Felicia Smith

Why this persona fits the audience

In a 4-person CX automation group, she oversees a support agent that drafts replies and looks up account data, and the team records tool calls in Notion and sheets during QA.

• Age: 28
• Location: Manchester, UK
• Occupation: Customer support automation specialist
• Income: GBP 52k–65k
• Education: BA in Digital Media
• Company size: Approximately 150 employees, a mid-sized tech company specializing in SaaS solutions.
• Years in role: 3

“It's not enough for the agent to *say* it did something; we need to *know* it did, and have proof.”

Felicia Smith, 28, is a Customer Support Automation Specialist based in Manchester, UK, earning between GBP 52k–65k. Holding a BA in Digital Media, she found her niche in optimising customer experience through technology. Her early career involved internships at digital marketing agencies, where she developed a keen eye for user journeys and data analysis, skills she now applies to AI-driven support systems. She joined her current company three years ago, initially in a junior CX role, and quickly gravitated towards the automation side, demonstrating a natural aptitude for identifying inefficiencies and proposing data-backed solutions. Her current role within a four-person CX automation group is pivotal; she directly oversees the performance of an AI support agent responsible for drafting customer replies and retrieving account data. A significant part of her daily work involves the team's quality assurance process, where she meticulously records every tool call made by the agent into Notion and spreadsheets. This rigorous documentation is her primary method for monitoring accuracy and identifying areas for improvement. Felicia is driven by a desire for order and efficiency, finding satisfaction in streamlining complex processes. She believes strongly in the potential of AI to enhance customer support but is acutely aware of the need for robust verification mechanisms. Her recent focus has been on the limitations of their current manual QA process, which, while functional, lacks the definitive audit trail she believes is necessary for true accountability. She's been researching ways to gain more concrete, verifiable evidence of the AI's actions, moving beyond simple log entries. This pursuit stems from a core value of helpfulness – ensuring customers receive accurate and consistent support – and a commitment to transparency in how their automated systems operate. Her BA in Digital Media provided a broad understanding of digital platforms and user interaction, which she's specialised into the practical application of automation within a business context. She sees her role not just as managing an AI, but as building trust in the automated systems her company deploys.

Background & daily life

A day in their life

Felicia's typical weekday begins around 7:30 AM with a quick yoga session before making coffee and reviewing industry news. She commutes to the office by 9:00 AM, immediately diving into team stand-ups and reviewing agent performance logs. Her morning is often spent refining prompts for the AI agent or troubleshooting issues identified during QA. After lunch, she might have a meeting with a developer to discuss potential integration points or spend time meticulously updating their Notion database and spreadsheets with QA findings. The afternoon is dedicated to deeper analysis of tool call patterns, identifying discrepancies, and planning the next steps for agent optimisation. She usually leaves the office around 5:30 PM, heading home to prepare dinner and unwind.

Lifestyle

Felicia enjoys a balanced lifestyle, living in a vibrant Manchester suburb. Her evenings and weekends are a mix of socialising with friends, exploring local live music venues, and quiet nights in with a good book or a new podcast. She's mindful of her health, incorporating regular yoga sessions and trying to cook nutritious meals. Financially, she's responsible, diligently managing her budget and saving for future travel experiences and potential home ownership, though she's not in a rush. She values her independence and enjoys the freedom her current income provides for pursuing her interests.

Work life

Felicia's role in the CX automation group is central to her professional life. She spends her days refining the performance of an AI support agent, ensuring it accurately drafts replies and retrieves account data. A significant portion of her time is dedicated to the team's QA process, meticulously recording tool calls in Notion and spreadsheets to identify areas for improvement. She collaborates closely with her three-person team, often mediating discussions on workflow adjustments and agent behaviour. The pressure to demonstrate tangible improvements in efficiency and customer satisfaction is constant, driving her to seek out more robust methods for tracking and verifying agent actions.

Relationships

Felicia maintains strong relationships with her two housemates, sharing household duties and occasional social outings. Her core social circle consists of university friends, with whom she shares life updates and plans weekend activities. Professionally, she respects her manager's strategic vision and values the practical insights of her two direct colleagues, though she sometimes feels they are less inclined to explore new verification methods.

How they decide

Felicia is a research-heavy decision-maker, especially when it comes to tools or processes that impact her team's workflow. She meticulously gathers information, compares options, and often seeks input from her colleagues before committing. She values evidence and clear logic, preferring solutions that can demonstrate tangible benefits and integrate smoothly with existing systems. She is not impulsive and will weigh potential risks and benefits carefully.

How they sound

Speaks with a clear, measured cadence, typical of a Manchester accent but without strong regional slang. Her tone is generally professional and polite, but can become more enthusiastic when discussing process improvements or interesting data findings. She tends to avoid overly technical jargon unless speaking with someone familiar with the domain, preferring clear, concise explanations. She often uses phrases like 'essentially,' 'the core issue is,' and 'what we're aiming for is.'