Purpose

This study aims to propose a new method to predict retail store performance using publicly available satellite imagery data and machine learning (ML) algorithms. The goal is to provide manufacturers and other practitioners with a more accurate and objective way to assess potential channel members and mitigate information asymmetry in channel selection and negotiation.

Design/methodology/approach

The authors developed an open-source approach using publicly available Google satellite imagery and ML algorithms. A computer vision algorithm was used to count cars in store parking lots, and the data were processed with a CNN. Linear regression and various ML algorithms were used to estimate the relationship between parked cars and sales.

Findings

The relationship between parked cars and sales was nonlinear and dependent on the type of channel member. The best model, a Stacked Ensemble, showed that parking lot occupancy could accurately predict channel member performance.

Research limitations/implications

The proposed approach offers manufacturers a low-cost and scalable solution to improve their channel member selection and performance assessment process. Using satellite imagery data can help balance the marketing channel planning process by reducing information asymmetry and providing a more objective way to assess potential partners.

Originality/value

This research is unique in proposing a method based on publicly available satellite imagery data to assess and predict channel member performance instead of forward-looking sales at the firm and industry levels like previous studies.

中文翻译：

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用于渠道成员选择的卫星图像和机器学习

目的

本研究旨在提出一种使用公开卫星图像数据和机器学习 (ML) 算法来预测零售商店业绩的新方法。目标是为制造商和其他从业者提供更准确、客观的方式来评估潜在的渠道成员，并减轻渠道选择和谈判中的信息不对称。

设计/方法论/途径

作者使用公开的谷歌卫星图像和机器学习算法开发了一种开源方法。使用计算机视觉算法对商店停车场的汽车进行计数，并使用 CNN 处理数据。使用线性回归和各种机器学习算法来估计停放车辆与销量之间的关系。

发现

停放汽车与销量之间的关系是非线性的，并且取决于渠道成员的类型。最好的模型是堆叠集成，表明停车场占用率可以准确预测渠道成员的表现。

研究局限性/影响

所提出的方法为制造商提供了一种低成本且可扩展的解决方案，以改进其渠道成员选择和绩效评估流程。使用卫星图像数据可以减少信息不对称并提供更客观的方式来评估潜在合作伙伴，从而帮助平衡营销渠道规划流程。

原创性/价值

这项研究的独特之处在于提出了一种基于公开卫星图像数据来评估和预测渠道成员绩效的方法，而不是像以前的研究那样在公司和行业层面进行前瞻性销售。