Data Analytics and Sales Forecasting Method -

 During the processing of the data the major requirement is to predict monthly sales for the progress in the procession of the data structuring of the E-commerce organization. In addition to the fact that the data mining method usage will also be discussed. Linear egression method will also be used in order to predict the monthly sales of the product in different regions. This methodology ensures the fact that the proper comparison between the data parameters are provided with respect to the relationship or the pattern of the data that are not previously explored.

As an example, the analysis of the monthly sales regarding the sales data, customer count with respect to the geographic region for creating the patterns of the data analytics of the processing the project management.

Analytical Findings

For the Given dataset we got the above statistical data which shows that there are 1201 records in the selected dataset. The minimum value, mean value and maximum value for the Product price is given by, $7, $27 and $16.85. For Monthly sales it is given by, $25, $145 and $86.31. Moreover, for the Customer_Number these values are, 7, 35 and 21.25.

Here, it is evident that, most of the customers are from South geographic region and the lowest number of customers for the E-commerce company is from North geographic region. 

Customers in different regions according to the shipping type

Calculation of the number of customers that enjoy the benefit of free orders and th customers that do not enjoy the free orders are done.

The above table provides the data that maximum number of customer management paid are form South region. On the other hand, minimum number of customer paid are from the North region.

Region wise sale of products

In this plotted graph different products in different regions are compared. Total value of the sold product is calculated or each and every region and it is seen that the Columbian in South and Darjeeling in South are the most highly ordered products from the e-commerce sites. For East region, Caffe Latte has been the highest sold product in East, green tea is the highest sold product in the north region ad west region.

In order to prioritize the sale of the products for improved revenue the revenue from the different products needs to be investigated which produces following result,

From the table below it can be concluded that the Chamomile is the costliest product among all other products, Chamomile has the largest monthly sales and Darjeeling has the hight number of customers.

The least sales is recorded by the Caffe Latte and the sales is counted to $2615424. Therefore, there must be a promotional and marketing strategy for the Caffe Latte in order to increase the sales of the products.

Most likely geographic region to target new customers

For targeting the new client base it is important to know the demand of the clients and for knowing the demands the graph is plotted with adequate data,

From the above table it is clearly visible that, the total number of sales that has the least quantity are Caffe Latte. This proves the fact that the need of the marketing policies needs to be strengthened for the processing of the Caffe Latte.

Use of liner regression model for the predicting the monthly sales

For the prediction of the Sales that are recorded monthly linear regression method is used for the procession of efficient marketing strategy. This is the reason linear regression method is chosen for this process. This technique is used for predictive analysis of the process. The main motive of this method is to check two techniques namely the performance of the set of prediction of the variables and the significant impact of the outcome variable.

Taking into consideration the processing of the marginal increase in the monthly sales of the e-commerce organization. The following recommendations will ensure the fact that there is an improvement in the business procession.

• For increasing the sales of the products, the business organization provides the free shipping of the products that are ordered by the customers, who prefers not to pay extra for the shipping of the products.
• As the total numbers of clients in the “East” the promotional offers must be provided in order to ensure there is a growth in the processing of the products. Marketing campaigns can be used as a tool that will be used for increasing the client base.
• The data that is used for the analysis of the product demand proves the fact that the “Choco latte” has lesser revenue and the demand is comparatively less for the product. Hence proper marketing strategy must be implemented in order to ensure the fact that there is proper marketing of the product which will lead to the increase in sales of the product.

Implementation plan based on the recommendations

The implementation of the strategies for the products that are lacking in pace in case of the public demand despite the fact that the organizations have been performing several marketing strategies are the productions must be stopped. The organization lo must take in feedback from the existing clients in order to influence the views regarding the products. The feedback that will be negative will ensure the fact that the organization will be able to understand the problems that are processed with the processing of te products and the laws will be detected and can be solved.

Personalized recommendations of the clients that has already bought the products acts as the positive side to the processing of the product. This ensures the fact that the potential clients will get influenced by the reviews hat are provided by the existing users of the product. 

Conclusion

The data analytics that are helpful in nature is completely dependent in the fact that it helps in the processing of the data is helpful in creating knowledge for the compression of unstructured data to a structured version of the data. Predicting the monthly sales with the help of linear regression will include the act that the information is recognizable for the business organization. The prediction of the monthly sales includes the usage of the linear regression methodology. This process is provident to the fact that it improves the business performance of the organization. By taking into consideration the recommendations stated above the organizations will be benefitted.

Bibliography

Arunraj, N.S. and Ahrens, D., 2015. A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting. International Journal of Production Economics, 170, pp.321-335.

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

Braun, M.R., Altan, H. and Beck, S.B.M., 2014. Using regression analysis to predict the future energy consumption of a supermarket in the UK. Applied Energy, 130, pp.305-313.

Chatterjee, S. and Hadi, A.S., 2015. Regression analysis by example. John Wiley & Sons.

Schroeder, L.D., Sjoquist, D.L. and Stephan, P.E., 2016. Understanding regression analysis: An introductory guide (Vol. 57). Sage Publications.