115. AI Chat Log Analysis Design
AI chat starts the moment it is implemented. By visualizing unanswered questions, misunderstandings, and drop-offs through log analysis, we will improve conversion rates and accuracy on a monthly basis.
The main reason why AI chat is not effective is that "logs are not being used for improvement." What did users ask? Where did they drop off? What did the AI misunderstand? Which questions led to conversions? Without examining these, accuracy and results will not improve. In this service, we will design the AI chat conversation logs in a "modifiable format." Rather than just viewing them, we will classify unanswered questions, incorrect answers, misunderstandings, insufficient FAQs, flaws in the user journey, guardrail activations, and escalations, and establish a dashboard and operational flow that highlights areas for improvement on a monthly basis. We will create a state where we can identify targets for improvement, including adding FAQs, terminology dictionaries, user journeys, question flows, and guardrails. ■ Provided Content (3 points) 1. Log collection and measurement design (what to record, granularity, anonymization) 2. Analysis design (classification axes, metrics, dashboards, alerts) 3. Improvement operation design (monthly cycle, responsibilities, priorities, KPIs) *First, please tell us the "purpose of the chat (estimation/consultation/download/self-resolution)" and "the symptoms you are currently facing (many unanswered questions/many drop-offs, etc.)." We will determine the analysis axes.
- Company：アンドワン 本社、東京支社、川崎営業所
- Price：Other
