You are also allowed to add any more attributes to describe product segment

Note: You must develop your own unique and original dataset - copying a dataset from another student or from the internet will result in reduced or zero marks.

 

    •    Research any specific data mining or classification technique and propose a suitable technique or model to determine any association or relationships among the attributes.

 

    •    Develop a predictive model to predict monthly sales for a given geographic region. You can use any of the methods such as Naive Bayes, decision trees or linear regression. You are also welcome to do a comparative analysis of all the methods you come across in your research and use the comparative analysis to justify your approach and research findings.
    •    Based on your analysis, present recommendations to the board for the following business problems:
    •    What is the most likely geographic region to target new customers to increase sales and profit?
    •    Which products should be prioritised for increase in sales?
    •    What will be impact on product sales if free shipping is provided to all products?
    •    Any new innovative ideas to improve company’s profitability supported by your data analytics.

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.

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 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.  

 

3. 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
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:

1. Fridge
2. Solar Panels

• Television Sets

1. Microwave
2. Air Conditioners
3. Smart Cookers

• Lawn Mowers

Additionally there were 2 methods of shipping goods sold (Paid and free), there were 2 types of business customers ( existing and new).

. Also, regions in which the company conducted business were: 

1. Africa
2. Australia

• Asia

1. America
2. 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

 

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

 

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
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$)	Customers
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
Television Sets	9243	59024	4295

From our analysis of the product sales across different regions, we found out that the company recorded most sales from air conditioners which had a sales return of 52,007 Us dollars followed by Microwaves which had a sales record of 54460, the least purchases were that of lawn mowers which surprisingly had more sales records at 61374 US dollars. Therefore, for the company to generate more revenue it ought to concentrate on the exportation of more lawn mowers which have higher value compared to lower value products. However, the company should not overlook the most popular product, i.e. Air conditioners and should therefore supply more to different regions in order to keep the flow of customers

Influence of shipping type

shipping Method	price	Sales(Us dollars$)	Customers
Shipping free	35925	177290	15544
Shipping paid	33136	173968	13763

 

Following our analysis we find out that most sales were recorded when free shipping was offered and therefore it proves that it would be fundamental enough to introduce free-shipping as an incentive to lure new customers as well as encourage multiple purchases.

Prediction model using forecasting

In order to explore and predict the sales in the American market, we applied a linear regression model . Through our regression model we established a relationship between different marketing methods and sales in the market, hence indicating need for better and innovative methods for competition.

 

1-Regression of sales and factors affecting sales In the US Market

5.  Recommendations

Persuasive selling: Following our analysis of the company’s records,  we did identified a relationship between the sales recorded by the company on different products and therefore, the sales sector would be encouraged to engage in persuasive selling where they persuade the buyer to purchase a given commodity now that they have purchased another through offering enticements such as free-shipping and gift coupons. This is mostly effective between related products such as Television and solar panels for instance. This would ensure more sales for the related products.

Endorsement campaigns for products: We advice the company to adopt new advertising techniques so as to foster product awareness as well as widen the product market through personalized ad campaigns by renown public figure such as sportsmen and entertainers. Public endorsements are often a way of ensuring public belief of the product as a good quality one or even essential.  

Provision of free shipping: Due to high sales recorded by the free-shipping option, the company ought to adopt the method for a wide range of products so as to encourage more purchases

After-sales services: Despite being unused by the company in the past, it would be an add-on for the shipping incentive in that given a certain type of goods purchased, the company offers after-sale services such as free training and installation. After-sales services may offer customers a reason to trust us as their suppliers.

6. Implementation plan based on the recommendations

 Following our recommendations, for the company to successfully oversee the implementation, the following basic plan is proposed:

1. The company should set aside a budget that will take care of endorsement deals and assign a marketing team the role of evaluating and assessing the suitable candidates for endorsing the line of the company products
2. The company should employ technical assistants who will help in the after-sale role and process for different clients both for abroad branches and the local region.

• The company should ensure free-shipping for all commodities so as to encourage more sales through subsequently ensuring shipping discounts

1. The sales team should employ the persuasive selling technique through adopting the client-seller rapport method, where apart from the sale, a marketer gets to discuss with the buyer of their other probable product interest and suggest some of our products.

Following the above strategy will give the company an upper-hand over our competitors and also ensure more sales for the subsequent business period. Additionally as a precaution, the company ought to declare redundant products out of cycle so as to ensure maximizing of the current profitable products

Conclusion

Conducting of business often requires just more than a product and investors. It calls for smart methods with which to make decisions with as well as cope up with competition. As a result methods such as Sales forecasting come in handy for exploring the business performance as well as predicting the next move of the business. Through forecasting the company can be able to predict sales, explore potential loopholes for adopting a given strategy and even be able to determine the underlying relationship between the performance of a given business aspect and a business activity, as in our case the activity of free shipping and the performance of sales.

Bibliography

Exterkate, P., Groenen, P.J., Heij, C. and van Dijk, D., 2016. Nonlinear forecasting with many predictors using kernel ridge regression. International Journal of Forecasting, 32(3), pp.736-753.

Fernandes,C & Nicole, I. (2018) The influence of corporate social responsibility associations on consumers’ perceptions towards global brands. Journal of Strategic Marketing. June 20^th , pp 39-57,  DOI: 10.1080/0965254X.2018.1464497. 

Montgomery, D.C., Jennings, C.L. and Kulahci, M., 2015. Introduction to time series analysis and forecasting. John Wiley & Sons.

Omar, H., Hoang, V.H. and Liu, D.R., 2016. A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles. Computational intelligence and neuroscience, 2016.

Baardman, L., Levin, I., Perakis, G. and Singhvi, D., 2017. Leveraging Comparables for New Product Sales Forecasting.

Donald H. & Tim S. (2008).Business leaders speak out: their real strategic problems.. Journal of Business Strategy, Vol. 29 Issue: 5, pp.32-37, https://doi.org/10.1108/02756660810902305.