comes

out to be 99.8% which means that they are highly positively correlated i.e. if

the company works towards increasing the number of active users the revenue

will increase in the same proportion. Below graph shows the net revenue and

active users’ data.

Recommendation

Basis the above analysis it is

recommended that data warehousing solution must be implemented in the company.

This will help the company in accurate forecasting and planning, assist in

taking strategic decisions and help in efficient utilisation of resources.

Implementation process, major risks and steps to overcome these issues while

implementing the data warehousing solution are mentioned in the report which

will help in effectively planning the implementation and will lead to a smooth

transition.

Word

count: 3982

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