Leveraging Logistic Regression Modeling in R to Analyze Future Relationship Perception with HBAT

In this analysis, we harnessed the power of logistic regression modeling in R to explore the intricate dynamics of future relationship perceptions with HBAT. Our extensive study uncovered a multitude of factors influencing this perception. Through a detailed examination of customer type, industry type, firm size, region, distribution system, and performance perception, we unveiled significant insights. These findings enable HBAT to make data-driven decisions, optimize customer relationships, and boost sales. Our approach showcases the practical application of implementing logistic regression models and the use of R for robust statistical analysis.

Problem Description

In this logistic regression assignment, we embarked on a comprehensive analysis to discern the complex web of factors that significantly affect the perception of future relationships with HBAT, a fictitious company. Employing the robust capabilities of R programming, we constructed a logistic regression model that serves as an invaluable tool to unearth insights from the data at hand. The central objective was to investigate the probability of a purchasing manager considering a strategic alliance or partnership with HBAT, given a multitude of independent variables, such as customer type, industry type, firm size, region, distribution system, and performance perception.

Logistic Regression Model:

Our logistical regression model ventured deep into the intricacies of these variables to reveal a wealth of statistical insights. Here is a snapshot of the model's results:

Table 1:Logistic Regression Model Results

The odds ratio divulged by this model yields fascinating insights into the multifaceted world of future relationships with HBAT. For instance:

• Managers who have been purchasing from HBAT for a duration between 1 and 5 years are six times more inclined to consider a future relationship compared to those with less than a year of buying history.
• Large firms boasting 500 or more employees exhibit five times greater proclivity to consider a future alliance with HBAT compared to smaller firms.
• In the grand tapestry of industries, the newsprint industry displays twice the inclination to consider a future relationship with HBAT in contrast to the magazine industry.

Significant Variables:

Notably, we identified variables with a p-value less than 0.05 as statistically significant. These are the forces that exert a substantial impact on the perception of a future relationship with HBAT. Our model illuminated the following pivotal variables:

• Buying history between 1 to 5 years
• Large firm size (500 or more employees)
• Product quality
• Advertising
• Sales force image
• Competitive pricing

Recommendations:

The culmination of our analysis has paved the way for actionable recommendations:

1. Increase advertising efforts, with a keen focus on managers with a buying history of 1 to 5 years and large firms.
2. Elevate the quality of the product and enhance the image of the sales force.
3. Maintain competitive pricing to wield a significant influence over the perception of future relationships with the company.

Cluster Analysis:

Beyond logistic regression, we delved into the intricacies of customer satisfaction through K-means clustering. This advanced analysis technique unveiled an intriguing revelation: both the magazine and newsprint industry segments appear equally satisfied with HBAT.

Model Accuracy:

To evaluate the real-world applicability of our model, we subjected it to a prediction exercise. The accuracy of the model was determined by calculating the percentage of accurately classified perceptions of future relationships with HBAT, resulting in an accuracy rate of 18%.

In summation, this rigorous analysis has unearthed valuable insights, empowering HBAT to make informed decisions, enhance customer relationships, and drive sales in a competitive market landscape.