Entendendo a Falha: Hipóteses e Métricas na Shopee
Quando uma transação na Shopee falha, a frustração é imediata. Contudo, por trás dessa mensagem de erro, existe a oportunidade de aprendizado e melhoria contínua. Para solucionar o desafio de forma eficaz, podemos utilizar o abordagem de experimento A/B, uma abordagem experimental que nos permite identificar a causa raiz e executar a alternativa ideal.
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A formulação da hipótese central é o primeiro passo: “A implementação de um sistema de notificação proativa sobre possíveis problemas com o abordagem de pagamento antes da finalização da compra reduzirá a taxa de falhas nas transações”. Para testar essa hipótese, definimos métricas claras de sucesso. A principal é a redução percentual da taxa de falhas nas transações. Adicionalmente, monitoraremos o tempo médio gasto na finalização da compra e a taxa de abandono do carrinho.
Por exemplo, imagine que muitos usuários estão tentando pagar com cartões de crédito que já expiraram ou que atingiram o limite. O experimento A/B nos auxiliará a validar se alertá-los sobre isso antecipadamente resolve o desafio. A jornada revela que a experimentação é a chave para o sucesso.
Testando a alternativa: Grupo de Controle vs. Experimental
Agora que temos nossa hipótese e métricas, vamos configurar nosso experimento. Imagine que o trajetória é como plantar duas sementes em solos diferentes para observar qual cresce melhor. O grupo de controle será a experiência atual da Shopee, sem nenhuma alteração. Já o grupo experimental receberá a notificação proativa sobre os possíveis problemas com o abordagem de pagamento antes da finalização da compra.
É fundamental compreender que a alocação dos usuários aos grupos deve ser aleatória para garantir que não haja viés nos resultados. Metade dos usuários observará a experiência original (controle) e a outra metade observará a nova experiência com a notificação (experimental). A duração do experimento será de duas semanas. Justificamos esse prazo com base no volume de transações diárias na Shopee, o que nos dará informações suficientes para tirar conclusões estatisticamente significativas.
Vale destacar que para executar o experimento, precisaremos de recursos como desenvolvedores para construir a notificação, designers para garantir que ela seja visualmente atraente e analistas de informações para monitorar as métricas e interpretar os resultados. Este é o preparo para o sucesso.
Resultados e Ações: Uma História de Transações Bem-Sucedidas
Após as duas semanas, os informações começaram a contar uma história. O grupo experimental, aquele que recebeu as notificações proativas, apresentou uma redução de 15% na taxa de falhas nas transações. Além disso, o tempo médio gasto na finalização da compra diminuiu em 5%, indicando que os usuários estavam conseguindo concluir o trajetória de forma mais rápida e eficiente.
Imagine um usuário, Maria, que sempre teve problemas para finalizar suas compras na Shopee. Ela tentava pagar com um cartão que já havia expirado e ficava frustrada quando a transação era recusada. Com a nova notificação, Maria foi alertada sobre o desafio antes de finalizar a compra, conseguiu atualizar os informações do cartão e concluir a transação sem problemas. Um exemplo claro do impacto positivo do experimento A/B.
O experimento demonstra que a implementação da notificação proativa foi um sucesso. Com base nesses resultados, a Shopee decidiu executar a alternativa para todos os usuários, garantindo uma experiência de compra mais fluida e eficiente. A jornada revela que, mesmo em um mar de transações, cada detalhe faz a diferença.
