Proposal2

Business Problem

The business problem related to the work is poor customer relationship management, which affects the business's success. At the workplace, I experienced that our customer management strategies are not advanced and fail to satisfy customer expectations on time. This greatly affects the retention and returning rate of customers. This problem can be addressed through a data science solution. This proposal will determine effective data mining techniques that will address the problem and add value to the organization. The proposal will discuss the use of predictive modeling in solving the issue by using data from the organization.

Predictive Modeling

To address the problem, predictive modeling is helpful because by using existing customer status, the management can predict future outcomes and make changes to improve these outcomes. By analyzing existing customer relationship patterns, management can examine the organization's likelihood of success or failure. But to utilize predictive modeling to solve CRM, the company should have a lot of customer interaction data or records. For example, records of 100 customers would help generalize customer experiences and design advanced and effective customer relationship management strategies.

Outcomes of Solution

Purpose of solving this problem is to help the company achieve long-term positive outcomes and develop a long-lasting positive relationship with customers. Positive customer relationships with the organization will add value by improving profitability and recognition. Customer retention and loyalty also bring huge advantages to the organization, which is what the solution wants to support and adds value to the organization.

An Integrated Model and Techniques of Data Mining

An integrated data mining model is helpful to address the problem, consisting of three types of data mining processes:

· Discovery

· Predictive Modeling

· Analysis

The data mining model's discovery stage helps identify new customers and the best ways to interact with them. Data mining techniques such as segmentation and association will help identify customers and interact with them using the best techniques possible. The predictive modeling stage will help better understand past and present customer behavior. And the technique response modeling will help set new strategies and goals regarding CRM. The data mining model's predictive modeling stage will also help attract and retain profitable customers for a long time. The analysis stage of the integrated model will conduct in-depth customer analysis and guide the organization on whether to retain customers for long or not. At the same time, techniques such as deviation detection and churn detection help determine deviation from the norm and help the organization make decisions.

The data required to train the model would be extracted from the organization. The organization required as much data as possible about customer relationships for predictive modeling. For predictive modeling, data scientists must take care of customer data, experiences, and satisfaction to help manage customer relationship management.