CUSTOMER SEGMENTATION AND LIFETIME VALUE PREDICTION

Customer segmentation and lifetime value estimation are two areas of development today. Today, the business world works differently, so machine learning uses different algorithms and different models to increase business efficiency. The value of life measured by its results is the bond between the customer and the company, as well as the value of the customer for the company. The reason for choosing this project is to examine the predictive behavior of future personalization models. From the company's perspective, this model can be very effective in terms of customer relations. Both segmentation and LVP are good for strategy development in business. As the world continues to evolve with major changes in technology, it is only natural to do such a project. LVP has a lot of predictive power when evaluating the customer buying process and choosing the best services to offer to different customers. Current methods for estimating CLV involve developing a model that uses all customer-based inputs. This not only loses the granularity of the data, but also makes the marketing plan less effective from the customer’s perspective. This article focuses on the advantages of intelligently combining small models by focusing on separate models for different customers. This allows companies to effectively use customer data to make informed decisions. Using the data as an example, the proposed multi-project study where the learning experience is shared with relevant groups increases the CLV estimation accuracy compared to using a single model size with some minor modifications. Moreover, the same method reduces the standard deviation of the error when compared with a large sample. Most importantly, when the groups have different data to train, multiple learning models will outperform single-shot models, which is normally considered a more difficult task. These results show that multi-task learning can be more effective than existing business models and can be a better alternative to existing models.