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Sales Performance Decomposition: Attribution Modeling & Predictive Sales Analytics

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Volume-10 | Smart Innovations in Computer Science and Applications

Last date : 25-Feb-2026

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Sales Performance Decomposition: Attribution Modeling & Predictive Sales Analytics


Sakshi Raut



Sakshi Raut "Sales Performance Decomposition: Attribution Modeling & Predictive Sales Analytics" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Smart Innovations in Computer Science and Applications, March 2026, pp.100-114, URL: https://www.ijtsrd.com/papers/ijtsrd101609.pdf

To do well in sales we need to understand what drives them. This is important for making marketing plans and keeping sales up over time. We are trying to figure out how much each marketing method contributes to sales when customers take a path to buy something. We also want to make a system to predict future sales. We suggest using a combination of Multi-Touch Attribution and advanced Machine Learning to make predictions our method involves looking at three ways to give credit to marketing methods. Linear, Time-Decay and Position-Based. To break down past sales data into what each channel did. We then use the method to help make a customized system to predict sales using XGBoost and a Neural Network to make the predictions more accurate. We tested this using data from an e-commerce site with 50,000 customer journeys across eight marketing channels the results show that using the Position-Based method with our suggested system works best giving us an idea of how well we can predict sales. This helps us understand what marketing methods work and makes it easier to predict sales so we can make plans. This study shows that combining attribution science with intelligence is a good way to make a system that works well for businesses, with many sales channels.

Sales Attribution Modeling, Predictive Sales Analytics, Multi-Touch Attribution, Ensemble Learning, XGBoost Marketing Mix Optimization, Machine Learning, Customer Journey Analytics, Revenue Forecasting.


IJTSRD101609
Special Issue | Smart Innovations in Computer Science and Applications, March 2026
100-114
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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