For E-commerce Teams
Pick the variant before it costs you the traffic.
You have ten ideas for the product page. You have traffic for two A/B tests. The other eight ship on instinct or never ship at all.
The new context
The world changed.
Has your process?
The generation tools changed e-commerce faster than they changed any other function. Product descriptions, hero images, lifestyle photography, email subject lines, ad creative, PDP layouts. Everything that used to require a brief and a turnaround now takes an afternoon. Your team can produce more variants than your funnel can statistically test.
That sounds like a good problem. It isn't.
Your competitor has the same tools. Every PDP in your category is getting AI-generated lifestyle copy. Every email subject line is being optimised by a generator. The customer's inbox is a wall of plausible, polished, generic noise. The brands cutting through are the ones whose variants actually mean something to a specific segment, not the ones producing the most variants.
A/B testing on live traffic still works, but it has two limits the new pace has exposed. It needs traffic volume you don't always have for niche segments. And it tells you which variant won without telling you why, which means the learning doesn't transfer to the next decision.
Faster time-to-conversion isn't shipping more variants. It's shipping the right ones, with the why already understood, so the next decision is faster too.
What Dilog does for e-commerce teams
Your discovery practice, extended.
Pre-test variants before they hit live traffic.
Five PDP layouts, three subject lines, four hero images. Run them past your customer panel before you commit traffic to the test. The variants that go live are the ones already validated against segments who matter, not the ones the team liked best in the standup.
Iterate the copy before the test goes live.
Generation gave you the ability to produce five drafts of a product description. Dilog gives you the ability to refine those drafts based on segment response, before any real traffic sees any of them. Be more impactful.
Find the qual insight that points to the quant test worth running.
Most A/B test backlogs are bloated with hypotheses nobody can rank. Dilog gives you the qualitative pattern first, so the A/B tests you commit traffic to are the ones with a real reason to win. Faster optimisation, fewer null results, results that actually move the number.
Get the why your analytics can't tell you.
Your A/B platform tells you variant B won. It doesn't tell you that variant B won because the headline addressed a fit anxiety variant A ignored, and that the same anxiety is now showing up across three other segments you haven't designed for yet. Dilog gives you the qualitative layer your quant stack is missing.
Test on segments you can't reliably A/B test on.
High-AOV customers. Lapsed buyers. The 5% of your traffic that drives 40% of revenue. Live A/B tests need volume to reach significance. Dilog lets you test against the segments that matter even when there aren't enough of them online this week.
Find the segment your average is hiding.
Your conversion rate is an average. Your AOV is an average. Your panel surfaces the segments where the average is misleading. The customer who buys six times a year doesn't behave like the customer who buys once. Designing the PDP for the average is designing for nobody in particular.
Sense-check merchandising and pricing decisions before they go live.
The bundle. The promotional message. The discount structure. The “compare at” pricing. Test the segment reaction before the merchandising calendar locks in for the quarter.
A/B testing is not enough on its own.
A/B testing is doing real work. Don't stop. But it has three limits worth naming.
It needs traffic to reach significance. Most e-commerce sites can run two or three meaningful tests at once. The rest of the decisions ship on instinct.
It tells you what won, not why. Which means the next decision starts from zero. The learning doesn't compound.
It can't test the thing you haven't built yet. By the time the test is live, the creative and dev work is already done. If the variant flops, that effort is sunk cost.
Dilog sits before the A/B test, not in place of it. Use Dilog to refine the variants, kill the obvious losers, and understand the segment dynamics. Then use A/B testing on the two or three variants worth committing traffic to. The combination ships faster and learns more than either tool alone.
And when it comes to synthetic shoppers — a statistical average doesn't shop. Real customers do, and they shop differently from each other. Dilog's ARPs are grounded in actual 2–3 hour interviews with real customers, evaluated at 86% average accuracy. The segments behave differently because the people behind them do.
Synthetic shoppers flatten exactly the variation that matters in e-commerce. Dilog preserves it.
The financial case
A balance sheet item, not a P&L line.
Most research spend disappears the moment the report lands. A Dilog panel works the opposite way. The setup is a one-time capital outlay against an asset that compounds. Year two is worth more than year one.
What you're replacing
A portion of recurring user-testing spend, the panel-research line items that come around at quarterly planning, the agency briefs you commission when a decision is big enough to justify it.
What you're enabling
Pre-testing on the 80% of e-commerce decisions that currently ship without research because the volume isn't there for an A/B test and the scale isn't there to justify a focus group.
What being wrong currently costs you
The variants that should never have shipped. The bundle that flopped. The PDP redesign that tanked conversion on the segment you forgot to design for. The traffic burned on tests that could have been resolved upstream.
How it works
From kickoff to live panel in 6–12 weeks.
From your priority customer segments through to a live, queryable panel — scoped to your priority customer segments and category decisions.
Define
We work with you to identify 4–6 priority customer segments. Who matters most to the decisions your team actually needs to make.
Build
2–3 hour interviews with real customers from each segment. Every interview becomes an ARP, validated against the source responses before joining your panel.
Activate
Your panel goes live. Any team member can ask any scenario question — concept, copy, price, flow — and receive cohorted responses with supporting quotes.
Update
Quarterly recalibration sessions keep each ARP accurate as attitudes, behaviours, and market conditions shift.
Then continuous from there.
Your customers are ready to talk.
Bring a real PDP, a real subject line, a real bundle structure to a 30-minute demo. We'll show you what your panel would say before you commit the traffic.
Ready to dive in? Start your free trial →