The Key to Successful AI Solutions? Behavioral Data

In recent years, it’s no secret that the proliferation of technological innovations has taken the world by storm. Generative AI, in particular, has become increasingly popular, with tools like ChatGPT reaching 100 million users just two months after it launched. But these advanced AI solutions are nothing without meaningful, quality data.

Unfortunately, the process of achieving this kind of data is not always simple – companies need to capture and process data in a way that removes bias, and format it to be easily consumed by AI. Especially as AI companies like OpenAI and Anthropic begin to run out of data to train their AI models, the importance of gathering usable and meaningful data grows.

As the world of AI expands, companies must understand the impact that quality data versus poor data has on AI solutions and the critical role behavioral data can play in building, training, and bolstering AI-driven tools.

The Impact of Quality vs. Poor Data on AI Solutions

AI models rely on data to learn patterns, make predictions, and perform tasks. If the training data is compromised, inaccurate, or error-filled, the model can produce biased and unreliable results, poor user experience, security vulnerabilities, and regulatory risks. In fact, Gartner estimates that poor data quality alone costs organizations an average $12.9 million annually.

Training AI models on quality data, on the other hand, allows organizations to make better, more informed decisions, implement reliable processes, reduce compliance issues, and avoid costly consequences. It can also lead to better customer and stakeholder experiences, increased revenue and market share, and reduced risk. Without holistic, meaningful, and accurate data, companies will have a hard time building and managing these increasingly complex AI applications and ecosystems.

The Rise of Behavioral Data

Behavioral data from product and service interactions can help provide companies with deep insights for AI models. In fact, McKinsey & Company estimates that organizations that leverage customer behavioral insights outperform peers by 85% in sales growth and more than 25% in gross margin.

Behavioral data describes user interactions with digital environments, revealing detailed preferences and patterns. It stands as a foundational element for uncovering the sentiment behind user clicks. For example, behavioral data can reveal critical metrics such as session duration, active time on site, number of error messages, or dead clicks to provide companies with a clearer picture of user engagement, preferences, and frustration points.

While this data can help enterprises identify flaws and enhance the users' experience, it also contains critical insights for companies to capitalize on new possibilities for their AI solutions, including better forecasting, enhanced security measures, and improved personalization:

Forecasting

Equipped with behavioral data, companies can glean valuable insights into user behavior, patterns, preferences, and pain points, allowing them to predict future behavior more accurately and, in turn, create better experiences. For example, website views, newsletter sign-ups, shopping cart activities, and social media engagements not only act as a digital breadcrumb trail, but can also be indicators of future purchase behavior, too. With this data in hand, companies will have a better indication of consumer behavior, enabling more informed predictions and strategic decision-making.

Identifying threats

Companies can leverage behavioral data to identify threats when developing AI solutions by analyzing patterns of user interaction for anomalies or suspicious activities. By monitoring metrics such as user session duration or click patterns, companies can detect deviations from normal user behavior that may indicate threats or potential vulnerabilities. For example, a financial services company may utilize behavioral data to see how users typically flow through the site and analyze discrepancies to determine if a particular behavior or pattern is fraudulent or not. This proactive approach allows companies to swiftly address security concerns, mitigate risks, and safeguard their AI systems.

Personalization

As the accelerated growth of AI reshapes our world, hyper-personalized customer experiences will soon be the standard. With behavioral data, engineering teams can predict behaviors and tailor user experiences. Businesses that track consumer purchase histories and develop detailed customer profiles can build stronger brand loyalty. Equipped with this data, retailers can offer experiences that are more personalized through targeted offers and relevant cross-selling opportunities. For example, behavioral data can identify shoppers who are more likely to abandon their cart. Retailers can use this information to serve up a personalized discount to the target segment, potentially turning lost sales into conversions.

Behavioral Data is the Key to Success

In an era marked by rapid technological advancement, the success and usefulness of AI solutions hinges on meaningful and accurate data. As the demand for quality data intensifies, behavioral data emerges as a pivotal resource for AI development. By leveraging insights gleaned from user interactions, companies can better predict and forecast user behavior, detect threats and safeguard systems, and deliver personalized experiences that anticipate and exceed user expectations.

As the AI landscape continues to evolve, embracing behavioral data not only ensures the efficacy of AI solutions but also paves the way for transformative and delightful user experiences.