Achieving Market Segmentation From B2B Insurance Client Data Using RFM & K-Means Algorithm

With innumerable players available in the market, it is no secret that the insurance industry is facing the heat of competition, struggling to outperform their rivalries and maintain customer engagement. This can be resultant due to the highly commoditized approach and superficial customer engagement methods. Such inadequate analysis as an end result can cause a performance dip if the insurance providers prioritize merely on cost management model leading to customer dissatisfaction and attrition. Research implications in this study denote overhauling antiquated customer segmentation models and showcasing varied yet smarter perspectives of deriving more reliable insights from data. Thereby, helping businesses to formulate better growth strategies by not missing out on the customer-centric approach. The way forward is to identify best-performing customer groups using RFM techniques and K-means algorithm that add value to the business. The target customers then must be offered personalized product offerings and services to embrace their behavioral needs or requirement. Design marketing campaigns tailor-made to the relevant customer(s) and that would best compliment the target groups. Generic traditional customer segmentation analysis results can be biased or imply lackluster outcomes, misleading, vain attempts try and can even lead to losing high performing customers. The outcomes from this study will aid in ease decision making for businesses by adopting a focused customer-led target market segmentation approach that will make the former easier to timely formulate or launch ideal marketing strategies to efficiently channelize the most relevant customer(s) for a defined set of product offering(s) based on their behavioral attributes.