Quando o feedback do cliente contradiz os dados, é essencial investigar mais a fundo. Enquanto os dados revelam padrões, o cliente traz o contexto real. Avalie se existe viés ou inconsistências nos dados internos, pois, se o cliente aponta algo errado, pode ser um sinal de que a coleta ou a análise de dados precisa ser ajustada. A combinação de ambos é o que garante decisões mais precisas e alinhadas à realidade.

Customer feedback is never wrong. Except for customers who lie fraudulently, customer feedback reflects ‘individual’ perceptions and feelings about their ‘unique’ experiences. It’s the perfect opportunity for you to act on an individual level. However, it is incorrect to assume that customer feedback represents the opinions of ‘all’ customers or that it fully explains the impact of your delivered customer experience on business value. When faced with conflicting information, it’s essential to bring in your experts. Market researchers provide representative insights for the total customer base, not just the ones reaching out to you, while data scientists use historical and transactional data to prove actual business impact.

Navigating the tricky terrain of customer feedback versus data analytics is an integral part of my journey. I listen to my customers, absorbing their feedback to understand their emotions and experiences. I analyze the data, uncovering patterns that tell a different story than what I hear directly. My team and I engage in open discussions, merging qualitative and quantitative insights to form a holistic view. Through experimentation, I test assumptions derived from both data and feedback, learning what truly resonates. This balancing act fuels innovation and helps me refine our approach, ensuring we not only meet customer expectations but also drive sustainable growth

Em customer experience têm-se um grande desafio em ouvir a voz do cliente e analisar a tendência dos dados. Neste caso é importante: - Ao ouvir o feedback dos clientes, garantir que o número de amostras sejam suficientes para uma análise de dados qualitativa e, caso seja necessário, realizar um método quantitativo para confirmar as hipóteses. - Apurar se os dados dos clientes e seus comportamentos estão atualizados e evidenciam tendências verdadeiras. - Realizar o cruzamento dos dados e voz do cliente e encontrar padrões de comportamento e necessidades para que a tomada de decisão seja mais assertiva. - Eliminar vieses que possam interferir na tomada de decisão.

When customer feedback conflicts with data analytics, start by analyzing both sources to uncover potential biases or blind spots. Dive deeper into the feedback to identify recurring themes and consider whether the analytics might be missing qualitative nuances. Cross-reference specific customer complaints with data patterns to see if there are underlying issues that numbers alone can't capture. Engage with customers for clarification if needed and ensure analytics tools are configured correctly. Use a balanced approach to align insights, leveraging feedback for context and data for scale, to make informed, customer-centric decisions.

I’d start by looking at both the customer feedback and data to see where they overlap or differ. Then, I’d try to understand why the feedback might not match the data, like potential biases or missing context. Finally, I’d use both insights to find a balanced solution that addresses the customer’s concerns while aligning with the data.