Data Science & Cluster Analytics: Profiling Customer Behavior and Other Patterns

Abstract 3D structure with Spheres and lines

You’ve no doubt worked with business data tools at some point in your business life, though realized one thing during use: The data wasn’t offering discernible patterns for more thorough insight. A major reason is, raw data hasn’t gone through enough processing to glean any reliable information. While some attempt processing, all it does is lead to a term that doesn’t always help: Cooked data.

With bad data a concern for companies in a time when analytics are extremely important for survival, you need other technologies to see all angles. Most importantly, you need something better defining customer behavior.

In other words, you should study data patterns, and raw data isn’t going to do this. A sub-segment of machine learning called cluster analytics is a major solution you’ve perhaps ignored in recent years.

If you’re new to cluster analytics and machine learning in general, it helps cluster elements in data that look similar to one another. It “learns” patterns in the data you accumulate and even helps distinguish which clusters of information aren’t alike.

While this is basic, you need to know more about what cluster analytics do so you can start personalizing your services and marketing strategies.

Understanding Cluster Analysis Methods

Despite this machine learning method continuing to evolve, it currently has three different methods top companies use to see customer patterns.

The first has a bit of a complex name: Agglomerative Method. In this process, you start with data in separate clusters. Then the program combines all data looking alike into a single cluster. Afterward, you choose the optimum number of clusters from the options given.

On the opposite end, you have the Divisive Method, which is almost self-explanatory. More precisely, it’s the Agglomerative Method in reverse. This helps remove non-alike data out of a single cluster for more clarity in your metrics.

You may prefer using non-hierarchical clustering methods for larger data sets. Some examples of this includeCanopy Clustering, which is a much faster method of grouping things together. It uses an algorithm to better sort data points looking the most alike.

However, you also have K-Means Clustering, which sometimes comes after Canopy Clustering. K-Means groups data sets together by numerical means for better organization.

So what things can you discover using cluster analytics? Let’s look at the insights possible and how much customer data you’ll receive if you have multiple stores across the region.

Average Customer Spend

If you have numerous business locations throughout Canada, cluster analytics helps sort out what customers spend throughout your market. Thanks to accumulating matching data, you’ll see how much customers spend every quarter in your stores. Plus, you’ll discover what they’re spending their money on.

These two data points alone are going to help you make smarter business decisions along your journey. You won’t have to go on generalities or second-guesses before making a strategic business move.

Average Customer Behavior

Digging deeper into your cluster analytics platform, you’ll start to discover basic patterns in customer behavior. Doing this is the most important of all, because it gives you a credible look into the future. Through these data sets, you’ll know the real reasons why customers buy what they do and determine what might make them occasionally change their minds.

You’ll save money and time in promoting products you think are going to sell, yet patterns show varying buying fickleness. Cluster analytics can even pinpoint why a certain product doesn’t sell based on customer personalities and pain points.

Helping Your Marketing Efforts

Don’t think cluster analytics only become important to your sales team. Using these metrics for your marketing team are just as essential. It helps greatly in market segmentation, which those in marketing need for better demographic targeting.

For market positioning, it’s very useful as well, and this includes better identifying test markets for your products. Clusters helps gather data on social media channels and where your customer base most likely hangs out. Now you can identify where your demographics are online and go there to start communicating with them.

All of this is an example in how machine learning turns your sales and marketing efforts into more of a personalized experience.

Machine Learning Works Silently in the Background

A recent blog on Oracle said it best when they described machine learning as being an invisible marketing force. It’s a good way to describe what it does while still helping you understand the customers you want to target.

The more sophisticated machine learning becomes, the more data it’s assembling for accurate customer portraits. With the online world now containing almost 25 years of e-commerce, machine learning can now take all this data and analyze millions of different people to give you a vivid customer persona.

It also helps if you’ve had customers for a while since it takes from what new and loyal buyers did in the recent past. Even better is machine learning now takes from online and offline sources. Never forget how offline data equally helps better define customers and leads to more transparency.

Cluster analytics now give you more purpose behind this data and assembles it within reasonable time frames. As such, you don’t have to wait to make valuable decisions. In a far more competitive business world, you can’t afford to wait and not make a move to one up a competitor.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

%d bloggers like this: