Experiments, not opinions

A/B Testing
in Aberdeen.

A/B Testing in Aberdeen the right way looks nothing like what most agencies sell. No call-it-early. No "the variant's trending up". One test at a time, properly powered, called at 95%.

I run a structured experiment programme: 1–2 properly-sized tests every month, each rooted in real conversion research, each documented win, loss or null result. After six months you have a private library of what actually works for your customers — and a measurably better-converting site.

1–2 tests / month · 95% significance · Rolling monthly

5.0 on Google
Every test documented · win, loss or null
A/B Testing in Aberdeen — live experiment dashboard on desktop, tablet and mobile
Old way vs new way

Bad A/B testing is worse than no A/B testing.

A test called three days early on 200 sessions is a coin flip with extra steps. It tells you nothing — but it feels like it told you something, so you ship the variant. That's not evidence; it's noise dressed up as data.

The old way

"Looks like a winner."

  • Test launched on a "what if?" idea from a Slack message.
  • Sample size never calculated. "We'll see what happens."
  • Called on day 4 — "variant is up 14%, let's ship it".
  • Three weeks later, conversion is flat. The "winner" was noise.
  • Test forgotten. No record. Six months later you run the same one.
Our way

"Test held. Result conclusive."

  • Every test starts with a data-backed hypothesis, scored, prioritised.
  • Sample size calculated up-front. We tell you the test length on day one.
  • 95% statistical significance threshold. Held until reached.
  • Mobile + desktop segmented. Blended winners that lose on mobile? Caught.
  • Every test logged — searchable knowledge base you keep.
5.0 on Google
Avg. across clients
24+
Local #1 rankings
0
Outsourced work
1
City we call home
Value 01 · Backlog

Hypotheses scored. Best one first.

Every test idea goes into a backlog and gets an ICE score — Impact, Confidence, Ease. The highest-scoring hypothesis runs next. Each one written in plain English: "If we change X because of Y, we expect Z to improve." You sign off before we build.

  • Hypotheses sourced from analytics, recordings, interviews
  • ICE scoring — Impact × Confidence × Ease (out of 10 each)
  • You see the full backlog — and pick what runs first if you'd rather
Experiment backlog
Ranked by ICE score
  1. #1
    Single-step checkout (mobile)
    I:9 C:8 E:7
    ICE 8.0
    Next up
  2. #2
    Sticky CTA on long pages
    I:7 C:8 E:9
    ICE 8.0
  3. #3
    Reviews above hero, not below
    I:8 C:6 E:9
    ICE 7.7
  4. #4
    Headline · concrete promise rewrite
    I:8 C:7 E:7
    ICE 7.3
  5. #5
    Pricing FAQ above the fold
    I:7 C:6 E:8
    ICE 7.0
  6. #6
    + 14 more in backlog
    Updated weekly from research
Live test · TEST-042
Single-step checkout
Running
A Control (3-step)
n=4,217
3.8% conv. rate
B Variant (1-step)
n=4,205
4.6% +21% rel.
Statistical confidence 92%
0% 95% threshold 100%
Day 13 of est. 16 Holding to significance
Value 02 · Rigour

We don't call tests early. Ever.

Before any test goes live we calculate the sample size needed to detect a meaningful effect at 95% confidence — based on your actual traffic, your actual conversion rate, and the minimum effect we'd consider worth shipping. Then we hold until we hit it. No early calls when "the variant's trending up". No mid-test peeks that fool you.

  • Sample size calculated up-front, test length quoted on day one
  • Held to 95% statistical confidence — no early shipping
  • Mobile + desktop segmented — blended winners don't fool us
Value 03 · Knowledge

Every test, documented. Forever.

Win, loss or null — every test we run gets written up: the hypothesis, the variant, the result, what we learned, what it means for the next one. Six months in, you have a private knowledge base of what actually works for your customers. That's the part most agencies skip — and it's the part with the longest payoff.

  • Searchable archive of every test — yours forever
  • Losses written up properly — they teach you the most
  • Each test informs the next — hypotheses get smarter monthly
Test archive
Last 6 months
3
Winners
3
Losers
3
Null
  • TEST-041
    Win
    Sticky CTA on mobile
    +8.4% rel. · 97% conf. · Shipped 12 Mar
  • TEST-040
    Null
    Free-shipping banner
    +1.2% · 38% conf. · No effect
  • TEST-039
    Loss
    Hero video instead of image
    -6.1% rel. · 96% conf. · Reverted
  • TEST-038
    Win
    Form: phone before email
    +14.7% rel. · 98% conf. · Shipped 28 Feb
All 9 tests searchable in your knowledge base
"Daniel produced a brilliant website for us which has had a lot of positive feedback for its clarity and ease of use. He listened attentively to all our suggestions and guided us towards a structure which would best reflect our content."
R
Rhona
Inverurie West Parish Church
Why us

Us, vs the other ways to run tests.

Bad A/B testing is worse than none at all — you end up shipping noise and trusting the wrong things. Here's the difference between our programme and the alternatives.

Us
DIY in-house
CRO freelancer
Big agency
Research-led hypotheses
Slack ideas
Sometimes
Sample size calculated
Sometimes
95% confidence held
Often peeked
Often called early
Often called early
Mobile/desktop segmented
Sometimes
Losses documented
Forgotten
Quietly
Engagement model
Monthly programme
40h of yours
Per-test
Retainer + lock-in
How it works

One loop. Repeated every month.

Same process every month. Each cycle ships one tested change and adds a learning to the knowledge base.

1

Backlog review

Top-scoring hypothesis from the backlog gets picked. Sample size calculated, test length forecasted, you sign off the brief.

Week 1
2

Build variant

Variant built and QA'd. Anti-flicker handled. Tracking double-checked. Variant ships behind a 50/50 split.

Week 1–2
3

Hold to significance

Test runs to the calculated sample size. No early calls, no peeking that affects the call. Status communicated weekly — without peeking at results.

2–4 weeks
4

Call + document

Winners shipped. Losers reverted. Nulls noted. Every test written up in your knowledge base — what we learned and what's next.

End of cycle
Common questions

Got a question? Likely answered.

What Aberdeen business owners ask before signing up for a structured testing programme. If yours isn't here, just ring.

How much traffic do I need to run A/B tests?

Practically: ~10,000 sessions per month per tested page, or 1,000+ conversions, gives you enough statistical power to detect a 10–15% lift in 2–3 weeks. Below that, a research-led page rebuild gets you more lift for the money. We tell you which fits on call one.

Which testing tool do you use?

VWO and Convert are our defaults — solid stats engines, fair pricing, server-side support. If you already use Optimizely or AB Tasty, we use those. For Shopify stores, we'll often recommend Intelligems. We're tool-agnostic.

How long does a typical test run?

Minimum 14 days (to account for weekly cycles), typically 2–4 weeks depending on your traffic and the effect size we're trying to detect. We tell you the projected length when we launch — and we don't call it earlier.

Why won't you call tests early?

Because every test ever run will, at some point, show a "winner" — even if there's no real effect. It's just random variance. Calling early bakes that noise into your site as permanent change, and you trust the wrong things forever after. Holding to 95% significance is the only honest way to do this.

Do you run multivariate tests?

Only when traffic supports it — multivariate tests need roughly N×M times the sample size of a simple A/B, and most mid-market sites don't have it. Sequential A/B is usually faster and clearer. We'll be honest about which gets you a real answer.

What happens to the knowledge base if I leave?

It's yours. The full archive — hypotheses, variants, results, write-ups — gets exported to your Notion, Google Drive, or wherever you want it. No ransom on your own data.
Locations we serve

Conversion Rate Optimisation across the North-East.

Based in the Granite City, optimising websites right across Aberdeenshire — wherever your customers are based.

Ready to test properly

Let's run experiments that ship real wins.

Drop me a line and we'll have a 20-minute chat about your traffic volume, your tracking, and whether A/B testing is the right next move for your business — sometimes it isn't, and I'll say so.