Improving Customer Segmentation With Machine Learning
Successful marketing is defined as offering the “right product to the right person at the right time”. In order to actually market to customers successfully, it’s important to sort them into segments so you can find patterns in their behavior. However, modern marketers continue to rely on their intuition for customer segmentation rather than employ technologies like machine learning.
So how can machine learning help you segment customers efficiently? Machine learning can significantly speed up the process of grouping customers into precise segments by analyzing them as data sets. Once your machine “learns” how to do this, it will automatically profile your best and worst performing customer segments for appropriate behavior targeting.
The Benefits of Using Machine Learning Methods With Segmentation Analysis
The first lesson a marketer has to learn is that no two customers are exactly alike. Every individual has very different needs, with unique profiles found across multiple channels. As a marketer, you can’t share the same content on the same channels with the same sense of urgency for all your customers; it would be like treating a new acquaintance the same way you would your best friend of over 10 years.
If you want to create relevant marketing messages your customers would actually respond to, you have to adapt to what they need or prefer. This is what you call customer segmentation. It is the practice of splitting up your business’s customers into groups that share similar characteristics. Essentially, customer segmentation allows you to customize your customer relationships and ensure all your marketing efforts are targeted.
Now, you may be wondering how machine learning comes into the picture.
Machine learning (ML) is the application of artificial intelligence to develop computer programs that can automatically learn or improve from experience without having to be programmed to do so. You can have supervised machine learning, where rules and settings can be adjusted depending on your operations or unsupervised machine learning, where you allow artificial intelligence to build the datasets and find patterns on its own.
Using advanced algorithms, machine learning can automate customer segmentation to find insights or significant groupings that marketers may not be able to discover on their own. Instead of manually analyzing large amounts of data to look for patterns, you can simply allow the ML-program to do the task for you. In this way, you can invest more time creating more sophisticated marketing campaigns and secure new business opportunities that drive better results for your bottom line.
Here are some ways machine learning methods can help improve your customer segmentation strategy:
Machine learning can manage complicated data analysis on its own.
Segments can easily get too broad and complicated for human handling. There are too many segmentation criteria that marketers are looking at such as “opened email campaign”, “looked at product x but did not purchase”, and so on — which is a far cry from segmenting based on demographic data. Machine learning is highly scalable and can sort through an unlimited number and size of segments. It requires little human intervention or maintenance so you can spend more time and resources elsewhere.
Machine learning can find hidden patterns a human marketer might not see.
Humans have biases that inform customer segmentation, which can give you an incomplete picture of the situation. For example, you might make the assumption that most video game lovers are young men and totally neglect other, equally passionate segments.
With machine learning, artificial intelligence algorithms will look at your data without these biases so you can see who your customers really are. ML-technology can make sense of multiple dimensions based on customer information and manage how customers migrate from one segment to another over time.
Machine learning can automatically update your segments.
In a rapidly changing market, allowing your customer segments to remain stagnant or one-dimensional won’t help you tap into and maximize the value of each customer to your business. As machine learning automates the segmentation process, the ML-model can constantly refine its segment definitions based on campaign results so you can see segment subsets that perform better (or worse) than the rest.
5 Steps for Integrating Machine Learning on Customer Segmentation
In order to use machine learning for customer segmentation, you will need to look into the different products available in the market. There are some CRM solutions and packaged software that can enable you to adopt ML-models for your business. The following steps are just one of the many approaches to do this, applying an unsupervised ML algorithm with Python software:
Step 1: Form your business case.
A business case is what you call the purpose of your deep learning model. Without a goal, the results you get would be messy and disorganized. In this case, you would most likely want to find the most profitable customer subset among your entire pool of customers. While you could approach this from demographic or geographic perspectives, behavior is probably the best indicator if you want to dig deep into customer spending habits.
Step 2: Prepare your data.
For machine data analysis, more learning data means more accurate models. Information about your customers will allow the machine to spot clearer patterns and trends within the datasets, although this may take more time. You have the option to train your model by using historical data to speed up the process.
Sorting through your data will allow your customers to become their own segment, defined by as many criteria as you want. You can look at features like client satisfaction, retention rate, or average lifetime value. Another feature you might want to prioritize would be sales or total spending. Just make sure your dataset is well-formatted and clean.
Step 3: Employ K-means clustering.
K-means clustering is an unsupervised machine learning algorithm method that can group similar data points together to discover any underlying patterns. It identifies your customers by feature and sort them into groups of “clusters” so you will have as many possible segments to interpret.
The algorithm will assign a data point to the closest centroid that forms different groups; a centroid represents the center of that cluster. It will move the average data point to the center of each cluster and check the sum of squared distance between the cluster point and each center, minimizing the distance and inertia of each cluster. Once the points converge, the iteration stops.
Step 4: Tuning your hyperparameter.
Tuning your hyperparameter means choosing the best set of hyperparameters for the learning algorithm to help you find your most rewarding customer groups. This is done by building different K-means models with the k values set from 1 – 15 with corresponding inertia values. With the elbow method, you would have to choose the k value where inertia decreases and stabilizes the most.
Step 5: Visualizing and interpreting your data.
Once you have chosen a k-value, plug it into the k-means model to see how the customer groups are created. You will be able to see your most favorable customer group and optimize your approach towards them. Finding your best and worst performing segments will allow you to improve feature launches, create product roadmaps, and launch targeted marketing campaigns that drive growth.
Boost Your Customer Segmentation Strategy With Commence CRM
Machine learning is just one way to improve customer relationships and offer more personalized experiences. Commence CRM can help you manage your customer data and gain insights on how to build a better connection with your clients. Contact us today to learn more about our products.