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Helping eCommerce marketplace improve seller satisfaction scores using Bayesian network

Author(s): Debasree Bhattacharya, Daya Patkar, Karishma Mulla, Ashi Garg

Businesses are increasingly focusing on brand loyalty and customer engagement as they move towards more customer-centric strategies. Besides end consumers, sellers also form a key customer base for ecommerce marketplaces. Bad customer experience can lead to negative brand perception and drive down NPS (Net Promoter Score). This study was to help an Ecommerce Marketplace scientifically identify key experience areas that were leading to a drastic decline in Seller NPS scores across regions over eight months. This would help business focus on the most important levers, prioritize budget and improve NPS. In this study, we modelled customer survey data on brand perceptions and experiences, with historical transactional data, and company policy information to determine drivers of NPS. It was important to understand the inter-relations among various aspects of customer data such as brand perceptions, issue resolution, customer service and macro factors to provide holistic and actionable recommendations. We used a path model approach with Bayesian network to determine causality between the key factors driving NPS as well as the inter relation among each factor. This was followed by a Structural Equation Modeling to quantify the relative impact of each factor on NPS. The study found that sudden policy changes without effective communication, poor customer service and lack of protection for sellers from false buyer claims, were leading to a poor selling experience. Sellers perceived the site as unfair to them and unsafe to sell. These insights were actioned upon by the company in an organization wide cross-team initiative which helped in addressing the seller’s dissatisfaction and eventually brought back NPS on an upward path.

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