Recently, in my machine learning coursework, we learned about and . This jogged my memory of my time at , where my team used these techniques to construct a . In this text, I explore the appliance of PCA within the healthcare industry and use that intuition to create customer segments for personalized marketing activities for a grocery firm dataset.
Back at Novartis, we used unsupervised learning techniques like clustering and PCA to discover physician segments and tailor marketing efforts accordingly. This allowed us to enhance the effectiveness of promoting campaigns and boost brand sales. Using these techniques, healthcare firms can gain insights into physician behavior and preferences to develop simpler marketing strategies.
PCA helps to scale back the dimensionality of a dataset by identifying an important variables that designate nearly all of the variance in the info. This is particularly useful when coping with marketing channel data, where there may be many variables reminiscent of customer demographics, channel engagement, purchase behavior, and product preferences. By utilizing PCA, we will discover the variables which might be most influential in explaining customer behavior across different channels. This might help to simplify the info and discover vital trends and patterns that could be hidden within the noise.
Once we now have identified an important features, we will move to clustering to discover customer segments who exhibit similar purchasing behaviors or channel engagement. This might help marketers to focus on their messaging and promotions to specific segments of their customer base, moderately than attempting to appeal to all customers directly.
Now that we now have an intuition in regards to the importance of doing PCA and clustering on marketing channel data, we will implement the identical on a grocery firm dataset. Since I actually have used a comparatively clean dataset, I only needed to do a number of additional data processing steps to investigate the info. I actually have created a number of frequency distribution plots and boxplots to visualise the info distribution. Eventually, I actually have implemented PCA to scale back the size to only two principal components that designate nearly all of the variation. Using this, I actually have run a K-Means clustering algorithm to discover 4 clusters which have unique attributes that may be used to tailor marketing efforts.
