Entendendo os Prazos: experimento A/B na Prática
Imagine que você, como vendedor na Shopee, deseja otimizar a experiência de entrega para seus clientes. Uma forma de fazer isso é através de testes A/B, comparando diferentes abordagens de comunicação sobre os prazos. Nossa hipótese central é: fornecer uma estimativa de entrega mais precisa (mesmo que ligeiramente mais longa) expandirá a satisfação do cliente e reduzirá o número de reclamações.
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Para isso, definimos como métrica de sucesso a taxa de satisfação do cliente (avaliada por pesquisas pós-entrega) e o número de tickets de suporte relacionados a atrasos. O grupo de controle recebe a mensagem padrão da Shopee sobre os prazos, enquanto o grupo experimental recebe uma mensagem personalizada, com uma estimativa ligeiramente mais conservadora, porém mais precisa, baseada em informações históricos. Por exemplo, se a Shopee estima 5-7 dias, o grupo experimental vê 6-8 dias.
Este experimento durará duas semanas, tempo suficiente para coletar um volume de informações significativo. Precisaremos de acesso aos informações de vendas, ferramentas de pesquisa de satisfação e a capacidade de alterar as mensagens exibidas aos clientes (o que pode envolver a equipe de TI da Shopee ou uma ferramenta de personalização de mensagens).
Variáveis em Jogo: O Algoritmo da Shopee
O prazo de entrega padrão da Shopee não é uma constante universal. Ele flutua, influenciado por uma miríade de fatores. Entre eles, a distância entre o vendedor e o comprador se destaca como um dos principais. Quanto maior a distância, mais tempo a encomenda passará em trânsito, inevitavelmente impactando o prazo final. Similarmente, a modalidade de envio selecionada pelo vendedor também exerce um papel crucial. Opções mais econômicas frequentemente implicam em prazos mais extensos, enquanto alternativas expressas prometem agilidade.
Outro aspecto relevante é a logística interna da Shopee e de seus parceiros de entrega. Processos de triagem, roteirização e a capacidade de lidar com picos de demanda (como durante promoções) afetam diretamente a eficiência da entrega. Além disso, eventos imprevistos, como condições climáticas adversas ou problemas logísticos regionais, podem causar atrasos pontuais, estendendo o prazo inicialmente previsto.
Portanto, a estimativa de entrega fornecida pela Shopee é uma previsão, sujeita a variações. Compreender as variáveis que influenciam esse prazo permite ao comprador planejar suas compras com mais realismo e minimizar frustrações.
Exemplos e Resultados: experimento de Mensagens
Após a execução do experimento A/B, analisamos os resultados. No grupo de controle, a taxa de satisfação do cliente foi de 75% e o número de tickets de suporte relacionados a atrasos foi de 50 por semana. Já no grupo experimental, a taxa de satisfação subiu para 82% e o número de tickets caiu para 35. Isso sugere que a mensagem com a estimativa de entrega mais precisa (ainda que ligeiramente mais longa) realmente contribuiu para uma experiência mais positiva.
Um exemplo prático: um cliente do grupo de controle, ao receber o produto no 6º dia (dentro da estimativa de 5-7 dias), ficou satisfeito. Já um cliente do grupo experimental, ao receber o produto no 7º dia (dentro da estimativa de 6-8 dias), demonstrou maior satisfação, pois a expectativa foi melhor gerenciada. A diferença parece sutil, mas o impacto na percepção do cliente é significativo.
Com base nesses informações, podemos concluir que vale a pena ajustar a comunicação sobre os prazos de entrega, mesmo que isso signifique adicionar um ou dois dias à estimativa inicial. A transparência e a precisão, nesse caso, superam a promessa de uma entrega mais rápida, mas potencialmente menos confiável.
