Improve B2B website

What are user insights?

User insights are the concrete observations that explain why visitors behave as they do. They can be a screen recording that shows hesitation on a pricing toggle, a heat-map highlighting ignored calls-to-action, or a survey response that spells out an objection in plain language. Insights turn raw numbers into a clear next step: rewrite copy, shorten a form, or move a button.

How do I collect user insights?

Start with low-effort tools: heat-maps for scroll depth, session replays for friction points, and a single on-page poll that asks, “What stopped you from signing up today?” Tag five recordings per funnel step, skim the poll answers weekly, and patterns surface fast. Pair these findings with built-in analytics to see whether fixes lift conversion or merely shift the blockage downstream.

What is the difference between qualitative and quantitative research?

Qualitative research digs into the why: comments, interviews, and open-ended polls. Quantitative research measures the how much: click rates, bounce rates, funnel drop-offs. A spike in exit rate is quantitative—“thirty per cent leave on the form step.” Watching a replay where users rage-click the same field is qualitative—“they cannot find the submit button.” Use numbers to spot the problem and narratives to understand it.

What is A/B testing?

A/B testing shows half your traffic version A and the other half version B, then measures which wins. It is the safest way to prove an idea works before rolling it out to everyone. For a deeper dive, see our separate page What is conversion optimisation?

How does multivariate testing differ?

Multivariate tests change several elements at once—headline, image, and button colour, for example—and evaluate every combination. This uncovers interactions you might miss with a straight A/B test but needs far more traffic to reach reliable results. In B2B, where session counts are lower, stick to A/B unless a page pulls thousands of visits a day.

Where do usability tests fit in?

Remote usability sessions add context numbers cannot reveal. Ask a prospect to share their screen, give them a goal—“Find pricing”—and watch silently. Note confusion points and missed cues. Ten sessions often uncover eighty per cent of issues, and the recordings become proof when you lobby for design changes.

How do I choose what to test first?

Rank insights by impact and ease using prioritsation frameworks. Fix the friction that blocks revenue directly—broken forms, vague headlines—before chasing minor aesthetic tweaks. One high-impact win beats a dozen tidier buttons.

When qualitative findings guide quantitative tests, and every result feeds back into the next round of insight gathering, conversion optimisation shifts from guesswork to an iterative, compounding process.

Start with insight, not guess-work

Interview users

Speak to five to ten recent prospects or customers. Ask what nearly stopped them buying, which proof points mattered, and what they expected to happen next. Patterns in language and objections translate directly into testable hypotheses for ad copy, landing-page proof, or proposal decks.

Session recordings

Use a tool from the user behaviour stack to watch how visitors navigate forms, scroll heat-maps, or rage-click buttons. Seeing hesitation or back-tracking pinpoints friction you cannot spot in aggregate data.

5-second tests

Show your page or email to someone for five seconds, then ask what they remember and what action they would take. If they cannot tell you the value or next step, your message is unclear—rewrite headline, hero, or call-to-action and retest.

Google Analytics & dashboards

Before running A/B tests, verify that pages, events, and revenue attribution flow correctly in your analytics and dashboarding setup. Mis-tagged goals lead to false “wins” or “losses” that waste optimisation cycles.

Once qualitative clues highlight friction points, quantify them with data. High-traffic pages move to A/B or multivariate testing; low-traffic assets rely more on qualitative tweaks and longer-running tests.

When you have data, experiment methodically

A/B testing

A/B (split) testing compares two versions—A (control) and B (variation)—served at random to visitors. If B produces a statistically significant lift in your metric, you keep it. Examples:

• Change an ad headline to emphasise pain, not product.
• Shorten a booking form from eight fields to four.
• Swap a generic case study for an industry-specific one on the landing page.
  

Tips: ensure you have enough traffic, run tests for a full buying cycle, and track profit as well as conversion so a higher rate does not erode margin.

Multivariate testing

Multivariate testing changes several elements at once—headline, image, CTA—creating multiple combinations. It shows which mix performs best and which element drives the lift.

Best for: high-traffic pages (e.g. product home pages) where many visitors allow quick, reliable results.

Watch-outs: complexity rises fast; stick to a limited set of variables and analyse interaction effects.

Practical optimisation ideas across the funnel

Build a repeatable programme

1. Log every hypothesis in a testing backlog—impact, confidence, effort.
2. Prioritise weekly; pull the top ideas into a sprint.
3. Run one change at a time per step to isolate effect (multivariate is the exception).
4. Declare a winner or loser; document the outcome so future hires learn what already failed.
5. Rinse and repeat: once a metric is healthy, move to the next bottleneck.
   

Keep profit front and centre

A change that lifts conversion but drops average deal size might hurt overall revenue. Always pair your conversion metric with a value metric such as pipeline value per visitor or profit per sale to avoid false victories.

By treating conversion optimisation as structured, data-led experimentation—from ad click to proposal sign-off—you turn every stage of your B2B funnel into a revenue lever. The payoff is compound growth: more deals, same spend, healthier profit.

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