When you get tons of unlabeled data and you want to find some pattern in data to be used for some purpose like segmenting the data on basis of certain characteristics machine learning algorithm can be a big help. Lets take a example of tons of customer data of Target or Amazon or Flipcart.
To use this data for building some value added service like recommendation engine or showing customer latest treds that he might be interested in or even showing ads that are most appropriate for customer based on his gender, age, location etc we need to first classify the customer into different segments.
According to a Forrester report, only 33% of companies using customer segmentation find it significantly impactful. The main reason companies fail is that they are still using traditional customer segmentation approaches, without leveraging the breadth of customer data and advanced analytics techniques available today.
What is Customer Segmentation?
Customer Segmentation is one the most important applications of
unsupervised learning. Using clustering techniques, companies can
identify the several segments of customers allowing them to target the
potential user base. In this machine learning project, we will make use
of K-means clustering which is the essential algorithm for clustering unlabeled dataset.
Before ahead in this project, learn what actually customer segmentation
is.
What is Behavioral Segmentation?
Traditional approaches to segmentation focused mainly on who customers are and segments were based on demographic attributes such as gender or age, and firmographic attributes like company size or industry. But just understanding who your customers are is not enough anymore. Behavioral segmentation is about understanding customers not just by who they are, but by what they do, using insights derived from customers’ actions.
Behavioral Segmentation is a form of customer segmentation that is based on patterns of behavior displayed by customers as they interact with a company/brand or make a purchasing decision. It allows businesses to divide customers into groups according to their knowledge of, attitude towards, use of, or response to a product, service or brand.
Why Segment Customers by Behavior?
Here are four main advantages of grouping customers into different segments based on their behaviors:
- Higher Level Of Personalization. Understand how different groups of customers should be targeted with different offers, at the most appropriate times through their preferred channels, to effectively help them advance towards successful outcomes in their journeys.
- Behaviourial Predictivity. Use historical behavioral patterns to predict and influence future customer behaviors and outcomes.
- Customer Prioritization. Make smarter decisions on how to best allocate time, budget and resources by identifying high-value customer segments and initiatives with the greatest potential business impact.
- Evaluating Segment Performance. Monitor growth patterns and changes in key customer segments over time to gauge business health and track performance against goals. At a high level, this means quantifying the size and value of customer segments, and tracking how “positive” and “negative” segments are growing or shrinking over time.
