ICT706 Data Analytics: Solving Business Problems with Python

This document presents a data analysis case study focused on solving business problems for NILA e-commerce company using Python. The analysis employs linear regression for sales prediction, explores descriptive statistics, and identifies key factors influencing sales across different regions and product categories. The report includes recommendations for persuasive selling, endorsement campaigns, free shipping, and after-sales services, along with an implementation plan. The goal is to improve the company's performance in a competitive market by leveraging data-driven insights. The document emphasizes the importance of forecasting and predictive modeling in making informed business decisions.

1Running head: ICT 706 DATA ANALYTICS
Title:Data Analytics
Assignment: Data analysis for a business case using python
Name of the Student
Name of the University
Authors note

2Business Data Analysis
1. Executive Summary
Purpose of study
The purpose of our report is to explore different data analytics methods in solving the
problem of diminishing sales as well as suggest new methods with which to conduct
business, partly due to rising competition in the e-commerce business as well as shifting trend
and nature of business.
Scope
We will cover a range of data analysis tools such as forecasting for classification and
predictive analysis. Moreover, we will explore the descriptive statistics of the company’s data
so as to draw gainful insights about the performance of NILA e-commerce company.

3Business Data Analysis
Table of Contents
2. Introduction............................................................................................................................4
3. Research Methodology...........................................................................................................5
4. Analytical Findings................................................................................................................7
5. Recommendations................................................................................................................15
6. Implementation plan based on the recommendations..........................................................16
7. Conclusions..........................................................................................................................17
8. Bibliography.........................................................................................................................18
9.Appendix...............................................................................................................................19

4Business Data Analysis
2. Introduction
E-commerce is one of the latest developing sectors of business, with both new and old
players and As a result drawing competition among the groups. As a result, there is need for a
firm to as given the company’s revenue sources, the number of customers purchasing a given
commodity and whether they are return or one-time purchasers as well as the region they
come from. As such, it is prudent to predict the product sales and performance of the products
both locally and internationally. Forecasting will enable the executive to apply new strategies
for product promotion and be able to export more of the best playing products in aim of
maximizing sales.

5Running head: ICT 706 DATA ANALYTICS
3. Research Methodology
To conduct our analysis paper we employ linear regression in developing a sales
prediction model for our given business regions for NILA e-commerce company. Our
regression analysis will be developed to incorporate a number independent variables to
enable us develop a more predictive and suitable model. Through linear regression
(Forecasting), the Company will be able to as given and exploit any available business gaps
and also curb the risks that may occur. Linear regression method in our business Company
will prove important in business decision making. Analysis of the company’s Product
demand is key in the forecasting on the the possible quantity of products that a customer buy
or which will be exported to a given region by our Company. However, the demand of the
company products is not only the factor to be considered in prediction of the performance of
the business. By the application of the linear regression method in data analysis, the business
Company will venture past estimating of just the sales into other factors which will be able to
display effect on the amount of sales made by the company in order to ensure more the sales
and consequently the revenue generated by the firms.
Moreover, Forecast analysis will be useful for a firm to establish specific relationship
among the different variables by determining the distribution patterns in the different
variables of our data-set which were previously absent. For instance, analysis of total sales
and customer records are useful in indication of the market patterns. Such patterns could
incorporate growth in demand for given products or in a given business regions of the
country.
application of this methods helps the company to reduce the tremendous amount
unstructured unprocessed data in applicable and productive information use by the business.
As a result, it will be stated that forecasting analysis promotes near accurate as well as better

6Business Data Analysis
business decisions aimed at improving the firms performance in the now competitive market.
In the forecasting model for instance, it may be concluded that the amount of products
purchased are dependent on consumer purchases in the specified region and on the total sales
of the product in the chosen geographical region. Analyzed statistics on these factors should
be taken into consideration and hence evaluation of best fit for the sales and other variables.
In our report we explore different stages of forecasting as a means of classification and
predictive modelling.

7Running head: ICT 706 DATA ANALYTICS
4. Analytical Findings
For the acquired data set, at first we collected the statistical details of it using python
language. The overall statistics of the data-set generates the following result,
price Sales(Us dollars$) Customers
count 1280.0 1280.0 1280.0
mean 53.95390 274.4203125 22.89609375
std 27.45342 687.8165506918891 11.4619805
min 3.0 50.0 5.0
25% 32.0 99.0 12.0
50% 54.0 183.0 23.0
75% 79.0 233.0 30.25
Max 99 9744 54
Table 1: Statistical data about the data-set
Sales(Us Customers
count 40.0 40.0
mean 208.35 23.75
std 373.4671 11.9587967
min 53.0 5.0

8Business Data Analysis
25% 88.0 15.0
50% 108.5 22.5
75% 211.25 30.0
max 2435.0 54.0
Table 1: Statistical data about Fridge sales
Following our analysis table we can interpret the statistics as in: the company data-set
incorporates total 1280 data entries with the maximum and minimum values for the product
price being $ 50 and $99. The cumulative product sales were such that: the minimum
recorded value by the company for sales was $50, mean value was 274.4203125 while the
maximum was $9744.
Through conducting further data analysis we realized there were 7 products that were
sold dealt by the company according to the records, including:
i. Fridge
ii. Solar Panels
iii. Television Sets
iv. Microwave
v. Air Conditioners
vi. Smart Cookers
vii. Lawn Mowers
Additionally there were 2 methods of shipping goods sold (Paid and free), there were 2 types

9Business Data Analysis
of business customers ( existing and new).
. Also, regions in which the company conducted business were:
i. Africa
ii. Australia
iii. Asia
iv. America
v. Europe
Business regions to target new customers
Data analysis a sample of total sales recorded and number of customers in relation to
price :
price Sales(Us
dollars$)
Customers
count 251.0 251.0 251.0
mean 54.30278 347.1752988 22.657370517
std 27.37904 943.7778261 12.123123997
min 3.0 53.0 5.0
25% 33.5 97.5 11.5
50% 52.0 180.0 23.0

10Business Data Analysis
Analysis of different regions according to sales
Africa
Sales(Us dollars$) Customers
count 40.0 40.0
mean 208.35 23.75
std 373.4671612241437 11.958796783657649
min 53.0 5.0
25% 88.0 15.0
50% 108.5 22.5
75% 211.25 30.0
Europe
price Sales(Us dollars$)
count 251.0 251.0
mean 54.30278884462152 347.1752988047809
std 27.379042207338713 943.7778261579414
min 3.0 53.0
25% 33.5 97.5
50% 52.0 180.0
75% 78.5 232.5

11Business Data Analysis
Asia
price Sales(Us dollars$)
count 230.0 230.0
mean 54.68695652173913 283.67826086956524
std 27.425282447667627 779.6865011892284
min 3.0 51.0
25% 33.0 102.25
50% 54.0 186.0
75% 79.0 236.0
Australia
price Sales(Us dollars$)
count 255.0 255.0
mean 55.043137254901964 235.32549019607842
std 27.917942576019772 466.2044007142466
min 3.0 51.0
25% 33.0 103.0
50% 56.0 186.0

12Business Data Analysis
75% 79.5 234.0
Given our table,we note that the average number of customers who made return
purchases were 22, also evidently there are lesser number of buyers following a -fold analysis
such that the variation is 12. As a result, it will be said that, it is important to regulate the
product price to favor more customers in order to attract new buyers so as to foster the
company’s revenue growth. Also we found out that Asia recorded the most average sales
followed by Australia, whereas Africa had the least sales recorded and therefore it would be
wise to export more to Africa while still maintaining the volumes exported to Asia and the
local market.
.
Products analysis to determine those key for sales growth
Product price Sales(Us
dollars$)
Customer
s
Air Conditional 9978 52007 4508
Fridge 9688 41221 4056
Lawn Mower 9883 61374 3861
Microwave 10784 54460 4353
Smart Cookers 9977 42692 4039
Solar Panels 9508 40480 4195