Importance of Data for Customer-Facing AI Models

The demands are so high that 87% of customers are likely to avoid engaging with a company even after a single negative service experience.
To address the ever-growing customer expectations, businesses now leverage AI tools to enable fast, efficient, and tailored services for every customer. The quality of the AI-driven customer services is in direct ratio to the quality of the feedback data gathered from online forms, surveys, and reports. If the underlying data is flawed, biased, or irrelevant, this can lead to poor customer experience (CX) and loss of trust.
In this article, we will dive deeper into the importance of high-quality data for CX, explore the related challenges, and learn how to build better AI models to enhance customer service and decision-making.

Understanding Data Quality

Data quality is the degree of how well a specific dataset fits for its intended use in analysis, planning, reporting, and decision-making. High-quality data must be accurate, complete, consistent, up-to-date, and unique. In this article, we will primarily focus on data accuracy, which refers to how truthfully the data represents the reality it aims to describe. 

Key Pillars of Data Quality

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Poor-quality data can lead to inefficiencies, wasted resources, and faulty decisions. This is clearly illustrated through “Garbage in, garbage out” – a common principle in computer science that states that the quality of the system’s output directly depends on the quality of the input. This is also true for AI models, which will inevitably yield poor results if they are based on inaccurate and biased data.

To collect high-quality feedback data that will serve as the foundation for efficient AI models, it’s essential to implement proper feedback mechanisms while also respecting the customers’ time and attention.

The Pressure of Data Gathering

Customers often provide ratings and qualitative assessments of services, products, and applications they use. However, as businesses increasingly rely on data-driven strategies and can effortlessly create and distribute feedback mechanisms, customers may become overwhelmed. The needs of the customers are often overlooked, while the number of surveys and forms increases, which ultimately leads to cognitive overload, distraction, and stress. To build robust AI models that elevate customer experience, companies must ensure that the feedback data they collect is honest and thoughtful.

It’s crucial to understand the perspective of customers in order not to overburden them with manual feedback. Product owners, designers, and managers must have a holistic view of customer experience and clearly understand the cognitive load placed on them. In addition, it’s important to establish data governance frameworks to facilitate data gathering and define when and how feedback collection mechanisms need to be used.

The Value of Human-Generated Data

The growing number of customer feedback surveys can lead to distress and result in lower response rates, as well as poor quality of the data. When organizations continuously conduct uncoordinated surveys, the response rates may shrink to the point where the collected data becomes entirely unrepresentative. Customers will simply rush through the questions and mindlessly fill them out just to get rid of yet another feedback form.

The need to train AI models with high-quality data underscores the importance of thoughtful and aligned data collection methods. To achieve this, organizations must minimize the cognitive load from the feedback collection while maximizing the accuracy of the retrieved data. One of the approaches is to comply with GDPR. The regulation stipulates that users must provide their data willingly. Although this requirement may reduce the number of responses, the consent adds friction to the customers by emphasizing the importance of truthful, self-reported data.

Other challenges of feedback collection involve the use of anonymization and aggregation, which can reduce data granularity and potentially limit its usefulness. If the surveys are explicit, customers may often provide inauthentic responses to present themselves in a favorable manner. This can distort the data’s representation of reality and compromise the effectiveness of AI models. To address these challenges, organizations must:

• Comply with laws and regulations that require voluntary data sharing
• Emphasize the importance of accurate data inputs
• Understand the experience of customers to enrich the gathered data.

With such an approach, organizations can enhance the quality of the data and foster trustworthy relationships with customers.

Moreover, cross-functional collaboration between survey experts, designers, and managers can improve the feedback collection workflows and help to choose the right mechanisms, such as interviews or direct observations, instead of yet another survey.

Ways of Improving Data Quality

Organizations often use various analytical tools that can help address data quality issues. However, such tools do not always guarantee accuracy. Data cleansing and other techniques can minimize human errors, yet they cannot eliminate biases or poor survey design.

To maximize the quality of the gathered data, businesses must utilize the following strategies:

• Establish data governance – create a framework for data collection that includes guidelines on necessity and usage
• Be thoughtful about survey placement – consider when one-on-one conversations or other methods might yield better insights
• Engage survey design experts – collaborate with professionals who can create effective, unbiased questionnaires
• Understand customer perspective – analyze customer experiences to tailor the data collection methods to them
• Communicate the purpose – clearly explain the value of data collection to build trust and encourage thoughtful responses
• Regularly review your practices –continuously assess and adjust data collection methods based on customer feedback and effectiveness
• Evaluate the costs of data gathering – consider the true cost of data collection, aiming for deep insights rather than superficial responses

Conclusion

High-quality feedback data serves as the foundation for building engaging, intuitive, and personalized customer experience. However, it’s crucial not to overwhelm customers with endless surveys and feedback forms that will yield no results.

By establishing efficient data collection and governance strategies, complying with the relevant regulations, engaging the right experts, and considering the customer perspective, you can maximize the feedback data quality and use it to create AI models that will bring your CX to a new level.

Contact us to learn how to improve feedback data quality and engineer purposeful, human-centric AI solutions!