To evolve your customer segmentation, you need to look beyond basic demographic segmentation.
Machine learning takes customer segmentation to this next level to provide a more detailed picture based on valuable data-driven insights. It does all the analysis for you, taking out the guesswork and providing you with specific, actionable improvement strategies for your customer service, marketing, sales, or product development teams.
With this information, you can:
- Design products to better meet customer needs
- Develop more targeted marketing campaigns
- Enhance the customer experience to increase customer satisfaction, loyalty, business revenue, and success.
This article discusses the basics of customer segmentation and machine learning and how you can use machine learning algorithms to uncover valuable and actionable insights from your data.
The customer segmentation problem
You already have a good customer base and lots of data to work with. The problem, for most businesses, comes when you need to take all this data and use it to inform business strategy and decisions. You can find patterns manually and draw conclusions based on these patterns, but it’s difficult and time-consuming.
Thankfully, you can solve this customer segmentation problem by using algorithms to look for those patterns and do the analysis for you. These systems will take your data points and highlight distinct groups and trends quickly and accurately.
What is customer segmentation in machine learning?
Customer segmentation in machine learning allows you to group your customer base according to similar characteristics to pinpoint trends and make more targeted marketing decisions. Traditionally, customers are segmented according to characteristics or demographic (such as male/female, size, location etc.) More complex machine learning algorithms like Idiomatic’s, however, allow you to use experiential data rather than hard demographical data points, to get a bigger picture view and the information you need to implement changes in business processes or products. For example, Idiomatic can group customers according to the same issue or experience (ie. had trouble checking out), giving marketing and product teams an even clearer picture of customer pain points and opportunities.
What data points can be used in machine learning?
Customer segments can’t be created without accurate data to create customer groups. There are many available data points that you can use for your customer segmentation model. These include, but are not limited to:
- Geographic region or city
- Buying history or preferences
- Browsing history on your website
- Payment preferences
- Support tickets
- Engagement stats (social media, email opens)
- Device usage (computer, iPhone, mobile)
- Other interactions with your business, products, or systems
You can use any legally obtained data point you have on your customers to create customer segments. Machine learning models can create customer segmentation on this data through their algorithms.
Where does your data come from?
The data given to the machine learning AI isn’t pulled from thin air. This data is gathered from your records, analytics reports, and other digital systems, including:
- Customer support systems
- Social media reports
- Customer surveys
- Forums and communities
- App reviews
- Product reviews
- Marketing data
What are the types of customer segments?
Customer data can be segmented by many different parameters. Machine learning can use the same data to create even more detailed customer segmentation.
Machine learning uses these and other data sets to better understand your customers by analyzing trends. You can eliminate pain points, develop better products, and improve your customer’s experience, to create loyal, repeat customers.
From a marketing perspective, segmenting your data allows you to send targeted messaging specifically to these customers. From a product development perspective, you can get a better idea of how many customers are having troubles (or success) with the same feature so you can improve where needed. From a customer service perspective, you can better help customers by knowing their needs or eliminating the need for them to contact support in the first place.
How machine learning can enhance your customer segmentation
Machine learning can help you optimize your business practices and grow your business to new heights. Here are a few results of using machine learning:
Decrease customer complaints and support costs
Are you noticing your support or help desk is busier than usual? Optimize your help desk ticketing system to better understand your customers and where they’re struggling to use your product. The algorithm checks aggregated customer data from your help desk system and analyzes it to find trends and look for potential inefficiencies.
Then, you can change business processes, systems, software, or even the product itself to eliminate the problem.
Increase customer satisfaction
The best way to increase customer satisfaction is to give your customers what they want without undue barriers to its implementation or use.
Customers usually won’t provide feedback unless they’re dissatisfied. Therefore, we use algorithms to paint a more complete picture of the customer, often beyond what they may tell you directly. Machine learning models can take your data from client feedback systems, app usage, bug reports, and other sources to determine:
- Which parts of your product or service your customers like the most
- Which are not performing as expected
- Where you can make improvements to lower support calls and increase product satisfaction
Analyze more data faster
Surveys and feedback forms can be an effective way to gather feedback on your company; however, they take significant manual work to solicit the feedback, tabulate the data, and present the results.
With machine learning algorithms and artificial intelligence doing the hard work, you simply need to look at the results and take action to improve your business practices or product. It could give you near-instant results and recommendations and help you analyze substantial amounts of data faster, minimizing any downtime and catching errors and problems before they become more serious issues.
Increase customer satisfaction scores
Your machine learning algorithm may uncover that you have had a 75% increase in people resetting their passwords on your system over the past week. It also noted an increase in help desk tickets regarding errors and broken content in your digital offering or platform. These are indicative of a more significant problem that you can now address.
Your artificial intelligence algorithm starts by segmenting all users who initiated help desk tickets with those who also reset their passwords, noting correlative relationships between the two. Based on this data, you can safely assume there is a problem, error, or bug in your system. You can fix it quickly before too many people experience a problem and get frustrated or angry and leave you a bad review.
Don’t forget that your customers may be forgiving of occasional bugs in your systems, only if you have already earned their trust by showing you can quickly identify and fix problems efficiently. Machine learning algorithms can quickly alert you to these problems to minimize downtime and increase customer satisfaction.
Save time on manual data analysis
Collecting data can be easy. The time-consuming part comes when you take all the data, calculate totals and averages, manually cluster and group the data, and present your findings in a visually useful format. Then, you need to use that data to make assumptions about what it means.
A machine learning algorithm can analyze mountains of data quickly and efficiently so you don’t have to.
Which algorithms can be used for customer segmentation?
Here are a few algorithms you can use to take customers’ data and use it to make a business case for updating marketing strategies or product development. These are classified as clustering algorithms.
This data science technique uses unsupervised machine learning algorithms to group customers who share similar attributes (or data). The clustering model assumes that customers with similar habits, demographics, or history will interact with your business in the same way and will have the same needs.
You can use clustering algorithms to determine the optimal number of clusters using one of two methods:
- Elbow method: The elbow method finds the optimal number of clusters by looking for an “elbow” point in the data.
- Average silhouette method: This algorithm test measures how similar a cluster is to its cluster set compared to other clusters.
Here are some common clustering algorithms you might use for segmenting customer data:
K-means clustering is an algorithm where the points of each cluster group are as similar as possible, and points in different clusters are as dissimilar as possible. It’s a type of exclusive clustering where two clusters are entirely different and do not overlap.
This algorithm is good for density-based clustering. It’s good for finding outliers in your data sets and works well for oddly shaped data.
Gaussian Mixture Model
The Gaussian mixture models don’t require circular paths like K-means does. It uses Gaussian distributions to fit oddly shaped data.
This algorithm only works with numeric values. It breaks data into summaries that are clustered, rather than clustering individual data points. The summaries store as much information about the data points as possible.
Affinity Propagation Algorithm
Using this algorithm is good when you don’t know how many clusters of data you are expecting. Your data points communicate with each other to indicate how similar they are and then use these similarities to reveal the final clusters.
Mean-shift Algorithm (aka “Mode-Seeking Algorithm)
When you have images and computer vision processing, this is likely a good algorithm to use. It finds the clusters for you through iterative processes and works best with smaller datasets.
This one is similar to DBSCAN but can find clusters when the density varies. Each data point is processed once and the special distance between data points are stored to indicate what cluster it belongs to.
How much does it cost to build a custom machine learning algorithm?
You can save time and money by using machine learning algorithms to perform customer segmentation and eliminate manual work. Your algorithms need to solve a real business problem to be effective, however, and turn a positive ROI.
This process, if done in-house, can cost anywhere between $60,000 to $95,000 over five years. This includes model infrastructure, data support, and engineering/deployment. If you add additional models, this price grows quickly.
Using a ready-made machine learning algorithm like Idiomatic’s can get you potentially better results at a fraction of the cost compared to developing a custom machine learning system. A ready-made system like Idiomatic’s, uses more than just “traditional” customer segmentation data like city, purchase history, and demographic information.
You can get detailed information about your users’ experiences too and use that to inform business decisions. Tracking users by how they interact with your company, products, and services gives you much deeper insight into what your customers’ real pain points are.
Which clustering algorithm is best for customer segmentation?
When creating customer segments for your company, the K-means algorithm is often the best as it’s the simplest. It’s best used for smaller data sets, but can be used for larger ones, but that could take more time.
How to get started with machine learning for customer segmentation
The entire process of taking a customer dataset and using machine learning techniques to discover correlations and inconsistencies is an effective form of data processing. You can use this to create customer groups to inform your sales, marketing, and customer service strategy. It can also provide valuable feedback for your future product and service development too.
Idiomatic’s powerful clustering algorithms use artificial intelligence to analyze your customers’ data to help you get an overall picture of your customers and business. It’s a ready-to-use product that’ll help you confidently eliminate customer points, taking the guesswork out of choosing algorithms and eliminating costly algorithm development in-house.
Check out our ROI calculator or request a demo today to see how you can use machine learning to create accurate customer segments based on customer data you already have in your business.