[Paper Presentation] Helping eCommerce marketplace improve seller satisfaction scores using the Bayesian network
Businesses increasingly focus 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 businesses focus on the most important levers, prioritize budgets 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 a Bayesian network to determine causality between the key factors driving NPS and the interrelation among each factor. Structural Equation Modeling followed this 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 led 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 address the seller’s dissatisfaction and eventually brought NPS back on an upward path.