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The sales and marketing processes rely heavily on lead conversion. Identifying the pool of potential clients and connecting with them is a resource-intensive and costly procedure. In the process of generating this pool and accumulating potential customers in this pool, there are both organic and inorganic inclusions of potential customers. The proportion of potential customers added to the pool via generic techniques is larger than the proportion added organically.
Adding potential customers to the pool is insufficient to turn these audiences into clients. Warm-up activities can be conducted within this group by identifying communication strategies that are unique to individuals or audiences. Warm-ups are used to increase the number of potential customers who turn into leads. But there’s a price to pay for getting in touch with a potential customer when it comes to warming activities.
In order to maximize the number of leads converted into paying customers, it is crucial to minimize the money spent on prospecting and contacting them. In recent years, artificial learning-based methods have grown increasingly prevalent within the scope of these cost optimization studies.
The most significant of these machine learning experiments is the segmentation of leads as a descriptive machine learning study. Our approach separates potential consumers into groups of similar people, making it easier to focus on the subset that will yield the greatest profit with the fewest outlays.
Using data collected from an empirical study conducted as part of Next4biz’s -Türkiye’s premier CRM software R&D business- research and development efforts, this manuscript explains how the company segments its potential customer base.
In light of the findings of this segmentation analysis, it is anticipated that a better chance to increase the efficiency with which leads are transformed into sales will be made available. Using the K-Means machine learning algorithm, over 300,000 samples were separated into four groups for the purpose of lead segmentation.
