Customer segmentation simply means grouping your customers according to various characteristics (for example grouping customers by age).
It’s a way for organizations to understand their customers. Knowing the differences between customer groups, it’s easier to make strategic decisions regarding product growth and marketing.
The opportunities to segment are endless and depend mainly on how much customer data you have at your use. Starting from the basic criteria, like gender, hobby, or age, it goes all the way to things like “time spent of website X” or “time since user opened our app”.
There are different methodologies for customer segmentation, and they depend on four types of parameters:
- geographic,
- demographic,
- behavioral,
- psychological.
Geographic customer segmentation is very simple, it’s all about the user’s location. This can be implemented in various ways. You can group by country, state, city, or zip code.
Demographic segmentation is related to the structure, size, and movements of customers over space and time. Many companies use gender differences to create and market products. Parental status is another important feature. You can obtain data like this from customer surveys.
Behavioral customer segmentation is based on past observed behaviors of customers that can be used to predict future actions. For example, brands that customers purchase, or moments when they buy the most. The behavioral aspect of customer segmentation not only tries to understand reasons for purchase but also how those reasons change throughout the year.
Psychological segmentation of customers generally deals with things like personality traits, attitudes, or beliefs. This data is obtained using customer surveys, and it can be used to gauge customer sentiment.
Implementing customer segmentation leads to plenty of new business opportunities. You can do a lot of optimization in:
- budgeting,
- product design,
- promotion,
- marketing,
- customer satisfaction.
Let’s discuss these benefits in more depth.
- Budgeting
Nobody likes to invest in campaigns that don’t generate any new customers. Most companies don’t have huge marketing budgets, so that money has to be spent right. Segmentation enables you to target customers with the highest potential value first, so you get the most out of your marketing budget.
- Product design
Customer segmentation helps you understand what your users need. You can identify the most active users/customers, and optimize your application/offer towards their needs.
- Promotion
Properly implemented customer segmentation helps you plan special offers and deals. Frequent deals have become a staple of e-commerce and commercial software in the past few years. If you reach a customer with just the right offer, at the right time, there’s a huge chance they’re going to buy. Customer segmentation will help you tailor your special offers perfectly.
- Marketing
The marketing strategy can be directly improved with segmentation because you can plan personalized marketing campaigns for different customer segments, using the channels that they use the most.
- Customer satisfaction
By studying different customer groups, you learn what they value the most about your company. This information will help you create personalized products and services that perfectly fit your customers’ preferences.
In the next section, we’re going to discuss why machine learning for customer segmentation.
Machine learning methodologies are a great tool for analyzing customer data and finding insights and patterns. Artificially intelligent models are powerful tools for decision-makers. They can precisely identify customer segments, which is much harder to do manually or with conventional analytical methods.
There are many machine learning algorithms, each suitable for a specific type of problem. One very common machine learning algorithm that’s suitable for customer segmentation problems is the k-means clustering algorithm. There are other clustering algorithms as well such as DBSCAN, Agglomerative Clustering, and BIRCH, etc.
Why would you implement machine learning for customer segmentation?
- More time
- Ease of retraining
- Better scaling
- Higher accuracy
K-Means clustering is an efficient machine learning algorithm to solve data clustering problems. It’s an unsupervised algorithm that’s quite suitable for solving customer segmentation problems. Before we move on, let’s quickly explore two key concepts
Unsupervised machine learning is quite different from supervised machine learning. It’s a special kind of machine learning algorithm that discovers patterns in the dataset from unlabelled data.
Unsupervised machine learning algorithms can group data points based on similar attributes in the dataset. One of the main types of unsupervised models is clustering models.
Note that, supervised learning helps us produce an output from the previous experience.
A clustering machine learning algorithm is an unsupervised machine learning algorithm. It’s used for discovering natural groupings or patterns in the dataset. It’s worth noting that clustering algorithms just interpret the input data and find natural clusters in it.
Some of the most popular clustering algorithms are:
- K-Means Clustering
- Agglomerative Hierarchical Clustering
- Expectation-Maximization (EM) Clustering
- Density-Based Spatial Clustering
- Mean-Shift Clustering
It’s not wise to serve all customers with the same product model, email, text message campaign, or ad. Customers have different needs. A one-size-for-all approach to business will generally result in less engagement, lower-click through rates, and ultimately fewer sales. Customer segmentation is the cure for this problem.
Finding an optimal number of unique customer groups will help you understand how your customers differ, and help you give them exactly what they want. Customer segmentation improves customer experience and boosts company revenue. That’s why segmentation is a must if you want to surpass your competitors and get more customers. Doing it with machine learning is definitely the right way to go.