Standard A/B testing playbooks break down with thin funnels, multi-month procurement committees, and field crews who rarely open a laptop. We create experimentation programs matched to your real traffic and designed around how construction buyers and users actually behave. Fewer, better-designed tests, each built to deliver a real answer rather than a coin flip.
The Challenge
Your traffic can't achieve statistical significance
Construction tech categories are narrow. A handful of qualified visitors land on your pricing page each week, not thousands. Standard split-test calculators need weeks of traffic most contech sites see in a quarter. Teams either ship 'winning' variants off underpowered samples or freeze testing altogether and revert to gut calls. Both choices waste the traffic you already have.
Pricing tests encounter procurement committees, not individual buyers
A GC's ops director, finance lead, and field superintendent all weigh in before a contract gets signed. That buying group moves on a quarterly budget cycle, not a two-week test window. A pricing or packaging experiment that looks inconclusive after 30 days might just be sitting in someone's approval queue. Teams that read early data as failure kill pricing changes that were about to work.
Trial-to-paid experiments don't reach the actual user
The person who signs the trial is rarely the person running the tool from a truck cab or a jobsite trailer. Email drips and in-app tooltips built for office admins don't reach field crews on a shared tablet with two bars of signal. Onboarding experiments that lift office-side activation can leave the crews who determine renewal completely untouched, so the test 'wins' on the wrong metric.
Attribution breaks before the experiment even begins
Trade show conversations, GC referrals, and review-site mentions all influence a deal that closes six months later through a different channel entirely. Running experiments off last-touch data built for short SaaS cycles means you're optimizing for the wrong variable. Teams ship 'winners' that only look like winners because the attribution model can't see the real path to close.
How We Can Help
We begin by auditing your real traffic and conversion volume rather than relying on industry benchmarks. That means counting actual pricing-page visitors, trial signups, and field-user logins, segmented by role. Most construction tech websites lack the volume to run a classic two-variant landing-page copy test and achieve significance within a quarter. Our assessment clearly shows which funnel stages can support a controlled experiment and which require an entirely different research method.
When pages and flows have thin traffic, we don't force a split test. We develop a testing strategy using sequential testing, higher effect-size thresholds, and qualitative signals – call recordings, session replay, sales rep feedback – alongside directional data, giving you a decision without waiting six months for a clean read. Where volume truly supports controlled testing, we create ship-worthy experiments with pre-registered hypotheses and stopping rules, so nobody glances at a shaky chart and declares the test finished.
Pricing and packaging experiments are designed around the buying committee rather than an individual visitor. We test during the proposal and quote-generation stage instead of on the marketing page, because that's where the ops director, finance lead, and field super actually contribute. We measure outcomes across the complete sales cycle, often 60-120 days, and reserve judgment until enough deals close for each variant to provide a real answer, rather than declaring a test dead on day 30.
Trial-to-paid experiments are segmented by who actually uses the product. We distinguish office-side activation, the admin who registered, from field-side activation, the crew operating it on a jobsite device, then design tests around the metric that predicts renewal: field usage rather than office login count. That means using SMS instead of email, in-app nudges designed to work offline-first, and onboarding flows a foreman can complete between calls.
As your fractional growth team, we operate as an internal function rather than an external agency that simply delivers reports. We develop the hypotheses, build each test, brief your product and sales teams on instrumentation, and join the room when a pricing experiment requires CFO approval. Much of what construction tech calls 'growth experimentation' would be better directed toward firsthand research than a doomed split test, and we'll say so when that's true.
Each experiment launches with a pre-defined read: what qualifies as a win, the sample size or timeframe required, and what follows if the result is inconclusive. We create a shared measurement log where wins, losses, and inconclusive outcomes are all documented – inconclusive is a legitimate result, not a failure to conceal. Across a few quarters, this becomes institutional memory, preventing you from repeating the same failed pricing test under a different name.
What we deliver
- Traffic and funnel audit – which stages can support a controlled experiment and which need qualitative methods instead
- Experiment backlog – prioritized test roadmap with pre-registered hypotheses, sample-size math, and stopping rules
- Pricing and packaging test framework – built around committee buying cycles, not single-visitor conversion
- Field-crew activation experiments – onboarding and engagement tests segmented by office vs. field user
- Experiment log and results archive – documented wins, losses, and inconclusive tests to prevent repeat work
In construction tech, the experiment that matters most isn't the one achieving significance fastest – it's the one designed to withstand a traffic pool too small and a buying committee too slow for standard testing.
Our Methodology
Our 90-day growth experimentation sprint begins with a traffic and funnel audit tailored to construction tech's volume reality. We map each funnel stage against real visitor counts, trial signups, and field-user logins, then identify which stages have sufficient volume for controlled testing and which instead require session replay, sales call reviews, or direct interviews with field crews.
Weeks three through eight are spent building and launching the experiment backlog. We prioritize based on expected impact and testability, not whatever is simplest to ship. Pricing and packaging tests are structured around your actual sales-cycle length, with read dates scheduled months ahead rather than automatically set at two weeks. Field-crew activation tests are instrumented separately from office-side activation so the wrong user segment doesn't muddy the data.
During the final weeks, we turn the results into a repeatable system. We hand over an experiment log, a testing calendar matched to your actual traffic, and a decision framework your team can use for routine tests without us present. By day 90, you'll have a handful of well-designed experiments with genuine answers, rather than a backlog of half-completed tests no one trusts.
Our Working Approach
Growth experimentation engagements for construction tech generally last 4-6 months, providing enough time to follow pricing tests through an entire sales cycle and field-adoption tests through a genuine trial-to-paid window. The initial 90 days establish the experiment backlog and launch the first testing wave. The remaining months take experiments through completion, assess outcomes honestly, and translate findings into product and pricing decisions.
We work 2-3 days each week, embedded across your product, marketing, and sales teams. You provide product access, CRM data, and sales-team availability to coordinate pricing tests. We contribute experiment design, sample-size math, and the discipline to keep a test running until it can truly be answered rather than ending it early because a dashboard appears interesting.
Weekly syncs cover active experiments and identify any needing a design adjustment or early termination – some tests should end before their scheduled completion because of faulty instrumentation, a sales-process confound, or a committee delayed for unrelated reasons. Monthly reviews consider the experiment log in full: what's been tested, what we've learned, and what's next in the backlog.
Most clients receive their first clean experiment read, not a guess but an actual answer, within 60-90 days. Because of committee buying cycles, pricing and packaging experiments usually require the entire engagement period to generate a reliable result. Field-crew activation experiments progress faster because usage data arrives daily instead of depending on a closed deal.
- Experiment discipline – a backlog of tests sized correctly for your traffic instead of copy-pasted SaaS testing frameworks
- Pricing clarity – packaging and pricing decisions validated against real committee buying cycles
- Field adoption signal – activation experiments that measure the user who determines renewal, not just the one who signed up
If your construction tech company needs growth experimentation leadership, we should talk.
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Frequently asked questions
What does a growth experimentation engagement cost for a construction tech company?
Engagements generally cost $12K-$28K per month, including experiment design, instrumentation planning, and results analysis. Pricing scales based on how many funnel stages have sufficient traffic for parallel testing and how closely we must collaborate with your sales team on pricing experiments.
How soon will we see results from growth experimentation work?
Field-crew activation experiments can deliver a usable read in 4-6 weeks because usage data arrives daily. Pricing and packaging experiments require more time, often 60-120 days, since they must span a full committee buying cycle to produce meaningful results.
How does the experimentation team collaborate with our current product and sales staff?
We work embedded within your teams rather than operating separately. Product provides access to instrument the correct events, sales coordinates pricing-test rollouts for specific accounts, and marketing works alongside us on landing-page and messaging experiments.
How is Winston Francois different from a typical CRO or growth agency?
Most CRO agencies use a testing framework made for e-commerce or high-traffic SaaS funnels – thousands of weekly visitors, fast reads, and straightforward splits. That approach falls apart against construction tech's traffic levels and buying cycles.
How is ROI measured for a growth experimentation program?
We measure experiment velocity, the number of tests launched and read through completion, win rate versus pre-registered hypotheses, and the downstream effect of shipped winners on trial-to-paid conversion and deal close rate. We also monitor a less obvious metric: tests correctly stopped before consuming more budget. An honest inconclusive outcome that prevents a poor pricing change is as valuable as a clear win.
What kind of construction tech company is best suited to this work?
The best fit is typically companies with a live product, paying customers, and enough baseline traffic or trial volume to run several parallel experiments, usually post-seed through Series B, with an existing trial or demo flow already in market. Companies that are still validating product-market fit are generally better served by direct customer interviews than structured experimentation. If procurement-led pricing decisions or field-crew adoption are slowing growth, that's the strongest indicator this work is a fit.
