Your customers perception of your brand or product is one of the most important indicators of success. You want to know who they are, what they like and dislike, and what exactly they want. This way, you know exactly what to provide for them.
The best success comes with an intimate understanding of the minds of your customer base. They provide this information willingly through customer feedback including reviews and ratings. The downside is that it can leave you overwhelmed with a load of unfiltered information.
Enter: Natural Language Processing and K-means clustering.
Learn to Thrive in a Data-Driven Market
Yogi offers the product insights platform you need to drive effective data insights. Specifically, it uses k-means clustering from data collected through customer feedback.
This article will help you understand the following key concepts in the field:
- What is machine learning
- How can e-commerce businesses effectively use k-means clustering
- Generating insights using Natural Language Processing (NLP)
- Grouping text into data clusters
- What Yogi’s ratings and reviews platform does for businesses
What is Machine Learning?
Machine Learning uses algorithms to “learn” new patterns in data independently. The machine learning algorithms function is to sort through raw data, also called “training data”, which is continuously being inputted over time.
Through this process, computing systems learn new ways of grouping data as they accumulate new information over time, making real-time changes to further improve functions.
Unsupervised machine learning methods work from unstructured complex data with the intent to organize the data to find meaning. The algorithm repeats a calculation repeatedly until it finds the most useful data groups.
K-means clustering is a commonly used algorithm to help businesses understand customer interactions.
How K-means Clustering Works
Successfully implementing k-means clustering requires multiple steps in which you input specific instructions to repeatedly teach the analysis software how to strategically categorize the data.
The first step is grouping the data into categories that will best meet your needs.
Clustering divides an entire data group into smaller chunks. You call these smaller data chunks “clusters.” Clusters are based around “centroids,” the central value that the rest of the data points group around, like an average.
For example, you can group customers based on what they buy, how much they spend, or their income levels. Another way to implement k-means clustering uses Natural Language Processing to group pieces of text into clusters based on what they say; more specifically, exactly what words and phrases they use.
How to Input the Algorithm
The following steps shows a high level overview of how to successfully run the k-means clustering algorithm with unsorted data.
- First, decide how many clusters k, or groups, you need.
- Select points as the centroids (you can write code to do this).
- Then all data points automatically move towards their closest centroid.
- Use new clusters to compute new centroids to create increasingly smaller clusters.
Each run-through is called a single iteration, which runs repeatedly until the data has reached the maximum number of possible iterations.
Once the algorithm ceases to learn new patterns, you can stop the training process and use those newly formed data groups.
You can also understand these steps by watching a live demo on this Youtube video.
How Can E-Commerce Businesses Effectively Use K-Means Clustering?
An easy-to-understand example of an eCommerce business case for K-means clustering is Customer Segmentation. This process allows you to separate your known customer base into groups based on the qualities they have in common.
Customer Segmentation helps you figure out exactly what traits to look for in order to set your customers into groups. As K-means clustering functions as an unsupervised algorithm, you don’t have defined conditions to start from. You use machine learning processes to find those conditions, and then figure out who fits into those groups.
This way, you gain insights on how to market to different customer groups based on their individual needs.
Get to know your customers with the following examples of customer segmentation:
- Demographics separate your groups based on key characteristics like marital status, age, gender identity, income level, and many other factors.
- Geographic data can be grouped based on where customers reside by country, by state, or even by town or neighborhood.
- Behavioral data can categorize people based on their spending habits, what features they use, or their browsing history.
Another, more complex, use of k means clustering in eCommerce involves in-depth text analysis of consumer feedback, such as ratings and reviews, in order to extract useful business insights. This use of machine learning enables organizations to decrease time and effort spent gaining insights by replacing focus groups, surveys, and other time consuming forms of market research with a product insight platform or customer sentiment analysis tool.
Below, we’ll dive into contextual text analysis by applying K means to Natural Language Processing.
Generating Insights using Natural Language Processing (NLP)
In order to create methods for complex text analysis, machine learning offers a special subset of automation. It teaches itself to contextually understand human languages. This happens through a special AI automation process called natural language processing, or NLP.
K-means clusters serve as a commonly used application for NLP text analysis. But how does it work?
We know that the k-means algorithms make data groups, or clusters, based on characteristics that data points have in common with each other. Natural language offers a means to find similarities across text-based data by grouping together similar phrases, words, or sentences.
Maintaining accurate and advanced NLP is necessary in order to declutter uncategorized data so that the algorithms runs effectively while also using k-clusters to determine key characteristics. Here you can read about how to maintain clean NLP processes in order to effectively apply a K-means algorithm so that it generates the most useful insights.
Grouping Text into Data Clusters
By analyzing and generating smart insights based on what your customers say online, you can fine-tune your brand messaging to be relevant to your customers. You can figure out specifically what customers do and don’t like about your product and which campaigns have been less successful.
They may talk about specific features or attributes of a product, or even leave a review describing a variety of aspects of their experience as a customer. This is where the ability to analyze text feedback data on a contextual sentence level comes into play.
By choosing Yogi to perform these processes for you, you can rely on a product that has been streamlined and tested to produce the most effective and useful insights while generating advanced data visualizations to help you map out strategies.
What Yogi Does for Businesses
Slower moving brands are stuck using first-generation tools, focusing only on a minimum review count & star rating, resulting in missed opportunities for differentiation. Forward looking brands, however, recognize that when analyzed properly, granular Review & Ratings analysis provides actionable insights that increase ROI throughout an organization.
Yogi’s proprietary method of using AI powered K-means based NLP analysis combined with easy-to-understand data visualizations provides the most in depth and actionable customer feedback analytics available.
As a result, you can quickly uncover data & insights from your UGC that enable your organization to make data-informed decisions. Whether you’re changing your PDP and marketing campaigns next week or prioritizing future product updates and planning a brand launch a year away, Yogi enriches your decision-making processes with real consumer data. Our laser focus on this differentiates us from the competition, both in terms of technology and platform.
Are you ready to enrich your decision making process with real consumer data from you and your competitors?