AI Campaign Case Studies: Before-and-After Results That Hold Up

The team had that familiar look, the one you see when a campaign has been “fine” for weeks. Clicks trickled in, sales limped along, and the dashboard felt like a stalled car on a wet hill.

Then one change went live, a small one. The next week, the chart finally moved with intent. Not because the budget doubled, or the offer changed, but because AI made the message fit the person.

This post gives you a before-and-after case study format you can copy for any AI campaign, plus real examples from 2024 to January 2026. You’ll see clear metrics (ROI, conversion rate, AOV, engagement), what changed, what stayed constant, what the effort looked like, and what to watch so the “win” is real.

The before-and-after case study template (copy this for any AI campaign)

A good AI campaign case study doesn’t read like a hype piece. It reads like a lab note, simple, tidy, and honest about what changed.

Use this fill-in-the-blanks structure:

1) Campaign snapshot

• Brand, product, and primary goal:
• Audience:
• Channels used:
• Offer and landing page:
• Budget and time window:

2) The “Before” baseline

• Time period measured (same length you’ll use for After):
• Tracking setup (UTMs, pixels, CRM source fields):
• Baseline metrics (pick 3 to 6):
  • CTR
  • CPC or CPM
  • CPA
  • ROAS or ROI
  • Conversion rate
  • AOV
  • Revenue per send (email/SMS)
  • Unsubscribe rate or complaint rate

3) The AI change (one change at a time)

• What AI did (plain words):
• Where it ran (which channel, which segment):
• What stayed the same (budget, offer, targeting rules):
• What changed (creative, timing, personalisation logic, scoring, on-site tool):

4) The “After” results

• Same time window length as Before:
• Same metrics as Before:
• Result summary (what moved, what didn’t):

5) Why it likely worked

• One to three reasons, tied to behaviour (relevance, speed, reduced doubt):

6) Costs, risks, and fixes

• Tool cost (if any):
• Time cost (hours, team roles):
• Risk checks (brand, bias, privacy, claims):
• What you’d tighten next time:

7) Next test

• The next single change you’ll run:

If you want more inspiration for how brands structure AI marketing results, the round-ups at Influencer Marketing Hub and Invoca are useful references. Still, your best case study is the one built from your own clean baseline.

Before: the baseline story in numbers, not opinions

The “Before” section is where most case studies go wrong. People write feelings. They need to write facts.

Capture these items, even if they feel boring:

• Goal: one sentence (for example, “increase purchases from returning customers”).
• Audience: who saw it, and how they were defined.
• Channel mix: email, SMS, paid social, search, on-site.
• Offer: discount, free delivery, bundle, value prop.
• Budget: total spend and split by channel.
• Time period: keep it clean (for example, the last 4 weeks).

A practical trick: pull a simple 4-week average for your baseline, then note what might distort it. Paydays, school holidays, Black Friday, and a heatwave can all shift behaviour. You don’t need perfect science, just fair comparison.

If you’re benchmarking against wider campaign trends, skim examples of creative-led AI campaigns at Superside to see what “normal” engagement looks like for big brands. Then bring it back to your own numbers.

After: what AI changed (one change at a time) and how success was measured

AI can change lots of things, which is the problem. If you change five levers at once, you’ll never know what caused the lift.

Describe the AI change in plain terms, like:

• “AI wrote 10 subject lines and we A/B tested them.”
• “AI chose which product set to show, per user.”
• “AI predicted lead quality and routed follow-ups.”
• “AI let shoppers try the product on their own face.”

Then write down what stayed the same. This matters more than it sounds.

Mini measurement checklist

• Tracking tags: UTMs, click IDs, and consistent naming.
• Holdout or A/B test: even a small holdout group beats guessing.
• Definition of ‘win’: decide before launch (for example, “ROI up 20% with unsubscribes flat”).
• Same time window: if Before is 4 weeks, After is 4 weeks.
• Same core metrics: don’t switch the scoreboard mid-match.

When teams skip this, they end up celebrating vanity metrics. A post can get views and still lose money. A subject line can spike opens and still tank conversions.

Case studies: real AI campaign results, before vs after (2024 to 2026)

Below are mini case studies written in the same rhythm, so you can compare them fast and steal the structure.

Retail SMS personalisation: boohooMAN went from average returns to 5x ROI (25x on birthdays)

Before: Standard promotional SMS sends, decent reach, weak ROI. Messages landed like flyers through a letterbox, same words for everyone.

AI change: Personalised SMS content and timing for UK customers, with special birthday logic (so the message hits when the customer is most likely to care).

After: 5x ROI overall, and 25x ROI for birthday SMS.

Why it likely worked: SMS is intimate. People don’t treat texts like ads, they treat them like taps on the shoulder. When the message matches the moment (birthday, back-in-stock, cart), it stops feeling random.

Try this next week: Pick one high-intent trigger and do it well.

• Birthday
• Back-in-stock
• Abandoned cart
  Keep it to one trigger first, then expand.

Beauty e-commerce confidence boost: ModiFace virtual try-on drove 3x higher buying likelihood

Before: Shoppers browsed shades and skincare with a quiet doubt. “Will this look right on me?” That doubt is expensive. It turns interest into hesitation.

AI change: A virtual try-on experience that lets users see products on their own face via camera.

After: Over 1 billion virtual try-ons, and users were 3 times more likely to buy than those who didn’t use the tool.

Why it likely worked: This isn’t just “cool tech”. It removes the gap between product page and real life. AI turns the screen into a mirror.

Try this next week: If you can’t build try-on, copy the principle: reduce doubt.

• Add shade guidance with plain language.
• Show “on-skin” photos across tones.
• Track engaged sessions to purchase, not only page views.

Social-first creative experiment: Heinz AI ketchup designs hit 850 million impressions and 38% higher engagement

Before: Normal creative, normal reach. The kind of campaign that fills a calendar but doesn’t get screenshotted.

AI change: Heinz used AI-generated bottle designs, powered by public prompts, as campaign fuel. The audience didn’t just watch, they helped make the outputs.

After: 850 million earned impressions, 38% higher social engagement, and roughly 25 times the media investment value.

Why it likely worked: Participation beats persuasion. People share what they helped create, and AI made creation fast enough to keep the feed fresh.

Try this next week: Run an AI prompt challenge with guardrails.

• Clear brand rules (colours, logo use, tone).
• Fast moderation (don’t let junk win).
• A simple prize that fits your brand, not a massive giveaway.

For more examples of campaigns that mix AI with creative production, DigitalDefynd’s AI marketing campaign list is a handy skim, especially if you’re building a swipe file for internal pitches.

Personalisation at scale: Estée Lauder’s AI skin tool lifted conversion by 396% and spend by 4x

Before: Standard browsing and product pages, lower conversion, average spend. Shoppers had to self-diagnose. Many won’t.

AI change: A selfie-based skin tool that gives advice and matches products to the user.

After: Users were 396% more likely to convert, they spent 4x more, and AOV rose 29%.

Why it likely worked: It acts like a helpful in-store expert, but online. The advice narrows choices and turns browsing into a guided path.

Try this next week: Copy the safe version: guided matching with clear consent.

• Ask for the minimum data.
• Explain what you’re doing with it.
• Add disclaimers so you don’t drift into medical claims.

Track conversion and AOV for “tool used” vs “tool not used”. That split is where the truth lives.

Promotion without heavy discounts: Cosabella generated 40% to 60% more sales with AI-tailored holiday content

Before: Holiday selling often pushes brands into discount habits. Without discounts, sales can lag, and margins take the hit when discounts appear.

AI change: Personalised “12 Days of Cosabella” content, tailored by customer, focusing on relevance rather than price cuts.

After: 40% to 60% more sales without discounting.

Why it likely worked: The customer didn’t need a lower price, they needed a better match. AI helped shape the story and product set around the person, which protects margin.

Try this next week: Personalise more than the subject line.

• Personalise the product group.
• Personalise the reason to buy (gift, comfort, upgrade, event).
• Measure return rate too, because bad matching can inflate returns.

If you’re collecting examples to show leadership that AI can support content strategy, Visme’s AI marketing case studies can help you frame results in a simple “before, change, after” way.

How to run your own AI campaign test without fooling yourself

AI tests fail in two common ways. Teams either change too much at once, or they celebrate a number that doesn’t matter.

Here’s a practical playbook you can run with a small team and a normal budget.

Pick the right first AI use case: start where the data is strong and the risk is low

Start where you already have signals and where mistakes don’t create harm.

Good first tests:

• Copy variants for ads or email subject lines, with A/B testing and brand review.
• Product recommendations based on browsing and purchase history.
• Lead scoring to prioritise follow-up (sales still decides, AI suggests).

Don’t start here yet:

• Sensitive targeting based on personal traits.
• Health or medical claims.
• Fully automated budget control with no human checks.

If you’re under pressure to prove paid performance quickly, it can help to compare your approach to structured case studies like Microsoft Advertising’s Marks & Spencer case study, because it shows what “credible reporting” looks like in an ad platform context.

Report results like a grown-up: the one-page scorecard stakeholders actually read

A stakeholder doesn’t need a 30-slide deck. They need a page that answers: “Did it work, why, and can we repeat it?”

Use this one-page scorecard:

• Goal: one sentence.
• Baseline window: dates, audience, budget.
• AI change: one paragraph, plain language.
• Test design: A/B, holdout, or time-based comparison (be honest).
• Key metrics (Before vs After): ROI/ROAS, conversion rate, AOV, CPA, engagement.
• Costs: tool cost, team hours, any agency support.
• Trade-offs: unsubscribes, complaints, returns, brand risk flags.
• Decision: scale, iterate, or stop.
• Next step: one next experiment, not five.

One extra tip: when you report a win, say what you kept constant. It builds trust fast. The boss hears, “We didn’t move the goalposts.”

Conclusion

AI campaign wins aren’t magic tricks. They come from focused changes, clean measurement, and honest reporting that includes trade-offs.

Copy the template, pick one small test, and ship it this week. Keep the budget, offer, and time window steady so the result means something.

What one metric will you improve first, and what will you keep constant so the lift is real?