By: Ram on Jan 07, 2021
Pricing Segmentation: E-retailers or e-commerce companies have taken the retail industry by storm. They are offering luring offers and discounts. They aim to move from discount led to convenience or differentiation led offering over a period in time. Some of them have been forced to start the journey.
One of the large retailer wanted to segment the customers based on customer spend patterns and understand price sensitivity of the customers. Some of the segmentation variables considered are – total spend, value of discounts, % discounts across transactions, number of items bought on discounts etc and used k means clustering to find the discount orientation of the customers.
Loyalty Segmentation: Every organization wants their customers to be loyal but some time defining loyalty could be a tricky. Some of the common expectations are frequent spend, higher spend, higher ticket size and diversified spend. A large Indian retailer was looking to understand customers from these 4 dimensions and wanted to develop segments for building a focus engagement strategy for each of the target segment.
Spend Behaviour Segmentation: Developed Customer segmentation for a grocery chain retailer using two year spend pattern on their loyalty card. Some of the key dimension considered for K Means clustering segmentation are Count of Transactions, Value of Transaction, Average Transaction Value and various transformation of these spend engagement indicators.
Branch Geo Segmentation: A large global ATM service provider wanted to define segmentation based on geo-location of ATMs. ATMs’ Latitude and longitude information were used for creating the clusters. These clusters were aligned to a particular ATM management unit.
Customer Need Segmentation: Based on various customer interest, life stage and product holding information have been used for creating customer need based segmentation. These segments were aligned to different groups of relationship managers to manage these clusters effectively. These customer clusters were different based on their needs, channel of preferences, service expectations.
Category Segmentation: Customer spend patterns vary by merchant category (e.g. Restaurant Spend, Grocery Spend, Entertainment & travel spend etc). Aim of the segmentation was to create different clusters which are significantly different based on spend across categories. Then, answer some of these questions.
Donor Segmentation: For a Non-Profit Organization, a segmentation was to understand level and response of people who will give donation and continue to contribute for a cause.
Some of the dimensions used for K Means clustering were
Server Clustering: For a financial services client, aim was to find clusters which should be given for annual contract together.
The details used for the clustering included usage and characteristics of servers. Some of the example of the variables are
Healthcare Fraud Detection Segmentation: For an Indian healthcare provider, aim was to find claims which could be fraudulent. K Means clustering method was used for anomaly detection and claim routing to right claim adjudicator. Some of the K Means clustering dimensions or variables used were
Customer Service Segmentation: For a large Australian Energy provider, the objective was to provide differentiated services to its customer base. Same segmentation would be used for targeted and differentiated offer and service solicitations. Some of the relevant variables used were