Today, I’m back with a reflection on where we are today.

This is the age old question. And usually for growth stage businesses, the answer lies somewhere in either product or marketing.

But perhaps the biggest factor is the one that we can’t control: the macro environment we all exist in.

We get glimpses of the macro as an agency when we analyse internal trends across £1.5-2m of ad spend each month. But sampling data like this is only part of a picture and is often more swayed by a particular strategy being deployed at any given time.

A lot has changed since the start of the year

This year has seen some pretty momentous change.

AI is now ubiquitous and we’re starting to see the real life impact of it, rather than the hypothetical (and this is before we get into future models).

But perhaps more immediately and tangibly impactful is the war in the Middle East.

Today’s post analyses a handful of sources including:

• Think tank releases

• Articles I’ve been reading the last few months from the Economist, FT etc

• Google Search Trends Reports

• Reddit

For most brands we work with, Q2 is a strong quarter that precedes the year’s weakest quarter, Q3. If I could predict the future, I’d be a rich man, but there’s still quite a few things I’ve seen so far that seem worth talking about.

Because I imagine this stuff will be having an impact for all of us in the coming months.

Josh

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29% of UK workers admit they’ve sabotaged AI tools at work

29% of UK workers admit they’ve sabotaged AI tools at work, growing to 44% for Gen Z.

The TUC finds 51% of the UK public concerned1, rising to 62% among 25 to 34 year olds.

There are hundreds of threads discussing AI anxiety in the last 30 days alone.

You won’t find this in Google Trends: searches related to “AI replacing jobs” have faded since April 2025. But ChatGPT usage in the UK tripled last year2.

Mortgage renewal, tax drag, and AI fear are hitting millennials at once

1.8 million mortgages expire this year into higher rates.

Frozen tax bands cost a £55k earner over £1,000 a year. And 52% of workers believe AI threatens their job.

On r/UKJobs, a 43-year-old on £50k described weighing a £27k offer.

On r/HousingUK, a first-time buyer watched their monthly payment jump £450 mid-conveyancing. No single dataset proves all three pressures act together. But the Reddit threads tell the same story from different angles: 30-to-45 year olds are absorbing rising costs, tax drag, and career uncertainty at the same time.

That’s the age group many premium DTC brands say drives their core revenue.

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Last time oil crossed $100, consumer mood fell 21 points in a month

In March 2022, Brent crude crossed $100 after Russia invaded Ukraine. Consumer mood (measured by the GfK survey) dropped from -17 to -38 in April.

By September it hit -49, the lowest in the survey’s 50-year history. In 2026, Brent spiked to between $109 and $1133 in early April on Strait of Hormuz tensions, and consumer mood is already at -21.

The pattern is the same: oil shock, energy prices rise, mood collapses. What’s different is the starting point, and what’s layered on top.

In 2022 the extremes won and the middle lost. It’s starting again.

After Brent crossed $100 in 2022, Aldi gained 1.4 percentage points of grocery share in 12 months.

Combined with Lidl, discounters grew 25% year on year against a market growing 7.6%.

Premium brands held.

In 2026, the same pattern is forming: Lidl is up 9.6%, Waitrose up 5.8%, Asda down 0.9%4.

If the 2022 pattern repeats, the middle loses the most share over the coming quarters. But there’s a difference this time: people are also dealing with AI job anxiety and a wall of mortgage renewals that didn’t exist in 2022.

People feel fine personally but are terrified about the economy

The UK’s main consumer-mood survey, run by GfK, asks two different questions.

How do you feel about your own finances?

And how do you feel about the economy?

The answers are diverging sharply.

The personal finances score sits at -7. Not great, but stable.

The economic outlook score collapsed to -37 in March, down 6 points in a single month and 8 points worse than March 2025.

That 30-point gap is the story. People aren’t struggling. They’re bracing. And bracing shoppers don’t stop spending entirely, they just get harder to convert.

People are saving more and spending less on big purchases

The GfK Savings Index rose 6 points in March to 27, the highest reading since January’s peak of 28. Context matters: it was 28 in January, fell sharply to 21 in February, then rebounded. So this is a recovery, not a breakout.

But GfK’s own consumer insights director put it plainly:

When people start saving instead of spending, brands need to earn the conversion harder.--

Health and beauty spending is up 6-8% while everything else flatlines

Note that this is February data and doesn’t capture most recent changes.

Barclaycard’s February data shows health and beauty up 6.4%, following 8.0% in January5.

Digital subscriptions grew 12.2%.

Entertainment grew 9.9%.

On the other side: household goods fell 2.9%, electronics fell 5.8%, airlines dropped 6.9%.

Overall spending grew just 1.0%, well below inflation of 3.2%.

People are choosing where to spend, and health is winning. Worth noting: this is February data, before the Strait of Hormuz oil shock.

Health and beauty was the most resilient category going into that shock, which is exactly the category you want to be in coming out of one. That resilience held through the oil spike. UK searches for strength training and gym memberships kept rising through March and early April, even as Brent crossed $100. Wellness isn’t a line item people cut first when the news gets worse.

“Strength training women” searches are up 261% in two years, protein powder peaked

Google Trends data for “strength training women” shows a 62% increase year on year and a 261% increase over two years. “Protein powder women” is up 80% over that same two-year period. Gym membership searches are up 88% year on year. This isn’t a January resolution spike. The trend has been compounding for two years and it’s still accelerating. The UK women’s health supplements market is worth an estimated £1.18bn, growing at 5.7% a year.

Vinted UK is up 80% and near all-time highs

“Vinted UK” sits at 93 out of 100 on Google Trends.

That’s up 80% year on year and 87% versus two years ago. It’s near the ceiling of search interest.

Meanwhile, ASOS reported sales down 9% globally and 5% in the UK in the first half of their financial year.

The secondhand market isn’t a niche. Every fashion DTC brand’s customer is now comparing prices against what they could get the same item for on Vinted.

The question isn’t whether resale competes with new. It’s whether “buy new” creative can make a convincing case.

Perimenopause is pulling health spending younger

Women as young as 33 are now spending on perimenopause solutions.

On r/AskWomenOver30, symptom management dominates the conversation: hot flushes during client meetings, brain fog at work, products that actually help.

The UK women’s health and beauty supplements market is valued at £1.18bn, growing at 5.7% a year.

Collagen supplements, a closely related category, are growing at 6.27%.

On Reddit, women describe buying fewer products but spending more per item, trading up to professional-grade solutions rather than adding more to the basket. That’s the pattern as people on these forums describe it, not confirmed market data, but it matches a market that’s growing in value faster than in volume.

One in six UK 16-24 year olds is unemployed

The UK 16-24 unemployment rate hit 16.0% in the three months to January 2026.

That’s 731,700 people and the highest rate since 2014.

There are now 3 unemployed young people for every vacancy, up from 1.9 a year ago. The King’s Trust reports6 that seven in ten teenagers wish they weren’t starting their careers in this economic climate. For brands targeting Gen Z, the spending power simply isn’t there for a growing share of this age group.

Closing thoughts

None of this is a prediction. If I could call the next six months with confidence, I’d be running a hedge fund, not an agency.

But there does seem to be a vibe shift going on – even before the war began. There already seems to be anxiety about work, the future, and cost of living, and while these things have for the last few generations impacted those already worse off, today they’re hitting the higher earners.

AI might be intangible, but petrol hitting £2 will not be, or flight routes will not be.

People are still choosing to invest in themselves for now. Health continues to be an area of prioritisation, but done in the right way. It’s strength training and gym memberships over the continual escalation of supplements.

We know inherently how impactful these things are, but we’re not yet seeing it on the spending power. But short of a big shift in direction, I imagine we’re about to start seeing one in the months to come.

This has been fun to put together, and I look forward to reflecting at the start of July to see how right or wrong these things ended up being.

Have a great week

Josh

Inspired by New York Mag’s recent 247 things you should know as a New Yorker, we thought we’d compile our own version for you.

On measurement

1. ROAS is a vanity metric in a profitability costume.

2. Contribution margin per session tells you more about a DTC brand’s health than ROAS ever will. CM3 = revenue minus cost of goods, minus fulfilment, minus paid media spend.

3. Blended ROAS conceals whether you’re winning or subsidising losses with organic.

4. If you’re optimising for ROAS, you are not optimising for incrementality.

5. MER (marketing efficiency ratio) is blended ROAS with a different name and the same limitations.

On Meta

6. Meta is a scale channel, not an efficiency channel.

7. CTR does not predict CPA.

8. Thumbstop rate does not predict CPA.

9. If you’re not running awareness spend, your conversion campaigns are doing the work of warming cold audiences and paying conversion CPMs to do it.

10. You should evaluate awareness investment after a minimum of three months. Judging it on last-click in week two is a category error.

11. The 45+ demographic is cheaper to reach and often has higher AOV and LTV. Most brands under £5m ignore it because their founders are 28.

12. Logos and CTAs in ads actually perform better on average, which is annoying if you built your whole brand around looking “authentic.”

13. Static ads frequently beat video because the message lands before the thumb moves.

14. The biggest mistake in DPA setup is combining incompatible product sets under one campaign.

15. I’ve seen accounts with 1x campaign, 1x ad set scale to £350k/month.

On creative

16. Jobs-to-be-done research starts not with what people buy, but with what they were doing before they searched.

17. The four forces of progress are: push, pull, anxiety, and habit. The most important two for paid social are anxiety and habit.

18. 1 star reviews are more valuable than 5 star reviews.

19. “I’ve tried everything” is not a customer complaint. It’s a hook.

20. UGC can be scripted. It only fails with bad copywriting or acting.

21. Yappers, talking-head videos of real people just talking, are quietly the top-performing format in several DTC categories.

22. When talking to customers always preface anxieties with “apart from price, what....”

23. Your brand tagline is almost certainly your worst-performing ad headline. Taglines are written for boardrooms, not for the scroll.

24. Review mining at scale is most efficiently done with AI, if you give it the right taxonomy first.

25. Creative fatigue isn’t as simple as many let on. Turn old ads back on. Go on.

26. Testimonial ads work because they remove anxiety rather than add aspiration.

27. “You put olive oil on that?” is a great hook because it assumes knowledge the viewer is compelled to validate. Use your customers’ language.

On strategy

28. Full-funnel advertising lowers CPMs on conversion campaigns. It’s not just brand investment, it’s efficiency investment.

29. At every growth stage, ask: what’s the single constraint that, if removed, unlocks the next order of magnitude?

30. “Diversify away from Meta” is advice given by those who don’t understand most consumer ecom/DTC.

31. Be aware of LTV debt: don’t get to £10m/year with the same LTV you had at £1m.

32. At a certain stage, you have to accept your natural CPA. If it doesn’t work, you don’t have product-market fit.

33. Incrementality testing is the closest thing we have to answering “would these customers have bought anyway?”

34. If your gross margin is under 50%, paid acquisition is extremely difficult to make profitable after fulfilment and media costs.

35. Track profit per employee. It forces you to confront whether each hire is actually driving margin or just making the org chart look right.

On experiments

36. Most creative testing on Meta is flawed because the algorithm redistributes spend within ad sets based on early signals. You’re not testing creative in isolation, you’re testing what Meta chooses to spend on.

37. A creative test needs roughly 20 purchases before you can call a result with any confidence.

38. 80% net new creative, 20% iteration.

39. Media buying experiments need a month, not 5 days.

40. Experiment velocity matters more than confidence per experiment, at early stage especially.

41. Statistical significance is misused in most DTC contexts because sample sizes are too small and observation windows too short.
    

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On AI and what it actually changes

42. AI lets a growth team produce the creative volume of a team three times its size. The advantage isn’t cost reduction, it’s test velocity.

43. AI video works best when it’s either immediately obvious, or not noticeable at all.

44. The gap between “someone who uses AI” and “someone who’s AI-native” is whether AI is the workflow or adjacent to it.

45. Learn to build with AI coding tools now. The growth people who can spin up custom dashboards, scrapers, and analysis tools without waiting for engineering will have a compounding advantage for the next two to three years.

46. AI fluency is a temporary advantage. As tools become easier and more embedded, the differentiator will shift back to judgement, taste, and domain knowledge.

47. “AI-native” will be a redundant phrase by 2028, for the same reason “internet-native business” became redundant. It’ll just be what you are.

On data

48. A blended CPA of £24 means nothing without knowing what the customer bought and whether they came back.

49. Power analysis before an incrementality test tells you whether your test cells are large enough to detect the effect you actually care about.

50. Most “data-driven” decisions in marketing are intuition-driven with data bolted on afterwards.

51. Invest in your data ETL and warehouse.

On category dynamics

52. Health and wellness DTC brands have a structural advantage in creative because pain is a universal hook.

53. Visual DTC (fashion etc) leans into huge volumes of different creator/people so someone can recognise themself somewhere.

54. There are no hard categories, just people who ignore the rules of Meta.

55. If you can get your F&B work DTC alone, then your retail will fly.

56. The 45+ female demographic is the most underserved audience on Meta and often has the highest LTV.

57. Whether you’re dtc or retail-first, don’t add a second sales channel until £5m of revenue.

On the industry

59. The claim that “native beats brand” in creative is true on average, but not exclusively. You need a mix.

60. Video receives roughly 2x the budget of statics across most accounts. Statics often convert better.

61. AI-generated ads are a volume lever, not a creative strategy.

62. The reason most performance agencies don’t evolve into strategy firms is that strategy is harder to sell and slower to prove.

63. The biggest structural shift in DTC since 2020 has been the collapse of reliable last-click attribution as iOS changed everything.
    

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On growth stage

64. From £0 to £1m, the constraint is almost always creative and targeting, finding what works.

65. From £1m to £5m, the constraint shifts to operational capacity and repeatable testing processes.

66. From £5m to £10m, the bottleneck is usually unit economics and whether the model holds at scale.

67. The moment to introduce a full-funnel strategy is when you can see CPMs rising on conversion campaigns without a corresponding CPA improvement.

68. A brand that can’t survive a three-month Meta creative drought doesn’t have a sustainable acquisition model. It has a hot streak.

69. Email and SMS matter most in the £1m to £5m revenue band, where repeat purchase rate determines whether the model is profitable.

On psychology and persuasion

70. Social proof works differently at the top and bottom of funnel. At the top you’re reducing scepticism. At the bottom you’re removing the last reason not to buy.

71. Fear-based creative underperforms in beauty and lifestyle categories where aspiration is the primary purchase driver.

72. Anxiety (one of the four forces of progress) is reduced most effectively in creative by social proof and specificity, not reassurance statements.

73. The “exhaustion arc” customer story works because it validates every previous failed attempt before offering a solution.

74. Reviews from the 45+ demographic convert better because they tend to be specific, context-rich, and written by people who’ve already tried everything else.

On what most people get wrong

75. Most DTC founders underinvest in creative volume and overinvest in creative polish.

76. Most performance agencies optimise for reported ROAS because it’s easier to present than contribution margin.

77. Most creative testing is actually budget testing masquerading as creative testing.

78. Most retention strategies are just email sequences with a better subject line. Not a repeat purchase model.

79. Most people who claim to understand LTV don’t understand the margin at which that LTV was generated.

80. Most agency pitches fail because they answer “why us” instead of “why now, and why does this matter to you specifically.”

On what almost nobody knows

81. Cost per reach going up while CVR also goes up is not a contradiction. It means Meta is spending into higher-value audiences.

82. A brand with a 3.5x ROAS might be losing money on every customer if COGS, fulfilment, and variable costs are high enough.

83. The incrementality of branded search is lower than most brands assume. Many of those clicks would have happened anyway. Assume it’s 0.1x.

84. “Meta’s algorithm has gotten smarter” usually means “we got better at brief writing and structure.” The credit rarely belongs entirely to the platform.

85. The measurement problem that MMM still can’t solve well is real-time responsiveness. It’s a retrospective tool used as a planning tool.

On writing and content

86. The most shared pieces of content make the reader feel smart, not informed, smart.

87. Contrarian takes work best when the conventional wisdom you’re attacking is genuinely prevalent, not a straw man.

88. The difference between Grade 7 writing and Grade 12 writing is that Grade 7 says what it means on the first read.

89. The best hook for a founder-vulnerability post opens in the middle of the failure, not with context.

90. The best Substack growth lever is not frequency. It’s having a clear reason to exist that nobody else fills.

On how growth teams work in 2026

91. Every growth team is now somewhere on a spectrum from “we use AI occasionally” to “we wouldn’t function without it.” The gap is widening fast.

92. The growth team that treats creative as a hypothesis rather than a deliverable will outpace the one that treats it as a production output.

93. “Move fast and break things” was a software company mantra applied to marketing by people who didn’t understand the difference. You can’t A/B test a brand perception.

94. The thing most growth teams under-budget for is time to think. The calendar fills with execution and the strategy atrophies.

On how consumer brands work in 2026

95. The brands that tried to be “authentic” by mimicking UGC without the actual user part have mostly been found out.

96. Founder-led content performs better than brand content for most sub-£20m DTC companies. Not because it’s personal, but because it’s legible, you know who’s speaking.

97. Most brands building “communities” are actually building email lists with extra steps.

98. The Dyson problem: great product, brutal creative. The reverse exists too. Creative that oversells a product is worse than creative that undersells it, because the reviews will eventually kill you.

99. The brands winning on TikTok in 2026 are mostly winning because someone in the building is genuinely interested in making content, not because they cracked an algorithm.

On what it actually feels like to work in growth right now

100. The feeling of having a great creative test result land on a Friday is still one of the best things about this job. It hasn’t been automated yet.

101. The worst thing about performance marketing culture is the tendency to confuse busyness with rigour.

102. Lean is the new default, and that’s going to require a mindset shift for psychological comfort.

103. The most underrated skill in growth is writing. Not copywriting. Writing, the ability to explain what you’re doing and why to someone who isn’t in the room.

104. The growth meeting that never ends is the one where nobody has established what decision needs to be made by the end of it.

105. Context switching is the new norm, and AI only accelerates that.
     

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On the agency relationship

106. The best agencies act like lawyers, not yes people

107. At the moment AI hasn’t helped with cost.

108. Your agency should challenge your thinking all the time.

109. Agencies get you access to usually over £1m of head count per account. That’s irreplaceable in-house.

On the craft

109. Writing a brief forces you to know whether you actually understand the customer. If you can’t write it, you don’t.

110. Best practice” in performance marketing has a shorter half-life than most people admit. What worked in 2025 is often actively wrong in 2026.

111. Spending two hours doing customer interviews is usually worth more than any AI assisted research.

On things that are genuinely changing in 2026

113. Creative production that used to take a week now takes a day. The time saved is not being reinvested in thinking, in most teams.

114. Incrementality is finally getting the attention it always deserved.

115. The post-cookie world turned out to be fine for everyone who wasn’t entirely dependent on third-party data. That group was larger than expected.

116. Growth people are becoming engineers.

117. The brands winning on YouTube in 2026 figured out that long attention and short attention require fundamentally different creative approaches, and stopped applying the same creative to both.

118. Subscription models in DTC turned out to be harder than they looked when every brand launched one in 2020-2022. The ones that work have genuine habitual use cases.

119. Some brands are realising they have audiences, not communities, and that’s fine.

On things that haven’t changed

120. Meta advertising is advertising.

121. Advertising has to sell.

122. Simplicity beats complexity.

123. Operating at 100% capacity creates a fragile business, build in space to crack.
     

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On the wider world that growth operates in

124. The cost of living crisis changed consumer behaviour more than most teams have updated their models to reflect.

125. The brands winning in grocery and everyday essentials are the ones that made the implicit trade-off explicit: this is cheaper, or this is better, pick one.

126. The environmental claims conversation has become complicated enough that most brands have quietly retreated from it. The ones with genuine credentials now have more white space.

127. Gen Z’s relationship with brands is not what most brand strategists predicted. They’re not inherently anti-brand. They’re just better at detecting nonsense.

128. The resurgence of in-person retail is real and most DTC brands still treat it as secondary. The ones blending both well are building the most defensible positions.

129. Social commerce is still not quite the thing it kept being predicted to be in the UK. The infrastructure isn’t there yet.

130. The creator economy has matured. The hobbyists have mostly dropped out. What’s left is more professional, more expensive, and more effective when briefed well.

131. Economic uncertainty has lengthened consumer consideration cycles in discretionary categories. Most brands haven’t updated their channel mix to reflect this.

On the culture around the job

132. Most growth Twitter/X is people restating what worked for them as universal law. Read it, but hold it loosely.

133. “Founder mode” became a phrase that was used to justify almost anything. The useful version of it is simple: stay close to the work.

134. The tendency of growth people to dress up industry gossip as market insight is a known failure mode. Name the pattern so you can catch yourself doing it.

And finally

135. The best DTC brand you know is probably one you heard about from a friend, not an ad. That’s still the industry’s most important unsolved problem.

136. The teams that ran the best experiments in the last year are the ones that wrote down what they expected to happen before they ran them.

137. The best briefs are written in the customer’s language, not the strategist’s.

138. Every successful DTC brand has a moment when a single creative unlocked scale. The lesson is not to try to recreate that creative. It’s to build the system that finds it again.

139. The teams most resistant to AI adoption are often the most skilled at their current way of working. Capability and change readiness are different things. This is the individual’s version of the Innovator’s Dilemma.

140. The growth industry in 2026 is better at measurement, better at structure, and still not great at the thing that actually drives results: making things people find interesting.

141. The question a founder should ask before hiring any performance agency: “Can you show me a client where you improved margin, not just ROAS?”

142. The question that reveals more about a brand’s health than any dashboard: “What’s your repeat purchase rate at 90 days?”

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Meta is shipping fast at the moment. When I initially wrote this, the latest ability to push spend to specific ads hadn’t been shipped. As I rewrite this, yet another new feature is launching. This is some of the most useful and fast-paced product development in Ads Manager we’ve seen yet.

Late last year, Meta started testing a ‘creative testing tool’ where you can launch five ads inside an ad set without disrupting learning. Five ads for most accounts is limited, but when in conjunction with the creative testing tool, it’s potentially a way to run accounts.

We’ve been testing these for the last few months across a series of our accounts. We’ve done a mixture of isolated AB tests as well as before and after experiments.

Here’s what we’ve learnt so far this year, and what we’re going to be doing with it.

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Flexible ad units: the promise vs the reality

The beginning of last year, we won an early-stage client who had one singular flexible ad that was working. It contained 10 separate, distinctly different ads within it.

One of the first moves we made was to move away from the flexible ad unit.

Our methodology is strongly rooted in creative testing, learning and iteration. We are intentional about creative tests, approach them strategically, and use every week’s experiment to inform follow-ons.

So for us, flexible ad units remove information that allows us to learn.

With that client, we were never able to find product-channel fit for the client. We eventually re-tested the flexible ad unit in case there was some magic in the delivery of it itself, but it never rebounded. For a long time we were confident in our decision.

Then late last year (2025), I saw rumblings on X about the unit. Some media buyers were claiming strong performance from these units over and above typical setups. The 99th percentile Consolidationists would argue that the more diversity you can fit into individual units the better. And lots of belated-Andromeda hype all pointed to the valuelessness of variants within individual units.

Throw into the mix the new creative testing tool, and it felt like it was time to revisit.

We started running a series of tests. Some before/after, and then a couple of stricter tests.

Flexible ad units: A/B test results

We ran two isolated AB tests using Meta’s testing tool. In each example, we ran the same setup.

• BAU contained 3-5 variants of an ad

• Flex ads contained a single ad unit with each of those variants within it

We ran this for a week.

The outcome in both tests was stark: the flexible ad unit variant ran at double the CPA of our BAU.

This was an annoying one for us. Had the flex unit won, this would have hugely increased our capacity for ad volume in a consolidated way.

As it stands, we have two very similar test results. With anecdotal before and after results showing similar things.

Summary: we’re derprioritising flexible ad units

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Meta’s creative testing tool: good for the wrong reasons

The other tool, which we first ran tests on in October 2025, is different.

The concept here is that you can run creative testing inside your main hero campaign and ad set, rather than inside a separate setup.

Most accounts these days are running some form of setup that encompasses: (1) dedicated testing, (2) scale/bau/hero setup. We are the same.

This new tool means being able to test inside those heroes without disrupting performance.

You force spend into them, and at the end of the tests, they can then scale.

The logic here is good: you get benefit of not having to disrupt something that’s working, and they build data in the place where they will live.

These have limits. You’re capped at five launches per week, which given we like to variant test still, means that we’re limited by what we can launch.

We’ve been testing this now in a series of accounts. Here’s what we’ve found.

Test-week performance is misleading

During the test week, almost every concept looks like a loser. CPAs during the learning phase were consistently 20-60% higher than the business-as-usual benchmark.

Depending on client stage, we expect to see a 20-40% win rate defined as concepts producing CPAs that are the same or better than the account average.

In these tests, no launches saw winning ads. Now an individual week doesn’t prove anything, nor does a single client experience. But we have now looked at this across a series of accounts, as well as across multiple weeks. Repeatedly we see the same things, win rate is about 6% rather than the usual 20-40%.

Note: we’ve not run explicit AB tests here where we ran usual creative testing structure alongside this setup. Given complexity and cost of isolating that test with audiences, we opted against it in favour of faster learning.

If you treated these on the test-week alone, we’d have binned all the ads.

But we decided to keep ads live to see what would happen afterwards, and this is where it got really interesting.

Post-test-week performance improved almost across the board.

One ad that had 60% higher CPA during test week graduated and had a CPA 40% below the account average.

Not only was the test week not a good indicator for CPA, nor did it indicate what would happen with scale.

Here was activity from one campaign we saw.

Note: numbers have been anonymised but % differences remain the same.

During test week, Concept A was one of the rare concepts that beat the test setup.

B,C, and D were all higher and what we would have deemed fails.

And yet in the post-test period, we saw:

• Concept A scale with better CPA than the account, as we would hope for such a definitive winner.

• Concept D, previously a marginal loser, scaled to about half of Concept A’s growth, but with an improving CPA.

• Concepts B and C both remained fails, and were ads we ended up turning off.

In other account tests, we saw more results like concept D: those with CPAs that were higher than account CPAs, which in usual structures we’d deem losers, becoming winners afterwards.

Creative testing tool is great for forcing spend

Our takeaway when we’ve round up internally is that the creative testing tool isn’t an effective way to test creative, but it is an effective way to force spend and give ads a head start in a hero campaign.

And so there are instances now where we’re looking to use this setup with this in mind.

A few examples:

1. Where overall account spend is low and running isolated tests is expensive

2. Accounts with high CPA where we use less correlative higher funnel events for testing, as a means of getting spend into a conversion-asset

3. Instances where certain ad sets have high spending, low performing ads that just can’t be shaken

The testing tool itself still helps identify rocketships – they seem to win regardless – but the lions share of winners don’t get picked up.

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Meta update: March 2026 – push delivery to an ad

As I write this (Friday 27th), Meta has this week rolled out a new feature allowing you to force spend to individual ads inside the hero.

Our current state with creative testing tool is essentially a way to force spend into a hero campaign to new assets before we see how they truly behave.

With this new launch in mind, it’s likely we’ll use the creative testing tool less and instead move to this new feature.

We are rolling out tests across the agency over the next two weeks and will be testing a variety of setups. Key things we want to understand:

• How much spend to force in

• Whether ads should be tested first

• Does this differ depending on creative testing method

If you’ve been playing with the new tool, I’d love to understand your setups.

Leave a comment as I’d love to hear how you’ve been finding these features.

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Josh Lachkovic is the founder of Ballpoint, a creative growth agency for DTC and ecommerce brands. Subscribe to Early Stage Growth for weekly insights on growth, creative strategy, and experimentation.

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“The way I interact with a computer has completely changed forever.”
– Mark Holland, our Head of Creative Strategy

Something changed over the last month. I and another colleague have become AI-native.

My colleague Mark, our head of creative strategy, told the team at Friday Beers & Cheers “the way I interact with a computer has completely changed forever.”

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With our first full week of Jan behind us, we’ve examined multiple categories and trading is on average slower than expected. As ever in times like this, I speak to friends who work in marketing and are founders.

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41 newsletters (42 including this one). Roughly 40,000 words. One year of thinking out loud about growth marketing, Meta ads, and building Ballpoint.

At the bottom of this newsletter, you’ll see a roundup of my top posts of the year. But first a reflection.

I’ve been blogging on some subject or other for over two decades now. No matter what it is I’m writ…

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Here’s my reflection on where we are as we go into 2026.

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1. Alcohol and the era of moderation

The IWSR recently published a report on drinking that did the rounds. There were two charts that showed the story.

First, is the absolute chart that reveals that Brits are now on a par with Americans for amount of alcohol drunk per capita each week.

There is a clear decline within the UK down to just over 10 drinks per week on average.

Gen Z’s role is shifting

The common narrative for a long time was it was Gen Z leading this charge, but data from summer showed that trend is bucking.

In fact, as that article suggests, a lot of Gen Z’s prior teetotalism can be accounted for being underage, unemployed or in less stable working environments generally. The generation is older, working and more affluent than they were in previous years.

And so overall declines in drinking in the UK must be broader.

Let’s look at that IWSR data again, but indexed rebased to 1990.

The shape of this chart got me digging into a few other thoughts of what correlates at this time.

A few key thoughts here:

• There’s a clear financial crash change around 2007-2010 that correlated with the downward trend

• Drinking rose throughout the 90s and peaked in early 2010s (just as I and other millennials starting drinking)

I was interested in how this correlated to public sentiment towards politics – whether the New Labour optimism was fuelling it.

What this means for brands

There’s a decline in drinking for sure, but it’s not as clear cut generationally as once thought. For anyone selling in the low or no alcohol space, this won’t be a surprise.

The bigger questions might be why this is happening. Understanding the motivations between reduced alcohol will help shape why and how to sell to someone.

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2. GLP-1 users are the new happy, premium consumers

GLP-1s are tricky beasts.

When I think of GLP-1s, my default is to think more of toxic culture of skinniness than it is to think of overweight people creating better relationships with their bodies.

I know statistically that isn’t correct. In the UK, the distribution of of weight leans far more to the overweight and obese end.

And when we look at global trends the picture is clear too:

Obesity is on the rise, and most sharply in the faster-growing middle income economies.

So this set of stats is interesting from the New Consumer report.

Life satisfaction is 14% higher for GLP-1 users than the average. And in fact is one of the best predictors for life satisfaction – second only to those who earn $150-200k/year.

Most interestingly for brands is what that increased life satisfaction correlates to in spending power.

Unlike the general population, where there is a mixture of trading up and down depending on category, GLP-1 users are almost trading up in every category from the Coefficient Capital survey.

Perhaps the surprising ones are in restaurant meals (17% up), and snack foods (24% up). But as their report also states a lot of this interest will come from new brands or products specifically catering for them.

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3. Drug use on the rise

While alcohol make be down, and low and no down with it, lots of drug use is on the rise.

18.1% of British 16-24 year olds have taken an illegal drug in the last year, which when you consider the 39% who never drink, might offer part of the picture.

In the US, while daily alcohol drinkers are flat or showing slow growth, daily cannabis users overtook drinkers a few years ago.

Meanwhile, lifetime use of cocaine has doubled since 2001 at a time when regular drinking has declined significantly. The overall numbers are still far apart, but the relative trend is clear.

It is too simple to look at alcohol moderation and say that society is moderating. For some, it is definitely about blanket moderation, but for others one drug is being swapped for another. And in many cases, some of those swaps will be worse rather than better.

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4. Longevity is on the rise

2025 started with the Don’t Die doc landing on Netflix. At the time, everyone laughed.

Today, the laughter seems to have quieted down.

Lots of biohacking is expected to rise.

Oura Ring sales have close to tripled in five years.

£200m is being spent on anti-ageing in the UK.

I had an IV in early December, and part of that was a NAD shot. NAD is still expensive to get as an injection or home shot. Most packages you’re looking at £300+ per month to properly get the benefits.

The number of people who will spend £3,600 per year on this is low. But this is the tip of the iceberg for the market.

5. Comms & storytelling is more important than ever

My old boss, colleague, board member Peter Sigrist used to consistently extol the value of storytelling. Whether it was in corporate comms, education, politics, or now quantum computing, I’ve never heard someone so stalwartly defend the value of being able to communicate a narrative.

Turns out the world is waking up to Peter’s worldview.

This year, we saw all the big AI companies invest heavily in storytelling, comms-focused content positions. It caused a few eyerolls given what these companies are doing for broader content around the world.

But as this WSJ article reinforced, it’s never been as important to find storytellers in the world. Source.

• The number of roles including the word storyteller have doubled year over year.

• This isn’t just marketing, companies want specific storytelling skillsets

• Continuing trend of owning your own content distribution

• Huge salaries to go with it

In advertising, this is important too, but while adverts try to capture attention in nano-seconds, this feels like the longer sell once you get someone into your world.

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6. Confidence in consumer investing is back

Sugar Capital, the venture arm from the POPSUGAR founders, posted a call to arms in early December with their post “The Golden Age of Consumer Investing.”

I read Brian Sugar’s post enthusiastically.

“A gummy vitamin company reaching $300 million in revenue on $20 million of primary capital. Contribution margins above 40 percent. Meaningful EBITDA before the Series B.”

“Product-market fit” is bandied around a lot in startup folk lore. I know it because I had my own startup fail without it. But so much discourse around PMF is focused on enterprise, b2b, and proper tech companies. You have comments referring to how sticky user engagement is or what your DAUMAU ratio needs to be. And how to solve it is focused on switching product categories or strategies with winners the likes of Twitch, Instagram, Notion, or in the UK more recently Fyxer.

But a lot of PMF discourse doesn’t talk to consumer non-tech problems.

At Ballpoint this year, we’ve worked with over 50 brands, and I’ve audited maybe another 150. We see up close and personal the companies that:

1. Don’t have PMF and is unachievable unless something massive happens

2. Do have signs of PMF, but haven’t worked out how to scale a growth engine yet

3. They have it, they’re scaling, but want to push harder because they’re in a growing category and need to be dominant

When Brian talks about contribution margins and ebitda, this is not an outcome of modern consumer PMF – it’s a part of it. PMF is about sustainable business models and huge accelerated growth opportunities. You need to a path to $200m and you need 30%+ CM.

Health is the focus

It’s unsurprising perhaps that this post has focused so much on health. Brian puts down this new wave of optimism to health specifically.

“Something shifted in American consumption over the past five years. People read labels. They actively avoid the processed food that built the twentieth century’s largest companies. Ozempic and its cousins rewired how America thinks about what it puts in its body.”

We share Sugar Capital’s optimism and focus.

7. There’s still scepticism in the M&A markets

Before you start pulling money from your index funds to invest in early stage consumer, there’s still some scepticism in the market.

The excellent Drew Fallon in his Substack Making Cents, posted this week the “2026 M&A Outlook.”

In it he highlighted just a few of the big issues we’re facing at the moment. Namely:

• Coca-Cola are trying to get rid of Costa for $2bn having bought it for over $5bn

• Waldencast are maybe selling Obagi

• Estee Lauder are likely to sell off some brands

• LVMH is trying to shift its 50% stake in Fenty

• Nestle wants to further investment in health and is reviewing elsewhere

And the list goes on.

It wasn’t all doom & gloom, as Drew pointed out interest rates are likely to continue dropping in the US and that will be good news for all. This will particularly be true for the portfolios with lots of DTC weighting which are now approaching the end of the 8-10 year fund cycles. Expect lots of pressured dealmaking for brands in this part of the cycle.

And the areas Drew has the highest confidence in:

“Protein and fiber [sic] companies (and hydration to a lesser extent) that are gobbling up market share right now are critical assets that must be owned”

This said, however, 2025 ends with broader M&A at an all-time high with $4tn in dealmaking done this year.

8. The two horsemen of the 8-figure world: LTV debt and Brand debt

I was reminded of Sean Frank (the Ride CEO)’s comment this month about Stanley:

Go back two years and Stanley were unavoidable. They were on every Meta ads inspo board out there. They were being case studied. And now they’re in a clearout section.

If you were YETI, who of course are broader in catalogue than Stanley were, you could have easily fallen for switching strategy. Fortunately their Google Trends data shows a slow but steady move up and to the right. They’ve remained stable and growing, while Stanley hit a hype curve they couldn’t follow back from.

We don’t know Stanley’s unit economics, but it’s a pattern we see often:

1. Hype doesn’t create a brand

2. Beware of LTV debt.

9. Beware of category cycles

This was a great chart shared by Ben recently about category/brand growth.

One key thing jumped out for me here about how to think through strategy.

Lots of businesses I know and meet aim for contribution break-even or contribution-profitability and their goal is to scale up to that point. In early days it’s a good strategy because it helps you build up to a level of net margin profitability by building enough revenue to cover your opex.

But once you’re there, you should think about category.

Now every founder is biased towards the strength of their own category. You are after all a founder and have long term belief in it.

But trends are cyclical. Just because GLP-1s and healthy snacks are big this year, it does not mean that’s a permanent state. We’ve seen in the space of a decade the explosion and death of artificial meats. The things that were rampant for millennials are unimportant for gen z and so on.

The move towards green looked like an inevitable change for the 2010s, but there’s backlash against it now. Governments, populism, consumers are shifting and moderating.

We are in a health cycle at the moment, and so lean in to your strengths. Lean into the growth.

But also make sure that you are not creating fragility. Running at £0 net profit is better than negative. But if you’re in the position to have customer growth for the benefit of cash flow growth – and you’re in a cycle that could change – then build in some optionality.

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10. The constant battle with affordability

The West seems to be battling away a recession every month. Only 42% of Brits believe they will ever be able to afford to buy a house. 56% of Brits say they’re impacted by the cost of living crisis this year.

Amongst young people, it’s the single biggest issue affecting the country. More than seven million people in the UK are going each month without essentials.

And amongst the highest earners, tax rates plus loss of child benefit mean that if you’re earning £125k per year, you’re actually worse off than earning £99k per year.

There is a higher number than ever who forgo basic necessities. There is a growing middle who who without support from Bank of Mum and Dad can’t afford housing. And the country’s best earners are disincentivised to earn more.

The ongoing joke about millennials is if they ditched flat whites, they’d be able to afford housing. Except for many, all that saving in the world won’t get them on the ladder. For them, buying premium makes sense.

There is of course lots to be optimistic about in the economy. But it’s also important that brands really think seriously about what type of company they are. Who they appeal to.

Are you really a mass market product? Or are you super premium? Does your growth and ambition support your customer?

And equally important are you actually talking to your customer in the right way? How you speak to middle England Facebook 50+ people is not how you speak to Real Housewives of Clapton London.

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Closing thoughts

The world feels more mixed than ever. Some macro signals are noisy and potentially worrying: ongoing affordability crisis, M&A scepticism, category cycles that could turn at any moment. On the flipsde, certain classes of consumer are spending more than ever in newfound categories, giving an opening to new types of brands.

Health, longevity, and alcohol moderation are certainly all singing in unison. But it’s not universal – THC products in the US continue to be prominent, and illict drug use is on the up in some categories as well. Not everyone is following the Bryan Johnston playbook quite yet.

Whatever type of consumer business you’re building, profitability remains king. Strong and early contribution margin is vital, but building net profit growth into your business is what is going to make M&A accessible.

The best founders and teams I’ve spoken to this year are all building optionality. They’re not content with cheap acquisition costs or break-even CM, they’re focusing on LTV debt and building cash into the business. Profitable enough to survive a downturn. Ambitious enough to lean into growth when the window opens. And perhaps most importantly, more realistic about who their actual customer is than ever.

If I was the founder of a growth business at the moment, I’d be using 2026 to think about ebitda profitability rather than fragile 100% growth maximisation.

We’re not in a universally good time, but for certain sectors it certainly feels like it.

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AI became a real possibility for ads creation this year.

At the centre of much of the AI backlash is the fear for …

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September was brutal, and by many degrees worse than last September.

I published mid September this chart on LinkedIn and it got a lot of conversation going.

Off the b…

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Since we’ve published this post, we’ve significantly updated our AI thinking, which you can read here.

AI is now the most frequent conversation topic of anyone in business I know now. One of our copywriters Mark said to me recently that “every conversation eventually turns to AI”. It’s become the modern Godwin’s Law.

There is a mixture of excitement and anxiety.

The AI influencer community is already in full swing accelerating that anxiety. If you believe every AI influencer then you might think there are entire companies now fully operated with just 25 AI agents and one person. You’re probably worried you’ll be left behind.

Anxiety in times like this is good. One of the best business books you’ll ever read is Only the Paranoid Survive by Andy Grove. The period we’re going through now is one of existential change, and if you bury your head in the sand, you probably will be left behind. Realising that this is an issue is important.

But at the same time, once you dig in to most of what the AI world is currently selling in today-terms, I’m often left wondering how many people actually use all of this day-to-day?

And what is there today that can be useful, recognising that ultimately every part of a system and workflow today will likely feel old in six months time.

I want to share very openly how we’re experimenting at the moment and our approach to it. I hope this is less a ‘here’s stuff you can borrow to be better at growth’ and more ‘here’s how we could experiment in our organisation' specifically.

Things to cover

1. How we ran our recent AI hackathon/weekender

2. Key lessons learnt

3. What we built

4. What comes next

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How we ran the AI Weekender

Most teams I know now are highly lean. Revenue per employee as a metric is back. And with AI companies now reaching tens and hundreds of millions with only a handful of employees, that will only continue.

But one of the issues with operational efficiency is that it doesn’t allow room for experimentation and play.

New systems and technology require experimentation and play. And there’s only so much snippets of time in evenings and weekends you can do to properly explore.

This was the backdrop to why Fiona, our head of ops and creative studio lead, and I went and did an our own Weekender. Except it wasn’t over the weekend, and we didn’t go clubbing. We spent two days of blocked out, deep work time, off site working only on AI.

Here’s how we did it

• Booked a workspace and hotel away from London to create different environment setting

• Blocked out calendars entirely, did handovers as if we were on holiday

• Came up with a long list of ideas ahead of the two days of what we wanted to build

For us this meant two days in Guildford where we worked solidly in deep work mode on AI ideas for two days.

What do we really mean by AI?

In this context important to point out the parameters.

1. The use of AI within automation frameworks to help us automate things we couldn’t have done previously

2. Exploration specifically of AI tools and how we could bring those into our workflows

How we worked throughout the day

We had a Notion board with our long list of ideas, and we then gave each an expected value.

We measured: (1) potential forecast contribution margin over a year the initiative could bring to the business and (2) probability we could finish it in two days

The CM forecasts looked at:

1. Can this reduce existing human time to the business?
   For example, can we replace 90 minutes of human time per week with 5-10 minutes of human time by automating a repetitive process? And therefore we can calculate 75 minutes of salary x 52 weeks of the year to give us a CM figure.
   

2. Can this accelerate our ability to do our jobs better and therefore increase client spend, which would then increase our revenue as well?
   Harder to forecast, but to be simplistic, let’s assume 1 ad in 20 we create allows us to push client spend by 15% per month, does this activity increase that ratio to be 1 in 18 and therefore offer a 10% increase in potential revenue to the business? Apply our gross margin to that revenue increase multiply it by number of clients it would impact, and hey bingo we’ve got a CM figure.
   

3. Does this activity reduce the likelihood of churn through better servicing of the client?
   Another hard to forecast one, but we have a set of principles we believe are important to client retention. Can we improve our ability to do that through an activity? And to do this we looked at our churn rate across the year, forecast a cost of churn in revenue terms, and again applied the CM calculation to it.
   

4. Does this improve our marketing of our agency?
   Very hard to quantify as measuring individual pieces of activity within the agency’s marketing is difficult. Yes we can look at substack reads/engagements, or LinkedIn likes, or follower increases, but ultimately, we don’t have a good answer to specifically how much that ties back yet.

And then we did a probability forecast of likelihood to complete within two days.

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Key lessons learnt from our AI experimentation

1. Computational thinking

First and foremost, this felt closest mentally to coding work that I’ve done in a long time.

This is process of approaching a problem in a way a computer scientist would do. It requires thinking through the ideas of like data structures, inputs and outputs, loops, and abstraction.

I’ve done CS50 in the past, and am fairly good at SQL, but I never really got into a proper language. I found it incredibly useful however, to understand things like defining variables, or iterating across loops.

For anyone who’s never done any coding or computer science stuff, in plain English this means:

• Breaking apart your day-to-day actions into the tiniest and most specific steps. For example, you might say:

  • press the button on the top-left of the coffee machine that has a has a circle with a dashed line through the top of it.

  • Wait for two minutes until the machine stops making any noises

  • Put your hand on the handle of the wooden block above the coffee machine, grab it, and move it to the right.

  • Pick up the bag that sits on the lowest shelf (position 0), on the far left of the shelf. Check that the bag says “coffee” on it.

  • and so on

• When presented with an either or action, using IF statements to set conditions and directions. For example:

  • IF the coffee machine has no coffee in it THEN add coffee to the back ELSE continue to next step

• Thinking about everything as data. I.e. every action or object or anything needs to be a data structure. And to use them properly, you’ll need to define them a lot. For example:

  • Every object in the cupboard is an object in a data array with different parameters

  • Every mug on the shelf is a different object

• When presented with a list of tasks you want to repeat for a list of things, ‘iterating’ with a ‘for loop’, i.e.:

  • For every object in the cupboard:

    • Check that it says coffee on it

I always find the first few easy to comprehend, but for the last one, it’s amazing how frequently you have to think like that when it comes so naturally as humans.

🌟 Key lessons: be prepared to think computationally and it would probably help to go and do lesson one of Harvard’s CS50.

2. What to automate

One of the first things we tested was seeing how we ChatGPT could automate analysis of different adverts.

It was one of the four hour deep pieces of work that worked based on a series of triggers or prompts. Ask a question about an ad in Slack? Great it gets an AI response reviewing the ad. Have an ad automatically hit a certain spend threshold, great get it to analyse.

It was technically beautiful.

Except the actual raw prompt at the end wasn’t something we ever do manually. We use AI a lot but we don’t use it that much in analysis and find humans far better analysts of visuals at the moment. So why did I think automating a route would be better.

🌟 Key lessons: only automate a process you have already tested

3. You do need the time

I context switch 9 or 10 times per day. Most work I do fits into 15, 30, or 60 minute chunks of time. If it doesn’t fit into that I have to make time for it.

With AI, it takes 60 minutes just to get your head back into a problem.

Inevitably, it could take another 60 minutes until you figure out how to solve it.

Then you need another hour to test it, let it break, and go and rebuild it.

And then you’re at a point that it’s done.

And so, you really do need to set aside big chunks of time. And given how many meetings and usual day-to-day work people have, I imagine that’s impossible to properly do without building that time in.

🌟 Key lessons: you can’t do AI properly in 60 minutes. It needs days.

4. It doesn’t naturally suit my mindset

I am very much a “done is better than perfect” person who is “vision, not detail.” I’ve hired exceptional people who are the opposite to that, but this level of work does require going very deep and stuff works or it doesn’t.

I still think AI sits with leadership to properly learn and implement, at least for us, but I’d murder a technical co-founder right now.

🌟 Key lessons: it suits perfectionist, detailed-orientated people

5. I now know n8n & how APIs work better

I always knew sort of how APIs worked before but now I get them a lot better. I’d still like to do a bit of a crash course on them rather than learn by doing. And want to explore Postman a bit so I get really comfortable – maybe even via Terminal. But overall my knowledge is 0 to 1.

🌟 Key lessons: n8n & APIs are great

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What we built

“This is all well and good Josh, but what did you build?” I hear you cry.

Ok. Here’s what we achieved.

Auto-generate simple statics using created backgrounds on nano-banana

GenAI comes down to great prompting. Great prompting is a skill that a handful of people have on the team but not every one else.

Workflow

1. Notion card where you drop a product link and a description of a scene

2. n8n takes that and sends it to ChatGPT

3. ChatGPT takes your input prompt and generates 10 variants of that prompt.

4. Each of those prompt get sent to nanobanana

5. Results come back to a thread on Slack and go to a Google Drive

Analyse recent creative winners

This was the ‘fail’ because we don’t use AI to do our visual analysis usually, but we got it working with a few inputs.

Workflow

1. Either (A) field updated on Notion, or (B) specific adname requested in an internal-client channel

2. Data is joined with other data from Notion related client insights

3. Claude prompt combines visual + insights and asks questions to analyse it

4. Result gets returned to Slack

Call actions shared to internal client Slacks

This never seems to work 100% but it does cover 90%.

Workflow

1. New Fathom call transcript

2. Transcript gets uploaded to Claude with a prompt about being marketer/exec assistant

3. Asks it to pull out all of the actions discussed on either side

4. Share those actions to client slack afterwards

High/Low Spend Alerts

We did get the non-AI version of this working. Still to build the next layer.

Workflow

1. Check Meta every hour across all clients to pull spend + conversions, then calculate CPA

2. Share the CPA update at a few key points to internal client Slacks

3. Check against a “danger” and “great” CPA range and if it’s good then either WhatsApp account owner / DM the account owner.

The bit here that’s not AI-ed is step 3, I’d love this to be relative to overall performance “recently” rather than set CPAs like this. If you’ve got ideas on how to build, let me know.

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What comes next

We’ve got 20 unfinished AI initiatives and dreamt up plenty more. I’ve also been tinkering away more at weekends and evening. It’s addictive.

We will also likely bring on someone to support with AI as a focus at least on a freelance basis while we build in additional workflows. But ultimately it’s still something I’m investing a lot of time in.

How we implement this amongst the team, tbc. Lunch & Learns are definitely one thing, but ultimately it’ll probably be something we do at the next offsite and dedicate a four hour chunk to so people can understand the true power of it.

I still haven’t done any vibe coding, but that is coming next.

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🔗 When you’re ready, here’s how Ballpoint can help you

→ Profitably grow paid social spend from £30k/m → £300k/m
→ Create full funnel, jobs to be done-focused creative: Meta, TikTok, YouTube
→ Improve your conversion rate with landing pages and fully managed CRO
→ Maximise LTV through strategic retention and CRM - not just sending out your emails

Email me – or visit Ballpoint to find out more.

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