Sales Trend and Forecasting Using Data Mining Techniques

The E-commerce industry is in dire need of an intelligent forecasting model of sales trends with the highest possible level of accuracy. There is a need to develop a tool that predicts sales and do the comparative analysis of sales prediction using data mining techniques. Proposed So

Project Title

Sales Trend and Forecasting Using Data Mining Techniques

Project Area of Specialization

Artificial Intelligence

Project Summary

Problem statement

The E-commerce industry is in dire need of an intelligent forecasting model of sales trends with the highest possible level of accuracy. There is a need to develop a tool that predicts sales and do the comparative analysis of sales prediction using data mining techniques.

Proposed Solution

Our proposed solution will provide a single platform for users to predict the sales of the products, their trends in the market and do a comparative analysis of sales prediction using different data mining techniques.

Summary

Almost 25% of the world’s population has shifted to online shopping. E-commerce Giants had a huge stake in the industry use the resources at hand to overwhelm the smaller stores. This in turn neglects the small store owners of fair market exposure. This project is capable of predicting and recommending productive ways to enhance sales of a store. Several data mining techniques will be used to perform a list of predictions and recommendations. There is currently no such application capable of accommodating small e-commerce stores with such credible information. So in this proposed project, we will build a system that does a comparative analysis of sales prediction by using the latest and innovative tools & techniques. Furthermore, the store owners will get a visual representation of the results which can help in decision making. Here proposed project (sales trend and forecasting using data mining techniques) provides a platform for these store owners to utilize the data at their disposal to grow and expand their services above and beyond the need of the valuable customer. The proposed project will use critical data mining techniques to implement a series of predictions and sales trends. The provided data by the user will be cleaned for a few known anomalies. Actions will be applied to this data set and certain results will be gained for each of the multiple actions. When the system is done with the actions, visualizations will be generated based on the results using some visualization tool. Moreover, the user will be offered a simple, streamlined, and efficient user interface. This proposed project will not only help the store owners (users) to understand the nature of the sales but will also guide them to enhance it. Our proposed project will provide a service to the store owners via a simple web application hosted on the cloud.

Project Objectives

To know the prediction and trend of sales within a specific time period has a huge impact on the progress of the business and also helps a lot for taking the right decisions at the right time. For this purpose, there are many data mining techniques & tools for extracting key knowledge from a large number of data-sets for the sake of forecasting. But the problem is that traditional forecasting systems are a little difficult to deal with large data sets and may not as much accurate as it should. So in this proposed project, we will build a system that does a comparative analysis of sales prediction by using the latest and innovative tools & techniques for a much accurate and reliable sales trends and forecasting system which would be up to mark and will fulfill all the requirements of this modern era of industry.

• To design and develop an online sales trend and forecasting web application using data mining techniques for store owners.
• To develop a web-based application only.
• To enhance the security and the cost efficiency of data storage on the cloud.
• To enhance the sale.
• To make financial planning.
• Do a comparative analysis of predicted sales

Project Implementation Method

Holt's Linear Trend Metod

For the implementation of this proposed system, we are using exponential smoothing also known as Holt’s linear trend method. It continues the procedure of simple exponential smoothing, but simple exponential smoothing does not follow any trending behavior. On the other hand, the linear trend method does follow. This method has three equations:

1. Level Equation
2. Trend Equation
3. Forecast Equation

Frequent Pattern Algorithm

First of all, it finds all the occurrence of items that are being bought in the same transaction, after that all the counts of a single item are maintained, which will be considered as support count. An FP tree is created; the root node is considered as null and then places the maximum support count at the top and places other nodes in descending order. This algorithm is thus better than Apriori since Apriori is costly and takes more time.

Customer Segmentation

This technique divides the customers based on recency, frequency, monetary. Clusters of customers based on their transactions are made – how recently, how often and how much did they buy. Likewise, which of your customers can be retained, has the potential to become valuable, the best customer, respond to promotions or campaigns.

Customer Lifetime Value Prediction

In this prediction, we need to identify a feature set that is used to predict the future. We used RFM (Recency, Frequency, Monetary) score as a feature set. We already find out the RFM score of customers in customer segmentations. We used the same code in this prediction for the feature set.

Uplift Modeling

In this prediction, we need to identify a feature set that is used to predict the future. We used RFM (Recency, Frequency, Monetary) score as a feature set. We already find out the RFM score of customers in customer segmentations. We used the same code in this prediction for the feature set.

Market Response

At times there is more than one offer from which the store has to opt for the best offer in order to enhance sales. For this purpose, we choose the market response algorithm. In the implementation, we chose a random dataset that included BOGO aka buy one get one free offer and discount offers.

Churn Analysis

In order to implement churn analysis, we needed more than the available feature set, so we explored and came up with a telecommunications dataset that had several features related to the sales of communication products. We performed a brief EDA aka Exploratory Data Analysis to find out the nature of a few columns.

Benefits of the Project

Benefits

The befit of this system is to provide ease to the whole business community including small store owners. So, by doing the predictions the user can easily analyze and grow their business progress and can have a better understanding of the trending products in the market in the current situation and after a specific time period which will assist them for a smooth business planning according to the situation.

The following are the main befits of our proposed system

• Enhancement of sales
• Boost your revenue
• To analyze the Market trend
• Customer Segmentation
• Sales Forecasting
• Market Basket
• RFM Segmentation
• Uplift Modeling
• Churn Analysis
• Customer Lifetime Modeling
• Market Response Modeling

Technical Details of Final Deliverable

Our proposed system will be an automatic prediction system that does a comparative analysis of predicted sales and identify their trends in the market. This will be a web-based system that easily accessible to anyone. It provides a platform for store owners to utilize the data at their disposal to grow and expand their services above and beyond the need of valuable customers. The proposed solution will use data mining techniques to implement a series of predictions and sales trends.

The provided data by the user will be preprocessed using data mining techniques and then some operations will be applied to this data set and certain results will be gained for each of the multiple actions. When the system is done with these operations, visualizations will be generated based on the results using some visualization tool. Moreover, the user will be offered a simple, streamlined, and efficient user interface. This proposed project will not only help the store owners (users) to understand the nature of the sales but will also guide them to enhance it.

Our proposed system will provide services to the store owners via a simple web application hosted on the cloud. It will do a series of predictions:

1. Sales Forecasting
2. Market Basket
3. RFM Segmentation
4. Uplift Modeling
5. Churn Analysis
6. Customer Lifetime Modeling
7. Market Response Modeling

Complete technical details of our final deliverable are available in the attached file as below

Final Deliverable 

Final Deliverable of the Project

Software System

Core Industry

IT

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Decent Work and Economic Growth

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com