The short answer: Evaluate a tech marketing consultant on evidence, not polish. Check their depth in your category, whether they diagnose before they prescribe, how they choose channels, whether they run structured experiments, how they report and attribute, how they actually use AI, who does the work, and what references say about outcomes. Test each in a discovery call or a short paid trial before signing a retainer.
We say this from the practitioner's side: Growth Division has grown 130+ startups through a test-and-learn process, selects channels using the Bullseye Framework, and works through a network of senior channel specialists directed by a growth strategist. We ran an internal AI hackathon in 2024 and have since built AI into our growth process, productised as GREX AI. These are the checks we would want to be held to ourselves.
A tech marketing consultant is an individual or small team hired to plan, and often run, growth marketing for a technology business, typically B2B SaaS, fintech, marketplaces or developer tools. Because results lag, evaluate process, not personality.
What to check: whether they have worked on your motion (self-serve, sales-led or product-led), your buyer and your price point.
How to check it: ask them to describe the two past engagements closest to yours, including what did not work.
What a strong answer sounds like: unprompted detail: named channels, conversion benchmarks stated as ranges, and a candid account of a failed test. A vague "we work across tech" is a weak signal.
What to check: whether they investigate your funnel before recommending tactics.
How to check it: describe your situation briefly, then stop talking. Count the questions they ask before offering a solution.
What a strong answer sounds like: questions about activation rate, sales cycle length, payback period and where deals stall. A consultant who pitches paid social in the first ten minutes has a product to sell, not a problem to solve.
What to check: whether they have a repeatable method for channel selection. We use the Bullseye Framework, which lists every plausible channel, ranks them, and tests the most promising few before concentrating budget.
How to check it: ask "how would you decide which channels we test first, and how would you know when to stop?"
What a strong answer sounds like: a scoring step, a few parallel tests with pre-agreed budgets, and explicit kill criteria. Weak answers list the channels they happen to be good at.
What to check: whether "test and learn" is a working cadence or a slogan. Growth experimentation means writing a hypothesis, defining a success metric, running for a fixed period and recording the result, win or lose.
How to check it: ask to see a redacted experiment log from a previous client, or in a paid trial ask for the first experiments to be written up in that format.
What a strong answer sounds like: a log with more failed tests than winners, each with a learning attached. In our experience only a minority of well-designed tests produce a clear win, so a log full of successes has been edited.
What to check: whether reporting connects activity to pipeline and revenue, not just impressions and clicks. Attribution is the method of crediting a conversion to the channels that influenced it; in B2B it is always partial, and a consultant should say so.
How to check it: ask for a sample monthly report, and how they would treat a deal that touched a LinkedIn ad, a webinar and a referral.
What a strong answer sounds like: a one-page report with qualified pipeline and cost per qualified lead above vanity metrics, plus an honest account of what attribution cannot tell you.
What to check: whether AI is embedded in their workflow or only in their pitch. Practical uses include research synthesis, creative testing and monitoring how your brand appears in AI-generated answers (generative engine optimisation, or GEO).
How to check it: ask for one recent task where AI changed how they worked, and one where they deliberately kept a human in the loop. Our guide to choosing an AI marketing agency applies the same test to larger firms.
What a strong answer sounds like: concrete tooling, a clear view on where AI output is unreliable, and a firm line on how your customer data is handled.
What to check: whether the person you are talking to delivers, directs or hands off. Subcontracting to freelancers is fine in itself, but a problem if you do not know about it.
How to check it: ask "who will be in the weekly call, who will build the campaigns, and what happens if that person leaves?"
What a strong answer sounds like: named roles, one point of accountability, and a bench. Our own model is a growth strategist directing senior channel specialists, and we say so up front; a consultant should be equally transparent.
What to check: whether previous clients can describe a measurable change and the process that produced it.
How to check it: ask for two references, one from an engagement that has ended. Ask what the consultant got wrong and how they responded.
What a strong answer sounds like: "they told us within six weeks that the channel we wanted was not going to work, and showed us the data." Case studies are useful, but a reference call shows how a consultant behaves when things go badly.
Bring these to the second call; each exposes process rather than charm.
If you are also weighing an in-house hire, see our guide to hiring your first growth lead, full-time or fractional.
Measure a consultant against outcomes agreed in writing before the work starts, at three levels. Leading indicators (weekly): experiments launched, qualified leads or sign-ups, cost per qualified lead. Lagging indicators (monthly to quarterly): qualified pipeline created, customer acquisition cost, activation or trial-to-paid rate. Learning (every review): channels validated or killed, and what the business now knows that it did not before.
Set a baseline in the first two weeks and agree a 90-day review. In our experience a new channel typically needs two to three months of testing before it produces repeatable results, so judge the first quarter on the quality of tests and the second on pipeline. For a structure to build that scorecard around, see building a growth marketing framework that scales your startup.
As a guide only: independent UK growth or tech marketing consultants typically charge in the region of £600 to £1,500 a day, with fractional retainers commonly landing between £2,000 and £6,000 a month depending on days and seniority. A specialist-led agency engagement, where a strategist directs several channel experts, typically runs £3,000 to £10,000+ a month, excluding media spend.
On the common objections: high cost is only a problem if you cannot see what it buys, so ask for a scope with hours or deliverables attached. Freelancer dependency is manageable if the consultant names their bench. Inflexible retainers can usually be negotiated to a shorter minimum with a review point. Uncertain outcomes are inherent to growth work, which is why honest consultants sell a process and a learning rate rather than a number.
A single consultant suits an early-stage company that needs strategy and light execution across one or two channels. An agency suits a company that needs several channels run in parallel by specialists, coordinated by one strategist. The checks above apply to both; the difference is capacity and cost.
Four to eight weeks is long enough to diagnose the funnel, launch the first experiments and produce a written report, without committing to a full quarter. Agree deliverables in writing, pay for the trial, and treat it as a live audition.
Thirty days is standard and fair for both sides. Anything above 60 days, or a minimum term beyond three months before the consultant has proven anything, should be negotiated down or treated as a red flag.
Full disclosure: Growth Division is an AI-enabled growth marketing agency, so we have an interest in how you buy. We would rather you run every check above on us than hire on instinct. If you want to talk through where your growth is stuck, book a strategy call.

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