本文源引自Shiv Gupta在《Martech》2024年3月13日的刊文，采用中英双语排版，由ImmersiveTranslate提供翻译支持。
Learn how AI-driven data mining and sentiment analysis can deliver more accurate and actionable customer insights.
了解人工智能驱动的数据挖掘和情感分析如何提供更准确、更可行的客户洞察。
Surveys and focus groups are the go-to methods for gathering customer insights to drive marketing strategy. However, they have major flaws like inherent biases, poor predictive power, high costs and responder fatigue. It’s time to move beyond these outdated tactics.
调查和焦点小组是收集客户见解以推动营销策略的首选方法。然而，它们存在重大缺陷，如固有偏差、预测能力差、成本高和响应者疲劳。是时候超越这些过时的策略了。
Today, AI-powered tools like data mining and sentiment analysis offer a powerful way to augment and improve customer research. By tapping into customer data and feedback, AI can provide deeper, more accurate insights with less bias and better predictive capabilities than surveys alone.
如今，数据挖掘和情感分析等人工智能驱动的工具提供了增强和改进客户研究的强大方法。通过利用客户数据和反馈，人工智能可以提供更深入、更准确的见解，比单独的调查具有更少的偏见和更好的预测能力。
This article explores two key use cases for how AI can enhance customer understanding more efficiently and effectively.
本文探讨了人工智能如何更高效地增强客户理解的两个关键用例。
Using AI to increase the predictive value and decrease the size of customer surveys
使用人工智能提高预测价值并减少客户调查的规模
Two major issues associated with surveys are dubious predictive value and responder fatigue due to size. Surveys have poor predictive value because they often present responders with choices or ask responders to identify pain points in isolation from the larger context of their lives. As a result, the survey findings often mismatch with actual customer behavior and preferences. In addition, response credibility decreases as the number of questions increases.
与调查相关的两个主要问题是预测价值可疑和由于规模而导致响应者疲劳。调查的预测价值很差，因为它们经常向受访者提供选择，或者要求受访者脱离他们生活的大背景来识别痛点。因此，调查结果往往与实际的客户行为和偏好不匹配。此外，随着问题数量的增加，回答的可信度也会降低。
Fortunately, customer interaction histories can be mined to better understand actual behaviors and preferences. Traditionally, marketing analysts have used data mining techniques on structured customer data to identify behavioral patterns and build predictive models. AI lowers the requirement for structuring customer data and improves the speed at which insights can be delivered.
幸运的是，可以挖掘客户交互历史，以更好地了解实际行为和偏好。传统上，营销分析师对结构化客户数据使用数据挖掘技术来识别行为模式并构建预测模型。人工智能降低了构建客户数据的要求，并提高了提供见解的速度。
While our experience tells us that AI still requires significant human supervision and direction, using AI we can evaluate a broader range of behaviors and scenarios in a shorter time. As a result, the insights generated have both predictive and explanatory powers.
虽然我们的经验告诉我们，人工智能仍然需要大量的人类监督和指导，但使用人工智能，我们可以在更短的时间内评估更广泛的行为和场景。因此，所产生的见解具有预测和解释能力。
A survey will still help to identify underlying drivers, needs and motivations. Customer data-driven segmentation and insights can help focus survey questions on observed behaviors, customer profitability, key demographics and other valuable dimensions. Furthermore, the survey can be shortened to address problems or opportunities identified specifically during the customer data mining stage.
调查仍然有助于确定潜在的驱动因素、需求和动机。客户数据驱动的细分和洞察可以帮助将调查问题集中在观察到的行为、客户盈利能力、关键人口统计数据和其他有价值的维度上。此外，可以缩短调查以解决在客户数据挖掘阶段专门识别的问题或机会。
Removing biases inherent in surveys
消除调查中固有的偏见
Surveys are significantly susceptible to biases. The very design of a study and the questions in the survey often reflect the company’s agenda.
调查很容易受到偏见的影响。研究的设计和调查中的问题通常反映了公司的议程。
Take the scenario of an innovative engineering-focused consumer products company looking to develop a new brand proposition for the market. Seeing themselves as innovative, the company will likely survey customers’ thoughts about innovation, and most would respond, “It’s great.” If you further ask them whether innovation is essential to them, they will likely respond, “Of course.”
以一家专注于创新工程的消费品公司为例，该公司希望为市场开发新的品牌主张。该公司认为自己具有创新性，因此可能会调查客户对创新的看法，大多数人会回答：“这很棒。”如果你进一步问他们创新对他们是否至关重要，他们可能会回答：“当然。”
However, when the customer goes to make a purchase decision, they are unlikely to consider innovation because that is not transparent or obvious. Instead, they may evaluate a product or service based on features and benefits that reflect a sense of innovation and relevance to their lifestyle.
然而，当客户做出购买决定时，他们不太可能考虑创新，因为创新不透明或不明显。相反，他们可能会根据反映创新感和与他们的生活方式相关的功能和优点来评估产品或服务。
This is just one example of a bias injected into market research projects based on what a company may believe is important to them rather than what is essential to customers. While it seems obvious in hindsight, in my experience, these biases (and others) are very difficult to detect and prevent.
这只是市场研究项目中注入偏见的一个例子，这种偏见是基于公司可能认为对他们来说重要的东西，而不是对客户来说重要的东西。虽然事后看来这是显而易见的，但根据我的经验，这些偏见（和其他偏见）很难被发现和预防。
An alternative, less biased way to understand what customers value is to evaluate minimally prompted feedback. This could be information on social media, chats, or simple free-form responses to open-ended questions such as, “How do you like the product?”
另一种了解客户价值的、偏见较少的方法是评估最低限度提示的反馈。这可以是社交媒体、聊天中的信息，也可以是对开放式问题的简单自由回答，例如“您喜欢该产品吗？”
This information has been challenging to mine because text mining and sentiment analysis capabilities have been limited. With AI, we can evaluate large volumes of open-ended responses and identify critical perceptions, attitudes and needs. Once these AI-driven needs are revealed, a more targeted and less biased market research project can be designed to yield deeper insights and support market strategies.
由于文本挖掘和情感分析能力有限，这些信息的挖掘一直具有挑战性。借助人工智能，我们可以评估大量开放式回答，并识别关键的看法、态度和需求。一旦这些人工智能驱动的需求被揭示，就可以设计一个更有针对性、更少偏见的市场研究项目，以产生更深入的见解并支持市场策略。
Unleashing the power of AI in customer insights
释放人工智能在客户洞察中的力量
The two use cases above are limited examples of using AI to create powerful insights at lower costs with less bias and better predictive powers. There are many more use cases for AI in market research. The challenge for marketing science is understanding how AI can augment and improve research methods that desperately need revamping.
上述两个用例是使用人工智能以更低的成本、更少的偏见和更好的预测能力创建强大洞察力的有限示例。人工智能在市场研究中还有更多用例。营销科学面临的挑战是了解人工智能如何增强和改进迫切需要改进的研究方法。
