Creating a Supplier Utility Metric and Researching it to Work with Segments

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Abstract

According to Marketplace Research 2022, the e-commerce segment is growing rapidly, but sellers don’t always understand what factors affect liquidity. Developing a recommendation system and set of measures based on seller usefulness will potentially improve service quality, positively impact the consumer experience, help the marketplace increase seller support and interaction with sellers, and improve the marketplace's competitiveness and reputation. The purpose of the study is to create a seller usefulness metric to further build a recommendation system and develop a set of measures to work with segments of sellers. Results: the algorithm for assessing the usefulness of the seller can be used to create a recommendation system and develop a set of measures that improve the quality of services and the potential profit of the site.

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About the authors

Natalia V. Grineva

Financial University under the Government of the Russian Federation

Email: ngrineva@fa.ru
ORCID iD: 0000-0001-7647-5967
SPIN-code: 1140-9636

Cand. Sci. (Econ.), Associate Professor, Associate Professor of the Department of Data Analysis and Machine Learning

Russian Federation, Moscow

Maria A. Sukhan

Financial University under the Government of the Russian Federation

Author for correspondence.
Email: 196865@edu.fa.ru
Russian Federation, Moscow

Natalia V. Kontsevaya

Financial University under the Government of the Russian Federation

Email: NVKontsevaya@fa.ru
ORCID iD: 0000-0002-9353-5463
SPIN-code: 3574-0050

Associate Professor, Associate Professor of the Department of Mathematics

Russian Federation, Moscow

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Supplementary files

Supplementary Files
Action
1. JATS XML
2. Fig. 1. Summary dataset statistics

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3. Fig. 2. Correlation matrix

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4. Fig. 3. Graph of the number of sellers on the GMV threshold

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5. Fig. 4. Graphs and tables of distributions of seller metrics

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6. Fig. 5. A set of constraints for regression testing

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7. Fig. 6. ROC-curve of logistic regression

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8. Fig. 7. Contribution of traits restricted to the 25th and 50th percentiles of GMV

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9. Fig. 8. Summary of the logit model

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10. Fig. 9. Model quality comparison

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11. Fig. 10. Contribution of features to the model

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12. Fig. 11. SHAP model for a randomly selected seller

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13. Fig. 12. Coefficients of regression model metrics

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14. Fig. 13. Coefficients of regression model metrics after removing the «number of account managers» attribute

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