Overview

Data Science | Marketing Science | Behavioural Science

Hello

My name is Mark Razzell.

I offer assistance with evidence/data/strategy to businesses and brands through WTQ Consulting.

Most people call me ‘Razz’, which is great, because it makes me sound much cooler than I am.

I trained as a behavioural scientist, transitioned to the commercial world, leaned in to strategy, and applied my Data Science skills to do that better. Now, I am a customer intelligence & insights leader, with notable strengths in Natural Language Processing, synthetic-research systems, and AI application to business development.

My background in behavioural science give me a ruthless curiosity and the ability to manipulate data with code. My commercial knowledge fast-tracks my understanding of a situation, and the diversity of my exposure allows me to collaborate fluidly within matrixed organisations.

Whatever I am doing, there’s one place I always start.

What’s The Question?

Not a slogan — a method.

Every defensible strategy starts as a well-formed question, and every good question begets the next one. That’s how science progresses, and it’s how good commercial outcomes are manifested too; simple, evolutive, and fun.

WTQ is an independent advisory, offering services within three quantitative time frames:

• Foresight; where is this category heading, and what’s the probable shape of the opportunity?
• Nearsight; what’s the sharpest way to execute against that opportunity, right now?
• Hindsight; did it work, and what does that tell us to ask next?

This is high-demand expertise, required at low frequency.

I’m brought in when the stakes or the ambiguity are high enough to warrant it, not as a standing resource.

I’m for marketers, innovators, and service providers who need a rigorous outside perspective without the overhead of a permanent one.

The underlying edge is breadth: fifteen-plus years spanning strategy, data science, and applied research, working across categories from FMCG and finance to retail and healthcare. That cross-domain pattern-matching — knowing how a pricing problem in banking rhymes with a positioning problem in FMCG — is often the difference between a plausible answer and the right one.

Core strengths:

• Data Science quantitative rigour, from segmentation to synthetic testing to NLP-driven text analytics
• Marketing & Business Strategy grounded in how categories, brands, and P&Ls actually behave
• Cross-Domain Knowledge pattern recognition earned across industries, not just one

Notable Experience

The proof of the pudding is in the eating

Outcome: NABs’ “More than Money”

Process: I led the research, guided the strategic thinking, and defended it. Netnography, quant, and experimentation.

Question: “When people think ‘bank’, what do they really think?”

Outcome: Myers’ brand direction as ‘customer-first’

Process: Built a customer-intelligence layer, stitching voice-of-customer survey data to transactional behaviour for a commercially actionable segmentation model and custom recommendation engine

Question: “Who is the real-and-ideal Myer customer?”

Outcome: Great Northern Brewing Co.s’ grow-and-defend media strategy

Process: Led customer research to define customer and non-customer cohorts, guided stakeholders around strategic pitfalls, and defined a sensible pathway to growth through geographic segmentation and custom algorithms.

Question: “How do you grow when you’re already the biggest?”

Outcome: Hootsuite’s ‘Social Performance Score’

Process: Instructured by sales-and-marketing, I partnered with Hootsuite’s product and engineering teams to develop the theory and prototyping using predictive NLP and custom text cleansing features.

Question: “Can we help our customers improve their engagement by benchmarking them against their category?”