Analytical Study

Open Access

|

Peer-reviewed

Analytics-Driven Channel Partner Profitability Enhancement and Product Mix Optimization

B. Nandhini , Dr. G. Rajini

DIP: 18.02.102/20261102

DOI: 10.25215/2455/1102102

Received: May 12, 2026; Revision Received: May 14, 2026; Accepted: May 16, 2026

Abstract

Lubrication manufacturers need to find ways beyond just increasing their sales revenues to maintain profitability in an increasingly competitive lubricants market. This project will be studying the potential application of an analytics framework to improve both product mix and profitability of the channel partners of a key lubricant manufacturer within southern regional distribution network in India. To verify the researcher’s hypothesis, statistical analyses will be performed on the internal sales data of the last two fiscal years (FY2023/FY2024 & FY2024/FY2025) for each of the six southern states of India and by sale per channel partner by performance and by contribution margin. All aspects of data analysis and visualisation will be done using both Microsoft Excel and Power BI. In conclusion, analysing sales and channel partner performance only on a volume basis convolutes the data set. Within this convoluted data set, the researcher will also conclude that the top partners represent a very small portion of total regional sales and that the regions high margin product categories (including PCMO, SCV, and Vehicle Care) are well below average in terms of being represented in the current channel partner’s product mix. This report recommends making the use of data dashboard software an institutionalized process for managing channels on a regular basis; rebalancing a product mix by moving toward higher profit margin categories; and developing a relationship between segmentation strategies and partner profitability. It was found that the switch from using intuition as the basis for managing channels to using evidence has a direct effect on generating higher profit margins and provides a sustained competitive advantage.

The author(s) appreciates all those who participated in the study and helped to facilitate the research process.

The author(s) declared no conflict of interest.

This is an Open Access Research distributed under the terms of the Creative Commons Attribution License (www.creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any Medium, provided the original work is properly cited.

Responding Author Information

Dr. G. Rajini @ rajini.sms@vistas.ac.in

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Analytics-Driven Channel Partner Profitability Enhancement and Product Mix Optimization

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Article Overview

ISSN 2455-670X

DIP: 18.02.102/20261102

DOI: 10.25215/2455/1102102

Published in

Volume 11, Issue 2, April-June, 2026: Special Issue

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