✶Explainer12:30
How to Test Lookalike Audiences From 1% to 10%
Timothy explains the number one tactic for a signs-of-life test: start with your own customer data loaded into the platform to build lookalikes. On meta you can build ad sets from a 1% match (most tightly correlated to your customers) up to a 10% match (a much wider base). With a limited budget, start at 1% because it's most tied to your existing customers.
- Start with your own existing customer data, not cold targeting
- Build ad sets at 1%, then 2-4%, then 5-7%, then 8%+
- The percentage is how closely the audience's behavior matches your customers
- Smaller percentage = closer match; 10% is a very wide base
- 8%+ usually doesn't work, but occasionally does — worth a test
- On a limited budget, just start at 1% for the clearest signal
“use your own data start with your own data”
“where 10% is pretty wide base that you're going to hit”
#meta-ads#audience-targeting#testing#lookalikes
✶Explainer14:30
How to Tell If Your Creative or Your Content Is Failing
When an ad isn't working, Timothy first checks that the targeting is correct and the right users are getting impressions, then uses click-through rate to see if they're engaging. Distinguishing whether the creative or the content is failing is harder — for infeed ads with headline, primary, and description, he says the real answer usually comes from a focus group, plus in-platform control-versus-test experiments.
- First confirm the target is correct — the right users are seeing the ads
- Click-through rate tells you if users are engaging with what they see
- Story creative stands on its own; infeed has headline, primary, description
- Data alone (CTR, reach, frequency) rarely explains why creative doesn't resonate
- Run a control-vs-test (e.g. logo + headline vs full creative) in the platform
- A focus group gives the qualitative 'why' the metrics can't
“it's a million-- dollar question to be honest with you”
#creative#diagnostics#testing#focus-groups
✶Explainer30:30
Don't Run Paid Ads Before You Have Product-Market Fit
Timothy shares that IBM ran ads across all of Africa but wasn't converting — the real issue was an operational product-market-fit problem: the continent has many currencies (franc, rand, shilling) while IBM only offered USD. Once they focused on South Africa and enabled the rand, performance improved dramatically. His broader point: without PMF, conversion stays low, you waste money, and you risk burning users who won't come back.
- IBM advertised across all of Africa but couldn't convert
- Root cause was operational PMF — multiple currencies, only USD accepted
- Focusing on South Africa and enabling the rand fixed conversion
- Without PMF, ads mostly annoy users and conversion stays low
- A bad first experience can lose a user's business permanently
- Big-budget awareness plays can run, but expect weak conversion
“in Africa there are multitudes of different types of uh currency there's the Fran there's the ran there's the shill”
“it's probably not going to convert very well”
#product-market-fit#conversion#case-study#strategy
✶Explainer48:00
Pausing One Poor Ad Can Dramatically Lift Your Whole Account
Walking through real Google Ads reports, Timothy shows how quality-score components (expected CTR, landing page experience, ad relevance) reveal where to act — improving ad relevance raises ad strength, which raises quality score and can add roughly 12% more impressions. He argues there's an account-level quality score, so pausing a single poor-performing ad with a high CPC can dramatically improve overall performance.
- Quality score breaks into expected CTR, landing page experience, ad relevance
- Improving a metric from below- to above-average can add ~12% impressions
- Google tells you exactly what to fix (more unique headlines, unpin assets, more keywords)
- He believes in an account-level quality score, not just per-keyword or per-ad
- Pausing one poor ad with a high CPC can dramatically improve the account
“so just turning this one off and removing it from the account this one ad could dramatically improve your performance”
#google-ads#quality-score#optimization#reporting
✶Explainer1:02:00
Multi-Touch Attribution and Measuring True Incrementality
Timothy favors multi-touch attribution, leaning toward time-decay because users quickly forget where they first found a brand. But he cautions attribution is biased — it can't tell you whether a user would have converted without the ad. That's why companies like Netflix and eBay run incrementality studies; eBay's ~2012 experiment led them to cut nearly all brand spend because users were finding them organically anyway. True incrementality is measured with geo experiments or conversion-lift tests where you withhold ads from a control group.
- Time-decay multi-touch attribution beats first-touch — users forget early touches
- Attribution is biased: it can't say if a user would've converted without the ad
- Netflix and eBay ran incrementality studies to answer that
- eBay's ~2012 test led them to cut almost all brand spend
- Conversion-lift tests withhold your ad from a control group when you win an auction
- Don't bother with incrementality tests below ~$50k/month — not enough signal
“they ultimately decided to cut almost all of their brands Ben because they found that users were already going to find them through organic anyway”
“when you're running a conversion lift test you intentionally do not show your ad to some users when you win an auction and instead Google…”
#attribution#incrementality#measurement#conversion-lift