Case Study Example

AI-Powered Customer Support Chatbot

Kris Jones
Professor of Information Systems
Tools
  • OpenAI GPT-4 API: For natural language understanding and generating accurate, human-like responses.
  • Bubble: To build the user interface for the chatbot, integrate customer support workflows, and manage backend operations.
  • Zapier: To automate connections between the chatbot and CRM, help desk software, or email systems.
  • Proposed Business Problem:

    Many small to mid-sized businesses struggle with the cost and resource requirements of maintaining a 24/7 customer support team. Customers expect instant responses to inquiries, but hiring full-time support staff for continuous coverage is expensive and difficult to scale.

    Solution:

    An AI-powered customer support chatbot that can handle FAQs, basic troubleshooting, and general inquiries. The chatbot will be integrated into the company’s website, helping to reduce the load on human agents by addressing common customer issues and escalating only more complex queries to support staff.

    Key Features:
    • Contextual Understanding: The chatbot will utilize GPT-4’s capabilities to understand customer queries in context, providing relevant answers and follow-up questions.
    • Multi-Language Support: The bot can be trained to handle multiple languages, expanding customer service capabilities without needing additional staff.
    • CRM Integration: With tools like Zapier, the chatbot will update CRM records, create support tickets, or send follow-up emails automatically after each conversation.
    Validation Indicators:

    Measure the number of customer interactions handled by the bot, customer satisfaction, and resolution times.

    De-Risking Strategy:

    By launching a minimum viable version of the chatbot with limited functionality (e.g., handling only FAQs and simple requests), the business can quickly determine if AI can reduce support costs and improve customer satisfaction before investing in additional AI features. Early user feedback will also highlight potential areas for improvement.

    Time Estimate

    4-6 weeks

    The time estimates are based on the solution's complexity, tool capabilities, and customization needs, ranging from 3 to 8 weeks depending on the project scope.

    Cost Estimate

    $3,000 - $6,000

    The cost estimates for these MVP ideas are based on tools/subscription fees, development time (at ~$50 to $150/hour), and customization complexity, with typical costs ranging from $3,000 to $9,000 depending on the project’s scope and AI requirements.

    Transform Your Business Today

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