Chatbot VRIO (DGO)

Virtual Assistant Evolution: Improving conversational efficiency
‍
My role:
Conversational Designer / UX Writer / Product Owner / Chatbot Strategy
Project duration:
6 months
Tools:
Miro, Google Analytics, Power BI, Microsoft Office, Nuance Microsoft.
Context

The DGO virtual assistant handled over 67,000 interactions per month but faced challenges in intent recognition, language consistency, and governance, which impacted efficiency and user retention. The goal was to reduce unnecessary escalations and improve user satisfaction.

I worked across DGO’s self-service channels (Brazil and Hispanic markets), responsible for shaping the customer experience with the mission of delivering clear, efficient interactions aligned with user expectations through the implementation of new processes.

I maintained chatbot and FAQ content continuously updated and relevant, while monitoring channel metrics to identify what was working well and where improvements were needed. I also explored new solutions and features to strengthen the brand experience and support revenue strategies.

To improve the team’s day-to-day workflow, I designed and implemented an Agile-based process that increased structure, clarity, and cross-team alignment. This reduced rework, minimized communication gaps, and improved predictability, enabling the team to focus on higher-impact initiatives and deliver better results in efficiency and, most importantly, retention.
Goals
🎯 Business goal: Reduce handoffs to human agents in critical flows;
‍
🎯 Product goal: Increase resolution rates in chatbot sessions;
‍
🎯 Experience: Ensure clear and consistent language for smoother conversations and lower chatbot rejection rates
‍
🎯 Business: Build a chatbot framework and governance model using Agile to support revenue, brand, and other company strategies;
My role and responsabilities
Conversational Designer / P.O
βœ… Conversation journey mapping and flow design
βœ… Intent redesign and microcopy optimization
βœ… Chatbot persona and system design (voice & tone)
βœ… Cross-functional alignment with product, engineering, CX, and data teams
βœ… Stakeholder relationship building and management
βœ… A/B testing and session analysis
βœ… Influencing and contributing to conversational product decisions
βœ… Development of guidelines, workshops, and team processes
βœ… Conversational QA and continuous optimization
Design Process
πŸ” Discovery
- Data, session, and ticket analysis
- Identification of improvement opportunities
- Market benchmarking
🧭 Definition
- Prioritized scope
- Success criteria
- Conversational architecture
πŸ’‘ Ideation
- Wireflows and microcopy
- Feature proposals
- Bot persona workshop
- Chatbot tone of voice guidelines
πŸ” Prototyping
- Prototyping in Miro
- Conversation simulations
- Stakeholder validation
πŸ” Testing
- Quantitative and qualitative testing (analytics and CSAT)
- A/B testing by period and implemented feature
- Daily monitoring
πŸš€ Launch & Iteraction
- Training sessions and workshops
- Implementations
- Continuous monitoring
- Post-launch iterations and improvements
Solutions and deliverables
All solutions were fact-based and validated with stakeholders and A/B testing after implementation.
Results and achievements
Some numbers and charts have been omitted due to data privacy reasons
Between Apr/2023 e Sep/2023
βœ… +17 pp increase in chatbot retention in Brazil
‍
βœ… 1,925 leads sent to the marketing team in one month
‍
βœ… +243 responses collected in the new CSAT form
‍
βœ… Improved clarity perceived in satisfaction surveys
‍
βœ… Reduced rework within the CX team
‍
βœ… Increased consistency and governance maturity
‍
βœ… No significant increase in operational costs (OPEX)