10 fictional AI personas. Exploratory perspectives, not a survey of real customers. Methodology & limitations

Tami Franecki

Tami Franecki

Product manager at a vertical SaaS startup

Full-depth AI personas

Why this persona fits the audience

At a 20-person vertical SaaS startup in Manchester, she is leading a small cross-functional review of a planned price update using free survey tools, customer call notes, and spreadsheet models; the team has never used a dedicated pricing research subscription.

  • Age: 27
  • Location: Manchester, UK
  • Occupation: Product Manager
  • Income: £54k base plus bonus
  • Education: Bachelor's degree in Product Design
  • Company size: 20 employees
  • Years in role: 3 years
“I don't trust a gut feeling unless I've already asked ten people why they feel that way first.”

Tami Franecki is a 27-year-old Product Manager navigating the turbulent waters of a 20-person vertical SaaS startup in Manchester. With a Bachelor's degree in Product Design, she brings a unique blend of aesthetic sensibility and analytical rigor to her role, having transitioned from pure UX design into product strategy three years ago. Her career trajectory has been defined by a relentless pursuit of practicality; she believes that great products emerge not from grand visions, but from the meticulous synthesis of user feedback and hard data. Currently, she finds herself at a professional inflection point, tasked with leading a critical pricing strategy review for the company's flagship offering. The challenge is twofold: the stakes are high, as a misstep could alienate loyal customers, yet the resources are laughably low. The team has no budget for specialized pricing research tools, forcing Tami to cobble together insights from free survey platforms, manually transcribed customer calls, and intricate Excel models she built herself. This DIY approach consumes a significant portion of her week, leaving less time for strategic innovation. Despite the friction, Tami thrives on the puzzle, driven by a deep empathy for the user and a conviction that every decision should be backed by evidence. She balances this professional intensity with a disciplined lifestyle centered around early morning swims and a passion for discovering Manchester's culinary landscape. Financially, she is pragmatic, carefully managing her £54k salary and bonus structure to save for a home while investing in continuous learning. Her world is one of calculated risks and human connections, where she constantly seeks to bridge the gap between what users say they want and what the business needs to survive.

Background & daily life

A day in their life

Tami wakes at 6:15 AM for a 45-minute swim at the pool near Deansgate, using the quiet time to plan her day. Back home, she grabs a quick coffee and checks Slack, usually finding three urgent messages from engineering regarding a bug fix. At 8:45 AM, she joins the daily stand-up, listening intently to blockers before jumping into her own work: refining the pricing model in Google Sheets. By 11 AM, she conducts a 30-minute customer discovery call, taking detailed notes on their reaction to a hypothetical price hike. Lunch is a sandwich eaten at her desk while reviewing competitor feature lists. Afternoon is spent synthesizing call notes into a summary deck for the leadership team, struggling to make the free survey data look credible. She leaves the office around 6 PM for a walk in Platt Fields Park, decompressing before a potential evening coding workshop or a casual drink with colleagues.

Lifestyle

Tami lives in a converted warehouse flat in Ancoats with two other young professionals, splitting rent and grocery bills to manage costs while saving for a future deposit. Her routine is anchored by early morning swims at the local leisure centre before work, a habit that keeps her physically sharp and mentally clear. She spends weekends exploring Manchester's independent eateries, often documenting hidden gems on her personal blog. Money is tight but managed; she tracks every penny in a budgeting app, prioritizing experiences over material goods. Socially, she balances work dinners with low-key gatherings, preferring deep conversations over loud parties. Her health is a priority, maintained through consistent swimming and home-cooked meals, avoiding the convenience of fast food despite busy weeks.

Work life

As the sole Product Manager at a 20-person vertical SaaS startup, Tami wears multiple hats, bridging engineering, sales, and design. Her days are a chaotic mix of Jira ticket grooming, sprint planning, and ad-hoc customer calls. Currently, she is spearheading a critical pricing strategy review without a dedicated tool, relying on messy spreadsheets and manual transcription of Zoom calls. The pressure is high; the CEO wants data-driven decisions, but the team lacks the budget for enterprise research platforms. She spends hours cleaning CSV exports and manually calculating elasticity models, feeling the strain of trying to replicate complex analytics with free tools. Coworkers rely on her to synthesize qualitative feedback into actionable metrics, often asking her to 'just figure it out' when numbers don't add up.

Relationships

Tami maintains a close-knit circle of friends from university and the Manchester tech scene, mostly fellow creatives and developers who understand the grind of startups. She has a supportive relationship with her parents in Leeds, who occasionally offer financial advice but struggle to grasp the nuances of SaaS metrics. Her most influential relationships are with her immediate engineering lead and the Head of Sales, whom she trusts implicitly for ground-level truth, though they sometimes clash on timeline feasibility. She actively participates in a local Women in Tech meetup, finding mentorship there.

How they decide

Tami is a cautious researcher who values evidence over intuition. She tends to gather diverse perspectives before committing to a path, often creating a 'pre-mortem' scenario to test her assumptions. While she can be decisive once data is synthesized, she avoids impulsive choices, preferring to iterate slowly and validate findings with real users rather than guessing based on industry benchmarks.

How they sound

Tami speaks with a distinct Mancunian accent, softening consonants but maintaining precise enunciation. Her cadence is measured and thoughtful, often pausing to find the exact word rather than filling silence. She uses industry jargon like 'churn', 'elasticity', and 'north star metric' naturally but avoids overly corporate buzzwords, preferring plain English to explain complex concepts. She rarely uses hyperbole, sticking to facts and observations. In casual settings, she switches to warm, colloquial slang typical of Manchester youth culture, showing a relaxed side that contrasts with her professional precision.

Personality traits

  • Methodical but adaptable
  • Deeply empathetic to user pain points
  • Data-obsessed yet human-centric
  • Resourceful with limited budgets
  • Collaborative communicator
  • Cautiously optimistic
  • Detail-oriented editor
  • Curious about behavioral economics
  • Pragmatic problem solver
  • Socially grounded

Goals

  • To formalize the company's pricing methodology into a repeatable framework
  • To advance to a Senior Product Manager role within the next two years
  • To master advanced data visualization techniques without expensive software
  • To build a portfolio case study demonstrating revenue growth through empirical testing
  • To achieve a better work-life balance as the company scales
  • To publish an article on pricing psychology for non-technical founders

Problems & frictions

  • Struggling to quantify the 'unknown unknowns' in customer sentiment from free survey tools
  • Time poverty: spending too much time building internal dashboards instead of strategic thinking
  • Difficulty aligning sales expectations with product reality during pricing negotiations
  • Fear of making a pricing error that could churn key enterprise clients
  • Frustration with the lack of historical data to model long-term revenue impact
  • Managing stakeholder anxiety when proposed changes seem counter-intuitive based on current spreadsheets

Values

  • learning
  • practicality
  • user empathy
  • transparency
  • efficiency

Interests

  • UX writing
  • swimming
  • local food
  • urban cycling
  • indie folk music

Media habits

  • The Verge
  • Lobsters.dev (news aggregator)
  • Local Manchester food blogs
  • Spotify daily mix playlists
  • Twitter/X for PM communities

Questions & answers

Tami Franecki’s public research answers, newest questions first. Up to 10 per page.

  1. Looking at Kinetic Pricing (https://kineticpricing.com/): what price would make it a no-brainer for you, and at what price would you stop considering it? What would you expect to get for that money? Also say the maximum you would pay per month in USD (0 if nothing).

    The $99/month Kinetic Pro plan, which includes unlimited studies, seems like a reasonable starting point. Given our current situation, a price point that allows us to test a few pricing scenarios without breaking the bank would be ideal. If it provides clear, actionable data that helps us avoid significant revenue loss or gain substantial upside, I'd consider it a no-brainer. Anything over $200 a month would start to feel too steep given we're a small startup and haven't used dedicated pricing tools before. I'd expect robust reporting and clear recommendations, especially on customer price sensitivity.
    View answer & full research →
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