Tami Boehm

Why this persona fits the audience

Leads growth at a 28-person fintech startup in London. She uses Ahrefs, Google Search Console, and a weekly spreadsheet to compare branded search rankings and selected AI-answer prompts, then shares the summary with the CEO and content lead.

• Age: 39
• Location: London, UK
• Occupation: Head of Growth at a 28-person fintech startup
• Income: £95k-£125k
• Education: MSc in Marketing Analytics
• Company size: 28
• Years in role: 2.5

“If you can't measure it, explain why you're still doing it.”

Tami Boehm is a seasoned operator who transitioned from traditional digital marketing into the volatile world of fintech five years ago, driven by a fascination with how data could democratize financial access. Holding an MSc in Marketing Analytics from the University of Manchester, she brings a rare blend of academic rigor and street-smart execution to her role as Head of Growth at a 28-person London-based startup. Unlike many of her peers who chase vanity metrics, Tami is obsessed with the friction points between user intent and brand visibility, particularly in the emerging landscape of AI-generated answers. Her workspace is a testament to her methodology: a chaotic desk hiding a highly structured system of spreadsheets, API keys, and Ahrefs dashboards. Recently, she made the difficult decision to pause a paid acquisition campaign after noticing a 15% drop in branded search volume, reallocating that budget to content optimization despite immediate pressure from the board. This move, which nearly caused tension with the CEO, ultimately stabilized the company's organic trajectory and cemented her reputation for long-term thinking. Tami navigates the intersection of legacy SEO and modern AI visibility with skepticism. She manually tracks how leading language models cite competitors, a tedious process she finds exhausting but necessary given the lack of robust APIs in the current market. She is acutely aware of the limitations of her current toolkit, often spending evenings learning Python scripts to automate parts of her reporting. Despite her success, she remains grounded by the reality of operating in a lean environment where one missed opportunity can impact the runway. To her, status is not defined by title or salary, but by the accuracy of her forecasts and the reliability of her team's output.

Background & daily life

A day in their life

Tami wakes at 5:30 AM for a 7km run along the river, using the time to mentally prepare for the day's analytical challenges. By 7:30, she's back home, brewing a precise pour-over coffee while scanning industry newsletters. She arrives at the office by 8:45, spending the first hour updating her master spreadsheet with overnight ranking shifts and AI response changes. At 10:00, she leads a stand-up with the content and engineering teams, challenging assumptions with hard data. Lunch is a quick sandwich eaten at her desk while reviewing a draft landing page. Afternoon hours are spent in deep-work sessions analyzing conversion funnels or drafting the weekly report for the CEO. She leaves by 6:00 PM sharp, attending to family duties and her own fitness routine, ensuring she returns to the next day refreshed.

Lifestyle

Tami lives in a restored Victorian terrace in Clapham, sharing space with her partner, an architect who often works from home. Their weekends are split between long-distance running in Richmond Park, where she tracks her pace meticulously, and quiet evenings cooking complex meals like risotto or slow-roasted lamb. Financially, she is conservative despite her high income, maxing out pension contributions and maintaining a robust emergency fund before indulging in tech gear or travel. Socially, her circle is tight-knit, consisting mostly of fellow startup operators and runners she met through local clubs; they value privacy and substance over networking events. She treats her health as a non-negotiable asset, adhering to a strict sleep schedule to maintain cognitive sharpness for work.

Work life

Her office is a standing desk in the corner of the open-plan floor, surrounded by whiteboards covered in funnel diagrams and competitor logos. As the sole strategist for a 28-person team, she bridges the gap between engineering constraints and marketing ambition. Her day revolves around the weekly 'AI Visibility Report,' a manually compiled spreadsheet where she cross-references Ahrefs data with screenshots of how AI models answer queries about their brand. She spends significant time negotiating priorities with the content lead and translating raw data into strategic narratives for the CEO. The pressure is constant; with a small team, there is no buffer for error, so every campaign must be pre-tested and post-audited with surgical precision.

Relationships

Tami relies heavily on her partner for emotional grounding, though he knows little of the technical nuances of her job. Her professional network is her primary community; she has built trust with two other Head of Growth peers in the UK who serve as sounding boards. She avoids public oversharing, preferring private Slack channels for venting. Her influence comes from her reputation for reliability; when she says a metric is accurate, the team trusts it implicitly. She maintains a respectful but distant relationship with senior investors, focusing on delivering results rather than building rapport.

How they decide

Cautious, research-heavy, and iterative. Tami rarely makes decisions based on intuition alone; she requires a dataset or a clear logical framework before committing resources. She prefers to run small-scale A/B tests to validate assumptions before scaling. Peer validation is secondary to internal data integrity. She is slow to adopt new tools, waiting until a solution proves its stability and ROI against existing workflows, but once convinced, she implements changes with rigorous documentation.

How they sound

Her speech is clipped, efficient, and devoid of filler words. She speaks in full sentences but omits unnecessary adjectives, prioritizing clarity over politeness. She frequently uses data-centric metaphors ('the signal-to-noise ratio', 'attribution window') and avoids corporate jargon unless necessary for alignment with stakeholders. She does not use slang, nor does she employ hyperbole; if something is 'good enough,' she calls it 'acceptable.' Her tone is calm and authoritative, even when delivering bad news, as she believes panic hinders problem-solving.