Which Store Should Get the Next Beverage Cooler?

Placing commercial refrigeration in retail accounts requires balancing upfront capital expenditure against projected volume lift. A 2025 machine learning study analyzing 3,119 retail accounts from an unnamed Central American beverage company evaluated algorithmic models designed to predict which accounts would achieve post-installation growth targets.

The study revealed a critical operational takeaway: the most effective predictive model changes based on the size of the equipment allocation budget. When evaluating a 30% volume growth target, CatBoost demonstrated the highest precision when selecting the top 100 candidate stores, correctly identifying qualifying accounts in 79% of test positions compared to 73% for LightGBM. However, when expanding the selection cutoff to the top 500 stores, LightGBM outperformed CatBoost, achieving 58.4% precision against CatBoost's 57%.

Matching the Model to the Equipment Budget

The model performance shift underscores why equipment allocation cannot rely on a single accuracy score. A distributor with budget to deploy only 100 cold-vault coolers requires a model optimized for precision at the top of the distribution curve. Deploying a broad-spectrum model risks allocating high-value capital assets to marginal accounts that generate insufficient case volume to amortize equipment costs.

Importantly, the Central American dataset examined historical performance exclusively among retailers that had already received coolers. Because the study lacked an untreated control group of accounts without new equipment, it measures predictive volume growth rather than direct causal incrementality. A high-volume store may achieve 30% volume growth due to foot traffic trends alone, whereas a smaller store’s growth may be entirely dependent on the cooler installation.

Understanding the difference between predicted account growth and equipment-driven incrementality is vital for commercial territory managers. Placing an expensive asset in a store that was already expanding naturally ties up capital without maximizing sales potential. Equipment allocation must prioritize accounts where cold availability unlocks entirely new shopping missions.

Managing the Cooler After Installation

Allocating refrigeration assets is part of a broader commercial strategy. In its 2024 operating review, Coca-Cola HBC reported using predictive outlet data to target 11,000 kiosks and small groceries in Egypt for combined cooler placement and commercial activation, delivering 33% higher sales compared to non-activated benchmarks. By 2025, the bottler expanded its deployment of IoT "Always-on" connected coolers by 20%, gathering real-time telemetry on internal cooler temperatures, door openings, and operating uptime.

Connected cooler telemetry introduces accountability to field placements. If an account secures a high-cost glass-door merchandiser but stocks it with non-authorized third-party products, or unplugs the unit overnight to save electricity, the asset's return on investment collapses. Furthermore, maximizing cold-vault profitability requires tailoring the internal package architecture—such as balancing PepsiCo’s entry-level 13-ounce PET formats against high-margin 20-ounce bottles.

Pairing predictive site selection with telemetry and localized assortments ensures commercial refrigeration investments translate into profitable case volume. A cooler is not just a cold box; it is an active sales platform whose financial return depends on precise placement, continuous operational monitoring, and disciplined product merchandising.

Managing refrigeration assets with this level of commercial discipline prevents wasted capital expenditure across territories. When equipment is allocated based on rigorous capacity modeling and monitored with live telemetry, bottlers protect equipment amortization while driving sustained volume growth across their account network.