Ecommerce The History and Future of Online Shopping Assignment

Assignment Task

Introduction

Online retailing has become an integral part of modern people’s life. Everyday people spend millions of money through internet sources such as: Amazon, Ebay, Walmart, Apple. According to Farfan (2019) The availability of products for people, fast buying and faster delivery is what driven attention to online retailing rather than usual high street shops. In addition, markets became very saturated as small firms entered the market with unique products and features which gains attention of online retail shoppers. As well as new technologies emerges faster, websites became more available for people and easier to use to any customer, delivery become faster and customer service is evolving which obviously gain attention of the customer to go online rather than high street.

Mintel (2020) claims that online-only stores are taking market share from store-based retailers where 52.7% of market are now online-only in the UK. Main customers of online retailing are both male and female aged from 16 to 55+. Mintel (2020) suggest that 30% of those people are making online shopping once a week, where most of the people make 2-3 purchases per month. The most popular items that consumers buy online is clothing, footwear and accessories amongst young people aged 16-24 which purchases remains growing on a regular basis. In comparison home furniture is popular amongst people aged 25-44 and amount of expenditure falls almost by 30% (Mintel, 2020).

In this repot will be investigated data about online retailing with visualization by “Tableau” software. The report will consist of following sections: Data brief, Data cleansing and preparation, identification of key metrics using the visualization with explanation and strategic inside with discussion of what has been analysed and identified.

Data brief

As was previously mentioned, the data set about “Online retail” will be used within this report. The data set has been taken from a website – Kaggle.com. Kaggle, is a website for data scientists created to solve problems with data science, machine learning and predictive analytics. It is a platform where people around the world can collaboratively work on data sets, sharing codes or host their own datasets. The “Online retail” dataset is open for people and free of use, meaning anybody can use the data and work with it (Usmani, 2018)

The data collected about online retailing throughout the world. The dataset shows many countries expenditure in terms of quantity, unit price for some items following the 2011 year. The data in chosen data set will help to identify many insights, such as where people have the largest quantity of goods bought, which items they buy and at what price. Ibrahim & Wang (2019) identifies that online shoppers are intended to cross-border purchasing, meaning people is willing to buy goods from other countries and such behavior might go from personal feelings of the online shopper and influences of social media.

This dataset consists of many Europe countries which are dominant in this dataset. The dataset identifies 38 metrics in such things as: Country, Customer ID, Invoice Date, Invoice No, Quantity, and Unit Price. This report aim is to identify which countries contributed more for buying of goods among those in the dataset, for what price they bought the goods, and quantity of goods sold as well as comparison and contrast

between these countries. Before the start of investigating, the data cleaning process should take place to avoid not useful information and identify key components that will be used in data analysis.

Data cleansing and preparation

Data cleansing process is used by many companies nowadays as they are working with a big data tools which needs to be analysed. Usually, the data with which they are working is dirty and contains many unmeaningful parts which they want to get rid of before starting to analyse. This is supported by Ridzuan & Wan Zainon, Wan Mohd Nazmee (2019) who states that data cleansing process is time consuming, but it is the most quickiest way to identify inaccuracies of the big data and solve them through that process. In addition, Hernández & Stolfo (1998) suggest that without the identification of these errors, such as duplicated information the data will produce failure statistics which then will lead to poor analysis and underwhelming results.

To have clearer vision of this data it is required to make some data cleansing process. As was mentioned previously, the data set contains a lot of information about many countries throughout the world. In this context, it has been decided to reduce the number of countries investigated to have clearer and more complex information. The 5 largest countries have been identified and chosen for the purpose of this report, they are all based in Europe (see appendix 1) which is clearly will help to identify, specifically this region. As you can see from the figure mentioned above, the largest countries that participated in online retailing according to this data set is UK, Netherlands, Germany, France, and EIRE.

By data cleansing process, it has been also identified an unclear information about countries such as: European community which does not belong to a particular country, and unspecified countries were cleared out from the data set as it does not provide any value to this report as it to identify specific countries. You can see the full list of all irrelevant and data cleansed countries in appendix 2. In addition, it has been identified that not all the countries from the chosen are bought the same products, so it has been decided to remove items that not match all the countries to have the clearer vision. To have a better demonstration of used items in this dataset please look at the appendix 3.

Key metrics and relationships

In this section it will be identified the key metrics used with illustration and explanation about 5 countries online retail.

Strategic Insight

A strategic insight is often used to show off the importance of big data analysis and to have more clearer and broader picture of the analysis. A strategic insight is a tool that used by companies to share knowledge more easily, improving business performance which creates several benefits including building customer loyalty. In this report, a strategic insight will show how the data was used and the main points that people can take from visualizations within the report.

The data visualization in this report showed a comparison in online retail between 5 countries: UK, EIRE, France, Germany, and Netherlands. In this report through data visualization these countries have been contrasted in which some have a greater amount of online retail purchases and some somewhere in between. All in all, this data set has not provided with the answer why exactly these countries in this data set have greater amount of online retail that other which were cleansed, but comparison and contrast between chosen 5 countries have shown some interesting information.

Visualisations have shown us that despite one country can dominate the online retail market with high number of purchases like UK, we can see that other countries that have purchased less quantity of products has the higher price per unit for those products. This can mean that products could be sold from the UK, meaning less price per unit for UK in the local market and higher price for other countries due to export. In addition, this report shows that tableau is a useful software that can be used to identify big data, for example the sales in each country buy quarters is a useful tool where everyone can see shifts in the pattern of online retails in these countries.

This report has also touched the idea of why online retails become so popular and why people choose to shop online rather than going to a high-street shop. Such things like social media impact, mental health impact, and the actions from companies to drive that act of purchasing by advertising and data collection about the consumers to successfully sell the product.

Finally, as the report touched the behavior of consumers, further it has been provided with visualization of number of items has been sold in these countries. That can state that probably these countries are most influenced countries for online retails from things like social media, or potentially have problems with mental health that consumers want to solve by shopping. This is also can mean that in these 5 chosen countries companies does collecting personal information of consumers by enormous amounts (especially the UK) to successfully sell products by analysing consumers behaviors, their needs and wants.

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