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.
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basic information
■Concerns like these - Implemented AI chat but can't see results - There are unanswered or incorrect responses, but the cause is unknown - The chat is functioning, but it doesn't lead to inquiries/quotes - Feels like there is a lack of FAQs and product information - The guardrails are either too strong or too weak, causing operational concerns ■Provided Content (Details) - 1) Log collection design (to create "analyzable logs") - 2) Classification design (to present improvement points "by category") - 3) KPI design (to create a state where "improvements are ongoing") - 4) Dashboard/report design (to fix the order of what to view) - 5) Improvement operation (the cycle of "analysis → measures → reflection → verification") ■Deliverables - Log item definitions (collection items, anonymization/masking policy) - Classification system (definitions and examples of unanswered/incorrect responses, drop-offs, CV, etc.) - KPI definitions (calculation formulas, target values, points of caution) - Report templates (monthly summary, improvement list, priorities) - Improvement operation rules (weekly/monthly procedures, responsibilities, SLA) - Improvement backlog (management sheet for adding FAQs, updating dictionaries, correcting pathways)
Price information
■1.5 million yen to 9 million yen (varies based on chat scale and measurement range) - Light (log definition + KPI + monthly report template): 1.5 to 2.8 million yen - Standard (classification design, CV measurement, operation procedures, improvement tickets): 2.8 to 5.5 million yen - Expanded (multiple channels/multilingual, alerts, ongoing improvements included): 5.5 to 9 million yen *Estimate required
Delivery Time
※Minimum 2-3 weeks / Standard 1-2 months
Applications/Examples of results
■Purpose - Reduction of unanswered/incorrect responses (accuracy improvement) - Optimization of conversion pathways (increase in inquiries/downloads/reservations) - Identification of improvement points for FAQs/dictionaries/information design - Proper adjustment of guardrails (excessive refusals/accident prevention) - Standardization of operations (establishing a state where monthly improvements can be implemented) ■Examples of Achievements - Many unanswered queries → Improved by adding FAQs for zero-hit terms - Not leading to conversions → Revised pathways by purpose to improve contribution rates - Fear of incorrect answers → Mitigated accidents through evidence presentation and guardrail adjustments ■Approach - 1. Confirm purpose: Establish KGI for chat (inquiries/downloads/self-resolution) - 2. Log design: Organize collection items and anonymization, and set up CV measurement - 3. Classification design: Enable judgment of unanswered/incorrect responses/abandonment - 4. Reporting: Fix the order of monthly reviews (in a way that shows improvements) - 5. Start operations: Immediate weekly improvements → Structural monthly improvements - 6. Establishment: Continuous operation with an improvement backlog
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