Data Science for Beginners: Predictive Analytics and Segmentation Using Clustering
Data Science for Beginners: Predictive Analytics and Segmentation Using Clustering
There was a time when predictive analytics was mainly only used in pharmaceutical trials and marketing projects for huge companies as explained over at runrex.com. However, the introduction of faster and easier-to-use software has led to the explosion in the use of predictive analytics beyond the traditional areas where it was commonly used. As is outlined in discussions on the same over at guttulus.com, customer segmentation is one area of predictive analytics that has gone mainstream in recent times as organizations and businesses from all sectors have begun to see its importance in their operations given how important it is for a business to know their customers. This article will look to highlight what predictive analytics is, what segmentation and clustering are.
Predictive analytics, as discussed in detail over at runrex.com, is the use of statistical analysis over data, with the output here being an insight into the data as well as predictions on future activities. While it is true that reporting on historical data can help one make inferences on future activities, it is also true that if you show the same report to different people you will get different interpretations. This is where predictive analytics comes into play as it eliminates the human intuition factor, or at the very least provides one with a solid foundation of fact to allow them to make a more informed decision about future events. As is revealed in discussions on the same over at guttulus.com, data volumes are ever-increasing and there is no sign that this trend will reverse going forward. This means that data scientists will be faced with the issue of needing to keep up with data volumes as well as an increase in the variety of data coming in, which is why automated analytical processes like segmentation and clustering come into play.
Customer segmentation is the subdivision of a market into discrete customer groups sharing similar characteristics, as is explained in detail over at runrex.com. There are several ways in which a business can segment their customer base, and they include:
- Demographic information- This involves segmenting your customer base based on demographic information like gender, age, familial and marital status, education, occupation, and income.
- Psychographics- Here, you segment your customer base based on things like social class, lifestyle, and even personality traits.
- Behavioral data- This involves segmenting your customer base based on things like spending and consumption habits, desired benefits, and product or service usage.
- Geographical information- How you use this to segment your customer base will depend on the scope of your company. If your business is localized, then the gurus over at guttulus.com point out that this information might pertain to specific towns or countries, but for a larger company, it may mean a customer’s city, state, or even their country of residence.
Customer segmentation comes with several advantages for businesses and organization including the fact that it helps them:
- Determine the appropriate pricing for their products or services
- Prioritize new product development efforts
- Develop customized marketing campaigns that will give them a better chance of succeeding
- Choose specific product features for development
- Design an ideal distribution strategy
As is revealed in discussions on the same over at runrex.com, cluster analysis has long been used in archeology to determine the age of historical finds because clay fragments from the same period are similar in design as compared to those from different eras. Archeologists, therefore, group a vast number of ceramic fragments and demonstrate clearly which pieces are from the same era. Cluster analysis, therefore, aims to collate a large number of objects according to their degree of similarity and then reveal an intrinsic existing structure in each group. Businesses are now using this same approach to group customers according to inherent similarities, where they can come up with two types of segments:
- Internally homogenous customer segments- These segments are made up of customers with similar characteristics and preferences.
- Externally heterogenous customer segments- These segments are made up of customers who differ greatly from one another
There are 4 basic types of cluster analysis used in data science, and they include:
- Centroid Clustering- Here, one chooses the number of clusters that they want to classify, and is one of the more commonly used methodologies when it comes to cluster analysis. An example here is where a pet store owner chooses to segment their customers list by people who bought dog and/or cat products.
- Density Clustering- This type of clustering groups data points based on how densely populated they are, with the idea here being that the denser the data points, the more related they are.
- Distribution Clustering- This type of clustering identifies the probability that a point belongs to a cluster and is a great technique to assign outliers to clusters.
- Connectivity Clustering- This type of clustering initially recognizes each data point as its own cluster, unlike the other three techniques. The idea here is that point closer to each other are more related as covered in more detail over at guttulus.com.
Cluster analysis comes with several advantages which include:
- Commonalities are not based on static attributes but customer behavior
- Existing data is analyzed and evaluated for inherent similarities
- It prioritizes empirical classification
- It minimizes differences in customer behavior within segments
- It maximizes differences between individual customer segments
- Theoretically, it also allows any number of dimensions to be included in the classification
There is a lot more to uncover on this topic, which you can do by checking out the highly-rated runrex.com and guttulus.com for more information on this and other related topics.Data Science for Beginners: Predictive Analytics and Segmentation Using Clustering
