Implementing Multi-Touch Attribution at Scale for a Global Sales and Marketing Team in an Enterprise Environment
Year: 2024
Type: article
Abstract: Multi-touch attribution (MTA) offers a sophisticated, data-driven approach to distributing credit across touchpoints within the customer journey [1] [2], thereby enabling more precise insights into channel and campaign performance. However, implementing MTA at scale for a global enterprise's sales and marketing team presents unique challenges, including the need to integrate disparate data sources, handle vast data volumes, apply advanced attribution models, and provide insights in real-time. This paper presents the design and implementation of a scalable MTA framework tailored to the needs of a global sales and marketing team, leveraging cloud-native infrastructure, big data processing, and machine learning algorithms to achieve accurate and actionable insights at scale. The proposed MTA solution addresses data heterogeneity by creating a unified data pipeline that integrates structured and unstructured data from CRM systems, web analytics, social media, paid media platforms, and offline sources. Keywords: Multi-touch attribution, sales and marketing, data integration, machine learning, cloud computing, enterprise analytics.
DOI: 10.55041/ijsrem9942
Author: Shreesha Hegde Kukkuhalli
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4404655246