Marketing customer response scoring model based on machine learning data analysis

Customer Relationship ◽

Customer Data ◽

Modeling And Analysis ◽

Scoring Model ◽

Model Based ◽

In marketing, problems such as the increase in customer data, the increase in the difficulty of data extraction and access, the lack of reliability and accuracy of data analysis, the slow efficiency of data processing, and the inability to effectively transform massive amounts of data into valuable information have become increasingly prominent. In order to study the effect of customer response, based on machine learning algorithms, this paper constructs a marketing customer response scoring model based on machine learning data analysis. In the context of supplier customer relationship management, this article analyzes the supplier’s precision marketing status and existing problems and uses its own development and management characteristics to improve marketing strategies. Moreover, this article uses a combination of database and statistical modeling and analysis to try to establish a customer response scoring model suitable for supplier precision marketing. In addition, this article conducts research and analysis with examples. From the research results, it can be seen that the performance of the model constructed in this article is good.

Machine Learning Based Predictive Action on Categorical Non-Sequential Data

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190417150421 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1020-1030

Author(s):

Pradeep S. ◽

Jagadish S. Kallimani

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Categorical Data ◽

Numerical Data ◽

Processing Technique ◽

Sequential Data ◽

Industry Standard ◽

Robust Model ◽

Future Work

Background: With the advent of data analysis and machine learning, there is a growing impetus of analyzing and generating models on historic data. The data comes in numerous forms and shapes with an abundance of challenges. The most sorted form of data for analysis is the numerical data. With the plethora of algorithms and tools it is quite manageable to deal with such data. Another form of data is of categorical nature, which is subdivided into, ordinal (order wise) and nominal (number wise). This data can be broadly classified as Sequential and Non-Sequential. Sequential data analysis is easier to preprocess using algorithms. Objective: The challenge of applying machine learning algorithms on categorical data of nonsequential nature is dealt in this paper. Methods: Upon implementing several data analysis algorithms on such data, we end up getting a biased result, which makes it impossible to generate a reliable predictive model. In this paper, we will address this problem by walking through a handful of techniques which during our research helped us in dealing with a large categorical data of non-sequential nature. In subsequent sections, we will discuss the possible implementable solutions and shortfalls of these techniques. Results: The methods are applied to sample datasets available in public domain and the results with respect to accuracy of classification are satisfactory. Conclusion: The best pre-processing technique we observed in our research is one hot encoding, which facilitates breaking down the categorical features into binary and feeding it into an Algorithm to predict the outcome. The example that we took is not abstract but it is a real – time production services dataset, which had many complex variations of categorical features. Our Future work includes creating a robust model on such data and deploying it into industry standard applications.

2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT) ◽

Construction of Driving Behavior Scoring Model based on OBD Terminal Data Analysis

10.1109/isctt51595.2020.00012 ◽

2020 ◽

Author(s):

Tianshi Liu ◽

Guang Yang ◽

Dong Shi

Keyword(s):

Data Analysis ◽

Driving Behavior ◽

Scoring Model ◽

Model Based

Interactive protocol for acquisition of migraine diaries with a mobile app and machine learning data analysis

10.1145/3472301.3484322 ◽

2021 ◽

Author(s):

Tatiane Vieira Alves ◽

Kamila Rios da Hora Rodrigues ◽

Moacir Antonelli Ponti

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Mobile App ◽

Supermarket Sales Prediction Using Regression

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2021/951022021 ◽

2021 ◽

Vol 10 (2) ◽

pp. 1153-1157

Keyword(s):

Machine Learning ◽

Performance Metrics ◽

Low Cost ◽

Machine Learning Techniques ◽

Customer Data ◽

Sales Data ◽

Online Marketplace ◽

Sales Prediction ◽

The Future

Sales forecasting is an important when it comes to companies who are engaged in retailing, logistics, manufacturing, marketing and wholesaling. It allows companies to allocate resources efficiently, to estimate revenue of the sales and to plan strategies which are better for company’s future. In this paper, predicting product sales from a particular store is done in a way that produces better performance compared to any machine learning algorithms. The dataset used for this project is Big Mart Sales data of the 2013.Nowadays shopping malls and Supermarkets keep track of the sales data of the each and every individual item for predicting the future demand of the customer. It contains large amount of customer data and the item attributes. Further, the frequent patterns are detected by mining the data from the data warehouse. Then the data can be used for predicting the sales of the future with the help of several machine learning techniques (algorithms) for the companies like Big Mart. In this project, we propose a model using the Xgboost algorithm for predicting sales of companies like Big Mart and founded that it produces better performance compared to other existing models. An analysis of this model with other models in terms of their performance metrics is made in this project. Big Mart is an online marketplace where people can buy or sell or advertise your merchandise at low cost. The goal of the paper is to make Big Mart the shopping paradise for the buyers and a marketing solutions for the sellers as well. The ultimate aim is the complete satisfaction of the customers. The project “SUPERMARKET SALES PREDICTION” builds a predictive model and finds out the sales of each of the product at a particular store. The Big Mart use this model to under the properties of the products which plays a major role in increasing the sales. This can also be done on the basis hypothesis that should be done before looking at the data

Machine Learning Data Analysis for Tool Wear Prediction in Core Multi Process Machining

Journal of the Korean Society of Manufacturing Process Engineers ◽

10.14775/ksmpe.2021.20.09.090 ◽

2021 ◽

Vol 20 (9) ◽

pp. 90-96

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Tool Wear ◽

Wear Prediction ◽

Tool Wear Prediction ◽

Applications of Artificial Intelligence in Electrical Engineering - Advances in Computational Intelligence and Robotics ◽

Introduction to Machine Learning and Its Application

10.4018/978-1-7998-2718-4.ch014 ◽

2020 ◽

pp. 262-290

Author(s):

Ladly Patel ◽

Kumar Abhishek Gaurav

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Learning Algorithm ◽

Human Interaction ◽

Machine Learning Algorithm ◽

Past Data ◽

Applications Of Machine Learning ◽

Learning Frameworks ◽

In today's world, a huge amount of data is available. So, all the available data are analyzed to get information, and later this data is used to train the machine learning algorithm. Machine learning is a subpart of artificial intelligence where machines are given training with data and the machine predicts the results. Machine learning is being used in healthcare, image processing, marketing, etc. The aim of machine learning is to reduce the work of the programmer by doing complex coding and decreasing human interaction with systems. The machine learns itself from past data and then predict the desired output. This chapter describes machine learning in brief with different machine learning algorithms with examples and about machine learning frameworks such as tensor flow and Keras. The limitations of machine learning and various applications of machine learning are discussed. This chapter also describes how to identify features in machine learning data.