Understanding unstructured customer interaction data — George Davis // Frame AI

Show Notes

• 01:36
  The rise of customer interaction data and Frame AI

  Between 2005 and 2015, businesses collected more customer communication data, yet its complexity and cost left it largely unused. Recognizing this, Frame AI saw an opportunity to leverage new advances in natural language understanding for insights and problem-solving.
• 03:37
  The major sources of unstructured customer interaction data

  Emails, chat interactions, survey interactions, and messages to support have drastically increased because of mobile access to customers. However, Frame AI found that hat a lot of data that people thought of as being structured was better viewed as unstructured.
• 07:52
  Unlocking customer interaction data value with data orchestration

  Frame AI believes that a perfect view of customers across the entire organization isn't feasible as different departments solve different problems. Instead, they focus on consolidating data at a technical level while tailoring relevant data to each stakeholder through data orchestration.
• 11:04
  Transforming AI insights into actionable steps

  Natural language and new AI technologies can reveal previously unnoticed insights, but businesses often lack actionable steps. However, translating those insights into recognized business objectives simplifies moving them to a place where someone can take action.
• 16:50
  Turning insights into actionable solutions with Frame AI

  Frame AI serves as a smart pipeline by connecting to various customer data sources and tagging signals using language models. This enables businesses to translate insights into actionable solutions that align with their objectives, fostering creativity and seamless integration into existing tools for effective decision-making.