AI for Marketing · Solutions

Test properly, then fund what earned it.

AI Campaign Optimisation records every test and campaign, says when a test has run long enough to read, and sets planned campaigns beside similar past ones. The marketer receives a suggested budget split with the evidence beside each line, and spend changes only when a person approves the move inside the ad account. Nothing moves by itself.

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What it is

Most small teams test the same way. Two subject lines go out, someone checks the open rate on day two, and whichever leads is declared the winner, and a month later nobody remembers why.

Every test is kept with its audience, dates, variants and result, and the software says plainly when the sample is too small to trust. Budget talks start from history. Before budgets are set, every planned campaign is lined up against similar earlier ones from your own results, so the meeting argues from records and not from hunches.

Where it goes wrong today

The problems this is meant to ease.

Tests called too early

Winners are declared too soon. A subject line leads after a few hundred sends, the team switches everything over, and the gap turns out to be the ordinary wobble between two lists.

Changes bundled together

Nobody can say what worked. A new variant changes the headline, the image and the offer in one go, so when results move no one knows which change did the work.

Learning that leaves with people

Test history lives in heads. The agency changes, the executive resigns, and the next campaign repeats an experiment that failed a year ago because nothing was written down anywhere.

Budget set by the loudest voice

Spend follows opinion, not records. The monthly split is argued from screenshots and memory, and a channel that quietly earns qualified leads loses money to one that simply reports more clicks.

Weak parts hidden inside good campaigns

One bad ad hides in the total. A campaign looks fine overall while a single audience or placement spends steadily without a lead, because nobody splits the report down to that level.

Tired audiences

The same ad appears repeatedly. Two campaigns reach one customer list in the same week, or one ad is shown to a small group until they stop looking, and annoyed customers cost you.

Inside the module

What AI campaign optimisation does.

Past results by audience

Learn from what ran before. Earlier campaigns are grouped by channel, season, offer and audience, so the marketer can see which customer groups responded and which ones simply received the message.

Message, channel and creative suggestions

Suggestions cite your own results. For a planned campaign the software proposes messages, channels and creative variations that worked for similar audiences, and shows the past campaigns behind each suggestion.

Test register for A/B tests

Every test is written down. Each test is logged with its question, variants, audience split, dates and result, and flagged when a variant changed the headline, image and offer at once.

Is it ready to read

A plain verdict on each test. You see how many people each variant reached and whether the gap is wide enough to mean anything or only the usual wobble between two lists.

Underperforming elements

Find the weak part. Within a campaign the software lists each ad, audience, placement and offer that spent money without leads, so the marketer can pause or rewrite one piece.

Planned against past

A range, not a promise. A planned campaign is compared with earlier ones of the same channel, season and offer and shown as a range with its history beside it.

Suggested budget split

Evidence sits beside every line. Across live campaigns it proposes moving spend from weaker ones to stronger ones, lists the evidence for each move, and lets the marketer edit or reject any line.

Weekly monitoring and warnings

One sheet each week. Running tests, finished tests, spend by campaign, fatigue warnings and open decisions are listed on one page, so the marketing meeting starts from facts instead of screenshots.

Where it gets used

What this looks like in a working week.

An online clothing retailer

Plan the seasonal sale. Last year’s sale campaigns are compared by channel and offer, and the marketer sees which audience groups responded before choosing where this year’s budget goes.

A travel agency

Test one change at a time. Two holiday packages are advertised with the same image and different offers, and the result is read only when the software says the sample is big enough.

A software company

Judge channels by qualified leads. Search, social and email campaigns are compared by sign-ups the sales team marked as qualified, and the head of marketing moves spend only after reading the evidence.

A hotel group

Catch overlapping campaigns early. Weekend breaks and spa packages are both pushed to the same guest list in one week, and the overlap warning lets the marketer stagger them.

A private clinic

Count bookings, not clicks. Bookings from a health-check mailer are counted from the appointment system and the practice manager compares them with the cost of that mailer.

Who touches it

The people who open this every day.

Performance marketer

Reads the verdict, then acts. Sets up tests, waits until the software says a result is ready to read, and decides what to scale instead of refreshing a dashboard every few hours.

Email or CRM marketer

Keeps every result. Runs subject line, send time and offer tests against customer segments and records each outcome, so next quarter’s send starts from what was learned.

Marketing head

Approves the weekly split. Reviews the budget split with the reasoning on every line, and can explain any change to the owner afterwards because each decision leaves a dated trail.

Finance partner

One view of spend. Sees planned and actual spend by campaign grouped in one place, and asks fewer questions at month end because the numbers arrive already sorted by campaign and channel.

How it is set up

From first conversation to daily use.

Discovery
List what runs today. We sit with the marketer and finance partner and list your channels, ad accounts, email tools and the places where past test results are kept, if anywhere.
Data readiness
Tags are fixed before testing. Ad accounts, email tools and the CRM are connected, campaign names are given consistent tags for channel, offer and season, and conversions are matched to qualified leads.
Trial on past campaigns
Start with past campaigns only. Past campaigns from one channel are loaded and the software lists its suggestions without any spend moving, so the marketer can read them against what happened.
Review by marketers and the head
The team judges the output. The performance marketer and the marketing head read each suggested budget move, mark the ones they would reject and say why, and those reasons are saved as rules.
Going live
Budget changes stay manual. One campaign family goes first, the weekly review sheet replaces the screenshots in the meeting, and every budget change is made by a person inside the ad platform.
Reviewing afterwards
Read the records back. After a few cycles the team compares the suggestions with what happened, retires rules that misled, and checks each measure against the figures taken before go-live.

Measuring it

How you would know it is working.

Record where you stand before it goes live, so the change is measured against your own figures.

Conversion rate counts visitors or recipients who took the action you care about, and the baseline is what your analytics recorded for those campaigns before go-live.
Cost per acquisition divides campaign spend by customers or qualified leads won, and the finance sheet and CRM for the last quarter before launch give the starting figure.
Return on marketing investment sets what a campaign brought in against its cost, using your invoices and orders from before launch as the starting comparison.
Test completion counts tests that reached a readable result instead of being stopped early, and the baseline is the number your team recorded in the quarter before.
Budget moves that followed the suggested split are logged with their outcomes, and the baseline is the spend allocation in the last plan approved before go-live.

Used responsibly

Where a person stays in charge.

A person approves every budget move

Nothing moves by itself. The software proposes a split and a person changes spend inside the ad platform after reading the evidence, and any line can be edited or rejected.

Small samples are labelled

Thin results say so. A variant that reached only a few hundred people shows as unreadable, and the software suggests a bolder change or a longer run instead of declaring a winner.

Prediction has limits

Ranges are not promises. Comparing a plan with earlier campaigns helps only where comparable history exists, so a new channel, product or market is marked as thin and carries little weight.

Privacy and audience rules

Audience rules differ by country. Targeting limits, consent for messages and customer-data use vary between countries and platforms, so settle them with a lawyer of your own. It gives no legal advice.

How it fits

It is one part of a system, not an island.

A module earns its place by what it passes to the next one. These are the connections we set up most often.

Ad accounts for search, social and display
Email and messaging tools
CRM, to count qualified leads and not only clicks
Web analytics and tracking tags
Finance or budget sheets

Already built in

Products that include it.

Digital Marketing Agency Management Software

Built for you

How we would put it in.

Common questions

Questions owners ask about AI campaign optimisation.

How much traffic do tests need?

More than most small accounts have. The software says so when a variant reached only a few hundred people and the gap sits within normal wobble, then suggests a bolder change or a longer run.

Does it move our budget?

No, a person does. It proposes a split with the evidence beside each line, and someone changes the spend inside the ad platform after reading it. Nothing is edited automatically.

Can it predict a new campaign?

Only from the history you have. The plan is compared with earlier campaigns and shown as a range, which helps when comparable history exists, while a new channel or market has little behind it.

Why count CRM leads, not clicks?

Clicks are cheap to win. Qualified leads in your CRM sit closer to sales, though matching them to campaigns needs tracking work first, and that work often changes which campaign looks best.

Who owns results an agency produces?

You do. Results sit in your own register, so ask the agency to log every test there, and the learning stays with you when the contract ends.

Is a small account too small?

It may be too small. When only a handful of campaigns run each quarter, a marketer with a sheet will find the same weak spots, and this adds little beyond a tidy record.

What does it connect to?

Your own accounts, with your permission. Ad platforms, email tools, the CRM and web analytics are connected through the access you grant, and where a platform offers no connection we work from exported reports.

What setup effort should we expect?

Mostly tagging and tracking. Someone must agree naming for channel, offer and season, and someone must match conversions to the CRM, after which connecting accounts and loading past results is lighter.

Who needs it most

Businesses that ask for this.

Next to it

Modules that work with it.

Talk to us

Tell us how you handle AI campaign optimisation today.

A spreadsheet, a register or another system: say which, and we will tell you plainly what is worth changing.

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