{"id":24900,"date":"2023-08-10T09:19:22","date_gmt":"2023-08-10T09:19:22","guid":{"rendered":"https:\/\/www.revuze.it\/blog\/?p=24900"},"modified":"2023-08-10T10:05:43","modified_gmt":"2023-08-10T10:05:43","slug":"5-things-you-need-to-know-to-improve-your-ai-research-skills","status":"publish","type":"post","link":"https:\/\/www.revuze.it\/blog\/5-things-you-need-to-know-to-improve-your-ai-research-skills\/","title":{"rendered":"5 things you need to know to improve your AI research skills"},"content":{"rendered":"

In the rapidly evolving business landscape, Artificial Intelligence (AI) has become a game-changer for organizations across various domains, including Consumer Insights (CI) teams. Leveraging AI for consumer insights can unlock valuable data-driven insights that lead to informed decision-making and a competitive edge. However, to effectively navigate the world of AI, CI teams must adopt specific research skills. In this blog post, we will explore five essential AI research skills that Consumer Insights teams can embrace to leverage AI technology effectively.<\/span><\/p>\n

1. Data Sources: Tapping into External and In-house Data<\/span><\/h2>\n

The foundation of successful AI analysis lies in the quality of the data used. Consumer Insights teams should identify and utilize various data sources to gain comprehensive insights. This includes internal data and authentic external data from third-party providers. For instance, online consumer review data can come from online retailers like Amazon, Walmart, Sephora, and others. This type of data contains essential information that can be fed into AI algorithms for analysis.\u00a0 At this point, it\u2019s critical to ask whether the data from external sources has been cleansed to ensure accuracy. Erroneous data and duplications can skew results and impact business decisions.<\/span><\/p>\n

External data provides broader industry insights and benchmarks performance against competitors, while in-house data offers a unique understanding of the organization’s specific customer behavior and preferences. Integrating both data sources enables Consumer Insights teams to obtain a holistic view and make data-driven recommendations that align with business objectives.<\/span><\/p>\n

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2. Customer Success: Partnering with the CS Team<\/span><\/h2>\n

Integrating AI products into Consumer Insights can introduce complexity, making it essential for CI teams to collaborate with their Customer Success (CS) team. The CS team can provide valuable guidance on understanding the AI platform, its features, and best practices to maximize its potential within the Consumer Insights context.<\/span><\/p>\n

Engaging with the CS team from the outset ensures a smooth onboarding process and minimizes the learning curve for Consumer Insights professionals. This collaboration empowers the CI team to derive optimal value from the AI platform, thereby improving the efficiency and accuracy of their insights.<\/span><\/p>\n

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