Home / Current Issue / Paper 1709056
Creating Budget Allocation Frameworks for Data-Driven Omnichannel Media Planning
Subject area: Science,Engineering and Technology · Area of research: Budget Allocation Frameworks
Abstract
In an increasingly fragmented digital ecosystem, media planners face mounting challenges in allocating budgets across a growing array of channels. Traditional budget allocation approaches often rooted in historical spending patterns or siloed analytics struggle to optimize for cross-platform efficiency and ROI. This paper proposes a comprehensive framework that leverages real-time, data-driven insights to guide omnichannel media budget allocation. Drawing from CRM integrations, machine learning forecasting, and conversion attribution modeling, the framework enables dynamic reallocation strategies that adapt to market conditions and consumer behavior. It also incorporates adaptive weighting algorithms, predictive analytics, and unified performance metrics to balance reach, engagement, and return. By testing the model across multiple campaign types in both B2B and B2C contexts, we demonstrate that data-driven omnichannel budget frameworks outperform conventional planning paradigms. The study not only enhances operational efficiency but also supports more agile, responsive media planning with quantifiable business value.
Keywords
Omnichannel media, budget optimization, CRM insights, marketing attribution, data-driven planning, machine learning.
How to cite this paper
@article{1709056,
author = {Omolola Temitope Kufile, Bisayo Oluwatosin Otokiti, Abiodun Yusuf Onifade, Bisi Ogunwale, Chinelo Harriet Okolo},
title = {Creating Budget Allocation Frameworks for Data-Driven Omnichannel Media Planning},
journal = {Iconic Research And Engineering Journals},
year = {2021},
volume = {5},
number = {6},
pages = {440-456},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709056.pdf},
abstract = {In an increasingly fragmented digital ecosystem, media planners face mounting challenges in allocating budgets across a growing array of channels. Traditional budget allocation approaches often rooted in historical spending patterns or siloed analytics struggle to optimize for cross-platform efficiency and ROI. This paper proposes a comprehensive framework that leverages real-time, data-driven insights to guide omnichannel media budget allocation. Drawing from CRM integrations, machine learning forecasting, and conversion attribution modeling, the framework enables dynamic reallocation strategies that adapt to market conditions and consumer behavior. It also incorporates adaptive weighting algorithms, predictive analytics, and unified performance metrics to balance reach, engagement, and return. By testing the model across multiple campaign types in both B2B and B2C contexts, we demonstrate that data-driven omnichannel budget frameworks outperform conventional planning paradigms. The study not only enhances operational efficiency but also supports more agile, responsive media planning with quantifiable business value.},
keywords = {Omnichannel media, budget optimization, CRM insights, marketing attribution, data-driven planning, machine learning.},
month = {December},
}