Extracting Meaningful Customer Insights Using Machine Learning
With more data than ever at their disposal, organisations who embrace the power of this valuable resource are becoming the new leaders, able to derive strategic, customer, and operational insights that lead to better experiences and decision making across their business.
Organisations have explored only a tiny fraction of the value data offers. In particular, mining customer data for insights – while offering immense business value – continues to lag, leaving organisations blind to huge chunks of customer intelligence. In this article we talk about how organisations can use machine learning to create meaningful customer segmentation and extract insights – using a technique called clustering.
Segmenting customers with Machine Learning – the definitions
Before we dive into the specifics, let’s first define some key terms we’ll be using:
Instead of directing the algorithm to group the customers based on labelled data, the program scans through customer data to infer (or learn) the patterns within datasets.
An unsupervised machine learning technique for identifying and grouping similar data points, such as customers, or a customer trait, together. This practice unearths hidden relationships between the data points within datasets.
Used for unsupervised machine learning, K-means is a powerful and popular clustering algorithm that draws insights into the formations and separations within data.
Because clustering works in high dimension, dimensionality reduction is used to make the output visually consumable.
Now we’ve got the key terms covered, let’s walk through the clustering process.
Clustering for meaningful customer segmentation
Customer segmentation involves dividing customers into groups that reflect similarities. This isn’t new, many businesses are already manually assigning their customers into segments – an approach that relies on structured, labelled data, which is very time consuming.
But the mere human brain can only take this so far; it’s simply not possible for people to trawl through volumes of data to find the many nuanced relationships that exist. This is where the data-driven clustering technique comes in – it intelligently augments existing segments without boxing customers into rigid groups. Using machine learning algorithms, clustering identifies how different types of data are related and creates new segments based on the variations between them.
From there, dimensionality reduction techniques can be used to create interactive visualisations – each based on two or three relatable, and valuable dimensions – of how these new clusters behave, and how they relate to each other. It essentially makes the data easy to interpret, understand and leverage.
Use cases for customer segmentation using Machine Learning
The intelligence garnered from clustering can unlock commercial possibilities that were previously hidden or unreachable. Here’s some of the many ways customer segmentation is being used by our customers to deliver real and rapid value.
Segmenting can be used to understand the customer on a deeper and more nuanced level. These insights can then guide the development of marketing , customer service, and even product development strategies – all of which can be targeted and individualised to these customer groups.
Grouping customers who behave similarly can help determine which kinds of customer behaviours are associated with a high likelihood to churn, providing an opportunity to proactively engage those customers before it’s too late. These projects typically achieve the biggest impact on our customers’ bottom line.
For example, a segmentation model can be built using clustering to uncover previously unknown niches in high-dimensional datasets. This can shed light on general trends in customer behaviour in terms of purchase frequency, credit worthiness, account status, etc.
Marketing leaders are increasingly seeing the potential of data for everything from optimising revenue streams, to increasing the effectiveness of campaigns and accelerating the sales pipeline. With improved insights and targeting through customer segmentation, marketers are better positioned to reach new audiences, identify up-sell and cross-sell opportunities, and ultimately convert more leads.
For example, one can analyse POS data and perform clustering based on customers who purchased the same types of products. This enables the identification of upsell and cross-sell opportunities by targeting under-performing products.
Customer segmentation is not just about understanding the customer base, it can also be used to look inwards at the organisation. For example, by linking sales data with external sources – such as industry, Government and Census data - one could create a more detailed customer profile which can inform executives about potential growth opportunities.
Ready to grasp the true power of your customer data?
This is just a small sample of customer segmentation use cases – there is almost no end to the ways this intelligence can be applied to business functions. Contino can help you develop a clear vision of your desired business outcomes and determine new ways to meaningfully segment your customer data for maximum impact. Get in touch with us today!