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This research aims to evaluate the effectiveness of cluster-based strategies on the profitability of FMCG retail chains in Russia.

May 22, 2026Journal of Monetary Economics and Management1 citationsOpen Access

Cluster-Based Assortment Optimization Strategies in Fmcg: An Empirical Assessment of Impact on Profitability (Ebitda) and Losses in Russian Retail Chains Vitaliy Borisovich Kolomiets

VKVitaliy KolomietsThe Russian Presidential Academy of National Economy and Public Administration

Key Points

This research aims to evaluate the effectiveness of cluster-based strategies on the profitability of FMCG retail chains in Russia.
Investigated two cluster strategy scenarios: Gross Margin Maximization and Optimal GM vs. Sales Turnover Balance.
Implemented matrix-based store reclustering and replaced non-performing FRESH SKUs with non-alcoholic/low-alcohol beverages.
Assessed impact on operational and financial KPIs for various store performance metrics.
Scenario 1 (GM Maximization) reduced FRESH category losses by 1.41 pp (>27%) but caused a 2.18% revenue decline for this category.
Scenario 2 (GM vs. ST Balance) achieved store revenue growth of 5.16% and GM growth of 7.57%, with moderate losses reduction.
Flexible management of refrigerated display cases (RDCs) increased SKU turnover by up to 18.4% while optimizing operational benefits.

Abstract

This study provides empirical validation of a cluster-based approach (CBA) for evaluating the economic efficiency (EE) of federal FMCG retail chains in Russia 1, and substantiates the application of cluster strategies (CS) for managing profitability and losses. Two CS scenarios were investigated: 1) the "Gross Margin (GM) Maximization" CS and 2) the "Optimal GM vs. Sales Turnover (ST) Balance" CS. Pilot interventions involved matrix-based store reclustering against benchmarks, replacing non-performing FRESH category SKUs with non-alcoholic/low-alcohol beverages (NALB/LAB) in refrigerated display cases (RDCs), and assessing the impact on operational and financial KPIs: • Scenario 1 (GM Maximization): Achieved significant FRESH losses reduction of 1,41 pp (>27%), but resulted in a 2,18% revenue decline for this category due to aggressive assortment reduction (~32% of FRESH SKUs). • Scenario 2 (GM vs. ST Balance): Generated substantial store revenue growth of 5,16% and GM growth of 7,57%, alongside moderate losses reduction through light assortment optimization (~17% FRESH SKU replacement). Adaptive RDC repurposing proved superior to radical reduction, maximizing ROI while preserving operational benefits. Scenario 2 is recommended for sustainable growth. Scenario 1 CS is effective as a corrective measure for high-losses stores. Flexible RDC space management to reduce "dead zones" can increase SKU turnover by up to 18,4%. However, limitations of the CBA for EE were identified regarding the projected 1,54 pp EBITDA increase (actual: 0,22–0,54 pp). The EE CBA model requires refinement in: incorporating operational costs (incl. associated CAPEX) for RDC repurposing/decommissioning; accounting for the adaptation time lag of assortment changes (up to 10 weeks for FRESH SKU delisting); incorporating cross-demand elasticity in FRESH → NALB/LAB substitutions; assessing store share within chains with complex logistics, extreme demand volatility, e-commerce services, and geographical specificities. This research establishes a new standard for integrating cluster analysis, A/B testing, and dynamic shelf-space EE management into the operational processes of large-scale retail chains.

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