Cody Wittick ✓
@Cody_Wittick · My Agency →→ · joined 01 Apr 2013
Co-CEO @kynshipco • Helping founders grow new customer revenue • Follower of Jesus • Proud Husband & Father •
12 607Followers
1 919Following
12 004Posts total
869.3KViews on collected posts
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Hold Up Displays increased ad spend 41% while aMER improved from 5.20 to 5.38.
Setting cost caps from Meta's seven-day click reporting alone would have undervalued part of what the traffic was worth.
Hold Up Displays sells a high-consideration, made-to-order product, and plenty
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I recently talked to a DTC brand with ~290,000 existing customers, yet somehow Google only knew about 11,000 of them.
That meant roughly 96% of its customer base could still be treated as new. Meanwhile, its campaigns were set to bid more aggressively for new customers.
So
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BTW: Partnership ads are a GREAT solve for creative diversity constraints.
Every creative has a ceiling, and an account with three genuinely distinct ads only gives its budget three lanes to flow through. Once those lanes clog, the next dollar has nowhere efficient to go, and
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Scaling brands like it's 1984 https://t.co/YzXtnM0wjQ
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@Cody_Wittick Nah… it hasn’t been simpler
It just requires people to admit they’ve almost assuredly been cosplaying as a brand…
When in reality they are a Covid era marketing salesman in denial
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You just read the exact system we use at Kynship to take brands from stuck at $5M to scaling past $50M.
Now you can either bookmark this and forget about it like everyone else, or actually do something about your dying CAC.
If you want help executing, link's in my bio.
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But protect human time for strategy, taste, judgment, and final decisions. AI should free your best people to think more clearly, not ask them to think less.
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The only 4 retention levers that actually move the needle:
- Send more emails
- Create moments through product releases
- Nail operational excellence
- Use a subscription where it naturally fits.
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5. Use AI as a production multiplier:
If your forecasting is wrong, AI won't save you. If your creative strategy is weak, AI will just help you produce bad creative faster.
Use AI to scale creative output, turn data into decisions, and maintain narrative integrity at scale.
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4. Use retention and LTV as your revenue multiplier:
No amount of email flows, loyalty programs, or SMS sequences will turn a low-frequency product into a high-frequency one.
If customers only need your product twice a year, no Klaviyo wizardry will get them to buy monthly.
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Test offers on siloed landing pages where the only traffic is the specific ad campaign. Break these at the campaign level because different offers lead to different AOVs, margins, and LTVs.
You can't optimize a "Buy 4 Get 4" offer against the same target as a subscription offer.
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Offer creation is a math equation based on four inputs: cost of delivery (COGS plus shipping), basket predictability, LTV, and existing customer behaviors.
Match the offer to your business model, not what you think sounds good.
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3. Test offers like you test creative:
At Kynship, if we aren't filling up ad capacity with creative wins, we integrate new offers to fill the rest of the real estate.
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When scaling, increase budgets by 15% increments. If your overall aMER is too low, tighten cost controls by $5. If aMER is above target, loosen by $5.
If your ads aren't spending or CPA is too high, don't hack the bid. Test fresh angles and hooks instead.
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Use the 50x Rule for budgets. Your target CPA x 50 = your daily budget.
If your target CPA is $50, your campaign budget should be $2,500 per day. You typically won't spend the full amount, but the high ceiling gives the algorithm room to find efficiency pockets.
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2. Change your Meta strategy to cost controls:
If you want to spend efficiently, you only need to do one thing: figure out how much you can afford to spend per customer.
Cost controls let Meta spend as much as it can, provided it hits your target CAC.
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Once you lock in historical data and unit economics, the forecast dictates your strategy. You get exact CAC ceilings, contribution margin goals per SKU, and clear payback limits.
A proper forecast removes emotion from decision-making completely.
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That means:
- You need SKU-level economics because a high-margin hero product allows for a completely different CAC target than a low-margin bundle.
- You need to split customers into three cohorts: New, Recently Acquired (last 180 days), and Active Non-Recent.
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1. Start with your P&L:
The biggest change in e-commerce right now is the shift from marketing-led growth to finance-led growth.
Brands used to set arbitrary revenue targets and force marketing to hit them. Top performers reverse engineer from unit economics instead.
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CPMs are climbing, CAC is increasing across every channel, and your winning ads are expiring faster than ever before.
Attribution has become more complex than it's ever been.
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Winning is still possible:
- MUD\WTR built a $400M brand in under 5 years.
- Purdy & Figg went from £452K to £50M in three years.
- Obvi scaled from $0 to $40M in three years.
The standards for efficiency, creative volume, and marketing expertise are just way higher now.
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For the last 10 years, you could launch a brand with a decent product, basic funnel, and a few ads and actually turn a profit.
That era is over.
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The last 10 years of scaling DTC brands were a cakewalk compared to what’s required in 2026.
If your brand is stuck at $5M and your CAC has been climbing for the past year, you may be executing against a dead playbook.
Here's the 2026 system that's actually working to scale DTC
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Face aux comptes de taille comparable
6 posts des 90 derniers jours, à côté de la tranche de 10K–100K abonnés. portée ordinaire pour sa taille, réaction plus faible que la moyenne.
Vues médianes440ce compte924médiane pour 10K–100K
Portée, %3.49%ce compte3.62%médiane pour 10K–100K
Engagement, %0.89%ce compte1.52%médiane pour 10K–100K
| Indicateur | Ce compte | Médiane pour 10K–100K | Rapport |
|---|---|---|---|
| Vues médianes par post | 440 | 924 | 0.48× |
| Portée (vues ÷ abonnés) | 3.49% | 3.62% | 0.96× |
| Taux d'engagement | 0.89% | 1.52% | 0.58× |
Autres comptes de cette tranche → Comparer avec un autre compte → Comment ces repères sont établis →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
21615 May
194
185
161
170
303
289
25116 May
1.2K5 Aug
96
45410 Sep
42511 Sep
395
639
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.93%15 May
1.03%
1.08%
1.24%
1.18%
0.66%
0.69%
1.20%16 May
0.68%5 Aug
2.08%
1.32%10 Sep
0.47%11 Sep
0.00%
1.10%
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes59.9%103 in total
Reposts2.9%5 in total
Replies16.3%28 in total
Bookmarks20.9%36 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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