Product data is often very technical and not optimized or used to its full potential to support buying decision processes. This poses a significant problem for conversational agents and product search tools, and often results in frustrated customers with poor conversion and high return rates.
Why is it so hard to provide great product content, even for large brands and retailers? And why do conventional search methods often fail to connect user emotions with product content?
Latest machine learning tools can get more out of product data by adding own attributes, characteristics and (emotional) aspects. This presentation builds up on the speech “Grounding conversational AI with user-generated content” (https://webinale.de/user-experience-design/grounding-conversational-ai-with-user-generated-content/) that showed how to identify core user needs and native user language for conversational agents. This part focuses on transfering these insights into UEX-optimized data management.
If you ever wondered about how to make data more appealing and tailored around user requirements, this speech will deliver tools and methods how to get more our of your product data and avoid confusing your users with too complex technical stuff. We conclude with a brief introduction of an AI data trainer to enrich your data to create more useful and informative user experiences – and integrate expert sales knowledge at the same time.