The Weekly Sharpener #111

👋 Hey fam!

I'm really excited to share this 👊 Monday roundup with you!

First, we get to have a great time learning about Influencer Seeding, Media Buying, and Intentional leadership with my friend, the Captain, Taylor Lagace (Co-CEO @Kynship).

Next, we get an amazing breakdown of media buying strategy from one of our favs, Charley Tichenor. You're going to be amazed with the value and depth he goes into and how simple his framework will be for you to implement and scale today.

Finally we get a little insight into some of the best media buyers POV on targeting on TikTok ads - to broad or not to broad? That is the question!

Thanks for spending a few minutes with us today, we appreciate you, and hope you find this valuable!

Cheers,

Chase

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Guest Content 🐐

This essay comes from our friend Charley Tichenor

How I do creative testing to build ads that spend 6 & 7 figures each *We are buying attention, not results!

DATA & CONFIDENCE

In the realm of digital advertising, it is crucial to consider the projectability of a test's outcome. This means determining whether or not the results can be predicted and replicated in the future.

It is important to give a test enough time to deliver this information. I often run my tests for at least a week, if not a month before deciding on their projectability.

Facebook’s machine-learning algorithm is an invaluable resource in this process. Ads that have proven themselves to be effective in retaining attention and driving engagement, will earn spend.

Think of Dynamic Creative Tests, or DCTs, as CBO, for the development of ads that are stable and scalable with confidence. It is important to allow the platform to do the decision-making, then champion ads that both earn spend, and deliver desirable business results.

When running a DCT, it is essential to consider whether the outcome is projectable and if it is desirable. If the result is projectable and positive, the next step would be to extract the post ID of the winning ad iteration.

On the other hand, if the outcome is not projectable or negative, it may be best to discontinue the test. Either way, when we are confident with our test’s outcome, we can take action with confidence and move on to the next test.

Ultimately, the goal is to make data-driven decisions that will lead to successful and efficient advertising campaigns.

DYNAMIC CREATIVE & the 3:2:2 Method

Facebook’s Dynamic Creative allows for the consolidation of all data from each ad iteration onto a single webpage.

This means that all information collected from each variation allows for a deeper understanding of how users are engaging with all variations in the test.

This is infinitely more powerful than having each ad compete in isolation and steal data from each other.

The 3:2:2 Method is a DCT built with 3 Creatives, 2 Headlines & 2 Primary Texts. Rather than building 12 individual ads, that all compete against each other, the 3:2:2 Method allows for all 12 ads to work together.

By utilizing the algorithm to align the ad experience with the target market and brand, the majority of ad spend is directed toward creating and delivering ads that are most likely to achieve our shared business objectives.

This not only benefits our business's goal of monetizing attention, but also aligns with Facebook's objective of retaining users and maintaining engagement.

The power of the 3:2:2 method comes from the fact that it is a test with very few moving parts.

If the ad set is doing well, we can go into the breakdown by Dynamic creative element and see which headline, creative, and primary text, earned the most spend.

We can have extreme confidence to the contribution margin of these elements to the overall results of our test.

From there we can preview the Facebook post with comments, and this will show us a dynamic web page featuring every iteration that has earned engagement, and they are ranked by merit.

When we see alignment between the elements that got the most spend, and the iterations that are shown in the preview, we can have extreme confidence of the projectable results of that post ID.

From here it's a simple matter of hitting the timestamp of that iteration and grabbing the post ID, so that we may insert that exact piece of content into our control environment.

Ultimately, what we are doing here is marrying the power of the scientific method, with the power of Facebook's machine learning, to produce high-confidence assets that are algorithmically aligned for not only Facebook's business objectives but our own as well.

Overwhelmingly, these ads are stable with projectable results, and when tested and leveraged against a broad audience, these ads will never fatigue.

When we utilize a complementary cohort of four to six of these ads created in this fashion, the success rate of our scaling efforts improves exponentially.

Now don't just take my word for it, here's a link to a YouTube video where I show you how I build this in real-time, sharing my ad account with dozens of these ads that have spent hundreds of thousands of dollars a piece, and one that spent nearly $2 million.

If you want to learn more from Charley check out his amazing Disrupter School 🔥

Thread of the week 🤌

Why it matters: Are you running TikTok ads? Or at least planning to sometime? Yes, then there is some information here for you then.

Targeting is always getting brought up in regards to FB ads - but we rarely talk about TikTok ad targeting.

This thread has some great insights from heavy hitters in media buying that is worth checking out.

My favorite quick targeting tip that I took away was 👇

Broad -> Household Targeting -> Top Interest -> Pur Intent Interest -> Broad Lookalike of Pur

Expert Strategy + Learnings

Some amazing learnings from thought leaders in DTC.

Carl's CRO Tips 🙌

This is why customers buy: > They are motivated > They understand the value > Little friction in the customer journey > They feel certain

For more 🔥 follow Carl Weische from Accelerated.

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